Stochastic Modeling to Allocate and Assess Disaster Response Capacity in Logistics Networks MASSACHUSETTS INSTITI ITF OF rECHNOLOLGY by Lauren A. Seelbach B.S. Civil Engineering Syracuse University, 2010 JUN 0 4 2015 LIBRARIES SUBMITTED TO THE ENGINEERING SYSTEMS DIVISION IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN TECHNOLOGY AND POLICY AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY JUNE 2015 C 2015 Lauren A. Seelbach. All Rights Reserved. The author hereby grants MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Signature redacted Signature of Author: Technology and Policy Program, Engineering Systems Division May 8, 2015 Signature redacted Certified by Jarrod Goentzel Director, MIT Humanitarian Response Laboratory Thesis Supervisor Accepted b y: Signature redacted Dava J. Newman Professor of eronautics and Astronautics and Engineering Systems and Policy Program Technology Director, Stochastic Modeling to Allocate and Assess Disaster Response Capacity in Logistics Networks by Lauren A. Seelbach Submitted to the Engineering Systems Division on May 8, 2015 in partial fulfillment of the requirements for the degree of Master of Science in Technology and Policy Abstract When a disaster occurs in the United States, individuals in the impacted areas look to the local, state and federal government agencies to provide aid in the form of food, water, shelter, and other essential commodities. The responding agencies have the task of preparing their logistics networks in advance of a disaster to enable them to meet the demand for these essential commodities shortly after an event occurs. This thesis uses stochastic modeling and related metrics to answer three central questions that inform the development of a framework for applying these metrics for evaluating the disaster response capacity of a logistics network. The thesis answers the questions of (1) where should inventory be placed, considering both normal, steady-state allocation of inventory as well as allocation of inventory in advance of a notice event such as a hurricane, (2) how does private sector involvement change the response capacity of a logistics network, and (3) what is the impact of reduced demand for critical commodities on the logistics network's ability to respond? Two case studies are conducted on the logistics networks of the Federal Emergency Management Agency (FEMA) and the Florida Division of Emergency Management (FDEM). Results from these case studies indicate that applying stochastic modeling and the associated metrics to inform the allocation of critical commodities in a logistics network demonstrate measurable benefits in terms of fraction of overall demand met, time to serve a disaster affected population, and related metrics. For prepositioning decisions in FDEM's logistics network the benefits were particularly convincing. By locating stock closer to the areas predicted to suffer the greatest losses, FDEM is able to decrease the time per unit served of two critical commodities by 10-15%. For FEMA's logistics network, results indicate that restructuring the terms of contract stock has the potential to increase fraction of overall demand served by 14% within 24 hours after a disaster and 16% within 36 hours after a disaster. Thesis supervisor: Jarrod Goentzel Title: Director, MIT Humanitarian Response Laboratory 3 Acknowledgements I am incredibly grateful to everyone who has challenged, inspired, and cheered me on throughout my graduate studies and the writing of this thesis. In particular, I would like to thank: Jarrod Goentzel, for consistently challenging me to think outside the box and push the limits of what I thought was attainable, and for the opportunity to work for the past two years in the Humanitarian Response Lab. Jason Acimovic, for your patience, advice, and lightning speed responses to my questions throughout the writing of this thesis. Marianne Jahre, for all you have taught me about approaching an academic paper, and especially for introducing me to the wonders of Norwegian cuisine. Adam Norrige and Gregg Hogan at Lincoln Laboratory, for supporting this research endeavor and providing thoughtful, inspiring insight throughout the process. The analytics team at DataCo and the logistics team at FEMA and in Louisiana, for providing data to make answering these questions possible. Michael, for lots of things, but most of all for your constant, unwavering support and for reminding me that there is life outside of MIT and making sure that I live it to the fullest. My family: Mom, Dad, Nikki and Leeza, for being awesome. I truly would not be writing these words if it weren't for your consistent love and encouragement (and personal moving services, programming tutoring, graphic genius and motivational messages) every step of the way. My Baltimore family, for laughter, inspiration, and a second home when I re-discovered what a northeastern winter entailed. A special thank you to Cassie, for keeping me grounded and laughing these past two years. My coworkers-and-friends, past and present in the Humanitarian Response Lab. Corinne, Julia, Emily, Mark, Tim, Jaime, and Erin. You have made some of the most challenging times in my time at MIT seem not so challenging after all. And, you guys make a mean lunch. My TPP family, the forty or so classmates turned friends who have inspired me, introduced me to more new experiences, foods, and ideas than I can count, and made these past two years so much fun. To Barb, Ed and Frank, for helping me navigate the maze of MIT requirements and for providing advice, listening, and coming through with the occasional piece of chocolate. 4 Contents A cknow ledgem ents...................................................................................................... 4 List of Figures..................................................................................................................... 8 List of Tables .................................................................................................................... 10 A cronym List .................................................................................................................... 11 I Introduction................................................................................................................. 12 2 Literature Review .................................................................................................... 15 2.1 3 Humanitarian Logistics: Preparing the Response Infrastructure ...................... 16 2.1.1 Needs assessm ent...................................................................................... 2.1.2 Prepositioning ............................................................................................. 2.1.3 Vendor-managed inventory and framework agreements........................... 2.2 Public-Private Partnerships............................................................................... 17 18 19 20 2.3 21 Indices and M etrics for Evaluation................................................................. 2.3.1 Logistics indices......................................................................................... 2.3.2 D isaster response and resilience indices.................................................... 2.3.3 Metrics for evaluating performance in disaster response .......................... M ethods....................................................................................................................... 22 22 24 26 3.1 Research Question ............................................................................................. 26 3.2 Research D esign................................................................................................ 26 3.3 The M odel............................................................................................................ 27 3.3.1 M odel form ulation .................................................................................... 3.4 M etrics D erived from the M odel ......................................................................... 28 31 3.4.1 The balance m etric.................................................................................... 3.4.2 Fraction served and fraction of disasters covered...................................... 3.4.3 Tim e per unit delivered............................................................................. 3.4.4 Response Capacity Index........................................................................... 3.5 D ata...................................................................................................................... 32 33 33 33 35 3.5.1 D isaster data................................................................................................ 3.5.2 Stockpile data............................................................................................. 3.5.3 Tim e and cost data.................................................................................... 3.6 Case Study Scenarios........................................................................................ 35 42 42 44 3.6.1 A llocation of inventory ............................................................................. 3.6.2 Impact of private sector commitments or contractual agreements ............ 3.6.3 Im pact of resilience goals .......................................................................... 3.7 Lim itations ........................................................................................................... 44 45 46 48 5 4 Case Study: Federal Emergency Management Agency........................................... 49 4.1 Background...................................................................................................... 49 4.2 A nalysis................................................................................................................ 50 4.2.1 EM-DAT disaster data............................................................................... 4.2.2 FEMA inventory data ............................................................................... 4.2.3 Analysis scenarios...................................................................................... 4.3 Scenario 1: FEMA Physical Inventory Allocation .......................................... 50 53 54 55 4.3.1 Ability to meet demand............................................................................. 4.3.2 Network inventory allocation strategies ................................................... 4.3.3 Delivery deadline cutoff times.................................................................. 4.3.4 Response Capacity Index........................................................................... 4.4 Scenario 2: FEMA Physical Inventory and Contract Inventory Allocation ........ 56 56 60 61 65 4.4.1 Delivery deadline cutoff times.................................................................. 65 4.5 Scenario 3: FEMA Physical Inventory Allocation with Reduced Demand......... 70 5 4.5.1 Ability to meet demand.........................................o .................................. 4.5.2 Network inventory allocation strategies ................................................... Case Study: Florida Division of Emergency Management...................................... 5.1 Background................................................ 74 5.2 A nalysis................................................................................................................ 75 5.2.1 Insurance risk data .................................................................................... 5.2.2 Florida inventory and demand data............................. 5.2.3 Analysis scenarios..................................................................................... 5.3 Scenario 1: Current Allocation of Inventory ................................................... 76 79 80 81 5.3.1 Ability to meet demand............................................................................. 5.3.2 Network inventory allocation strategies .................................................... 5.3.3 Delivery deadline cutoff times.................................................................. 5.3.4 Response Capacity Index........................................................................... 5.4 Scenario 2: Prepositioning of Inventory ........................................................... 81 82 87 89 91 5.5 6 70 72 74 Scenario 3: FDEM Physical Inventory Allocation with Reduced Demand......... 95 5.5.1 Ability to meet demand............................................................................. 5.5.2 Network inventory allocation strategies .................................................... D iscussion .......................................................... ................................................... 95 96 98 6.1 Federal Emergency Management Agency .............................. 6.2 Florida Division of Emergency Management.................................................... 100 6.3 Cross-Case Findings ......................................................................................... 101 6.3.1 Allocation of critical commodities............................................................ 6 98 102 7 6.3.2 Impact of partnerships in m eeting needs .................................................... 6.3.3 Resilience goals .......................................................................................... 6.4 Fram ework ......................................................................................................... 104 104 105 6.5 109 Future Research ................................................................................................. 6.5.1 Impact of model and data limitations .9.......................... 6.5.2 Com plimentary future research................................................................... Bibliography ............................................................................................................. 7 110 113 List of Figures Figure 2-1 Conceptual framework of the literature review .......................................... 16 Figure 3-1 National Hurricane Center cone and warnings for Hurricane Wilma on October 21, 2005 at 4PM CDT (5PM EST) (National Hurricane Center, 2005)..... 41 Figure 3-2 National Hurricane Center cone and warnings for Hurricane Wilma on October 23, 2005 at 10AM CDT (1 1AM EST) (National Hurricane Center, 2005) 41 Figure 4-1 EM-DAT disaster locations and affected population, continental United States 1990-2013 (Esri, 2015).......................................................................................... 51 Figure 4-2 FEMA permanent stock warehouse locations (Esri, 2015).......................... 53 Figure 4-3 FEMA contract stock warehouse locations (Esri, 2015)............................. 54 Figure 4-4 Actual and optimal allocation of MREs (minimizing time or cost)............ 57 Figure 4-5 Actual and optimal allocation of water bottles (minimizing cost or time) ..... 58 Figure 4-6 Optimal allocation of MREs (minimize time) with increasing total system inventory, current inventory level indicated with dashed line.............................. 59 Figure 4-7 Optimal allocation of water bottles (minimize time) with increasing total system inventory, current inventory level indicated with dashed line................... 60 Figure 4-8 Fraction of demand served (minimize time) actual vs. optimal allocation of physical inventory only........................................................................................ 61 Figure 4-9 Locations of potential FEMA warehouses used in the sensitivity analysis for the balance m etric (Esri, 2015)............................................................................ 64 Figure 4-10 Fraction of demand served (minimize time) actual vs. optimal allocation of physical inventory only........................................................................................ 66 Figure 4-11 Fraction of demand served (minimize time) actual vs. optimal allocation of contract inventory only .......................................................................................... 67 Figure 4-12 Fraction of demand served (minimize time) actual vs. optimal, physical and contract stock ............................................................................................................ 68 Figure 4-13 Actual vs. optimal allocation of FEMA contract inventory only (minimize tim e).......................................................................................................................... 69 Figure 4-14 Actual vs. optimal allocation of inventory with original and reduced demand for M REs (m inimize time).................................................................................... 72 Figure 4-15 Actual vs. optimal allocation of inventory with original and reduced demand for water bottles (minimize time) .......................................................................... 73 Figure 5-1 Count of disasters by county, disaggregated disaster dataset, for counties with TAP>100 (Esri, 2015)........................................................................................... 77 Figure 5-2 Maximum total affected population by county, disaggregated disaster dataset, for counties with TAP>100 (Esri, 2015) .............................................................. 77 Figure 5-3 Map of Counties in Florida, noting the county seats (Geology.com, n.d.)..... 78 Figure 5-4 Florida Logistics Staging Areas (Including Orlando, the location of permanent warehouse) (Florida Division of Emergency Management, 2013)....................... 79 Figure 5-5 Actual and optimal allocation of MREs (minimizing time or cost)............ 83 Figure 5-6 Actual and optimal allocation of water bottles (minimizing cost or time)..... 84 Figure 5-7 Optimal allocation of MREs (minimize time) with increasing total system inventory, with current inventory indicated by dashed line................................. 85 Figure 5-8 Optimal allocation of water bottles (minimize time) with increasing total system inventory, with current inventory indicated by dashed line...................... 86 8 Figure 5-9 Fraction of demand served, actual allocation of Florida MRE inventory, m inim ize time ....................................................................................................... 87 Figure 5-10 Fraction of demand served, actual allocation of Florida water bottle inventory, minimize tim e...................................................................................... 88 Figure 5-11 Optimal allocation of MREs, minimize time, for two prepositioning time intervals..................................................................................................................... 91 Figure 5-12 Optimal allocation of water bottles, minimize time, for two prepositioning tim e intervals............................................................................................................. 92 Figure 5-13 Fraction of demand served, minimize time, for water bottles in Report 1 (10/22/2005 at 7PM CDT)....................................... 94 Figure 5-14 Fraction of demand served, minimize time, for water bottles in Report 2 (10/23/2005 at 1PM CD T)........................................................................................ 94 Figure 5-15 Actual vs. optimal allocation of inventory with original and reduced demand for MREs (minimize time)............................................. 97 Figure 5-16 Actual vs. optimal allocation of inventory with original and reduced demand for water bottles (minimize time) ................................... 98 Figure 6-1 Framework for assessing logistics and other preparedness decisions. Graphic designed and illustrated by Nicole Seelbach (Seelbach & Seelbach, 2015)........... 107 9 List of Tables Table 3-1 Structure of disaster risk data (DataCo, 2015) ............................................ Table 3-2 Summary of case study analysis process...................................................... Table 4-1 FEMA case study scenarios........................................................................... Table 4-2 Ability of FEMA physical inventory to meet demand ................................. Table 4-3 Response Capacity Index for FEMA physical inventory ............................. Table 4-4 Balance metric sensitivity analysis results ................................................... Table 4-5 Ability of FEMA physical inventory to meet demand with 10% reduction in Florida dem and ..................................................................................................... Table 5-1 Florida Case study scenarios ........................................................................ Table 5-2 Ability of Florida physical inventory to meet demand.................................. Table 5-3 Number of counties within one to four hours driving distance from Orlando, F L .............................................................................................................................. Table 5-4 Response Capacity Index for Florida .......................................................... 38 47 55 56 62 65 71 81 82 89 89 Table 5-5 Average time to serve per unit of commodity for prepositioning scenarios .... 93 Table 5-6 Ability of Florida inventory to meet demand with 10% reduction in demand in M iam i-Dade County .............................................................................................. 95 Table 6-1 Logistics decisions and associated metrics evaluated and data required for analysis.................................................................................................................... 108 10 Acronym List API Application programming interface FA Framework agreement FDEM Florida Division of Emergency Management FEMA Federal Emergency Management Agency LP Linear programming LSA Logistics Staging Area MRE Meal ready to eat NFIP National Flood Insurance Program NIMS National Incident Management System NRF National Response Framework PPP Public-private Partnership RCI Response Capacity Index VMI Vendor-managed Inventory 11 1 Introduction When a disaster occurs in the United States, individuals are displaced from their homes or are faced with prolonged interruptions in essential services such as water and power. This interruption in service or displacement means that disaster survivors look to their local and state government emergency response organizations and, in the case of large disasters that exhaust local supply, the Federal Emergency Management Agency (FEMA) to fill the gap and provide supplies to meet basic needs. One of the most important functions of FEMA, as well as state and local disaster response agencies is to ensure that the basic needs of a disaster-affected population are met. However, it is currently difficult to evaluate how well these agencies are prepared to meet the needs just described. How do the decisions that response organizations make in the planning and preparedness phases before a disaster strikes impact their ability to respond? The response to a disaster reflects the continuous efforts of communities led, in many cases, by an emergency management organization or other government entity and supported by private, non-profit and faith-based organizations, as well as individuals. FEMA has coined the term "whole community" to refer to this broader collective of organizations, government entities, and individuals that is essential to a successful response to a natural disaster or other emergency (Federal Emergency Management Agency, 2015b). The response to a disaster not only reflects the efforts of this group of organizations and individuals in the moments immediately after a disaster, but also their efforts throughout the preparedness cycle. The preparedness cycle is defined by the National Incident Management System (NIMS) as "a continuous cycle of planning, organizing, training, equipping, exercising, evaluating and taking corrective action in an effort to ensure effective coordination during incident response" (Federal Emergency Management Agency, 2015a). FEMA emphasizes the importance of preparedness because it is, as the NIMS definition suggests, a cycle, and decisions made long before a disaster strikes can have a huge impact on the response. An important preparedness decision made long before a disaster occurs is where resources should be located throughout a federal, state, local or tribal government's logistics network. Logistics Management is defined as "that part of supply chain management that plans, implements, and controls the efficient, effective forward and 12 reverse flow and storage of goods, services, and related information between the point of origin and the point of consumption in order to meet customers' requirements (Council of Supply Chain Management Professionals, 2013). In the disaster response context, logistics management means ensuring that survivors whose homes and livelihoods have been damaged or destroyed have food, water, and shelter. Humanitarian logistics is the term that has developed to encompass this aspect of logistics management, and similarly to logistics management in general, includes considerations for effective storage (allocation) of goods in a logistics network, highlighting the importance of this decision before a disaster occurs. This thesis aims to answer three central questions that will inform the development of a framework for evaluating the capacity of disaster response organizations to meet the needs of a disaster-affected population after an event using a set of metrics and an index recently developed by Acimovic and Goentzel (2015). The questions inform first where critical stock should be allocated long before a disaster as well as shortly before a disaster where an emergency management organization would have notice, such as a hurricane second, how the private sector's involvement changes the response capacity of a logistics network, and third, what the impact of reduced demand for critical commodities is on the logistics network and the ability of the network to meet demand. The framework will outline how these considerations relate to the types of organizations involved in a response, and metrics and an index can be applied in the United States context for evaluating performance of a logistics network. Two case studies are conducted on the logistics networks of the Federal Emergency Management Agency and the Florida Division of Emergency Management to answer the research questions. The case studies also explore the implications of varying quality of disaster risk data, assumptions about how to determine affected population, and stockpile data. They present the existing frameworks in place governing disaster response at the organizations studied, with a focus on the logistics guidance and/or requirements. The case study analyses rely on forecast disaster impact data to evaluate strategic logistics decisions made in the preparedness cycle. Decisions made in the preparedness cycle are evaluated here with forecast disaster data to facilitate an evaluation of the ability of these decisions to hold up against reality in a response. 13 The goal of this thesis is to provide a framework for assessing domestic disaster response capacity based on analytical metrics of how well equipped the supply chain is to respond with critical commodities to meet the needs of disaster survivors. This framework and the associated models and metrics are proposed to further the discussion about a broader index for assessing disaster resilience, and to aid decision makers at local, state and federal emergency response organizations as well as in the private and non-profit sectors in making decisions about allocation of critical commodities before, during, and after a disaster. The Literature Review section presents the relevant literature in humanitarian logistics, with a focus on needs assessment, prepositioning, vendor managed inventory, framework agreements, as well as the concept of public-private partnerships, and also includes a discussion on indices and metrics as they relate to this research. The Methods section describes the overall research design, model and data used in conducting the case study analyses. The FEMA Case Study section describes the assumptions and data unique to FEMA in greater detail and presents the findings of the case study on the FEMA logistics network. The FDEM Case Study section describes the assumptions and data unique to FDEM in greater detail and presents the findings of the case study on the FDEM logistics network. The Discussion section summarizes the key findings from each of the two case studies. It also includes a discussion of relevant cross-case findings as they relate to the literature, and finally it presents the framework for assessing disaster response capacity under the broader umbrella of preparedness decisions. 14 2 Literature Review Logistics is a critical aspect of any disaster response because it ensures that supplies get to those in need. An understanding of logistics in the humanitarian context is important because it differs from traditional supply chain and logistics theory and because this understanding will facilitate the more detailed evaluation of certain aspects of humanitarian logistics here. The aspects of humanitarian logistics that covered here are elements of preparing the response infrastructure - how are needs determined, how are goods stored and moved to reach the end user, and what policies and procedures support that. Within the topic of preparing the response infrastructure the focus is on prepositioning resources, vendor managed inventory and framework agreements. Next, public-private partnerships (PPP) will be covered as an overall approach to engaging the private sector in emergency response, presenting the general concept of PPP as well as their application to logistics is discussed. Mechanisms that govern the overall disaster response, such as federal or private organization policies and procedures, are not included here but are presented with the individual case studies as they constitute the context for these studies. Following the discussion of humanitarian logistics, literature related to logistics and disaster response and resilience indices is reviewed. An index often represents the quantification of a framework using benchmarks or ratings, and takes a framework from conceptual to something that can be measured. Similarly, literature and examples of performance metrics and evaluation were reviewed as a compliment to the literature on indices for their application to the development of a framework. The literature was identified through the review of several reports and articles related to the central themes of this research, including humanitarian logistics, publicprivate partnerships, prepositioning, and indices (Acimovic & Goentzel, 2015; Gustetic, 2007; L N Van Wassenhove, 2005). Subsequently a backward look at papers that had been cited in these initial papers was conducted to identify and supplement the review. Then, a search looking forward at papers that had cited these original papers was conducted (Webster & Watson, 2002). Some papers reviewed were newer and had not yet been cited. 15 Figure 2-1 provides a visualization of the conceptual framework for this literature review. The headings represent the broad topic, and the bullets beneath each heading represent the main subtopics covered. Preparing Response Infrastructure -prepoddonWng Public-Private Partnerships - in giesnd aer"SOeY ru~ponsn Indices and Metrics _nratrutpoure - Me' tndkoe id aMetrics In the Hmiralits meve V man~d -Disaster Response framewok of the liertue Figure* -1 Cocpta Context I Pramawrk Figure 2-1 Conceptual framework of the literature review 2.1 Humanitarian Logistics: Preparing the Response Infrastructure Humanitarian logistics is defined as 'the process of planning, implementing and controlling the efficient, cost-effective flow and storage of goods and materials, as well as related information, from point of origin to point of consumption for the purpose of meeting the end beneficiary's requirements' (Thomas & Mizushima, 2005). 'Requirements' in this definition is similar to needs, which drive the demand for goods and services in a disaster. The planning, implementation and control processes are akin to the activities for preparing the response infrastructure covered here. This section will discuss both the needs as well as four strategies for preparing the response infrastructure in advance of a disaster: prepositioning, vendor-managed inventory and framework an agreements. These strategies are presented because they are examples of methods that emergency management organization might employ to streamline the response and reduce the time to serve the disaster-affected population. 16 2.1.1 Needs assessment Needs is a broad term that encompasses many things in daily life as well as in a disaster. Darcy and Hofhiann (2003) present need as having several applications in the humanitarian context including to describe basic needs (e.g. food), to describe a lack of something considered essential, and to describe the need for assistance of some sort. The focus in this study is most closely aligned with the first application outlined by Darcy and Hofmann (2003), where needs are made up of items or supplies that fall into the 'basic needs' category. In a disaster response, an individual's needs are not satisfied by just one organization. Needs are satisfied through a patchwork of resources provided by federal, state and local government as well as non-governmental and faith-based organizations. FEMA, through its Ready campaign, provides an 'Emergency Supply List' of items to keep on hand in case of a disaster. At the top of this list are water, food, a means of battery-operated communication, and a flashlight (Federal Emergency Management Agency, 2014). Similarly, in Florida's Emergency Management Handbook, the top needs listed are medical, water, food, shelter, and fuel (Fugate, 2009). These basic needs drive the demand for goods that the humanitarian logistics network must respond to. In order to ensure an effective response to meet the needs of disaster survivors, organizations must make decisions on how to structure their logistics network. These decisions are for the most part made in the preparedness phase, before a disaster occurs. The goal of preparedness is to ready the whole community, including emergency management organizations, state, local, tribal, and federal governments, faith based organizations, as well as the non-profit and private sectors and individuals in the community to more effectively respond in the event of a disaster. The compliment to the preparedness mission is mitigation, reducing the risk of catastrophic damage in the event of a disaster. Mitigation can be defined as "a sustained action to reduce or eliminate risks & to people and property from [such] hazards and their effects" (Haddow, Bullock, Coppola, 2011). Sustained action is the operative phrase in the definition of mitigation, as it implies a goal of long-term reduction of risk, which has real potential to drive reduction in demand for critical commodities. This reduction in demand can in turn impact the planning and allocation of inventory within the logistics network. 17 In order to prepared for and respond to these needs quickly and at minimal cost, humanitarian logisticians rely on a number of strategies that are an important part of the overall infrastructure of a response. As Kovics and Spens note, one key difference between humanitarian and industry supply chains is that in the humanitarian supply chain, 'relationships are built "just in case" and suppliers are needed with a capacity at the time of need' (Kovacs & Spens, 2011). It is these "just in case" relationships, enabling physical strategies such as prepositioning, how they are accomplished (e.g. public-private partnerships, vendor-managed inventory), and the impact that these strategies have on the overall response that is emphasized in this thesis. 2.1.2 Prepositioning Prepositioning is defined as 'the storage of inventory at or near the disaster location for seamless delivery of critical goods' (Ukkusuri & Yushimito, 2008). Because the goods are located closer to the disaster affected population, the time that it takes responding organizations to provide these life-saving goods to people in need is far less than if prepositioning had not been initiated (Duran, Gutierrez, & Keskinocak, 2011). Because the goods must be in place prior to an event occurring in order to be the most effective, prepositioning requires additional coordination by the responding organization to ensure that enough of each of the essential commodities are placed in the appropriate locations. The literature on prepositioning in the humanitarian context relates to either where the optimal location of prepositioning sites should be (e.g. G6rmez, K6ksalan, & Salman, 2010; Ukkusuri & Yushimito, 2008), or what quantity of goods should be placed in & designated prepositioning locations (e.g. Guo, Wang, & Liu, 2014; Lodree Jr., Ballard, Song, 2012; Ozbay & Ozguven, 2008), and sometimes both (Duran et al., 2011). Specifically focusing on the allocation of goods in designated prepositioning locations here, Guo, Wang, and Liu have proposed a methodology that takes into account risks and dynamics in the humanitarian supply chain on both the supply and demand sides to optimally allocate supplies throughout a logistics network. The stockpile capacity model developed by Acimovic and Goentzel (2015) and adapted for use here suggests a method for answering the question of: given locations for prepositioning supplies, what quantity of each item should be stocked in each location within a network to minimize the response time or cost. Similarly, Duran et al. (2011), work with the relief organization 18 CARE International to determine the optimal warehouses of a finite set of nine warehouses to open, and how much to stock in each warehouse. The key differences between the work of Acimovic & Goentzel (2015) and Duran et al. (2011) are that the former considers cost as well as time in decisions about where to locate stock, and also includes truck travel. Acimovic & Goentzel (2015) also develop a set of metrics through their analysis in order to facilitate future evaluation of a logistics network. Also similar to these is the method used by Lodree Jr. et al. (2012), where a two stage stochastic linear programming model is used to evaluate the optimal allocation of supplies throughout the logistics network of a commercial retailer to meet the pre-event demand for commodities. 2.1.3 Vendor-managed inventory and framework agreements In addition to the tool of prepositioning resources closer to the disaster-affected population, emergency response organizations have options for how they procure and store essential goods like water and food. In Spens and Goentzel's case study on the Florida Division of Emergency Management they present the concept of vendor-managed inventory (VMI), where the vendor of the good rather than the emergency response organization, in this case the State of Florida Division of Emergency Management (FDEM) owns the good (Goentzel & Spens, 2011). The FDEM purchases the good only when it leaves the warehouse in a disaster response. This benefits both the FDEM as well as the vendor. The FDEM does not have to stockpile large quantities of goods in case of an event and risk these goods expiring, and the vendor gains additional space to store goods to supplement their own supply chain in the event of a disruption. VMI is a concept that has been used in commercial supply chains (Waller, Johnson, & Davis, 1999), but is relatively new to humanitarian logistics. Similar to VMI, Framework Agreements (FAs) are another tool used by humanitarian agencies to ensure that they have the critical commodities to meet the needs of disaster survivors immediately following an event. In FAs, suppliers "reserve inventory for the relief organization and promise to deliver supplies according to prespecified terms (such as pricing, packaging, etc.) once an order is made" (Balcik & Ak, 2014). The response organization then decides, after a disaster, whether or not to activate the agreement. Similar to prepositioning, depending on the locations of the supplier's warehouses, FAs could place critical commodities closer to the disaster-affected 19 population. Compared to VMI, FAs mean that the stock is stored in the suppliers' warehouses rather than a response agency's warehouse. Another key mechanism that disaster response organizations rely on is public-private partnerships, which can and often do go hand-in-hand with prepositioning, VMI, and framework agreements. 2.2 Public-Private Partnerships Humanitarian logistics research often cites the benefits of pulling strategies from and working with the private sector (Van Wassenhove, 2005; Van Wassenhove & Pedraza Martinez, 2012). This makes intuitive sense because private organizations are moving goods through their supply chains daily, whereas humanitarian supply chains are activated relatively infrequently. Additionally, in these infrequent activations, humanitarian supply chains are moving essential and often life-saving goods in less-thanoptimal conditions and under immense pressure to shorten delivery time. Van Wassenhove and Pedraza Martinez (2012) stress that a "successful response implies quickly building a supply chain," and that "responding is a less difficult task if the response system is well prepared". Partnerships between private sector organizations that are moving goods on a daily basis and emergency response organizations are an important avenue through which expertise and resources can be shared to ensure that the response system is well prepared. Public private partnerships (PPP), or public private collaboration is defined by Donahue and Zeckhauser as 'the pursuit of authoritatively chosen public goals by means that include engaging the efforts of, and sharing discretion with, producers outside of government' (Donahue & Zeckhauser, 2006). This definition is similar to those in other literature and reports on public private collaboration or partnerships (Gustetic, 2007; National Council for Public-Private Partnerships, n.d.; Osborne, 2000). A few have looked at applying public-private partnerships to disaster or emergency response or to logistics in a broad sense. Gustecic (2007) suggests a separation of responsibilities between public and private that is divided between emergency preparedness and emergency response, with the private sector handling the response and recovery side. The Council on Foreign Relations report titled 'Neglected Defense' states that the private sector is "adept at providing material, logistics, and know-how to provide relief in the aftermath of a disaster" and echoes the sentiment expressed in many papers on public- 20 private partnership in disaster response: that the private sector should not be an afterthought in planning (Flynn & Prieto, 2006). How then, should these observations be enacted? How should the private sector be included in the disaster response process? Several make the observation that private organizations, like individuals, feel some sense of responsibility to their communities and have a sense of civic duty, and that public organizations charged with disaster response should take note of this and engage with the private sector (Business Response Task Force, 2007; Flynn & Prieto, 2006; Tomasini & Van Wassenhove, 2009). In a review of several case studies on public private partnerships undertaken at each stage of the disaster response life cycle, Chen et. al found that "trust, reciprocity, and commitment to the collective", as well as alignment of incentives were key factors of success for a PPP (Chen, Chen, Vertinsky, Yumagulova, & Park, 2013). The Business Response Task force suggested systematic integration of the private sector into the nation's response to disasters, including removing legal hurdles that make working with the government less predictable and efficient (2007). This is especially important, they note, for the purchasing and vendor relationship regulations. In summary, the interests and incentives of public and private entities must be aligned, and legal and procedural hurdles to partnerships between the two should be removed for public-private partnerships to be successful. Once partnership can be achieved, the information and resource sharing can benefit both the public and private sectors (Bergqvist & Pruth, 2006). The public sector gains the expertise from companies moving goods on a daily basis, and the private sector benefits from a business perspective with the opportunity for learning and growth as a result of their involvement (Tomasini & Van Wassenhove, 2009). 2.3 Indices and Metrics for Evaluation Having sound strategies such as prepositioning and vendor managed inventory, as well as strategic relationships - including public private partnerships - are all essential in facilitating disaster response. An understanding of each of these mechanisms is essential in framing this research. As part of the development of a framework for assessing a community's ability to respond to a disaster, it was also important to understand the body of research around indices. The review of these indices will inform what is needed in a 21 framework to get towards an index. An index combines quantitative benchmarks for each aspect of a framework, and in doing so allows for a normalized means of comparison across sectors, organizations, states, etc. Much of the literature on indices focuses on economic indices, but for the purposes of this research, only logistics, disaster response, and resilience indices are reviewed. 2.3.1 Logistics indices Many of the logistics indices that exist were developed by international organizations like the World Bank, and not unsurprisingly focus on metrics related to international trade. For example, the air connectivity index was developed as a new measure of connectivity in the global air transport network. The intent of the index is to show how connected a given country is via air as an important indicator of the overall level of service provided by the global air transport system (Arvis & Shepherd, 2011). Hoffmann (2012) developed the Liner Shipping Connectivity Index (LSCI) in an effort to measure the containerization of trade and access to containerized transport services because these are important factors in determining a country's competitiveness in trade. It is calculated as the normalized average of five components. Next, the Logistics Performance Index (Arvis et al., 2014) "measures the on-the-ground efficiency of trade supply chains, or logistics performance." The index is developed based on results from a survey of freight forwarders, and uses Principal Component Analysis to weigh the responses to the survey questions into one number. 2.3.2 Disaster response and resilience indices As with other indices, the disaster response and resilience indices are aimed at quantifying the state of some service or entity, usually with the intent of providing a decision-maker or policymaker with more data and evidence to support their decisions. Berkeley's Institute of Governmental Studies BuildingResilient Regions Report presents the Resilience Capacity Index. The index is accessed through a web interface, similar to the National Health Security Preparedness Index, which is described next. The index, from the webpage is a "single statistic summarizing a region's score on 12 equally weighted indicators-four indicators in each of three dimensions encompassing Regional Economic, Socio-Demographic, and Community Connectivity attributes" (Foster, 22 201 1).The purpose of this is to gauge economic resilience and to give decision makers a tool for making more informed decisions on shoring up economic resilience. It is a simple tool to use, and in that is powerful in its messaging. The National Health Security Preparedness Index is an index that aims to quantify health security preparedness in the United States by looking at health security preparedness in each individual state (Robert Wood Johnson Foundation, 2013). It is updated annually, and the target scores for each portion of the index were determined by an expert panel, by regulatory requirements, by the leading literature, or were statistically determined. The purpose of this index is to "Strengthen preparedness, inform decision making, guide quality improvement, and advance the science behind community resilience" (Robert Wood Johnson Foundation, 2013). While not explicitly focused on developing an index, Cutter et. al (2010) formulate a set of indicators and baseline characteristics to measure community resilience. Indicators for social, economic, institutional, and infrastructure resilience as well as community capital are included in what Cutter et. al (2010) term the disaster resilience index. The main focus of the study is to develop baseline metrics from which to measure improvement within a community or compare across communities on similar metrics that would inform an index (referred to as a disaster resilience index), rather than on an index as a whole. Similarly to the Resilience Capacity Index (Foster, 2011), measures of socioeconomic resilience are included, but in addition to the Resilience Capacity Index, the indicators proposed also look at the role of the physical and community infrastructure in disaster response (Cutter et al., 2010). The indicators and sub-indicators included by Cutter et. al are fairly comprehensive and were developed to use readily available data. They do not include indicators to measure resilience of a logistics network or a community's stockpiles of critical commodities, which might be informed by the set of indicators and index that were developed recently by Acimovic and Goentzel (2015). Acimovic and Goentzel (2015) have developed the Response Capacity Index (RCI), which "measures the quality of stock deployment in the global disaster response network." This index, which is formulated using the results from a twostage stochastic linear programming (LP) model that analyzes stockpile capacity, will be applied in this thesis, along with several related metrics. The metrics and index 23 specifically evaluate the ability of a logistics network to meet the needs of affected individuals after a disaster, and could be used in complement to a broader set of disaster resilience indicators or index, like that outlined by Cutter et al. (2010) to inform community decision-making. 2.3.3 Metrics for evaluating performance in disaster response A number of the topics covered thus far have related to or been relevant for an evaluation of a logistics network's ability to meet the needs of a disaster-affected population. Prepositioning, if implemented effectively, has the potential to greatly reduce the time to serve a disaster-affected population (Duran et al., 2011). Indices are tools that can be used on a continuous basis to update and evaluate the allocation of supply throughout the logistics network. In addition to indices, literature on developing metrics in the humanitarian context in general is reviewed here to provide additional background on techniques to evaluate a logistics network. FEMA's National Disaster Recovery Framework (2011) stresses the importance of metrics for tracking and evaluating the progress of disaster recovery, and is one of the few frameworks reviewed that includes detail on how this might be achieved for disaster recovery. It does not suggest specific benchmarks or metrics but it does walk the reader through a number of activities that should be done to facilitate tracking progress in disaster recovery, and developing metrics for assessing how well a community is doing. The three overarching activities suggested include conducting a baseline impact assessment after a disaster to understand the extent of the disaster's impact on the community, identifying the desired outcome as holistic results rather than simply numerical targets for units delivered or infrastructure constructed, and conducting a cross-sector assessment that includes sectors like environmental, housing, health, business, and infrastructure in the picture for what it means to conduct a successful recovery (Federal Emergency Management Agency, 2011). This is a good point of reference, but does not provide clear benchmarks or metrics for evaluating progress. Developing clear, effective metrics in logistics is challenging. Davidson (2006) cites some of the challenges of implementing metrics in the non-profit sector, specifically focusing on the humanitarian sector. She notes that the culture of the non-profit sector may make the implementation of a set of performance indicators or metrics difficult, 24 citing overall organizational culture as well as communication within the organization, across departments. Caplice and Sheffi (1994) develop a method for evaluating logistics metrics in general, suggesting eight criteria for assessing the overall quality of a metric, including the metric's validity, robustness, usefulness, integration, economy, compatibility, level of detail, and behavioral soundness. While developed for application to traditional logistics, these criteria can be used to evaluate metrics in humanitarian logistics as well, and to inform the use of metrics to assess the logistics network in humanitarian response organizations. The background literature in humanitarian logistics, including needs assessment, prepositioning, VMI, FAs, as well as PPPs, indices and metrics were used to frame the case studies and discussion around how this research can be applied. An understanding of these concepts informed the methods used and the analyses conducted in the case studies. Finally, the Discussion section is used to describe how the concepts presented here relate to the findings of the research. 25 3 Methods This section describes the methods used to answer the three central research questions First, the research questions and the overall research design are presented, including an outline of the case study analysis, the formulation of the model used in the study and the metrics derived from the model. Next, the data collected and the data collection and cleaning process is described. Finally, the methods by which the three central research questions will be analyzed using the model and metrics derived from the model are described. 3.1 Research Question This thesis answers three central questions that informed the development of a framework for evaluating the capacity of disaster response organizations to meet the needs of a disaster-affected population after an event. These are: 1. Where should inventory be placed, considering both normal allocation of inventory as well as allocation of inventory in advance of a notice event such as a hurricane? 2. How does private sector involvement change the response capacity of a logistics network? 3. What is the impact of reduced demand for critical commodities on the logistics network's ability to respond? These questions can inform important decisions regarding the logistics network in an emergency management organization. The research relied on metrics developed by Acimovic and Goentzel (2015) to evaluate these questions, including an index combining several evaluation metrics. The sections that follow describe the specific research design and the model, as well as the metrics used to assess these three research questions. 3.2 Research Design An exploratory case study method was used here for two reasons. First, because this thesis aims to describe natural phenomena in depth and in the context of realistic scenarios and assumptions, and second, because the study incorporates multiple data sources with the understanding that there may be items of interest for which no data are available (Yin, 2009). Many emphasize the connection of theory to reality as an important reason for applying case study research in general, and in particular emphasize the use of exploratory case studies to advance theory (e.g. Edmonson & McManus, 2007; 26 Pedraza Martinez, Stapleton, & Van Wassenhove, 2009; Stuart, McCutcheon, Handfield, McLachlin, & Samson, 2002). Furthermore, the case study method has been used some in humanitarian logistics literature but could and should be used more often, Kunz and Reiner (2012) assert in their meta-analysis of the literature. The research was conducted using a two-stage stochastic linear programming (LP) model (Acimovic & Goentzel, 2015), actual supply data from emergency response organizations, and disaster risk data generated for use in the insurance industry. The LP model is used to further advance theory around the application of this type of model to inform decision-making in the humanitarian context. Actual data from emergency response organizations are used to simulate the intended context as accurately as possible. Two exploratory case studies were conducted on the disaster response logistics networks for the Federal Emergency Management Agency (FEMA) and the Florida Division of Emergency Management (FDEM). More specifically, an embedded multiplecase study design was used in this study. This design was chosen as the results from the analysis embedded within each case will not be pooled across all cases, rather it will be applied to the specific case in which it was conducted (Yin, 2009). The cases were selected on the basis of available data as well as presence of natural hazards. Florida experiences frequent natural hazards, and logistics data such as location of warehouses and stock levels could be obtained either through agency contacts or publicly available sources. In the case of FEMA, the Agency responds to disasters of all types across the country, and inventory data from their warehouses were provided in support of this study (FEMA Staff 1, 2015). The unit of analysis in each of the case studies is the logistics network in the emergency management organizations studied (FEMA and FDEM), with embedded units of analysis on specific decisions that would be made by these emergency management agencies in preparing for a disaster. The analysis was conducted using the stockpile capacity model as well as relevant metrics and the associated Response Capacity Index (RCI) developed by Acimovic and Goentzel (2015). 3.3 The Model In order to conduct the evaluation in the case studies, the stockpile capacity model, a twostage stochastic LP model ("the model") (Acimovic & Goentzel, 2015) was used. The 27 model takes in the locations of and stock in existing warehouse facilities as well as the locations and quantities of post-disaster needs and seeks to minimize the time or cost to meet the needs. It uses the existing locations of the warehouses and the existing stock levels to determine both the optimal allocation of stock across warehouses to minimize time or cost, as well as the time and cost of keeping the commodities in their original locations. It is important to note that the model is intended to inform the positioning of emergency response capacity in advance of a disaster's impact and it is not intended to be precisely predictive of when supplies will arrive in a given response effort. The model was originally developed to assess the stock allocation in the six United Nations Humanitarian Response Depots located across the world to serve disasters that are also spread across the world. Despite its development for this international context, the model is flexible to analyze smaller geographic areas depending on the data that are input (Acimovic & Goentzel, 2015). In this study, the model will be applied to the United States as a whole and to the individual state of Florida. 3.3.1 Model formulation The model has two stages, the first of which records the allocation of inventory (if utilizing actual allocations) or determines the optimal allocation (if utilizing optimal allocations) and the second of which allocates the resulting supply to demand. The demand can occur at multiple disaster nodes for each disaster scenario. The model minimizes either total expected time or cost to deliver items from the supply nodes to the disaster locations. A dummy supply node is used to satisfy disasters where the demand exceeds the available inventory in the system. Additionally, in certain cases a delivery deadline can be imposed to include only solutions able to meet the needs within a given time window. 28 The model formulation is as follows (adapted from the model developed by Acimovic and Goentzel (2015) to include multiple locations per disaster event): I 3 i - Set of all depots and warehouses except the dummy node iw Iw =I U Lw - the dummy supply node - Set of all depots including dummy node K 3k - Set of possible disaster scenarios J 3j - Set of possible disaster locations M 3 m - Set of disaster types N 3 n - Set of line items R 3 r - Set of transportation modes Cijnr Tijr cwTw - Cost in dollars to transport a single item n from i toj via mode r - Time to ship a single item from i to j via mode r (item independent) - The cost (time) from the dummy supply node to a disaster ) Pk - Probability of scenario k occurring (1 pk = 1 SJmnt - The domestic/local capacity to respond to a disaster in locationj of type m at period t for item n X - The inventory vector dictating how many items are stored at each depot i. X's are the elements of this vector. XE N - Starting inventory in the system as a whole, not including the dummy node TAP - The population vector dictating the Total Affected Population at each locationj for disaster scenario k. TAPj,k's are the elements of this vector fljmnt dk,; - Factor converting number of people affected into the demand for item n at locationj at time t for disaster type m max (TAP X/3j,m,n,t - Sjm,n,to 0) - Actual demand for item n for disaster k ykjnr - Decision variable for how much of n to send from i to the disaster in scenario k, which has locationsj, via mode r. (Note n is in in the subscript, but for now the problem is decomposed by item, so it is redundant) X - The III dimensional vector of starting inventory in each supply node. Its elements are X. (Depending on the formulation, this may be an input by the 29 user or a decision variable) 0 - Delivery deadline: If 0 < oo, arcs whose Tyr > 0 are removed for both cost and time objectives The stochastic LP that minimizes time is (note: the LP that minimizes cost is analogous): Vw(X,n) min pk k I Ti,j,rkYnr iElW,j,r Vk yy.dk S. t. i EIW,r I Y~nr yunr - niIJ XVi E I, k r Yynr 0 Vi, k,r The formulation that optimizes inventory allocation is very similar, with X as a decision variable, and with an additional constraint to ensure the sum of the supply is equal to X. VoPT,W x =n)min k k k iEiW,j,r S. t. yL = dnk i EIW,J,r y nr X Vi E I, k r Xi =,x tel >0nr y X1 30 0 ViE Iw,k,r ViEI The objective values with the dummy costs subtracted are defined as: Vw(X, n) - V(X,n) E p w, 1 r Yw,n,r r k VOPT (X, n) j VOPTW(X, n) - pk k TLwjr Ywjk r r & where the yiw,n,r's are fixed as the respective solutions for the above LPs (Acimovic Goentzel, 2015). 3.4 Metrics Derived from the Model The model solutions are used to derive several operational metrics, some of which were applied in the analysis. The most relevant metrics for this study and how they are derived are explained here. The dummy value TMw can be replaced by cw if cost if being minimized or measured instead of time. V(Xn) VOPT (, n) I y Balance Metric pk dk Weighted average of demand pkmin (dkX) Average demand met k I I' k Y' >Lpk Fraction of disasters served completely k:dk X V(X) It I Average time (cost) per unit delivered , S8 Fraction of demand served 31 To calculate A requires solving both LPs. The metrics y, it', y, and 6 can easily be calculated without solving any LPs. The following sections describe the metrics selected for this analysis from those described in Acimovic & Goentzel (2015) in detail and how they were used to evaluate inventory decisions. 3.4.1 The balance metric The balance metric is intended to measure whether a given amount of inventory is generally in the correct place or not. More specifically, it estimates how far out of balance the actual allocation of inventory is relative to the optimal, and is based on the metric set forth by Acimovic and Graves (2015). Properties of the balance metric, as outlined in Acimovic and Goentzel (2015) include: 1. It is an approximation of the fractional increase in cost (time) to serve beneficiaries given that one's inventory is allocated as it actually is as opposed to being allocated optimally. As such, if inventory should be in Mountain View, CA but it is actually across the country in Frederick, MD, the metric will suffer. However, if the inventory should be in Frederick, MD, and it is actually nearby in Cumberland, MD, the balance metric value will not change substantially from optimal. 2. The optimal value is 1. Anything greater than 1 is considered out-of-balance. 3. It is not strongly affected by abnormally large disasters in the dataset, i.e., it is relatively robust to outliers. Often, the largest few disasters in a set of scenarios far exceed the on-hand supply for any item. The objective functions and solutions of the model depend only on those people who can be served by the on-hand inventory. People affected by a disaster who cannot be served by the current inventory due to a lack of supply have no bearing on the optimal allocation. Thus, if an item can serve only 1,000 people, then the optimal allocation, the time-toserve those who can be served, and other output metrics will be identical whether the largest disaster across the scenarios affects 1,000 people or 10,000,000 people. 4. The metric is affected by which depots are considered. If a new depot is opened in a disaster hotspot and no inventory is actually moved there, the balance metric will increase (because V"PT(X, n)will decrease). In this sense, operational 32 managers can be alerted to the fact that the inventory is out-of-balance given the new depot. 3.4.2 Fraction served and fraction of disasters covered Fraction served (X) represents the fraction of the weighted average of demand met, where the weights are derived from the disaster scenario probabilities. This value gives a sense as to whether the inventory of items stored in the depots in total is appropriate. It does not depend on how the items are allocated among the depots (unless the demand deadline 0 < oo). This metric can be influenced by very large disasters. The inclusion of a disaster scenario affecting tens or hundreds of millions of people will have a significant impact on g, the denominator in the fraction that defines X. Thus, these values in general may appear low, and must be interpreted with this in mind. Fraction of disasters covered (6), on the other hand, is relatively robust to outliers. If there are only 1000 items in stock of an item, then whether an unserved disaster affects 1001 or 10,000,000 people is irrelevant in the calculation of S. This robustness comes at the cost of not conveying the magnitude of the disasters that go unserved. 8 provides different information from X, and the two together can help operational managers understand the adequacy of the total inventory level (Acimovic & Goentzel, 2015). 3.4.3 Time per unit delivered The value <p represents the average time (cost) to deliver one unit to a beneficiary from a depot. This will, of course, not be equal to the actual time to deliver an item (which would be stochastic itself and would depend on specific factors such as traffic and weather), but rather will be the time from the depot to the location of the disaster as calculated using the assumptions and data described in Section 3.5.3. Even though this number should not be used to estimate how long it will take for supplies to arrive at a disaster site, it can provide valuable and objective information when comparing scenarios and strategies with each other (Acimovic & Goentzel, 2015). 3.4.4 Response Capacity Index The RCI is an index to evaluate the quality of stock allocation for an organization or set of organizations providing humanitarian aid (Acimovic & Goentzel, 2015). It is meant to 33 be a tool to allow decision-makers to evaluate and compare options for positioning of inventory, because it provides a common standard on which to compare these decisions. Each of the five metrics included in the RCI is scaled from 1-10 according to the best and worst possible values for that metric. In the case study sections where RCI is used, the specific values used as the best and worst for each of the metrics are explained, as well as where the actual value fell between those values and how it was scaled. 1. Optimal time to serve (OPT) refers to the average time to serve if the inventory is optimally allocated. This evaluates not only the total inventory level, but also the network of warehouses because it takes into account the optimal allocation of inventory. In essence, it is a way to evaluate how well positioned the warehouses in a logistics network are because the stock allocation cannot be improved further from the optimal. 2. Balance metric, time (Ar) refers to the balance metric when minimizing time. The balance metric is calculated as shown earlier to describe how well allocated the inventory is currently as compared to the optimal allocation. The best and worst possible values may differ depending on the range of potential scenarios present in the logistics network being evaluated. 3. Balance metric, cost (Ac) refers to the balance metric when minimizing cost. It is calculated similarly to the balance metric for time. 4. Fraction of disasters covered (6) refers to the fraction of disasters that are served completely given the current inventory level. This number is robust to outliers. The worst value is 0, and the best possible value is 1 for all cases, so the value is simply multiplied by 10 to scale it for the RC. 5. Fraction of disasters covered in 12 hours (612) refers to the fraction of disasters that are served completely in 12 hours time. This is included in the RCI to serve as another way of evaluating the overall positioning of warehouses, independent of total inventory in the system. Similar to the fraction of disasters covered, the worst value is 0 and the best possible value is 1 for all cases, so the value is simply multiplied by 10 to scale it for the RCI. 34 It is important to note that the RCI represents an initial proposal of a methodology for evaluating the overall capacity of a logistics network to meet the needs of disaster survivors. It could benefit from additional input from experts in terms of what metrics should be included as part of the RCI and how each of the metrics should be weighted. The intent of the application of the RCI in this thesis was to apply it to a new context and evaluate how well it transfers to this new context, as well as to suggest methods for evaluating components of the index. 3.5 Data This section describes the data that are used in this thesis, as well as key assumptions made regarding these data. First, the disaster risk data are described, followed by stockpile and finally time and cost data are described. 3.5.1 Disaster data The model used in this thesis is not a model focused on forecasting potential future disasters and their impact. Instead, it assumes that the future is similar to the past, and uses a catalog of past events to estimate future damage, assuming each has an equal probability of occurring in the future (Acimovic & Goentzel, 2015). The model is flexible to any proposed disaster forecast, and two types of disaster data are used in the analysis conducted for this thesis. They are described in detail below: 3.5.1.1 EM-DA T data The Centre for Research on the Epidemiology of Disasters manages the Emergency Events Database (EM-DAT). This database includes records on the impacts of over 18,000 disasters across the world from 1900 to present (Centre for Research on the Epidemiology of Disasters, 2015). For the purposes of this thesis, EM-DAT data were used for disasters impacting the continental United States from 1990 until the summer of 2013 in the assessment of FEMA stockpile data, and are only considering sudden onset natural disasters. The key adjustments to these disaster data for analysis include: 1. Each disaster event is associated with only one location. While the model as adapted from Acimovic & Goentzel (2015) can accommodate greater than one location per disaster event, this assumption is a function of the EM-DAT dataset. Most disasters in the dataset only have one location and where events have 35 multiple locations the affected population is not separated by location, which makes it difficult to discern the distribution of affected population throughout the locations given. There were a total of 317 events in the database impacting the continental United States, 24 of which had locations that were difficult to reconcile (e.g. impacted locations that were far apart geographically, or included a county name with no state name, or were blank). These 24 records were removed from the total set of 317 events. For events where multiple locations were recorded in the EM-DAT record, a central location was estimated (e.g. for Caroline County, VA the town of Milford, VA). 2. In line with Acimovic & Goentzel (2015) the "Total Affected" field in the EMDAT database was used to measure the number of people affected by a disaster. The EM-DAT database description of "Total Affected" is the sum of "people suffering from physical injuries, trauma or an illness requiring medical treatment as a direct result of a disaster", "people needing immediate assistance for shelter" and "people requiring immediate assistance during a period of emergency; it can also include displaced or evacuated people" (Centre for Research on the Epidemiology of Disasters, 2015). This number was used instead of "Number Killed" because it provides a better estimate of the number of people requiring assistance in the form of the critical commodities which are evaluated here: water and food. Records with null values for "Total Affected" were excluded from this study. 3. The estimate for affected population is translated into units of an item demanded by multiplying the per-person units of need for the initial push of supplies that a logistics network would need to support following a disaster by the number of individuals affected. In line with Acimovic and Goentzel (2015), the initial push of supplies is assumed to be enough supplies for 72 hours (e.g. 2 MREs/person/day, over 3 days equals 6 MREs/person). 4. Only sudden onset disasters and epidemics affecting the continental United States were included in the study of the FEMA disaster response logistics network. This includes: earthquakes, epidemics, floods, mass movement dry, mass movement wet, storm, volcano, and wildfire. Not included were: complex disasters, 36 droughts, extreme temperature disasters, industrial accidents, insect infestations, miscellaneous accidents, and transport accidents. & 5. Post-1990 data were used for completeness and homogeneity (Acimovic Goentzel, 2015). 3.5.1.2 Insurance risk data Data were obtained from DataCo, a firm specializing in catastrophe risk quantification and decision analytics firm serving the risk and insurance industry, among others, to enhance the model that estimate the residential and commercial damage as a fraction of total insurable value in each county in Florida. DataCo combines a catalog of natural and man made catastrophes with exposure data, vulnerability functions and mitigation measures to develop estimates of property damage. For this project, the perils considered included only hurricane wind, storm surge and associated precipitation caused damage impacting the State of Florida, and did not take into account the man made events in DataCo's catalog of events (DataCo Staff, 2015a; DataCo, 2015). The DataCo Hurricane Model for the United States contains a 10,000-year stochastic catalog of potential hurricane events, which provides 10,000 iterations of next year's potential hurricane activity under current climate conditions. The Hurricane Model used to generate the damage data for this project begins with the historical hurricane events that have occurred since 1900. For each historical event, a number of characteristics for each type of event, such as radius of maximum winds, minimum central pressure, wind speed, and depth of storm surge in hurricanes, for example, to generate the stochastic catalog of events. Each unique combination of these characteristics defines a simulated event. For the State of Florida the catalog included thousands of disaster event scenarios. The characteristics of the disasters combined with physical characteristic data and vulnerability functions for each property allow DataCo to provide an estimate of expected damage. The data used for this study are the aggregated expected damage levels of all insurable commercial or residential properties for all 67 counties in Florida. It is important to note that these are out of all insurableproperty, rather than all insuredproperty. This gives a more accurate representation of the damages experienced in a disaster, as disasters do not solely impact properties that are insured. The dataset used included both historic and stochastic disaster events. 37 The disaster risk data are structured in the format shown in Table 3-1: Table 3-1 Structure of disaster risk data (DataCo, 2015) Event ID County FIPS Industrial Damage Commercial Damage Residential Damage X1,C% X1,1,R% 1 1X1,1,% 1 3 XI,3,I% XI,3,C% X1,3,R% 1 5 X 1,5,1% XI, 5,c% X1,5,R% The Event ID field is a unique identifier for each disaster event in the dataset. The County FIP field is the field for the County Federal Information Processing Standard (FIPS) code, a unique identifier given to counties and county equivalents in the United States. The industrial, commercial and residential damage fields represent the fraction of damage or loss over total insurable value. For each Event ID, industrial, commercial and residential damages were reported for each county in Florida. The key assumptions and adjustments made to the disaster risk dataset for Florida include: 1. Each disaster event is associated with multiple locations. This is different than the EM-DAT disaster database, where each disaster event is associated with only one location. Only disaster-county pairs where the impact was greater than 100 persons were considered, as it is assumed that the county has the ability to respond to events affecting less than 100 persons. A disaster-county pair represents the unique combination of the disaster event ID and the county. That is to say, no disaster impacted the same county twice in the dataset. 2. Total Affected Population for each event-county pair was determined by multiplying Residential Damage (%)x 2014 County Population(State of Florida Office of Economic & Demographic Research, 2015). Additional methods for determining Total Affected Population were considered, but ultimately this simple and intuitive method was used. This method was chosen over others considered as it did not risk double-counting affected individuals by including the effects of commercial and industrial losses (e.g. an individual whose home is damaged and whose local grocery store is also damaged), or risk misinterpreting 38 the demand for critical commodities by estimating this figure based on the typical demand for commodities in non-disaster times. 3. The estimate for affected population is translated into units of an item demanded by multiplying the per-person units of need for the initial push of supplies that a logistics network would need to support following a disaster by the number of individuals affected. In line with Acimovic and Goentzel (2015), the initial push of supplies is assumed to be enough supplies for 72 hours (e.g. 2 MREs/person/day, over 3 days equals 6 MREs/person). 4. Each disaster event, whether historic or stochastic, was assumed to have an equal probability of occurring in the future. 5. Only hurricane wind, storm surge and associated precipitation damages were included in the disaster risk dataset used in this project (DataCo Staff, 2015a). 6. The damage level was modeled at the property location level, then aggregated for each county. 7. The location of disaster events was assumed to be the county seat for the affected county identified in the disaster risk dataset, because this is most likely to represent a central location within the county where individuals might go to receive aid, or where local emergency management or response organizations might set up distribution points. 3.5.1.3 Simulated propositioningdata Data from a disaster response training exercise were also obtained from DataCo to run the prepositioning scenario analysis. The data provided were based on an actual hurricane event: Hurricane Wilma in 2005, and consisted of disaster impact forecasts at several time intervals prior to the time when the hurricane made landfall on October 24, 2005 at approximately 6:00 a.m. CDT. The data from these forecast reports that were used for the prepositioning analysis was the population at risk reported in two state level reports (DataCo, 2014). The first report and associated analysis were generated for the conditions and forecasts that existed at October 21, 2005 at 7:00 p.m. CDT, and the second report and associated analysis were generated for the conditions and forecasts that existed at October 23, 2005 at 1:00 p.m. CDT. Each analysis used DataCo's stochastic modeling 39 techniques to expand the operational forecast model ensemble (40-50 discrete simulation tracks) to create an enhanced ensemble (500 discrete simulation tracks). The population at risk is reported as a statewide average displaced population, and is broken into population at risk from just storm surge, just wind, and both storm surge and wind. The combined figure for displaced population from wind and surge is used here. For each member of the enhanced ensemble considered in each report (forecast period), the displaced population was considered to be100% of the census block population if estimated winds in a census block were over 74 mph or storm surge was over 4 feet. Summing that figure across the state for each scenario and taking the simple average of the 500 scenarios gives the average population at risk estimate for that forecast period (DataCo Staff, 2015b). In addition, for each report, every county in the state is classified as having a high, medium, or low risk of being displaced by wind only, surge only, or wind and surge. To arrive at a county-by-county distribution of the displaced population for each of the two forecast periods considered these two pieces of information were combined. First, counties were coded with a (1) if designated as low risk, (3) if medium, and (5) if high, the sum of scores for the state was used to normalize each county as a fraction of the overall state score. This normalized fraction was then multiplied with the average displaced population to estimate the total affected population in each county for the two forecast periods considered in this analysis. Hurricane Wilma made landfall at on October 24, 2005 at approximately 6:00 a.m. CDT (NOAA, 2005). The two forecast periods considered for the prepositioning analysis were Module 1 on October 21, 2005 at 7:00PM CDT, and Module 3 on October 23, 2005 at 1:00 p.m. CDT. Traditionally, a key piece of data that would be used by decision makers in the United States to make prepositioning decisions is information on the storm's path from the National Hurricane Center. Figure 3-1 and Figure 3-2 show the National Hurricane Center's cone forecasts of the disaster impact for the closest time approximations to the reporting times available from DataCo, for comparison of the data that would have been available and used in decision making in Hurricane Wilma. 40 Figure 3-1 National Hurricane Center cone and warnings for Hurricane Wilma on October 21, 2005 at 4PM CDT (5PM EST) (National Hurricane Center, 2005) ) :ox. Distance Scale ( Statute Miles 125. tSO 3S 500 4urfcane Wma :ctobor 23, 2005 10 AM CDT Sunday WS TPCiatlonaI Hurricane Center kdvlsory 33 :urrent Center Location 22.7 N 5.8 W Wac Sustained Wind 100 mph iiiM Movement NE at 8 mph Cunent Center Location Forecast Center Positions H Sustained wind , 73 mph S Sustained wind 39-73 mph Potential Day 1-S Track Area Hurricane Warning ' Hurricane Watch - Tropical Storm Warning Figure 3-2 National Hurricane Center cone and warnings for Hurricane Wilma on October 23, 2005 at 10AM CDT (11AM EST) (National Hurricane Center, 2005) 41 The intent of the analysis for prepositioning is to answer the question of whether the allocation of goods to prepositioned locations made, in this case, 57 hours (Report 1) before an event differs significantly from prepositioned locations made 17 hours (Report 3) before landfall of a hurricane. While these intervals do represent relevant planning intervals, ideally a third time window even earlier than 57 hours before the event would have been provided, as that would more likely be when decisions about prepositioning would first be made. 3.5.2 Stockpile data Local, state and federal emergency management organizations keep stockpiles of critical commodities in preparation for disasters. These stockpiles allow the organizations to meet the immediate needs of disaster survivors faster than would be possible if supplies had to be procured from a third party following an event. This section describes the stockpile data that was obtained from the two organizations studied. Stockpile data, including quantity in each warehouse and location of warehouses were obtained from FEMA Logistics for both water bottles (liters) and meals ready to eat (MREs). The stockpile data used for water bottles is current as of March 2014, and the MRE stockpile data is current as of March 2015. In the State of Florida, data were obtained from publicly accessible sources. Warehouse location data were estimated based on the State of Florida Unified Logistics Section Logistics Operations and Management training course and stockpile data are assumed to be similar to those cited by Goentzel and Spens in their case study on FDEM (Florida Division of Emergency Management, 2013; Goentzel & Spens, 2011). 3.5.3 Time and cost data This section describes how the distance, time, and cost from each warehouse to each disaster site were calculated. These figures are calculated for truck transportation only, and air and sea transportation are not considered as the study focuses on immediate deployment of the stockpile. In the case of disasters impacting the continental United States, it is assumed that stockpiles will be moved via truck. In line with Acimovic and Goentzel (2015), it is also assumed that the cost of transporting goods is linear in the number of units being transported. Thus, for each warehouse-disaster-mode triplet, the 42 time and cost-per-metric-ton-km are calculated for the route. This, paired with information on the weights of each item, allows the cost of shipping to be calculated for a single unit on a specific route (Acimovic & Goentzel, 2015). In order to calculate the distance and time between a warehouse and a disaster, Google's "Distance Matrix" application programming interface (API) (Google, 2014) is used. This provides the duration and distance between two points based on driving. Although one can prefer to not use ferries with this API, if a ferry route exists, it will return the distance using a ferry. In line with Acimovic and Goentzel (2015), this is assumed to be reasonable because if a ferry route exists, organizations may use this route (or a similar one) to transfer goods via boat. With the distance for truck routes, the time and cost are then calculated assuming there is a fixed and variable component. For cost, moving a truckload of goods is assumed to incur 237USD fixed cost and 0.59USD per metric ton per km variable cost via truck. In line with Acimovic and Goentzel (2015), the time and cost parameters were estimated based on data spanning numerous commercial and humanitarian projects conducted at the MIT Center for Transportation & Logistics. As such, the parameters may vary among different organizations working in different contexts in different locations. To calculate the times associated with truck travel along the routes, the Google API travel times were used for driving, rounding up to the nearest whole hour for all drive times output using the Google API (Google, 2014). Driving is possible for all scenarios studied in this thesis, including for FEMA and the State of Florida. In addition, a fixed time was added to truck travel in the United States for loading and unloading goods. This time varies for different organizations, but the standard time used for all organizations was 6 hours. For inventory that is owned by the emergency response organization being evaluated (e.g. FEMA physical inventory) this fixed time represents the sum of the time that it takes for the truck to arrive at the distribution center where supplies are located which was assumed to be 3 hours, the time to load the truck which was also assumed to be 3 hours. For inventory that is not owned by the emergency response organization but that is obtained through existing contract agreements, this fixed time of 6 hours to acquire and load a truck was assumed to hold, and that there is an additional fixed time of 6 hours added to the time to place and process the order. For the 43 purposes of this study it is assumed that driver rest will not be an issue as team drivers will be hired to meet the need in the fastest time possible. 3.6 Case Study Scenarios For each of the case studies an embedded analysis was conducted using the model output and relevant metrics developed by Acimovic and Goentzel (2015) to evaluate a set of key resource allocation decisions, representing the central research questions. The decisions represent the types of issues that an emergency manager or logistician would face in preparing for a disaster, and they are (1) Where should inventory be placed, considering both normal allocation of inventory as well as allocation of inventory in advance of a notice event such as a hurricane? (2) How does private sector involvement change the response capacity of a logistics network? (3) What is the impact of reduced demand for critical commodities on the logistics network's ability to respond? The following sections describe how the metrics will be applied in the case studies to answer these research questions. 3.6.1 Allocation of inventory Allocation of inventory refers to decisions about where to locate stock of critical commodities. These decisions are made during non-disaster times as well as in advance of a notice event (e.g. a hurricane), and both applications are studied here. The analysis of physical inventory allocation in non-disaster times will be done in both case studies and will aim to assess the current allocation using several of the metrics derived from the model output, including the RC. It will compare the actual and optimal distribution of commodities across warehouses; show the allocation of critical commodities by inventory level (as a unit of inventory is added, where should it be placed?), and an idea of the demand served by hour with the current allocation as compared with the optimal allocation. These metrics will be evaluated for FEMA's current allocation of inventory, as well as its contract (private sector) inventory, and FDEM's current allocation of inventory in one warehouse, and prepositioning of inventory in staging areas throughout the state. 44 In addition to evaluating metrics for the current allocation, the model is extended from Acimovic and Goentzel (2015)'s application to evaluate prepositioning strategies for a notice event in the State of Florida, such as a hurricane. In this case, emergency managers can move supplies closer to locations most likely to be impacted by the event. To conduct this analysis, the catalog of disaster risk scenarios considered in the model is limited to only those that may potentially occur given the current trajectory of the storm and date of prediction, as is described in Section 3.5.1.3. In general, the geographic scope of these scenarios is narrowed as the storm approaches land and the forecasted track becomes more accurate. To evaluate prepositioning decisions using the model, the metrics for the actual allocation of stock in the system in one central warehouse will be compared with metrics for the optimal solution when sites that have been pre-identified as locations for prepositioning supplies in a notice vent are included in the model. The metrics that will be most important in evaluating a prepositioning strategy include the allocation of inventory, the change in demand met by hour when adding a prepositioning location, and the change in fraction of overall demand served when adding prepositioned locations. Prepositioning makes sense when the disaster-affected population can be served faster, but the emergency manager must also consider costs in decision-making about early deployment of supplies. 3.6.2 Impact of private sector commitments or contractual agreements Private sector organizations become involved in disaster response through a few mechanisms. The details of the mechanisms (contracts, donations, etc.) by which they are involved in the supply chain are not explored. Instead, this study is concerned with the amount of goods that private sector organizations have committed to the response, when they will be available, and where those goods are located. The impact of private sector commitments will be evaluated here for contracted private sector commitments to FEMA's logistics network in order to provide a method for conducting future analyses of other forms of private sector commitments. The key metrics that will be evaluated include the RCI, the actual and optimal times and costs to serve for the inventory owned by the organization as compared with adding capacity from private sector commitments, and the change in overall demand served with the 45 addition of the private sector capacity. Using FEMA as the test case, the metrics for the current inventory levels will be compared with those when outside suppliers stock becomes available after 24, 48, and 72 hours, according to the contract structure set up by FEMA Logistics. This assumes the same demand, in aggregate, as is used to assess physical inventory allocation. 3.6.3 Impact of resilience goals To evaluate the impact of resilience goals put in place by emergency response organizations or state governments, a change in demand is used as one way to quantify the effect of resilience goals. Methods for estimating how much demand changes as a result of resilience goals are not covered in this thesis, rather a demand reduction of 10% is modeled to evaluate its impact on response metrics. Any value could have been chosen, but 10% was selected as it is high enough to be expected to have some impact, but low enough to be a plausible reduction in demand that could be achieved with increased resilience. The metrics evaluated to measure the impact of resilience goals include the overall demand met with reduced demand as a result of resilience goals, as well as the cost and time to serve the original demand as compared to the decreased demand. Table 3-2 summarizes the case study analyses just described, including the decisions informed by the analyses, the scenarios and metrics evaluated, and the data required to conduct a given analysis. This provides a general guide for how the case studies will be structured. 46 Table 3-2 Summary of case study analysis process Decision Scenarios _________Considered FEMA: Current Allocation of Inventory Where to allocate inventory? Where to preposition inventory in a notice event (e.g. hurricane?) Florida: Current allocation of inventory (all in one warehouse) vs. Allocation evenly throughout the state Metrics Evaluated MtisEaued * Response Capacity Index e Ability to meet * Current allocation of inventory with option to stock closer to disaster events in LSAs for two time intervals before a notice event * (e.g. hurricane) * * demand Comparison of current vs. optimal inventory allocation Comparison of current vs. optimal inventory allocation Ability to meet demand Time to serve Data Required DaRqird * Case Applied To Warehouse locations * * Warehouse stock Disaster data (historic and forecast) * * FEMA FDEM All from base allocation, plus: prepositioning * locations Cone of expected isaster impact several * FDEM storm Should I enter into a contract with the private sector? Compare current inventory levels with consideration Current allocation strategy for Private sector perspective: How much am I improvng the of ude t ader capacity added after 24, 48, 72 hours, according to the contract structure contract stock vs. optimal allocation Demand served by hour * All from base allocation, plus: e Contractual * FEMA private sector agreements Locations of private sector warehouses response? What is the benefit, in terms of cost and time to serve the affected population, if resilience goals reduce overall demand? * Reduction in demand of a given state or county of a set percentage e Ability to meet demand Comparison of current vs. optimal allocation of inventory 47 All from base allocation, plus * Resilience goals for a county or state * e FEMA FDEM 3.7 Limitations The limitations of this study are largely related to two factors. First, limitations exist related to the data available for analysis. This section discusses the limitations to the study, and areas where the results would be strengthened with better data. First, the DataCo disaster risk data provided for Florida (DataCo, 2015) to represent the hazards present include only hurricane hazards. This means that they exclude riverine flooding hazards as well as other natural disaster risk that is present in these states (e.g. tornado, severe thunderstorm). Another limitation of this study related to data availability was the availability of accurate stockpile data. For example, in the state of Florida, while data were obtained from publicly available sources, these data are outdated. As a result, the FDEM analysis findings are more useful in understanding general concepts and cannot be used to inform current decision-making. Also, data on stock committed through partnerships and contract agreements with the private sector were not obtained for Florida, which meant that analysis on the impact of these commitments could not be conducted as was done for FEMA's logistics network. This study would benefit from a comparison of the impact of private sector commitments on state-level response to the impact of these commitments on federal emergency response. The second set of limitations relate to the model as currently formulated. First, the model is not formulated to consider time-phased availability of stock in the optimal solution (it can accommodate time-phased availability in the actual solution). That is to say that if stock was not made available through contract terms until 72 hours after the disaster, the model cannot, as currently formulated, delay the availability of that quantity of stock for the optimal solution. Second, additional costs or time constraints associated with decisions such as hiring team drivers or running into traffic slowdowns in transit are not included. This is something that could likely be incorporated into the model relatively easily. Third, the model does not consider the cost that would be incurred if the demand for a disaster were overestimated and the goods had to be sent back to warehouse facilities. These model limitations as well as limitations on data availability are important to note, and if remedied would further enhance the results of the study. 48 4 Case Study: Federal Emergency Management Agency This section presents the results of the case study on the disaster response logistics network at the United States Federal Emergency Management Agency (FEMA). First, the framework used within FEMA to guide disaster response is presented with a focus on the parts of this framework that address logistics concerns. Next, the analysis assumptions and process are described, followed by the results of the analysis and finally a discussion of the implications of these results for FEMA. 4.1 Background The National Response Framework (NRF) is a framework for response to natural and man-made disasters and emergencies in the United States. The framework serves as a guide for all responding agencies, including FEMA, and aims to coordinate the efforts of the multiple stakeholders involved in a response operation. It "describes the principles, roles and responsibilities, and coordinating structures for delivering the core capabilities required to respond to an incident and further describes how response efforts integrate with those of the other mission areas" (United States Department of Homeland Security, 2013). The response mission area, according to the NRF (2013), is focused on ensuring that the Nation is able to respond to all types of incidents, and the priorities of the mission area are to "save lives, protect property and the environment, stabilize the incident and provide for basic human needs". The FEMA logistics network is essential to the response mission area, as it facilitates the movement of goods to a disaster-affected population. When a disaster exceeds or, in certain cases, is expected to exceed the resources of a state, the state may request Federal assistance under the Robert T. Stafford Disaster Relief and Emergency Assistance Act (Stafford Act). It is in these cases where the FEMA logistics network may be used to deliver essential resources. The NRF describes the coordinating structures that serve as a framework for organizing resources from state and tribal governments, the federal government, and other actors in a response (e.g. private organizations). The federal government and many states organize their coordinating structures into Emergency Support Functions (ESF), which combine resources and actors into key functional areas. ESF #7 is logistics, and includes a number of core capabilities that are to be carried out by the ESF coordinators, 49 one of whom is DHS/FEMA. Relevant to this study are the response core capabilities of providing the broad categories of public and private services and resources as well as mass care services. ESF #7 is responsible for "comprehensive national incident logistics planning, management, and sustainment capability" (United States Department of Homeland Security, 2013). While the NRF sets up a framework for coordinating response, and together with the Response Federal Interagency Operations Plan (FIOP) (U.S. Department of Homeland Security, 2014) provides guidance to state and federal agencies on critical tasks and responsibilities in a response, what is lacking in either of these documents is a set of target performance metrics or measures of a successful response. Many of the decisions that impact response are made in the preparedness phase, before a disaster strikes. This case study evaluated FEMA's logistics network using forecast disaster data to assess stock level and warehouse decisions, and it suggests metrics on which the ability of the logistics network to meet some of the goals outlined in the NRF and Response FIOP can be evaluated. These metrics were selected from the Stockpile Capacity Model ("the model") (Acimovic & Goentzel, 2015) presented in the Methods section, and here those metrics that can be used readily in decision-making for a logistics network are presented. 4.2 Analysis This section will describe the analysis that was conducted using the model, associated metrics and the Response Capacity Index (RCI) (Acimovic & Goentzel, 2015). Supplementing the description of disaster and inventory data presented in the Methods section, this section will describe the dataset for FEMA's logistics network in detail, including any assumptions made for the purposes of this study. 4.2.1 EM-DAT disaster data For the evaluation of FEMA's logistics network, only sudden-onset natural disasters impacting the continental United States from the EM-DAT database were used. A total of 285 disasters are included, with total affected populations ranging from 11,000,048 to 1. The geographic distribution and sizes of the disasters included in this analysis are shown in Figure 4-1. The top 10 disasters range in total affected population from 11,000,048 to 50 -250,000, and are represented by the largest symbol on the map. The next 17% of disasters represented by the medium-sized symbol range from -250,000 to -14,000 total affected population, and the remaining 80% of disasters have a total affected population below 14,000 and are represented by the smallest symbol. As described in the Methods section, the EM-DAT field 'Total Affected' is used as 'Total Affected Population' in the model calculations. According to conversations with FEMA Logistics staff, FEMA estimates the affected population using a combination of modeling using FEMA's Hazus model and assumptions based on the population in the impacted geographic area. Specifically, the impacted population is made up of the "at-risk population" who would come to a shelter to receive food, but would not stay overnight and the "shelter-seeking population" who would seek overnight shelter following a disaster. If utility outages continue longer than 72 hours after an event, the shelter-seeking population is expected to increase significantly (FEMA Staff 3, 2015). Vancouver Sa Montreal @ OR * 0fVc-c die L s II ~Havana Mexico Santo Cty Domingo 44 8 , LU,, Guatemala > 14,000 to 250,000 Caraca5 1 to 14,000 Figure 4-1 EM-DAT disaster locations and affected population, continental United States 1990-2013 (Esri, 2015) 51 The disaster events in the EM-DAT dataset of 285 disasters shown here are fairly evenly distributed across the continental United States, accurately reflecting the different types of hazards that FEMA must respond to. A basic comparison of the data from EM-DAT with the disaster risk data provided by DataCo for the state of Florida was conducted to assess the extent of disaster events covered in both datasets. Of the 14 events in the EM-DAT database that were recorded as having occurred in the state of Florida, only 6 were identified by a comparison of the dates provided in the EM-DAT database with the dates in the DataCo dataset to also be in the DataCo dataset. This may be a function of differences in how historic disaster data are collected in each organization, or on how assumptions are made to arrive at total affected population. In comparing the total affected population as estimated by the methods used for the DataCo data to the total affected population as estimated by EM-DAT, estimates by EM-DAT were higher than those for the DataCo data for half of the common events (three events), and lower for the other half. This split is likely a result of several factors, the first being that EM-DAT does not break down affected population by location, even if multiple locations for one event are recorded in the database. This means that for each disaster there is only one loss figure, regardless of the number of locations listed. As described in the Methods section, if multiple locations were recorded, the geographic center of these locations was used for the EM-DAT data, because there was no good way to break down the affected population between the multiple locations identified. A second reason for the discrepancy between the two datasets might be the method used here for determining total affected population from the DataCo disaster risk data. As outlined in the Methods section, this assumption is used here for simplicity and intuitiveness. A final reason for the discrepancy may be that the EM-DAT total affected number includes injured, affected, and homeless. This may represent potential double- or triple- counting of individuals who fall into multiple categories (e.g. injured and homeless). The method used for interpreting the DataCo disaster risk data here was intended to eliminate the possibility of double-counting individuals. In summary, improved historic or predicted disaster data will improve the accuracy of the model in determining where critical commodities should be placed to minimize time and/or cost to respond. In the absence of a common methodology for 52 estimating the impacts of disasters in terms of total affected population, the best available data and assumptions are used here. 4.2.2 FEMA inventory data Inventory data were provided by FEMA for this study for water bottles and shelf stable meals or meals ready to eat (MREs). Water bottle data used here is current as of March 2014, and MRE data is current as of March 2015. In addition to data on the current physical stock of inventory for these two items, data on contracted stock of MREs were also provided. Physical inventory was used here to mean the organic inventory owned by FEMA and kept in FEMA warehouses. This is different from contract inventory, which is procured from outside vendors through pre-arranged contract vehicles. FEMA's physical inventory is located in five warehouses throughout the continental United States. Contracted inventory is located in three locations in the southeast and midwest. Figure 4-2 and Figure 4-3 show the locations of these warehouses. tinrtivJ Montreali Chicago Dewrri Was ngton At M a Los Angeles Havana Esri, HERE, DeLorme, NGA, USGS Mexco Figure 4-2 FEMA permanent stock warehouse locations (Esri, 2015) 53 I Esri, HERE, DeLorme Seattle Monteal Washington Los Angde A at Figre cntrct -3FEM d fdE rcatis (Esi, tok Hrwowre7us 015 Havana 2 2M ant Es , HERE, DeLorme, NGA, USPr I Es , HERE, Del me Figure 4-3 FEMA contract stock warehouse locations (Esri, 2015) To estimate demand by item, 3L of water per person per day and two MREs per person per day were assumed based on conversations with FEMA Logistics staff and the international Sphere Standards for water needs for drinking and food (FEMA Staff 2, 2015; Florida Division of Emergency Management, 2013; The Sphere Project, 2011). Finally, it is important to remember how affected population is translated into units of demand. The estimate for affected population is translated into units of an item demanded by multiplying the per-person units of need for the initial push of supplies that a logistics network would need to support following a disaster by the number of individuals affected. In line with Acimovic and Goentzel (2015), the initial push of supplies is assumed to be enough supplies for 72 hours (e.g. 2 MREs/person/day, over 3 days equals 6 MREs/person). 4.2.3 Analysis scenarios Three scenarios were run to evaluate the FEMA logistics network, and are summarized in Table 4-1. First, the current physical inventory allocation of water bottles and MREs were run to evaluate the base FEMA logistics network. Next, the allocation of the contract inventory of MREs was run to evaluate the additional benefit that adding this stock to the physical inventory provides. Finally, the current physical inventory allocation 54 was run against reduced demand in Florida to evaluate the impact of reduced demand in one area of the country. The sections that follow describe the results of each scenario analysis. Table 4-1 FEMA case study scenarios Decision cnsdered Metrics Evaluated e Where to allocate inventory? Should I enter into a contract with the private sector? Private sector perspective:How much Current Allocation of Inventory Compare current inventory levels with consideration of current and outside am I improving the response? supplier capacity added after 24, 48, 72 hours, according to Where would an additional unit of stock be the most valuable? the contract structure What is the benefit, in terms of cost and time to serve the affected population, if resilience Reduction in demand in the state of Florida by 10% * * 0 * Current allocation strategy of contract stock vs. optimal allocation Demand served by hour 0 * * Warehouse locations Warehouse stock Disaster data (historic or forecast) All from base allocation, plus: 0 Contractual private sector e agreements Locations of private sector warehouses * o goals reduce overall demand? 4.3 Response Capacity Index RespitsetCameettyeIade Ability to meet demand Comparison of current vs. optimal inventory allocation Data Required Ability to meet demand All from base Comparison of current vs. optimal inventory allocation allocation, plus * Resilience goals for a county or state Scenario 1: FEMA Physical Inventory Allocation This scenario analyzed FEMA's current allocation of physical stock of water bottles and MREs. The key metrics assessed were the ability of the physical stock to meet demand, the quality of the inventory allocation, a comparison of current and optimal allocation strategies and finally an assessment of the physical inventory using the RC. 55 4.3.1 Ability to meet demand The first metric assessed for physical inventory was the ability of this inventory to meet demand. Demand is represented as the weighted average of demand, as defined in the Methods section, where each disaster has an equal weight or probability of occurring. With this, large disasters carry no more weight than small disasters. It is important to note that units of demand are reported here in terms of number of items, rather than in terms of people demanding the items. Table 4-2 includes several metrics that are used to evaluate the ability of FEMA's physical inventory to meet demand. Table 4-2 Ability of FEMA physical inventory to meet demand Demand Demand Met Fraction of demand served Fraction of disasters served Item Units MRE 7,319,000 1,281,604 477,673 0.37 completely (6) 0.97 Water Bottle 9,144,876 1,922,983 656,000 0.34 0.97 (t) (i )mn Units here refer to the number of units of each item in stock in the entire network of FEMA warehouses. Demand and demand met refer to weighted average demand and average of demand met, respectively, as defined in the Methods section. Fraction of demand served is also defined in the Methods section. In reviewing these metrics, it seems contradictory that the fraction of overall demand served (r) would be low while the fraction of disasters served completely (6) is high. This is the case because y is more susceptible to outliers than 8. Many small disasters are covered completely by the physical inventory, but there are a few very large disasters that cannot be served by the physical inventory. This results in the high fraction of disasters served completely (0.97), but a lower fraction of total demand is served (0.37 for MREs and 0.34 for water) because there are a few large disasters that skew this result with high levels of demand. 4.3.2 Network inventory allocation strategies In order to evaluate the allocation strategies in FEMA's logistics network, it is useful to compare the current allocation to the optimal allocation when minimizing time and/or cost. The optimal solutions when minimizing time are very similar to those when minimizing cost here because truck is the sole mode of transport being used in the model, so the model cannot choose between air and truck when optimally allocating goods to 56 minimize either time or cost. This is because disasters in the continental United States can all be served by truck. Figure 4-4 compares the actual allocation of MREs to the optimal allocation of MREs when minimizing time and the optimal allocation of MREs when minimizing cost, and Figure 4-5 does the same for water bottles. I Frederick, fD Actual *Mountain View, CA j Cumberland, MD Minimize Cost *Frederick, MD U Atlanta, GA Fort Worth, TX Minimize Time 2,000,000 4,000,000 6,000,000 8,000,000 Units in Inventory Figure 4-4 Actual and optimal allocation of MREs (minimizing time or cost) 57 Fre rick, MD AmtG Actual mberlan otWrh MD Mountain View, CA Cumberland, MD Cumerlnd MD 0 Frederick, MD Minimize Cost N Atlanta, GA 'Fort Worth, TX A Minimize Time - G 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 Units in Inventory Figure 4-5 Actual and optimal allocation of water bottles (minimizing cost or time) The actual allocation of both MREs and water bottles is fairly evenly distributed across all five depots, with greater inventory in Atlanta, GA and Fort Worth, TX. While the stock in Maryland is in two separate warehouses (Cumberland and Frederick), they are approximately two hours driving distance apart from one another so are likely serve the same disasters and have a similar impact on the model that having only one location in Maryland would have. When stock is optimally allocated (minimizing either time or cost) the majority of the inventory is placed in Atlanta and Fort Worth. Referring back to Figure 4-1 which shows the geographic distribution of disasters this is likely because a greater number of disasters occur in the Southeast, South, and Midwest than the MidAtlantic and Northeast. In addition to evaluating inventory allocation strategies for existing and optimal inventory, decision-makers may be increasing or decreasing the system inventory levels across multiple warehouses and need to determine where inventory should be allocated. Figure 4-6 and Figure 4-7 show the optimal allocation of inventory in each warehouse as the total inventory in the system increases for MREs and water, respectively, minimizing 58 time to serve. The current system inventories of MREs and water for FEMA are 7.3 million and 9.1 million respectively, and are indicated by dashed lines in the figures. 100% 90% 80% PC B S4 70% 60% U Mountain View, CA 50% U Frederick, MD, USA 40% U Fort Worth, TX, USA 30% *Cumberland, MD, USA 20% N Atlanta, GA, USA 10% 0% , S~~~~~~~~~N ej P0 q R P51S 11 IIil Total Inventory in the system Figure 4-6 Optimal allocation of MREs (minimize time) with increasing total system inventory, current inventory level indicated with dashed line Similarly to the optimal allocation of the current level of physical inventory, when progressively increasing the total inventory of MREs in the system, the optimal solution (minimizing time) is to place stock primarily in Fort Worth, TX and Atlanta, GA. With up to 2,000 units of stock, the optimal location to place 100% of stock is Fort Worth, and then Atlanta is introduced. With approximately 200,000 units of total stock in inventory, Mountain View, CA is introduced. Frederick and Cumberland, MD remain a low percentage of the total inventory even up to 10,000,000 units of inventory. 59 100% 90% I- 80% Mountain View, CA 70% " ca 60% " Frederick, MD, USA PC 50% * Fort Worth, TX, USA 40% " Cumberland, MD, USA 30% " Atlanta, GA, USA 20% 10% 0% pQ 'ep v'4> y~) Total Inventory in System Figure 4-7 Optimal allocation of water bottles (minimize time) with increasing total system inventory, current inventory level indicated with dashed line Like MREs, the optimal allocation of water bottles as the system inventory increases is mostly placed in Fort Worth, TX and Atlanta, GA. Atlanta is not utilized as a second warehouse location until there are 5,000 units of inventory in the system, and Mountain View, CA is introduced when total inventory in the system is at approximately 200,000 units. At 2,000,000 units and after 5,000,000 units it appears as though the inventory in Atlanta decreases, but it is important to remember that this graph shows the percent of total inventory in each of the warehouses. This is showing that Atlanta decreased in percent of total inventory held, but since the total inventory in the system simultaneously increased this does not mean that the quantity of inventory in Atlanta decreased. 4.3.3 Delivery deadline cutoff times To understand the performance of the current allocation of inventory over time, the fraction of total demand served was evaluated for intervals of time from 0 to 72 hours, the maximum time to respond considered by the model. Figure 4-8 compares the fraction of demand served by the physical inventory as actually allocated and in the optimal 60 allocation. This figure is also presented later when contract inventory is added to the analysis. Time to respond here refers to the time to deliver supplies to those in need and it is based on a combination of the fixed and variable time associated with obtaining, loading, and transporting goods as explained in the Methods section. 0.4 0.35 0.3 -o 0.25 0.2 -+-- Actual Optimal 0.15 0.1 I 0.05 ii 0 0 10 20 30 40 50 60 70 80 Time to Respond (hours) only Figure 4-8 Fraction of demand served (minimize time) actual vs. optimal allocation of physical inventory For FEMA's physical inventory, actual and optimal fraction served follow roughly the same trend, except at 18-24 hours, the optimal allocation would increase the fraction served above the optimal allocation by approximately 5%. 4.3.4 Response Capacity Index for an The RCI, as explained in the methods section is an index to evaluate the quality of stock allocation was organization or set of organizations providing humanitarianaid (Acimovic & Goentzel, 2015). The RCI calculated for FEMA's current allocation of physical inventory. The results are shown in Table 4-3 and calculations for each metric in the index are described in the paragraph that follows. 61 Table 4-3 Response Capacity Index for FEMA physical inventory Item Optimal tmto serve OPT) Balance metric time Balance metric cost (Ar) ((pOP) (AC) Fraction of dstes covered cvr (6)(612) Fraction of disasters covered in 12 hours RCI (simple average) Water Bottle 7.64 9.79 9.84 9.67 5.04 8.40 MRE 7.68 9.81 9.83 9.67 5.12 8.42 Each of the metrics included in the RCI is scaled from 1-10 according to the best and worst possible values for that metric. The descriptions below describe what values were used as best and worst for each of the metrics, where the actual value fell between those values, and include a sample calculation showing how the scaling is done. Descriptions of what each metric means can be found in the Methods section. 1. Optimal time to serve ($ OPT) The best time is assumed to be 6 hours, because that is the fixed time associated with acquisition and loading of a truck. The worst time is assumed to be the longest travel time that might potentially exist between a warehouse and a disaster when traveling by truck within the continental United States. This distance is from Point Arena, CA to West Quoddy Head, ME (United States Geological Survey, 2001), and according to Google's driving distance function represents a travel time of 52 hours. The values for optimal time to serve per unit shipped for water bottles and MREs are 16.8 hours and 16.7 hours respectively. Scaling is done by calculating the ratio of the difference between the optimal time to serve per unit shipped for both commodities and the best possible time to the difference between the best and worst possible travel times, subtracting this ratio from 1, and multiplying by 10. 2. Balance metric, time (AT) The best possible value here is 1, meaning that the current and optimal allocations are perfectly in balance. The worst possible value is considered here to be 1.6 for cost, and 2.6 for time, based on a sensitivity analysis that is described in this Section 4.3.4.1. The balance metric values for water bottles and MREs (minimize time) were 1.032 and 1.030, respectively. 62 Intuitively, this means that the allocation of water bottles is 3.2% off from the optimal allocation, and the allocation of MREs is 3.0% off from the optimal allocation. As described in the Methods section, the balance metric increases if warehouses are added closer to the locations of disasters but no stock is added to them, or if new warehouses are added and all stock was moved to one warehouse while leaving the others empty. 3. Balance metric, cost (Ac) refers to the balance metric when minimizing cost. It is calculated similarly to the balance metric for time, with the same high and low bounds. The balance metric values for water bottles and MREs (minimize cost) were both 1.010. 4. Fraction of disasters covered (&) refers to the fraction of disasters that are served completely given the current inventory level. This number is robust to outliers. The worst value is 0, and the best possible value is 1, so the value is simply multiplied by 10 to scale it for the RCI. 5. Fraction of disasters covered in 12 hours (612) refers to the fraction of disasters that are served completely in 12 hours time. This is included in the RCI to serve as another way of evaluating the overall positioning of warehouses, independent of total inventory in the system. Similar to the fraction of disasters covered, the worst value is 0 and the best possible value is 1, so the value is simply multiplied by 10 to scale it for the RCI. For FEMA, the RCI can be used as a tool to evaluate the tradeoffs between, for example, adding and stocking another warehouse in a location that is closer to where frequent disaster activity occurs (e.g. Miami). This might decrease the balance metric because the optimal allocation across all disasters would not place inventory in Miami over more centrally located warehouses, but it would also likely improve the fraction of disasters served completely in 12 hours. Looking forward, the RCI will benefit from additional expert input on the metrics to include and how each of the individual metrics should be evaluated. 63 4.3.4.1 Balance metric sensitivity analysis In order to evaluate the upper bound of the balance metric more accurately for FEMA (the lower bound will always be 1), four scenarios were run in which warehouses were added in all locations of FEMA regional offices (except in Atlanta, GA, where there is already a warehouse) as well as the cities of Miami, FL, Minneapolis, MN, Billings, MT, Salt Lake City, UT, Reno, NV and Memphis, TN to increase the geographic diversity of the warehouse locations from what currently exist. Figure 4-9 shows where the additional warehouses were located. The current warehouse locations are also included in this analysis. Vancouver SA9h"I.WA MNMontreal W wiNng,merMT eMinnmapolis, MN Ne )Yrr I0 Salt Lake CI 0 SanF Ph Remno,CANV Dn s co %A", S Los Angeles a s tonT 0 3 H1avana 600mI Esri, HERE, DeLorme, NGA, USGS I Esri, HERE. DeLorme metric (Esri, Figure 4-9 Locations of potential FEMA warehouses used in the sensitivity analysis for the balance 2015) Once the additional warehouses were added to the logistics network in the model, all FEMA physical stock was assumed to be located in Bothell, WA, Oakland, CA, Miami, FL, Boston, MA, and Billings, MT while the remaining warehouses remained empty to represent five extreme cases of where stock could reasonably be located within the continental United States. The balance metrics for these scenarios are used to conduct a sensitivity analysis of the balance metric for FEMA and appropriately scale the balance metric scores in the RCI for FEMA. This process should be repeated in other cases in order to appropriately scale the balance metric for the purposes of the RCI. The resulting balance metrics for cost for and time for water bottles and MREs are shown in Table 4-4. 64 Table 4-4 Balance metric sensitivity analysis results MRE Location of Stock Balance Metric Cost Water Balance Metric Time Balance Metric Cost Balance Metric Time Bothell, WA 1.53 2.54 1.52 2.50 Oakland, CA 1.46 2.27 1.45 2.23 Miami, FL 1.14 1.38 1.14 1.39 Boston, MA 1.28 1.81 1.28 1.81 Billings, MT 1.32 1.90 1.31 1.88 As a result, the upper bound for the balance metric for cost is scaled to be 1.6 and the upper bound for the balance metric for time is scaled to be 2.6. The lower bound remains at 1. 4.4 Scenario 2: FEMA Physical Inventory and Contract Inventory Allocation In the second scenario analyzed for FEMA, both physical and contract inventory were considered. Physical inventory is the inventory owned by FEMA and stored in FEMA owned warehouses. Contract inventory is inventory that FEMA has pre-emptively set up contractual agreements to purchase in the event of a disaster. In this analysis, only contract inventory from private suppliers was considered. Contract inventory from private suppliers will likely be up for re-negotiation, and decision makers could use the results of this analysis to restructure these contracts. 4.4.1 Delivery deadline cutoff times To understand the performance of the current allocation of inventory over time, the fraction of total demand served is evaluated for intervals of time from 0 to 72 hours, the maximum time to respond considered by the optimization. Figure 4-10, which is the same as Figure 4-8 compares the fraction of demand served by the physical inventory as actually allocated and in the optimal allocation, minimizing time. Time to respond here refers to the time to deliver supplies to those in need and it is based on a combination of the fixed and variable time associated with obtaining, loading, and transporting goods as explained in the Methods section. 65 0.4 0.3 / PC / 0.35 0.25 0.2 - 0.15 Actual - Optimal 0.1 0.05 n 0 10 20 30 40 50 60 70 80 Time to Respond (hours) Figure 4-10 Fraction of demand served (minimize time) actual vs. optimal allocation of physical inventory only For FEMA's physical inventory, actual and optimal fraction served follow roughly the same trend, except that at 18-24 hours, the optimal allocation would increase the fraction served above the optimal allocation by approximately 5%. Next, the optimal and actual allocations are compared for the contract inventory only, minimizing time. In order to conduct this evaluation, the locations of the contract inventory were constrained to not be available to contribute to the network inventory until the time specified in the contractual agreements with FEMA. Based on conversations with FEMA staff, the times specified in the contract terms represent the time range within which the stock is expected to arrive at the disaster location, rather than when it is available at the warehouses. As the model does not predict arrival time, assumptions were made to estimate time constraints for when inventory would need to become available to arrive at disaster sites within the contract terms of 24, 48, and 72 hours. For contract terms where the stock was expected to arrive within 24 hours, stock was assumed to become available according to the base fixed time constraint of 12 hours as described in the Methods section. If stock was expected to arrive within 48 hours, 36 66 hours was used as the fixed time for the stock to become available (24 hours after the first round of stock became available at 12 hours), and if stock was expected to arrive within 72 hours, 60 hours was used as the fixed time for stock to become available (48 hours after the initial round of stock became available at 12 hours). Figure 4-11 shows the comparison between the actual and optimal allocation of the contract stock, minimizing time. It is important to note that the optimal solution in this case factors in the best-case scenario of the ability to have all contracted inventory available immediately, rather than the current delay imposed by the contract terms and/or limitations on production ramp up for individual suppliers. Based on discussions with FEMA staff, it seems that this is a scenario that may indeed be feasible, which is why it is included here. The actual results here factors in the delay of availability of supplies from certain vendors. 0.3 0.25 Ar 0.2 S0.15 -e- Actual Optimal S0.1 0.05 0 0 10 20 30 40 50 60 70 80 Time to Respond (hours) Figure 4-11 Fraction of demand served (minimize time) actual vs. optimal allocation of contract inventory only The largest benefit for re-allocating contract inventory and/or changing when this inventory is available comes between 24 and 48 hours, as Figure 4-11 shows. Later in this section the optimal allocation of contract stock across vendors is presented, and 67 shows how the allocation of inventory in the warehouses throughout FEMA's logistics network change at these different delivery deadline cutoff times. 0.7 0.6 V 0.5 V Cu 'aC 0.4 -+-- Actual 0.3 - C Cu I.E - Optimal 0.2 0.1 0 0 10 20 30 40 50 60 70 80 Time to Respond (hours) Figure 4-12 Fraction of demand served (minimize time) actual vs. optimal, physical and contract stock Figure 4-12 combines the fraction served by the physical inventory and the contract inventory over a 0 to 72 hour period to respond. It incorporates the fraction served by the physical inventory only beginning at the 6 hour response time as well as the benefit to reallocation of contract inventory as the time to respond moves towards 72 hours. The overall fraction of demand served also increases with the combined contribution of physical and contract stock to meet demand, as can be seen by comparing Figure 4-12with Figure 4-10 and Figure 4-11. 68 I I Optimal-72 E Mullins, SC, USA-12 N Mullins, SC, USA-36 Optimal-48 0 Mullins, SC, USA-60 * Norcross, GA, USA-12 Optimal-36 SA-1 E Norcross, GA, USA-36 Norcross, GA, USA-60 U Evansville, Optimal-24 IN, USA-12 Evansville, IN, USA-36 'Evansville, IN, USA-60 Actual Inventory - 1,000,000 2,000,000 3,000,000 4,000,000 Figure 4-13 Actual vs. optimal allocation of FEMA contract inventory only (minimize time) Figure 4-13 compares the current allocation of contract inventory across the three main suppliers that are located in Mullins, South Carolina, Norcross, Georgia, and Evansville, Indiana. The "-12", "-36", and "-60" appended onto each of these warehouse locations represent the time-constrained availability of the supplies. For example, after 12 hours, the warehouse in Mullins, South Carolina has contracted capacity to provide 150,000 MREs which are expected, according to conversations with FEMA officials, to arrive within 24 hours (FEMA Staff 2, 2015). This is reflected in the model by adding a fixed time constraint of 12 hours to the transportation parameters for the Mullins, South Carolina warehouse. The model, however, is limited in its allocation of optimal inventory - at present it will optimally allocate all contract inventory, regardless of when it becomes available, to the warehouses with only a 12-hour fixed time constraint. Despite this limitation, what the optimal solution does provide is a better idea of what would be needed from a contract or set of contracts to serve a greater fraction of the demand overall. At 36 hours, having stock distributed through Norcross, GA and Evansville, IN with some in Mullins, DC is optimal, but at 48 and 72 hours, the optimal solution is to 69 place most of the stock in only Norcross, GA and Evansville, IN with a very minimal contribution of stock from Mullins, SC. This is insight that may be used to structure future contracts or to restructure existing contracts. 4.5 Scenario 3: FEMA Physical Inventory Allocation with Reduced Demand This section evaluates FEMA's allocation of physical inventory with a 10% reduction in demand in the state of Florida. This reduction in demand was chosen according to the reasoning specified in the Methods section. Furthermore, in the case of the FEMA study, it seems like a realistic estimate based on a presentation given by a representative of Florida Emergency Management at a conference stating that the state emergency management leadership has set a goal of increasing participation in the National Flood Insurance Program (NFIP)'s Community Rating System (CRS) from approximately 40% to 100% over the course of 18 months (Kay Roberts, 2015). Participation in the CRS makes NFIP participants living in the community eligible for varying levels of insurance benefits, depending on the community's CRS rating. The CRS rating is improved when the community takes steps to promote reduction of flood vulnerability through a variety of activities. Ten percent would likely represent a conservative estimate of the impacts of this relatively aggressive goal of 100% CRS participation statewide in Florida. This is because not all of the activities that qualify as CRS participation directly reduce vulnerability on an individual household basis that would decrease the number of people requiring aid after a disaster. This scenario is focused on assessing the impacts of reduced demand on assessment metrics, but these resilience goals (CRS participation) represent the type of activities that might cause a real reduction in demand in the long run. 4.5.1 Ability to meet demand Similar to the evaluation of FEMA's physical inventory with the original disaster risk data, the ability of the current physical inventory to meet the new reduced demand for disasters in Florida was evaluated. 70 Table 4-5 Ability of FEMA physical inventory to meet demand with 10% reduction in Florida demand Item MRE Units Demand Original Item Demand Demand Met Reduced Us OOriginal O(') Demand Met Reduced (V') Fraction of demand served Original Fraction of demand served Reduced (y) (y) Fraction of disasters served Original and 7,319,000 1,281,604 1,239,351 477,673 475,082 0.37 0.38 Reduced (8) 0.97 9,144,876 1,922,983 1,859,584 656,000 652,112 0.34 0.35 0.97 Water Bottle As is observed here, the fraction of demand served with the reduced demand is only slightly higher, with the fraction of disasters served remaining exactly the same. Additionally, the demand met with the reduced demand is less. To understand how these three variables have changed (or not) with reduced demand in the FDEM logistics network, one must consider how disasters are generally distributed. In the case of disasters in the continental United States that were considered for the FEMA analysis, there are a few very large disasters, but the total affected population for the remaining disasters drops off significantly beyond these large events. In reducing demand in one state weighted average demand (y) is reduced because some of the disasters have become smaller in terms of total affected population. This information, combined with the fact that the fraction of demand served (p')/(i) increased, means that with the current stock FDEM's logistics network is able to meet a greater proportion of the demand given the current allocation. As the demand overall has decreased, the average demand met still (y') decreases, just less than the decrease in (p). In summary, the increase in fraction of demand served means that the FDEM's logistics network is serving proportionally more of the demand given the current allocation of inventory, but the network is not serving any more disasters completely, which is why fraction of disasters served remains the same. To serve more disasters completely, demand would have needed to be reduced such that the current allocation of stock that was unable to serve some of the larger disasters originally could now serve those disasters. While this is only a slight benefit, an increase in fraction served might still be used as justification for promoting resilience planning that is intended to reduce overall demand for critical commodities in a disaster. 71 4.5.2 Network inventory allocation strategies In addition to evaluating the figures described above, the actual allocation of physical inventory was compared to the optimal allocation with original demand and the optimal allocation with reduced demand in Florida. It was hypothesized that a reduction in demand in one geographic location would change the optimal allocation of inventory across warehouses. That was not the case for the reduction in demand in Florida, as is shown in Figure 4-14 and Figure 4-15. The allocation for both water bottles and MREs remains unchanged despite a reduction in demand in Florida. This is likely because the reduction in the weighted average of demand was only 3% for both commodities being considered. A larger reduction in the weighted average of demand in the state might have pushed the optimal allocation to a warehouse farther from Florida. T G FW e Actual * Mountain View, CA Cumberland, MD 1 1t ( OptTime-RedDmd E Frederick, MD N Atlanta, GA * Fort Worth, TX A i OptTime-Orig - F ( 2,000,000 4,000,000 6,000,000 8,000,000 Units in Inventory Figure 4-14 Actual vs. optimal allocation of inventory with original and reduced demand for MREs (minimize time) 72 Actual E Mountain View, CA ' Cumberland, MD OptTime-RedDmd 0 Frederick, MD N Atlanta, GA E Fort Worth, TX OptTime-Orig - 2,000,000 4,000,000 6,000,000 8,000,00010,000,000 Units in Inventory Figure 4-15 Actual vs. optimal allocation of inventory with original and reduced demand for water bottles (minimize time) Despite not having observed significant change of the metrics considered thus far aside from fraction of demand served, this method might still be used as a preliminary way of putting a value on resilience. For example, the change in total cost of delivering the initial push of supplies in a response can be estimated with this method. In this case, the resulting change in total cost given the current allocation of inventory and a reduced demand in the state of Florida of 10%, a savings of $493 is realized for MREs and $1160 for water bottles. These figures are small, but they show a savings and begin to answer the question of how much money will be saved by reducing the number of people who will require aid in a disaster. 73 5 Case Study: Florida Division of Emergency Management This section presents the case study on the disaster response logistics network for the Florida Division of Emergency Management (FDEM). First, the procedural framework used by FDEM to guide disaster response is presented, with a focus on the parts of this framework that address logistics concerns. Next, the analysis assumptions and process unique to FDEM are described, followed by the results of the analysis and a discussion of the implications of these results for FDEM. 5.1 Background The FDEM has a robust logistics section and is often used as a best practices model of state emergency response in the United States (e.g. Goentzel & Spens, 2011). The state has also made publicly available much of their logistics documentation, which is reviewed for this study. The state of Florida Unified Logistics Plan, which is an annex to the State Comprehensive Emergency Management Plan, "includes plans, procedures, and supporting documentation needed to ensure the state of Florida maintains a strong and viable logistics capability" (State of Florida Division of Emergency Management Unified Logistics Section, 2013) is primarily reviewed. This plan is authorized by Section 252.35 (1) of the Florida Statutes, which state that the "division is responsible for maintaining a comprehensive statewide program of emergency management" which includes coordination with the federal government as well as other agencies in the state government, the private sector, and county and municipal governments. The statute also requires that the state prepare a "comprehensive emergency management plan", which the state has done, and which this logistics plan is part of (The Florida Legislature, 2014). The majority of the Unified Logistics Plan focuses on operational activities and coordination mechanisms, including the risk assessment, hazard analysis, roles and responsibilities, the locations of key response, mobilization and distribution sites, and other operational considerations. In addition, the state has outlined a resource management system for supply chain management, which specifies the order in which various resources will be called upon in a disaster, beginning with the state's physical inventory. The state has also conducted a gap analysis to identify, for three levels of disasters (10,000, 100,000 and 1,000,000), the resources that it has and the gaps that exist, as well as plans for filling those gaps. The analysis conducted in this case study 74 compliments the plans and analyses already conducted by FDEM by expanding the possible disaster scenarios and suggesting where the inventory of resources should be allocated to best serve the disaster affected population. In conclusion, the Unified Logistics Plan states the logistics goals, objectives, and actions. The response goal cited is "to be able to effectively respond in an emergency with Logistical Support to all emergency operations and field operations factions", with a key objective to "ensure a timely and effective response to the many consequences that may be generated by an emergency/disaster situation" (State of Florida Division of Emergency Management Unified Logistics Section, 2013). This is one of four key objectives for the response goal and most closely relates to the goals of this study. The objectives outlined are qualitative in nature, which make the attainment (or not) of them difficult to measure. Also reviewed for the purposes of this case study was the County Logistics Planning Standard Operating Guideline (State of Florida Division of Emergency Management, 2006). This document is meant to serve as a guideline to communities in developing their own individual logistics plans. It outlines what logistics finctions counties are responsible for, such as designating points of distribution (POD) for critical commodities, and how counties can request assistance from the state for events that it is not able to respond to on its own. The document is primarily operational in nature and does not include frameworks for evaluating performance, but does provide useful insight into how communities operate their logistics response, which feeds into the overall state logistics response. This case study will aim to evaluate FDEM's logistics network and suggest metrics on which the ability of the logistics network to meet the goals outlined in the Unified Logistics Plan can be evaluated. These metrics were selected from the output of the Stockpile Capacity Model ("the model") (Acimovic & Goentzel, 2015) presented in the Methods section, and here those metrics that can be used readily in decision-making for a logistics network are presented. 5.2 Analysis This section will describe the analysis that was conducted using the model, associated metrics, and the Response Capacity Index (RCI) (Acimovic & Goentzel, 2015). It is 75 presented as an addition to the description of disaster and inventory data presented in the Methods section, and will describe the dataset for FDEM's logistics network in detail, including any assumptions made for the purposes of this case. 5.2.1 Insurance risk data For the evaluation of FDEM's logistics network, the disaster risk data were obtained from DataCo (DataCo, 2015). These data are described in detail in the Methods section and as noted, include only hurricane disaster data (storm surge, precipitation, and high winds). This represents a portion of the disasters experienced in the state of Florida, but not all. A more robust dataset would also include damages from riverine and severe storm flood events, as well as other hazards that Florida is susceptible to. The original dataset from DataCo for Florida included 9,842 disasters, 103 historic and 9,739 stochastic and their impacts on the 67 counties in Florida. Figure 5-1 and Figure 5-2 show the geographic distribution of the count of disasters by county and the maximum total affected population by county for the disaggregated disaster dataset. In this dataset, each disaster-county pair with a total affected population of greater than 100 was included, because this is assumed to be the maximum number of individuals affected in an event that a county within the state would be able to respond to on its own. The figures separate the disaster data into the top 10% (largest symbol), the next 20% (medium-sized symbol), and the bottom 70% (smallest symbol) for the count of disasters per county and the maximum total affected population by county. Disaster-county pairs with less than 100 TAP are not shown on the map. This means that the top 7 counties for both count of disasters and maximum total affected population are represented by the largest symbol, the next highest 13 are represented by the medium sized symbol, and the rest are represented by the smallest symbol. As anticipated given that this dataset includes losses from only hurricane wind and storm surge, the counties along the coast and in the southern portion of the state experience the most frequent and the largest disasters. 76 > 2,000 to 3,421 > 1,040 to 2,000 1 to 1,040 Esr, HERE, DeLorme, NGA, USGS I Esn HERE, DeUrme 2015) Figure 5-1 Count of disasters by county, disaggregated disaster dataset, for counties with TAP>100 (Esri, * 100,000 to 1,213,072 > 15,000 to 100,000 142 to 15,000 Havana Esn, HERE. DeLorme, NGA, USGS I Esr., HERE, DeLorm Figure 5-2 Maximum total affected population by county, disaggregated disaster dataset, for counties with TAP>100 (Esri, 2015) The disaggregated dataset of disasters with total affected population greater than 100 persons includes 48,699 unique disaster-county pairs ranging in total affected population from 100 to 1,213,072. The total dataset, however, included 9,842 disasters, with loss data for each of the 67 counties in Florida, so a total of 659,414 unique disaster-county pairs, meaning that 93% of disaster-county pairs had a total affected population less than 100 persons and were not included in the analysis. Even so, this represents a rich dataset 77 of historic and predicted disaster data that will improve the accuracy of the model in determining where critical commodities should be placed to minimize time and/or cost to respond. The disaster data provided estimates losses on a county basis. In order to estimate the location of disaster, the county seat was used as an estimated location for where the demand would be located within the county. This location was used because the county seat is a likely location where supplies would be distributed to individuals, and/or a place where individuals from the community would come for shelter. Figure 5-3 shows a map of the counties in Florida with the county seats identified. Paname Cn Porl SI -Vaf:Jw te Spnngs t Avgustne noll I4w, PSaM880: Lee Monroe Figure 5-3 Map of Counties in Florida, noting the county seats (Geology.com, n.d.) 78 5.2.2 Florida inventory and demand data Inventory data were assumed for this study based on a 2011 case study by Goentzel and & Spens for water bottles and shelf stable meals or meals ready to eat (MREs) (Goentzel Spens, 2011). These data are current as of March 2010. At present, all of the FDEM's physical inventory of MREs and water bottles is located in one warehouse in Orlando, but the FDEM has identified nine logistics staging areas (LSAs) throughout the state that are also evaluated in this study as potential locations for prepositioning goods. The physical addresses of these LSAs are included in the model for estimating drive time and distance, but in analysis only the city name is included for simplicity (Florida Division of Emergency Management, 2013). Figure 4-2 shows the locations of these logistics staging areas, which does include a location in Orlando which represents the physical warehouse where all stock is currently located. Duke Field AFS, FL 0 Lakeland, FL Ocala, Orlando, e Ta FL FL nsaoa FLPunta Gorda, FL * Tallahassee, FL * West Palm Beach, FL Esr, HERE, DeLorme NGA, USGS IEsn, HERE, DeLorme Figure 5-4 Florida Logistics Staging Areas (Including Orlando, the location of permanent warehouse) (Florida Division of Emergency Management, 2013) To estimate demand by item, 3L of water per person per day and two MREs per person per day were assumed based on reviewing Florida's logistics documentation and the international Sphere Standards for water needs for drinking and food (Florida Division of Emergency Management, 2013; The Sphere Project, 2011). Finally, it is important to remember how affected population is translated into units of demand. The estimate for affected population is translated into units of an item demanded by multiplying the per79 person units of need for the initial push of supplies that a logistics network would need to support following a disaster by the number of individuals affected. In line with Acimovic and Goentzel (2015), the initial push of supplies is assumed to be enough supplies for 72 hours (e.g. 2 MREs/person/day, over 3 days equals 6 MREs/person). 5.2.3 Analysis scenarios Three scenarios were run to evaluate the FDEM logistics network, and are summarized in Table 5-1. First, the current physical inventory allocation of water bottles and MREs in Orlando only was run to evaluate the ability of this inventory to meet the needs of disaster survivors across the state in aggregate and at varying time intervals. Then, the LSAs were added on and considered as additional locations for placing goods in the event of a disaster to compare metrics for when all stock is located in Orlando to metrics when additional locations are available. Second, county level disaster risk data from a training exercise for Hurricane Wilma was run for two time intervals of warning before the disaster to evaluate the optimal locations for prepositioning goods, and whether they changed significantly with more warning. Finally, the current physical inventory allocation was run against the original disaster risk data with reduced demand in just Miami-Dade County to evaluate the impact of resilience goals in one county. The sections that follow describe the results of each scenario analysis. 80 Table 5-1 Florida Case study scenarios Scenarios Considered Decision Current allocation of inventory Where to allocate inventory? What is the benefit, in terms of cost to serve the affected population, if resilience goals reduce overall demand? 5.3 * * Current allocation of inventory with option to stock closer to disaster events in LSAs Current allocation of Where to preposition inventory in a notice event (e.g. hurricane)? Metrics Evaluated inventory with option to stock closer to disaster events in LSAs for two time intervals before a notice event Response Capacity Index Ability to meet demand of current vs. optimal inventory allocation Data Required * e * * * * e Reduction in demand in Miami-Dade County by 10% Comparison of current vs. optimal inventory allocation Ability to meet demand Time to serve Ability to meet 0 demand Comparison of current vs. optimal inventory allocation * * Warehouse locations Warehouse stock Disaster data (Comparison historic or forecast) Warehouse locations Warehouse stock Disaster data (forecast) for notice event All from base allocation, plus alctopu Resilience goals for a county or state Scenario 1: Current Allocation of Inventory This scenario analyzed FDEM's current allocation of physical stock of water bottles and MREs. The key metrics assessed were the ability of the current inventory to meet demand, the allocation strategies (current and optimal), and the fraction of demand met when varying delivery deadline cutoff times are imposed. 5.3.1 Ability to meet demand The first metric assessed for physical inventory is the ability of this inventory to meet demand. Demand is determined to be the weighted average of demand, as described in the Methods section, where each disaster has an equal weight or probability of occurring. With this, large disasters carry no more weight than small disasters. It is important to note that units of demand are reported in terms of number of items, rather than in terms of people demanding the items. Table 5-2 includes several metrics to evaluate the ability of FDEM's physical inventory to meet demand, as predicted using the dataset of disaster risk data from AIR Worldwide (2015). 81 Table 5-2 Ability of Florida physical inventory to meet demand Demand Units ItemUnis Item 1,600,000 5,292,000 MRE Water Bottle I _ _ __ 435,909 I _ _ __ 228,223 0.79 ( 290,519 _ Demand Met demad sev ed demnd srve ~ 407,377 _ I _ _ __ _ Fraction of disasters served completely (6) 0.96 0.93 _ I _ _ _ _ 0.99 _ _ I _ _ __ _I_ In reviewing these metrics, FDEM looks to be well prepared to meet demand overall given the level of inventory it currently keeps in stock for both MREs and water bottles. The fraction of demand served is much higher than was seen in the FEMA analysis, and the fraction of disasters served completely is nearly 100%. There is a difference between fraction of demand served and fraction of disasters served completely because y is more susceptible to outliers than 6. Many small disasters are covered completely by the inventory in stock, which results in the high fraction of disasters served completely (0.96 and 0.99), but a lower fraction of total demand is served (0.79 for MREs and 0.93 for water) because there are a few large disasters that skew this result. 5.3.2 Network inventory allocation strategies In order to evaluate the inventory allocation strategies in FDEM's logistics network, it is useful to compare the current allocation to the optimal allocation when minimizing time and the allocation when minimizing cost. While FDEM currently keeps its entire inventory of MREs and water bottles in one central warehouse in Orlando, additional warehouse locations that could be added in the locations that Florida currently has identified as LSAs were considered in order to provide metrics for comparison of the current centralized strategy to one where stock is allocated to warehouses across the state. The optimal solutions when minimizing time are very similar to those when minimizing cost because truck is the sole mode of transport being used in the model, so the model cannot choose between air and truck when optimally allocating goods to minimize either time or cost, and the variable time and cost for truck travel are directly related to distance traveled. This is because all disasters in Florida can be served by truck transport. Figure 5-5 compares the actual allocation of MREs to the optimal allocation of MREs when 82 minimizing time and the optimal allocation of MREs when minimizing cost, and Figure 5-6 does the same for water bottles. 1 Actual " Orlando, FL " Pensacola, FL Airport Punta Gorda, L 0 Pensacola, FL " Ocala, FL Minimize Cost Lakel " Tallahassee, FL d, Punta Gorda, FL Punta Gorda, 11 ELakeland, FL Minimize Time West Palm Beach, FL lL - 500,000 Duke Field AFS, FL 1,000,000 1,500,000 2,000,000 Units in Inventory Figure 5-5 Actual and optimal allocation of MREs (minimizing time or cost) 83 Actual 0 Orlando, FL 0 Pensacola, FL Airport Punta Gor ia, FL 0 Pensacola, FL 0 Ocala, FL Minimize Cost MTalahassee, Lakeh d, FL FL Punta Gorda, FL Punta Gor Ja, FL Lakeland, FL West Palm Beach, FL Minimize Time _ - Duke Field AFS, FL 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 Units in Inventory Figure 5-6 Actual and optimal allocation of water bottles (minimizing cost or time) The actual allocation of both MREs and water bottles is currently all in Orlando, so by adding eight additional locations where stock may be placed, the allocation shifts drastically for both commodities. The optimal solutions for minimizing both time and cost for water bottles and MREs place most of the inventory in West Palm Beach, FL, with the second highest quantity placed in Punta Gorda, FL, and the third highest quantity placed in Lakeland, FL. Referring back to Figure 5-1 and Figure 5-2, this is likely because there is a high concentration of disaster events in the southeastern portion of the state, and these are some of the largest disasters in terms of total affected population. While allocating inventory to West Palm Beach would minimize time and cost in the long run, there may be additional factors that would make locating the majority of stock in that location permanently a risk, and may be why FDEM currently has 100% of its stock located in Orlando. West Palm Beach is where the disasters are largest in size and is where disaster events are the most frequent, so locations in the county are likely at a greater risk of being flooded or damaged by high winds in a hurricane event, and as such might not be a safe location for long term storage of critical commodities. 84 In addition to evaluating inventory allocation strategies for existing and optimal inventory, decision-makers may be increasing or decreasing the system inventory levels across multiple warehouses. Figure 5-7 and Figure 5-8 show the optimal allocation of inventory in each warehouse as the total inventory in the system increases for MREs and water, respectively, minimizing time. The current system inventories of MREs and water for FDEM are 1.6 million and 5.3 million respectively. Similarly to the evaluation of actual and optimal allocation of inventory, the evaluation of where to place inventory as total system inventory increased was evaluated for all potential staging areas in the state as well as the central warehouse in Orlando. 100% 90% *Punta Gorda, FL 80% - -- West Palm Beach, FL 70% E Lakeland, FL Duke Field AFS 50% " Tallahassee, FL 40% HOcala, FL 30% " Pensacola, FL 20% " Pensacola, FL - Airport 10% " Orlando, FL 0% ' sqp dV' Total Inventory in System Figure 5-7 Optimal allocation of MREs (minimize time) with increasing total system inventory, with current inventory indicated by dashed line When minimizing time, the optimal allocation of inventory as the total inventory in the system is increased from 10,000 to 10 million units is to place most of the inventory in West Palm Beach, with Punta Gorda, Lakeland, and Orlando holding smaller fractions of the overall inventory. This is similar to what is observed with the optimal allocation of the current inventory, minimizing time. 85 100% 90% K *Punta Gorda, FL West Palm Beach, FL 80% 70% E Lakeland, FL 60% 0 Duke Field AFS, FL 50% 0 Tallahassee, FL 40% 0 Ocala, FL 30% 0 Pensacola, FL 20% 0 Pensacola, FL Airport 10% N Orlando, FL 0% Total Inventory in System Figure 5-8 Optimal allocation of water bottles (minimize time) with increasing total system inventory, with current inventory indicated by dashed line Like the optimal allocation of water bottles when minimizing time and the allocation of MREs as overall system inventory increases, the allocation of water bottles when inventory in the system is increasing from 10,000 units to 10 million units consistently places the majority of inventory in West Palm Beach, FL. The current inventory level of water bottles is shown here with the dashed line. Orlando makes up about 15% of the total inventory when inventory levels are around 10,000, but as overall inventory increases, the amount in Orlando decreases as a percentage of the overall inventory in stock. The allocation across warehouses as total inventory in the system increases is nearly identical for MREs and water, as is expected given that the demand for these items is on a units per person basis, and does not take into account other factors. This analysis would be useful for decision makers in determining where to allocate new stock into the system to move towards a more optimal solution. 86 5.3.3 Delivery deadline cutoff times To better understand how well prepared the current stock is to meet demand, the fraction of demand served at several time intervals was evaluated. This evaluation assumes, as is actually the case, that all stock is located in Orlando. It does not consider the LSAs as potential locations to allocate stock. This evaluation is useful for evaluating how quickly the demand can be met with the current allocation. Figure 5-9 and Figure 5-10 show the ability of the current inventory allocation to meet demand over a period of 18 hours after a disaster for MREs and water bottles, respectively, minimizing time to serve. 0.9 0.8 0.7 0.6 Cu w C C 0.5 0.4 --4-Actual 0.3 0.2 0.1 1 1 0 0 5 15 10 20 Time to serve (hours) Figure 5-9 Fraction of demand served, actual allocation of Florida MRE inventory, minimize time 87 1 0.9 .0 0.8 0 0.7 0.6 0.5 4 -Actual 0.4 0.3 ~0.2 0.1 0 0 5 15 10 20 Time to Serve (hours) Figure 5-10 Fraction of demand served, actual allocation of Florida water bottle inventory, minimize time No demand is met in the first six hours because that is the fixed time assumed for a responding organization, such as FDEM, to obtain and load a truck. After that time, stock is deployed and can serve first the disasters located closest to Orlando, then farther away. Since the disaster locations are assumed to be the county seat of each of the 67 counties in Florida as described in the Methods section, an evaluation of the drive distance from Orlando to any county in Florida explains why the fraction of demand served jumps from nearly 40% at 9 hours post disaster to over 90% at 10 hours post disaster. Table 5-3 shows the number of counties within 1, 2, 3, and 4 hours of Orlando. Some of these counties may be inland and may not have a large number of disasters or a high demand, but this table shows that within the first ten hours after a disaster, including the fixed time of six hours, stock leaving Orlando can reach 52 (nearly 80%) of the 67 counties in Florida. 88 Table 5-3 Number of counties within one to four hours driving distance from Orlando, FL Driving distance from Orlando (hours) 1 2 3 4 Time after disaster (hours) (driving distance from Orlando + 6 hour fixed time) 7 8 9 10 Number of counties 6 19 20 7 As has been shown here Orlando is a central location and positioning stock in Orlando still allows FDEM to respond to 40% of the demand within just about 9 hours after a disaster occurs, assuming a fixed time of 6 hours to mobilize and load trucks. The optimal solution remains to place the majority of inventory in West Palm Beach, but that location may be vulnerable to flood or high wind hazards. 5.3.4 Response Capacity Index The RCI, as explained in the Methods section is an index to evaluate the quality of stock allocation for an organization or set of organizations providing humanitarian aid (Acimovic & Goentzel, 2015). The RCI was calculated for FDEM's current allocation of physical inventory, considering the logistics staging areas as options for allocating stock to evaluate the balance metric and optimal time to serve. The results are shown in Table 5-4 and calculations for each metric in the index are described in the paragraph that follows. Table 5-4 Response Capacity Index for Florida Item Optimal tim serve Balance metric time Balance metric cost T (OOP ) (A) (AC) Fraction of Fisasters covered Fraction of disasters covered in 12 hours RCI (simple average) Water Bottle 7.29 7.67 9.49 9.58 5.48 7.90 MRE 7.11 7.94 9.55 9.90 5.38 7.97 Each of the metrics included in the RCI is scaled from 1-10 according to the best and worst possible values for that metric. The descriptions below describe what values are used as best and worst for each of the metrics, and where the actual value fell between those values. 89 1. Optimal time to serve (4 0PT) The best time is 6 hours, the time assumed to acquire and load a truck, as outlined in the Methods section. For Florida, the worst time is assumed to be the longest travel time that might exist between a warehouse and a disaster when traveling by truck. This distance is from Pensacola, FL to Key West, FL and according to Google's driving distance function represents a driving time of 12 hours. The values for optimal time to serve per unit shipped for water bottles and MREs are 7.6 hours and 7.7 hours respectively. 2. Balance metric, time (A&) The best possible value here is 1, meaning that the current and optimal allocations are perfectly in balance. The worst possible value is considered here to be 2, because it is small and easy to interpret, and no results & obtained in the analyses for FDEM's logistics network went above 2 (Acimovic Goentzel, 2015). To test the bounds of this, a sensitivity analysis similar to that done for FEMA could similarly be conducted for Florida. The balance metric values for water bottles and MREs (minimize time) were 1.233 and 1.206, respectively. 3. Balance metric, cost (Ac) The balance metric for cost is calculated similarly to the balance metric for time, with the same high and low bounds. The balance metric values for water bottles and MREs (minimize cost) were 1.051 and 1.045, respectively. 4. Fraction of disasters covered (6) refers to the fraction of disasters that are served completely given the current inventory level. This number is robust to outliers. The worst value is 0, and the best possible value is 1, so the value is simply multiplied by 10 to scale it for the RCI. The values for fraction of disasters covered for water bottles and MREs were 0.958 and 0.990 respectively. 5. Fraction of disasters covered in 12 hours (612) refers to the fraction of disasters that are served completely in 12 hours time. This is included in the RCI to serve as another way of evaluating the overall positioning of warehouses, independent of total inventory in the system. Similar to the fraction of disasters covered, the worst value is 0 and the best possible value is 1, so the value is simply multiplied by 10 to scale it for the RC. The values for fraction of disasters covered in 12 hours for water bottles and MREs were 0.548 and 0.538 respectively. 90 The RCI can be used as a tool to evaluate the tradeoffs between various metrics. For example, by moving stock closer to the coast and to where many disasters occur, Florida could increase the fraction of disasters covered in 12 hours, but they may decrease the fraction of disaster covered overall. 5.4 Scenario 2: Prepositioning of Inventory An evaluation of allocation strategies for prepositioning stock in advance of a notice event was conducted to understand how the model could be applied to this type of decision. The Methods section includes a detailed description of the input for this analysis. Figure 5-11 and Figure 5-12 below shows a comparison of the allocation of inventory for two intervals of time before Hurricane Wilma (minimizing time), which made landfall in Florida on October 24, 2005 at approximately 6:00 a.m. CDT (NOAA, 2005). Report 1 represents disaster risk data estimated on October 21, 2005 at 7:00PM CDT, and Report 2 represents disaster risk data estimated on October 23, 2005 at 1:00 p.m. CDT. Actual d AFS, Fl H Orlando, FL N Pensacola, FL Airport Tall West eeFL m Beach, FL E Pensacola, FL Ocala, FL Report 1 D Tallahassee, FL Orlando, FL all ee, FL est Palm Duke Field AFS, FL ach, F ELakeland, FL Report 2 West Palm Beach, FL Punta orda, FL |Punta Duke Field AF, FL - 500,000 1,000,000 1,500,000 Gorda, FL 2,000,000 Units in Inventory Figure 5-11 Optimal allocation of MREs, minimize time, for two prepositioning time intervals 91 I 1 II Actual A Orlando, FL 0 Pensacola, FL Airport Pun allahassee, Gorda, FL * Pensacola, FL M Ocala, FL Report 1 M Tallahassee, FL Lakeland, FL Duke Field AFS, FL T Report 2 s e FL Punta G rda, FL .West ELakeland, FL Palm Beach, FL Laelan Fl E Punta Gorda, FL - 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 Units in Inventory Figure 5-12 Optimal allocation of water bottles, minimize time, for two prepositioning time intervals It is interesting to observe how the allocation of inventory changes between Report 1 and Report 2, which are themselves about 40 hours apart. For MREs, Orlando is not allocated any stock in Report 1, but as the area of impact of the storm becomes clearer with Report 2, it is added back in as a warehouse location. The changes in inventory levels for water bottles are less pronounced between Report 1 and Report 2. This is likely because the overall quantity of water bottles in the system is much larger than that for MREs, so with a relatively even allocation at the time of Report 1, only small changes are made in Report 2 to serve the expected demand. In addition to comparing strategies for allocating inventory, it is also interesting to compare the average time to serve from the actual allocation of all inventory in Orlando to the optimal allocations for Reports 1 and 2. Table 5-5 summarizes the benefits, in terms of average time to serve (per unit delivered) for actual and optimal inventory allocations for both Report 1 and Report 2 (minimizing time). 92 Table 5-5 Average time to serve per unit of commodity for prepositioning scenarios Water Bottles MIREs Percent Percent Actual Optimal decrease (to Actual Optimal optimal) decrease (to optimal) Report 1 7.18 hours 7 hours 2.5% 7.62 hours 7 hours 8.9% Report 2 7.72 hours 7 hours 10.3% 8.4 hours 7.28 hours 15.4% In the case of water bottles, optimally allocating supplies in advance of a notice event would reduce the per unit time to serve by 9-15%, depending on when the decision was made to allocate supplies. Looking closer at water bottle allocation in Report 1 and Report 2, it is observed that the biggest advantage of optimally allocating inventory throughout the LSAs in Florida comes in the first seven hours of the response. In Figure 5-13, at a time of 7 hours, the actual allocation of supplies will serve approximately 11% of demand, while the optimal allocation will serve approximately 30% of demand. 93 0.35 0.3 41 0.25 eu 0.2 C 0.15 241 -+-Actual - C Cu 5- WOptimal 0.1 0.05 0 0 2 4 8 6 10 12 14 16 18 20 Time to Respond (hours) Figure 5-13 Fraction of demand served, minimize time, for water bottles in Report 1 (10/22/2005 at 7PM CDT) 0.8 0.7 41 0.6 0.5 Cu 241 0.4 /l '4- + Actual C C 0.3 Cu I- 0.2 - aOptimal 0.1 0 0 2 4 6 8 10 12 14 16 18 20 Time to Serve (hours) Figure 5-14 Fraction of demand served, minimize time, for water bottles in Report 2 (10/23/2005 at 1PM CDT) In Figure 5-14, which compares the fraction of demand served over time to serve for the actual and optimal allocations at Report 2, which represents forecasts on October 23, 94 2005 at 1:00 p.m. CDT, approximately 17 hours before landfall of Hurricane Wilma. Compared to Report 1, optimally allocating water bottles in Report 2 increases the fraction of demand served at both 7 and 8 hours after the disaster. Additionally, note that the overall fraction of demand served in Report 2 increases to approximately 70%, as compared to about 30% when estimates are made in Report 1. A more focused storm track allows the allocation of inventory to be more precise and meet demand in the areas most likely to be impacted by the disaster. 5.5 Scenario 3: FDEM Physical Inventory Allocation with Reduced Demand This section evaluates FDEM's allocation of physical inventory with a 10% reduction in demand in Miami-Dade County. Miami-Dade County was chosen because it has frequent disaster activity and has some of the largest disasters by total affected population in the disaster risk database used. Ten percent was chosen as a conservative estimate of the impacts of a disaster risk reduction plan in the county. In the future, higher values could be tested to evaluate the relative impacts of varying levels of demand reduction. This additional sensitivity analysis would give decision makers an idea of the threshold for demand reduction that would result in shifting supply, or allow them to meet given targets for response with their current stock levels. The following sections assess several metrics related to the allocation of stock given this reduced demand. 5.5.1 Ability to meet demand Similar to the evaluation of FDEM's physical inventory with the original disaster data, the ability of the current physical inventory to meet the new reduced demand was evaluated. Table 5-6 compares a few key metrics for evaluating the ability of FDEM's current inventory to meet demand when demand is reduced in one location, Miami-Dade County. Table 5-6 Ability of Florida inventory to meet demand with 10% reduction in demand in Miami-Dade County demand served Reduced (Y) Fraction of disasters served Origial and Reduced 0.80 0.94 (8) 0.96 0.99 Fraction Item Units Demand Original () MRE Water 1,600,000 5,292,000 290,519 435,909 Demand Reduced () 278,907 418,486 Demand Met Original Demand Met Reduced Fraction of demand served Original (y) 223,032 394,191 0.79 0.93 V( 228,223 407,377 95 of Bottle Similar to the results observed for FEMA, it is observed here that while the fraction of disasters covered completely remains the same for the original and reduced demand conditions, the fraction of demand served (y) increases as demand is reduced in MiamiDade County. This means that the current allocation of inventory is able to serve a greater fraction of the overall demand. What this shows is that while the overall demand was reduced, the portion of overall demand that can be met given where stock is currently positioned (all in Orlando) is greater when demand is reduced in Miami-Dade county. 5.5.2 Network inventory allocation strategies The actual allocation of physical inventory was compared to the optimal allocation with original demand and the optimal allocation with reduced demand in Miami-Dade County. It was hypothesized that a reduction in demand in one geographic location would change the optimal allocation of inventory across warehouses. While not explicitly evident in Figure 5-15 and Figure 5-16, reducing demand in Miami-Dade County did change the inventory levels in the LSAs across the state slightly. It is important to note that FDEM currently keeps its entire inventory in Orlando, FL, and would only move stock to these locations in an impending disaster, but these locations are available for short term storage of stock and as shown in Section 5.3.4, balance metric, do give FDEM significant opportunity to improve on a number of their metrics. 96 1 I Actual " Orlando, FL " Pensacola, FL Airport P nta Gorda, FL " Pensacola, FL " Ocala, FL OptTime-RedDmd Lakelan " Tallahassee, FL , FL Punta Gorda, FL P nta Gorda, FLI E Lakeland, FL OptTime-Orig West Palm Beach, FL *Duke Field AFS, FL Lakelan, FL 500,000 I I 1,000,000 1,500,000 2,000,000 Units in Inventory Figure 5-15 Actual vs. optimal allocation of inventory with original and reduced demand for MREs (minimize time) 1 I I Actual " Orlando, FL U Pensacola, FL Airport " Pensacola, FL Punta Gorda, FL " Ocala, FL OptTime-RedDmd " Tallahassee, FL . Punta Gorda, FL P ta Gorda, FL N Lakeland, FL * West Palm Beach, FL OptTime-Orig N Duke Field AFS, FL I ~~ - 4,000,000 2,000,000 Units in Inventory 97 6,000,000 Figure 5-16 Actual vs. optimal allocation of inventory with original and reduced demand for water bottles (minimize time) In the case of both MREs and water bottles, when demand is reduced in Miami-Dade County, the optimal solution minimizing time moves units of stock out of West Palm Beach and distributes that stock throughout the other warehouses in the system. In the case of water bottles, this represents a 26% increase in stock in Tallahassee and a 13% increase in stock in Orlando when demand is reduced in Miami-Dade County. For MREs, stock in West Palm Beach is also reduced, and Duke Field AFS (now Eglin Air Force Base) sees a 12% increase in stock of MREs when demand is reduced. This is interesting because if demand were reduced further in Miami-Dade, a more even allocation of inventory across the state might be beneficial. Looking at overall cost savings as a result of the reduced demand, there is a total savings of $843 for MREs and $3426 for water bottles. These numbers are insignificant in terms of the cost of an overall response, but do represent savings that could be factored into a larger accumulation of savings as a result of mitigation or other efforts to reduce demand. 6 Discussion The case study analyses for FEMA and FDEM represent an expansion in the application and function of the Stockpile Capacity Model (Acimovic & Goentzel, 2015) to domestic response agencies logistics networks, and to disaster scenarios where more than one location (e.g. county) is impacted at any one time. Additionally, the managers of the emergency logistics networks studied can use the results of these analyses to make decisions in the near term. This section will summarize the findings of the FEMA and FDEM cases studies and build upon them with discussion of how these findings relate to the central research questions and previous literature, and how they inform the development of a framework for evaluating logistics and other preparedness decisions. 6.1 Federal Emergency Management Agency As part of its responsibilities as one of the coordinators of ESF #7, FEMA must coordinate "comprehensive national incident logistics planning, management, and sustainment capability" (United States Department of Homeland Security, 2013). A 98 review of the policies and other documentation around disaster response revealed that performance based metrics for making logistics relevant preparedness decisions were lacking. The analysis conducted in the case study highlights several metrics that can be evaluated to assess how well equipped the FEMA logistics network is to meet the needs of disaster survivors after an event, including the allocation of inventory throughout the network, the fraction of demand served over time given the current and optimal allocation of inventory, and the RC. The takeaways from the FEMA analysis include (1) overall allocation of inventory was well balanced, but including a large portion of stock in the mid-Atlantic region is sub-optimal based on the historic distribution of disaster risk across the continental United States, (2) restructuring contracts with MRE vendors to only two locations as opposed to three, and negotiating shorter wait times for vendor stock would allow FEMA to serve a greater fraction of overall demand, and (3) that while reduced demand in one state may not necessarily change the optimal strategy for allocating critical commodities within the logistics network, it will improve the response by other metrics. In all three findings, the optimization model output does not tell the entire story, but does provide important metrics on which to compare and contrast benefits derived from logistics decisions. The metrics provide a benchmark for assessing whether the reasoning for current stock allocation decisions make sense in light of benefits that can be gained by optimizing allocation of resources. For example, in the case of the allocation of physical inventory, FEMA's decision to place a large portion of stock in the mid-Atlantic in two warehouses in Maryland may not be optimal, but it may serve the purpose of satisfying political or organizational requirements for geographic diversity of stock allocation, because these organizational factors take into account more than cost and time to serve, while those are important metrics. Furthermore, while the restructuring of contract inventory to just two suppliers may represent the optimal solution in terms of time and cost to serve the disaster affected population, the optimally located suppliers may not have capacity to increase supply to the desired levels. Additionally, from a policy standpoint having diversity in suppliers will reduce the risk that a significant portion of supply is interrupted due to an issue with a given supplier. In the case of reduced demand, despite not being observed to change the allocation of commodities 99 throughout the logistics network for FEMA, it did increase the fraction of demand served for both commodities that were evaluated, which is an important metric for emergency management organizations. It means that they are able to do more, to meet more demand, to serve more people with the inventory they currently have. The analysis conducted for FEMA develops metrics that the organization can use to evaluate current and future logistics planning and preparedness decisions. 6.2 Florida Division of Emergency Management The FDEM's logistics planning documents have been developed with the intent of ensuring that the state is well equipped to respond to the needs of its citizens in the event of a disaster response. The analysis conducted for FDEM suggests that overall, the FDEM logistics network is well equipped to deliver on its plans. However, similarly to FEMA, the planning guidance and documentation in Florida lacks metrics on which to evaluate the response capacity. Using the robust DataCo disaster risk dataset, the case study assessment of FDEM's logistics network found that (1) the current inventory level is able to meet a large fraction of the overall demand in the state, but the allocation of inventory in one central warehouse means that metrics evaluating the time and cost to deliver goods to the disaster affected population suffer, (2) deploying inventory to prepositioning sites several days in advance of a hurricane event has the opportunity to improve the overall time to serve as well as the fraction of demand served, and (3) reducing demand in one location may impact both the optimal allocation of inventory throughout the logistics network as well as the fraction of demand that can be served. Similarly to FEMA, the analysis of FDEM's logistics network does provide useful metrics but there are a few key tradeoffs that are likely to factor into decision-making that are not reflected in the optimal solutions. First, allocating all stock in one central location in Orlando may not allow FDEM's logistics network to meet demand as fast as would be possible with a more disaggregated allocation of supply, but the geography and nature of hazards in Florida may make locating stock in multiple warehouses not feasible or prudent. The scenarios do not analyze, for example, the potential risk of a warehouse in West Palm Beach to damage in a hurricane event. That is analysis that could show that from a risk standpoint, Orlando actually is the best option for placing stock. Interestingly, the findings from the scenarios where focused storm tracks and projected affected 100 population data in advance of a hurricane were used to preposition stock represent the best of both worlds. The results of this analysis support keeping stock in a centrally located warehouse for most of the year, as FDEM does in Orlando, and highlights the benefits of prepositioning stock to pre-identified locations closer to the impact of a notice event. This is not possible for certain types of hazards, such as tornadoes and earthquakes, but is feasible for hurricanes and some flood events. The findings related to reduced demand in Miami-Dade county resulting in a shift of inventory out of West Palm Beach shows that as demand changes, the best option for allocating stock may also change. In this case, the warehouse that holds the most stock in the optimal allocation, West Palm Beach, loses stock to other locations throughout the state, spreading the existing inventory out a bit better. This is intuitive, but does show a real impact of reduced demand as a result of resilience or preparedness work that reduces demand for critical commodities following a disaster. 6.3 Cross-Case Findings The types of decisions explored using the case study analyses fall into the broad category of preparedness decisions, decisions that are made in advance of identified needs. Given the definition of humanitarian logistics as set forth by (Thomas & Mizushima, 2005), which emphasizes that the field is concerned with the planning as well as the implementation of a flow of goods to meet the beneficiary's requirements, these decisions are a key part of the function of a response organization's logistics network. In this definition of humanitarian logistics, efficiency and cost-effectiveness of the flow of goods is also emphasized. Despite the sometimes unique constraints placed on the supply chain in a disaster response, efficiency and cost-effectiveness remain important considerations. The metrics assessed in this study across both cases were directly related to minimizing time or cost to meet the needs of disaster survivors. The analyses used simulated disaster forecast data to evaluate the implications of preparedness decisions, and focuses on three types of decisions: allocation of resources, structuring of contracts with private partners or outside vendors, and decisions related to preparedness goal setting and implications. This section discusses the cross case similarities and differences in the findings for each of these decisions, and relates the findings of this study to previous work. 101 6.3.1 Allocation of critical commodities The findings related to allocation of critical commodities represent an addition to the discussion on logistics preparedness decisions. Even if the solutions suggested by the model are not feasible or desirable given current policy, physical or other constraints, they provide a starting point on which to make informed decisions about allocating inventory. While an organization may not completely agree with the optimal allocation of goods because it concentrates all stock in one area of the state or country (e.g. TX and GA for FEMA and West Palm Beach for FDEM), decision makers now have better intuition and metrics to consider alternate strategies. The results also quantify the impact of suboptimal solutions that are driven by policy or organizational justifications to better inform the debate around such decisions. This ability to make decisions on the basis of a benchmark or starting point is a complement to the frameworks already in place, such as the NRF (United States Department of Homeland Security, 2013), in which the importance of planning, operational coordination, and mass care services, among other objectives of the Response mission area. The ability to include a benchmark for delivering critical commodities to a mass care operation, for example, would facilitate the application of the NRF or other similar state or local framework because there would be a way to quantitatively assess whether a logistics network as currently organized and stocked was able to deliver on the core capabilities outlined in the NRF to provide certain services or goods. For example, the balance metric (Acimovic & Goentzel, 2015) applied in this study to FEMA and FDEM is a quick and intuitive indicator of how out of balance, in general, the allocation of stock across a logistics network is, and the analysis results suggest what specific actions can be taken to improve the overall balance of stock within the network. This metric as well as the other metrics applied in this study is not intended to be static measures of the allocation of inventory, which is important to note when suggesting that they complement a framework like the NRF. Similarly to the fact that the NRF serves as a guide to response but can be tailored to scale up or scale down for a given disaster, the metrics used here to evaluate the allocation of inventory throughout a logistics network represent a framework, or a toolkit from which a response organization can utilize the most applicable metric or set of metrics to assess a given issue. The metrics can also be 102 updated as the allocation of inventory throughout a logistics network shifts, for example when stock is deployed to a disaster but unused stock must be returned to warehouses following the response. The case for using the model output in making prepositioning decisions is strong, with the findings from the FDEM case study clearly presenting the benefits in terms of time to serve and fraction of overall demand met. This is in line with intuition, as well as previous findings that the time to respond when goods are located closer to the disaster affected population the time to respond is lower (Duran et al., 2011). Also, the results from this study reinforce the findings in Lodree Jr. et al. (2012), which were from the perspective of a commercial retailer but used a similar methodology as was used here. What this study adds to both studies mentioned here is a set of metrics and a framework that decision makers can use this to strengthen the case for prepositioning goods. It also extends the application of the methodology to a state logistics network (in addition to the international context explored by Duran et al. (2011) and the private context in Lodree Jr. et al. (2012)). Lodree Jr. et al. (2012) have a similar question of where to allocate supplies in advance of a notice event, but for a commercial supply chain, and arrive at a similar conclusion that prepositioning is beneficial to the overall ability to respond. In addition, Lodree Jr. et al. (2012) conduct a more in-depth sensitivity analysis to estimate the impact of cost parameters on the expected benefit of prepositioning and also reformulate the approach to the problem, using a Percentage of Demand Scenario solution Approach in addition to the two-stage stochastic linear programming model to increase efficiency in obtaining results and reduce computation time, an important consideration if this approach were to be used in practice. In this study, the model formulation to evaluate prepositioning decisions had a computation time of only a few minutes, as the number of disaster scenarios was reduced significantly to just two scenarios (Report 1 and Report 2) from the statewide analysis for FDEM's logistics network that considered the entire catalog of disaster scenarios, so a reformulation of the model or approach was not necessary to conduct the analysis. However, when conducting the analyses for statewide allocation decisions, the computation time for an individual scenario was between 20 and 50 h. As these analyses would be performed to inform decisions that would be made well 103 in advance of an event, this length of time can be accommodated. In the case of prepositioning decisions, output is needed more urgently. 6.3.2 Impact of partnerships in meeting needs The inclusion of the private sector in planning for disasters has been identified as an important consideration for emergency managers (Flynn & Prieto, 2006), and the analysis conducted with the FEMA contract inventory only further supports this theory. Hurricane Katrina remains a constant reminder of what can go wrong in disaster response, but as Horwitz (2008) finds in a review of private sector and Coast Guard engagement in the Hurricane Katrina response, public emergency management organizations can benefit from and learn from the policies of private organizations in their response to this historic hurricane event. In his study, he cites the ability of retailers including Wal-Mart and Home Depot to deliver critical commodities to disaster survivors much faster than FEMA was able to do at the time. While this study did not explicitly focus on the capacity of Wal-Mart or Home Depot, it does suggest a methodology for assessing the involvement of any private organization in the response in addition to the stock already in the logistics network for the responding organization. This type of analysis is relevant as many responding organizations will have at least some organic or physical supply that is either owned outright or is stored in an agency's warehouse through a VMI agreement, but will need to initiate contracts prior to an event to provide additional capacity. The metrics from and results of the analyses conducted for FEMA can inform long term structuring of contracts to optimize on time or cost to serve the disaster affected population. Where should strategic surge contract supply be located to ensure that it is able to serve a large fraction of the disaster-affected population across the country? 6.3.3 Resilience goals The decisions modeled in the case study scenarios can be classified as preparedness decisions. They are made assuming that a disaster will occur and that it will impact people and property such that demand for commodities such as water and food will be generated. There is, however, another closely related emergency management function that impacts preparedness decisions and can sometimes be confused with preparedness. 104 This is the mitigation function. As Haddow et al. (2011) point out, mitigation is different from preparedness because mitigation attempts to "prevent the event altogether" through methods that reduce or eliminate the hazard itself (e.g. flood control structures) or the negative consequences as a result of the hazard (e.g. elevation of homes), whereas preparedness seeks to "improve the abilities of agencies and individuals to respond to the consequences of a disaster event once the event has occurred." The scenarios modeled in this study aim to quantify the benefit to or impact on the logistics network as a result of activities that reduce demand for critical commodities in a disaster, which could fall into the categories of mitigation or preparedness, or might be classified as resilience goals. As the term is being used here, resilience goals refer to goals that might fall under preparedness, mitigation, or another function of emergency management but share the common goal of improving a community's ability to return to a pre-disaster state quickly and effectively following a disaster. Here, the assumption is being made that these types of goals might reduce the demand for critical commodities, and scenarios are run to evaluate the impact of reduced demand on the logistics network. Reduced demand might occur in a number of ways in addition to resilience goals, mitigation, or preparedness decisions, so the insight derived from these scenarios apply broadly to cases where demand is reduced. Reduced demand as a result of resilience goals may not change the allocation of inventory in the system, but, as observed in both the case of FEMA and FDEM, it will allow a logistics network to serve more of the overall demand with its current stock. In the case of FDEM, the reduced demand in Miami-Dade county did shift the optimal allocation of inventory slightly among Florida's LSAs, so there is the potential that if a large enough consistent shift in demand were observed, optimal allocation of inventory throughout a logistics network would shift. 6.4 Framework The framework outlined in Figure 6-1 encompasses the assessments conducted in this thesis through the FEMA and FDEM case studies and positions these assessments as part of the broader set of preparedness decisions. Further, it suggests how these decisions and the RCI and related metrics could contribute to an overall disaster resilience index, similar to the one proposed by Cutter et. al (2010) or the Resilience Capacity Index 105 proposed by Foster (2011), including the other aspects of such an index that would compliment the research done here. The Resilience Capacity Index developed by Berkeley's Institute of Governmental Studies (Foster, 2011) combines twelve equally weighted indicators, four in each of three dimensions to understand how a region might respond to a future stress. Similarly, the Resilience Index indicated in the framework would combine metrics from a set of indices or indicators for different phases of the disaster response lifecycle, such as preparedness, mitigation, response, and recovery into one metric for comparing the resilience of a given government, commercial, or other organization or entity to its own identified benchmarks/goals, its own past performance, or other similar entities or organizations. One of these incorporated indices or indicators into an overall disaster resilience index would be the RCI (Acimovic & Goentzel, 2015), as an example. What the RCI and related metrics used in this study and the disaster resilience index included in the framework do is provide tools for decision makers and leaders to evaluate their decisions within disaster lifecycle and set goals, benchmarks, and standards. The lack of widely used standards currently limits critical evaluation and accountability within organizations in the disaster mitigation, preparedness, response, and recovery space, and makes it difficult for comparisons to be made across and/or within entities involved in disaster response. Within the logistics decisions for preparedness, the framework breaks down the types of decisions into decisions relevant for a public emergency management organization and those relevant to private organizations participating in disaster response through public private partnerships, vendor managed inventory arrangements or other contract mechanisms. It is split this way because the two sectors have different responsibilities when it comes to meeting the needs of disaster survivors after an event. Public emergency management organizations are mandated by state or federal statutes to serve disaster affected populations, so have several key decisions to consider, whereas private organizations may choose to be involved in disaster response, committing goods or agreeing to provide additional capacity to public emergency management organizations through contracts. 106 A number of outside factors impact the logistics decisions for both entities but one could argue that the most significant factor in planning decisions for delivering critical commodities after a disaster is the expected demand for these items. This input is highlighted here as it relates to other preparedness decisions that might be expected to change demand. The RCI, as shown here, distills the results of an analysis of various logistics decisions into one figure and is suggested as part of a future disaster resilience index. PREPAREDNESS DECISIONS Rsduces Demand for Critical Comrnnowts Laqisks Dodsios Pr*paednew DOcMOIsi 0Othff Ir a C p dt d ds MllraMliktoyrodng xdwkMasucture Promoe hrdual readines atus s Graphic -emiinn & Puttic E!1w9eKy Mnament e em loaino mnd em gsies df used s S-Wreourk aNbembn(s with pb&ateyConact I d ongattgons fh Private Orgaizations s e ova NOlutf CdCal coemodtai ey * e - Pnewsitank.o jdcxiwPonncnun, eic. esponse Plans Fth ' Respcows Capacitaem * indexm 0t0wlrsdlces rfoMmgauon, Planning. etc. Figure 6-1 Framework for assessing logistics and other preparedness decisions. Graphic designed and illustrated by Nicole Seelbach (Seelbach & Seelbach, 2015). In addition to the framework presented in Figure 6- 1, the analysis conducted in the case studies can be summarized into a table listing the logistics decisions informed, the metrics evaluated, and the data needed to conduct the analysis. Table 6-1 shows this. 107 These metrics could also be a complement to the benchmarks in the categories of institutional resilience or infrastructure resilience as put forth by Cutter et. al (2010). Institutional resilience is defined there as containing "characteristics related to mitigation, planning, and prior disaster experience", whereas infrastructure resilience is defined as "an appraisal of community response and recovery capacity" (Cutter et al., 2010). Indicators within these categories currently cover benchmarks like shelter capacity (as measured by the number of vacant rental units in a community), mitigation (as measured by the percent of the population participating in CRS for Flood, the percent of the population covered by a recent hazard mitigation plan, and the percent of population in Storm Ready communities), and recovery (number of public schools per square mile), but do not include benchmarks specifically related to a community's ability to provide individuals with critical commodities like water and food after a disaster, which the metrics applied here do. Some of the benchmarks might serve as proxies for the ability to provide these commodities (e.g. previous disaster experience), but none explicitly do so. Table 6-1 Logistics decisions and associated metrics evaluated and data required for analysis Decision Metrics Evaluated e Where to allocate inventory? e e Data Required Response Capacity Index e Ability to meet demand Comparison of current vs. optimal inventory allocation e e Warehouse locations Warehouse stock Disaster data (historic and forecast) All from base allocation, plus: Where to preposition inventory in a notice event (e.g. hurricane?) * C omparison of current vs. optimal inventory allocation Ability to meet demand Time to serve e e Potential prepositioning locations Cone of expected disaster impact several days out from storm Should I enter into a contract with the private sector? Private sectorperspective: How much am I improving the response? * * Current allocation strategy for contract stock vs. optimal allocation Demand served by hour Where would an additional unit of stock be the most valuable? 108 All from base allocation, plus: Contractual private sector agreements Locations of private sector What is the benefit, in terms of cost and time to serve the affected population, if resilience goals reduce overall demand? * * Ability to meet demand Comparison of current vs. optimal allocation of inventory All from base allocation, plus * Resilience goals for a county or state Similar to Cutter et. al (2010), this study uses metrics to evaluate an aspect of a community's resilience. However, it adds to the findings of that study in that it uses data that are not yet publicly available, and speaks to one specific area of overall resilience, rather than Cutter et. al (201 0)'s proposal of a comprehensive set of benchmarks for assessing disaster resilience. The area of overall resilience covered by the metrics and index used in this study and applied to the overall framework is not explicitly included in Cutter et. al (2010)'s set of benchmarks, so could serve to inform an added indicator or set of indicators in the set proposed there. 6.5 Future Research This thesis focused only on answering the questions/decisions that are classified in the framework as logistics decisions, and also employed the RCI in assessing logistics decisions. It also included assessment of the impacts on logistics decisions to changing inputs through resilience goals and potentially reduced demand. It did not focus on the other preparedness decisions listed in the framework, nor did it aim to precisely estimate the impact of these other preparedness decisions on overall resilience or on demand. This section discusses the impact of the model and data limitations on the research, suggesting additions that would enhance the work done here. 6.5.1 Impact of model and data limitations The case studies were subject to limitations in terms of the data available for input as well as limitations due to the current stockpile capacity model formulation. Limitations to input data are addressed in detail in the future research section, but the key challenge is the quality of disaster risk data and the translation of disaster risk data into persons affected. The FDEM analysis benefit from a more robust disaster risk dataset, but the lack of approved and validated methods for determining affected population may have resulted in over or under estimating the total affected population for that analysis. That said, the methods used in the FDEM case study analysis are intuitive and simple, aligned 109 with the private sector assessments used by the insurance industry, and produced reasonable estimates of total affected population. The FEMA analysis uses data with a different methodology for estimating total affected population, and includes fewer historic/predicted disaster events, so could benefit from a robust dataset of disaster events. In terms of the impact of limitations of the stockpile capacity model on results, the most notable limitation was observed in the FEMA analysis for the contract stock. Because the optimization will force all available inventory, regardless of when it becomes available into the warehouse locations with the least restrictive time delay, it is not possible to impose a delay on warehouse availability within the model. In the case of FEMA, this limitation was not relevant because conversations with logistics personnel revealed that the assumption that contract stock will be available immediately or shortly after a disaster event is realistic. However, a model enhancement would improve the ability of the stockpile capacity model to assess varied availability contract terms. Finally, two improvements to the RCI are suggested. First, a method for conducting thorough sensitivity analysis on each of the metrics is needed. The FEMA case study presents a method for how this would be done for the balance metric time and cost metrics, by creating a matrix of potential locations and moving all contract stock to the plausible extreme locations in the matrix to evaluate the bounds of the balance metrics. This method worked for FEMA, but a generalizable method is needed that can be used regardless of the unit of analysis. Second, focused discussions with potential users of the RCI is needed to evaluate both the range of metrics that are appropriate to include in the RCI as well as the weighting of each of the metrics relative to the others. Should additional metrics be included in the RCI? Should the current and potential additional metrics all be weighted equally, or should individualized weights be developed? Focused discussions with logistics experts in emergency management, private sector, and other organizations who would use the RCI is needed to ensure that the index is robust and can be applied across the range of organizations who would use it. 6.5.2 Complimentary future research here (e.g. mitigate risk to flooding) and others would impact demand for critical 110 ' Future research could explore how preparedness decisions such as the ones mentioned commodities in a disaster response, and also how these decisions themselves might be measured in an index. The creation of other indices to measure aspects of the disaster response lifecycle including preparedness, response, recovery, and mitigation would supplement the response capacity index in creating a comprehensive resilience index. Additionally, future indices could evaluate preparedness decisions for other hazards, supplementing the research on natural hazards done here. In addition to research to inform the overall framework, additional research on a few key inputs to this study would improve not only the conclusions made here but future analysis in this space as well. The key inputs that would benefit from additional focused research include (1) disaster risk data (2) affected population data. In this study, disaster risk data were obtained from EM-DAT, a publicly available dataset with data on disasters that have occurred all over the world, as well as from DataCo, a firm specializing in catastrophe risk quantification and decision analytics serving the risk and insurance industry, among others. The data from EM-DAT had the benefit that it included information from disasters that had occurred in the past, and thus might better be able to describe the scenario. On the other hand, the EM-DAT data is also limited by the fact that it only represents historic events. It therefore cannot predict future events that have a larger impact than events that have occurred in the past. The DataCo dataset included both historic events and events generated stochastically from a catalog of characteristics of hurricane events. The limitations of the DataCo dataset were that it did not include an estimate of affected population for each event, and it only included loss estimates from hurricane wind and storm surge. Each dataset had merits and drawbacks, but a more comprehensive set of data on disaster events, applied to the analysis done in this study would render the results even better for informing preparedness decisions. As presented in the FEMA case study, a comparison of the disaster affected population as reported by EM-DAT and the population as assumed using the DataCo disaster risk data was done, and the results point to challenges in reaching a commonly agreed upon methodology for determining the total number of people that were or will be impacted by a disaster event. EM-DAT's figures, based on the injured, homeless, and affected populations as reported by governments and media, compared to the simple assumption used here to relate residential property damage to affected population III produced different estimates of affected population for the same historic events. The hypothesized reasons why these figures are different is covered in the FEMA study, but the broad question of how to assess affected population remains. FEMA estimates total affected population using a combination of Hazus and census data for the area that has been affected in a disaster (FEMA Staff 3, 2015), while EM-DAT relies on reported estimates and when that is not available on relationships between damaged homes and average family size (Centre for Research on the Epidemiology of Disasters, 2015), and DataCo relies on a hazard threshold to make a binary affected or not affected decision on a county-by-county basis, as described in the methods section. 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