Modeling and Analysis of Commercial Finished Goods Inventory By Oliver Stiles Schrang B.S. Operations Research, United States Military Academy, 2005 SUBMITTED TO THE MIT SLOAN SCHOOL OF MANAGEMENT AND THE ENGINEERING SYSTEMS DIVISION IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREES OF Master of Business Administration and Master of Science in Engineering Systems In conjunction with the Leaders for Global Operations Program at the Massachusetts Institute of Technology JUNE 2014 @ 2014 Oliver S. Schrang. All Rights Reserved. The author hereby grants to 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. Signature redacted S Signature of Author Certified by MIT Sloan School of INbagement, MIT Engineering Systems Division May 9, 2014 Signature redacted Leigh Hafrey, Thesis Supervisor Senior e cturer, MIT Sloan School of Management Certified by Signature redacted p Signature redacted Accepted by A Accepted by David Simchi-Levi, Thesis Supervisor fessor okCivil and Environmental Engineering and Engineering Systems Richard C. Larson, Mitsui Professor of Engineering Systems Chair, Engineering Systems Division Education Committee Signature redacted Maura Herson, Director of MIT Sloan MBA Program MIT Sloan School of Management MASSACHUSETTS INMftE OF TECHNOLOGY JUN 13 201 LIBRARIES This page intentionally left blank. 2 Modeling and Analysis of Commercial Finished Goods Inventory by Oliver S. Schrang Submitted to the MIT Sloan School of Management and the MIT Engineering Systems Division on May 9, 2014 in Partial Fulfillment of the Requirements for the Degrees of Master of Business Administration and Master of Science in Engineering Systems Division Abstract As the commoditization of the PC market erodes product margins, increasing emphasis is placed on cost optimization within the supply chain. One critical component of this is the financial impact of inventory policies and the transportation choices affecting these policies. Overseas manufacturing and ocean transportation are the most cost-effective solutions, but this requires building products to a forecast. The uncertainty induced by forecasts affects the inventory volumes necessary to achieve specified service levels. Inventory volume and its associated holding cost can be reduced through air transport, but this must be balanced against the increased expense of this particular shipping option. This thesis seeks to develop a framework informing inventory levels, transportation policies, and replenishment decisions. Holding inventory to a target level that does not vary across product type or replenishment method has the advantages of ease of management and low inventory variability within merge centers, but is sub-optimal from a customer satisfaction and cost perspective. The model presented introduces a flexible approach that considers variations in product characteristics to determine optimal inventory and transportation strategies. Differences between generalized target inventory levels and the levels achievable through a non-uniform approach are demonstrated. The implications of these inventory levels on required forecast accuracy levels are also considered. From these differences are extrapolated cost savings under current commercial finished goods volumes for the North American region as well as target volumes for the same. Current and target ocean volumes are discussed, with an analysis of their effect on inventory levels and costs. Thesis Advisors Thesis Supervisor: Leigh Hafrey Title: Senior Lecturer, MIT Sloan School of Management Thesis Supervisor: David Simchi-Levi Title: Professor of Civil and Environmental Engineering and Engineering Systems 3 This page intentionally left blank. 4 Acknowledgments First I would like to thank Dell for their longstanding support of the LGO program and for sponsoring this internship. I was provided with opportunities to work with and learn from a tremendously talented group of individuals and this made the experience all the more rewarding. I would like to thank my supervisor, Jen Felch, and my project champion, Cathy Arledge, for their mentorship and support during my time with Dell. Josh Freeman and Jerry Becker were instrumental in outlining and explaining relevant processes and ensuring I connected with the right individuals in order to access any and all relevant data. Many thanks to Juan Correa, LFM '07, for his feedback and advice throughout the duration of this internship. I would like to thank my two advisors, Leigh Hafrey and David Simchi-Levi, for the time and effort they invested in my project. Their guidance and input is greatly appreciated. Thank you to the LGO staff and our Director, Don Rosenfield, for supporting not only this thesis, but also my educational growth throughout the program. Additionally I would like to thank my classmates. The past two years have been a blast, and I'm proud to know you and call you friends. Finally I would like to thank my family. Without their love and support none of this would have been possible. 5 This page intentionally left blank. 6 Note on Proprietary Information In order to protect proprietary Dell information, the data presented throughout this thesis has been altered and does not represent actual values used by Dell, Inc. Any dollar values, product names or logistic network data has been disguised, altered, or converted to percentages in order to protect competitive information. 7 Table of Contents A bstract ...................................................................................................................................... 3 A cknow ledgm ents ............................................................................................................ 5 N ote on Proprietary Inform ation .................................................................................. 7 Table of Contents .................................................................................................................... 8 List of Figures........................................................................................................................... 9 List of Equations ...................................................................................................................... 9 List of Tables .......................................................................................................................... 10 1 Introduction: Background and Findings .......................................................... 12 1.1 Introduction.....................................................................................................................12 1.2 Background...................................................................................................................... 1.2.1 Dell's History as a PC M anufacturer ................................................................................ 1.2.2 Historical Fulfillm ent M ethod .............................................................................................. 1.2.3 New Business M odel ...................................................................................................................... 1.3 Project M otivation ..................................................................................................... 1.4 Approach........................................................................................................................... 13 13 14 15 16 17 1.5 19 2 Literature Review ................................................................................................... 2.1 2.2 2.3 2.4 3 Introduction..................................................................................................................... Data Collection/Analysis ........................................................................................ Process fulfillm ent characteristics ...................................................................... Current state process.....................................................................................................................27 Proposed future state ................................................................................................ Service Level ...................................................................................................................................... Forecast Accuracy and Standard Deviation of Forecast Error .............................. Mixed Replenishm ent M odes............................................................................................... Costs associated w ith inventory ............................................................................ Purchase costs................................................................................................................................... Transportation Costs ..................................................................................................................... Holding Costs.....................................................................................................................................37 M odeling Techniques................................................................................................ Results/Recom m endations .................................................................................. 4.1 4.2 5 Introduction..................................................................................................................... Inventory M odels ........................................................................................................ Methods of Forecasting/measuring forecast accuracy.................................... Effects of Under-stocking cost ................................................................................. M ethodology .................................................................................................................. 3.1 3.2 3.3 3.3.1 3.4 3.4.1 3.4.2 3.4.3 3.5 3.5.1 3.5.2 3.5.3 3.6 4 Findings............................................................................................................................. Forecast accuracy im pact on inventory .............................................................. Comparison between DSI and base stock methodology................................. Conclusion ...................................................................................................................... 5.1 Recom m endations ..................................................................................................... 8 20 20 20 21 22 23 23 24 26 28 30 32 34 35 36 36 39 40 40 45 50 50 5.2 5 .3 6 51 51 Appendices.....................................................................................................................53 6.1 6.2 7 Opportunities for further work ........................................................................... C o da .................................................................................................................................... Appendix A: Model Dashboard.............................................................................. Appendix B: W eekly Screenshot............................................................................ 53 54 References......................................................................................................................55 List of Figures 14 Figure 1: Dell revenue by business segm ent............................................................................ Figure 2: Global supply chain rankings from 2007-2013.................... 17 Figure 3: China - US supply channels.......................................................................................... 24 Figure 4: North American commercial product dem and patterns ................................ 25 Figure 5: Dem and m anagem ent review cycle. ............................................................................. 26 Figure 6: China - US shipping tim es............................................................................................... 27 Figure 7: Inventory profile..................................................................................................................... 30 Figure 8: Norm al demand distribution....................................................................................... 33 Figure 9: Monte Carlo output. .............................................................................................................. 40 Figure 10: DSI levels by forecast accuracy given 100% ocean replenishment............ 41 Figure 11: DSI levels by forecast accuracy given 10% ocean replenishment..... 42 Figure 12: Forecast accuracy required to hit a target DSI for various transportation m ix es . ..................................................................................................................................................... 43 Figure 13: DSI by ocean transportation for a given forecast accuracy......................... 44 Figure 14: Shipping and holding costs per box across North American smart selectio n po rtfo lio ............................................................................................................................ 47 Figure 15: Sales volume and smart selection percentage for analyzed product p o rtfo lio ................................................................................................................................................ 48 Figure 16: Global smart selection sales as a percent of volum e ...................................... 49 List of Equations Equation Equation Equation Equation Equation Equation E q ua tio n Equation Equation Equation Equation 1: Coefficient of Variation. ........................................................................................... 2: DSI target m odel ................................................................................................................ 3: Base stock for air replenishm ent ........................................................................ 4: Base stock for ocean replenishm ent................................................................... 5: Type II fill rate.................................................................................................................... 6: Expected units short. ................................................................................................. 7 ...................................................................................................................................................... 8: Follows from eq. 6............................................................................................................ 9: Follows from eq. 8 ............................................................................................................ 10: Partial loss function .................................................................................................. 11: Forecast accuracy. .................................................................................................... 9 25 28 29 29 31 31 31 31 32 32 33 Equation 12: Root mean square error. ........................................................................................ Equation 13: Mixed replenishment base stock model. ...................................................... 34 35 List of Tables T ab le 1: Discou nt sch edule.................................................................................................................... Table 2: Inventory mismatch under current conditions.................................................... Table 3: DSI difference between current and proposed model across North American smart selection portfolio.................................................................................... Table 4: Predicted savings under proposed base stock model........................................ 10 38 45 46 49 This page intentionally left blank. 11 1 1.1 Introduction: Background and Findings Introduction This thesis represents the culmination of six months of collaborative work between the author, Dell employees, and MIT academic advisors. It was born of both commercial necessity and academic interest, and as such seeks to strike a balance between these realms. Dell has a long history of operational excellence and also a longstanding partnership with the MIT Leaders for Global Operations program, to include a seat on the Governing Board. This partnership is reflected in the numerous internships conducted by LGO Fellows and the extensive collaborative work between Dell and members of the MIT staff and faculty. Strong operational performance, once achieved, can be short lived if continual efforts at improvement are not made. The business environment is nothing if not fluid, and a static approach can quickly become outdated. In the larger scheme, then, this thesis can be seen as a continuation of previous Dell/MIT partnerships, even if the specific content matter is not identical. As a company with an international manufacturing footprint and a global distribution network, Dell's supply chains are correspondingly long and networked. Globally optimal solutions can be difficult to derive, let alone implement, so local solutions are sought which can be integrated into the larger whole. One such local component is the role of inventory within the supply, manufacturing, and distribution networks. Specifically: where to hold it and how much to hold. These questions can be further specialized to accommodate raw material, component, work in progress, and finished goods inventory. This thesis primarily focuses on the ultimate category, and further concerns itself with the question of how much inventory to hold. Finished goods inventory, while to a certain degree inescapable in any manufacturing and distribution process, has a varied history within Dell. Many of its most impactful operations successes came through the near elimination of finished goods inventory in its factories. It is useful, then, to examine more closely the role inventory has played throughout the company's evolution. 12 1.2 1.2.1 Background Dell's History as a PC Manufacturer In 1984, three years after the introduction of the IBM PC, Michael Dell founded his namesake company in a dorm room at the University of Texas, Austin (Holzner, 2006). Originally the company modified existing IBM hardware, but by 1985 it introduced its first purebred PC. A voracious market coupled with IBM's inability to fulfill all orders meant Dell grew at a phenomenal rate. The company moved into new, larger facilities in the first year, and by 1987 opened its first international subsidiary in the UK. In 1988 the decision was made to go public, in no small part due to the company's 80% annual growth rate. Soon after followed the construction of Dell's first overseas manufacturing facility, in Limerick, Ireland, to meet rising customer demand from Europe, the Middle East, and Africa. Much of this success was built on Dell's direct to customer model. In the classic business practice of eliminating the middle man, Dell eschewed retailers and instead sold their computers directly to customers over the phone. Holzner (2006) suggests that Michael Dell himself estimated that going direct saved 25% to 45% of mark-up costs on each machine, an advantage that would prove to be decisive in the early PC market. As technology evolved so too did Dell's direct business model: dell.com launched in 1996 providing an online order platform. Within six months the site was generating over $1 million in sales each day; by 2000 that amount had risen to $40 million per day. Not content to remain solely a manufacturer of PCs, Dell developed a line of blade servers in 2001 and continued to expand into new segments, either through internal development or acquisition of external firms. By 2012 the company was organized and focused on four key areas: end user computing, enterprise solutions, software, and services. As of the end of fiscal year 2013 (the fiscal year ending in February, 2013) over 100,000 13 employees supported these four areas and the Revenue mix by segment (Dollars in billions) company posted revenues in excess of $56 $70 billion.' Figure 1 depicts Dell's revenue across $60 these four areas. $50 $611 $611 FY'08 FY09 $615 $621 FY'1 FY'12* $40 1.2.2 Historical Fulfillment Method $30 In addition to introducing a direct to $20 customer sales model, Dell also championed a $* just-in-time manufacturing philosophy. "Dell's model direct customer's has way given culture," it a that do-it-the- the way manufactured in which (Holzner, UPublic Large Enterprise FY'10 Consumer SMB extended beyond how computers were sold and shaped $0 Figure 1: Dell revenue by business segment. they were At 2006). manufacturing sites in Ireland and the United Taken from Dell 2012 Year in Review, available at http://www.dell.com/learn/us/en/uscorp1 /about-dell-investor?s=corp States, Dell routinely built PCs directly to each individual customer's specifications. The manufacturing process was refined and streamlined in an iterative process with a goal of reducing as many unnecessary process steps as possible. Internals were redesigned to eliminate the need for screws. Components instead snapped together, reducing the time it took to assemble a computer. According to a senior design engineer, "every screw you design out of a product reduces assembly time by approximately eight seconds" (Holzner, 2006). This devotion to efficiency resulted in PC assembly times as low as three minutes. The entire process, from order to entry to a PC leaving the factory was four to eight hours. As impressive as these statistics are from a manufacturing perspective, combined with an innovative supplier relationship they had an even bigger impact on inventory. Because process lead times were so short, Dell could afford to operate solely based on realized demand. No finished goods were ever built to forecast or stock, because the 1 More information regarding Dell's history and financial information can be accessed at http://www.dell.com/learn/us/en/uscorpl/about-dell-company-timeline, and http://www.dell.com/learn/us/en/uscorp1/about-dell-investor?s=corp, respectively. 14 manufacturing process didn't begin until after a customer entered an order. Combined with in-region manufacturing, a customer who ordered a pre-built PC from another manufacturer could expect to receive it at the same time as a custom built PC from Dell. Additionally, because products were built to order, there was no need to hold finished goods inventory. As soon as a PC was completed it could be shipped to the customer. Such inventory reductions occurred upstream in the supply chain as well. Suppliers maintained warehouses near Dell manufacturing facilities and assumed responsibility for much of the component inventory. The close proximity between supplier and manufacturer enabled re-supply shipments to occur on an hourly instead of daily basis. From 2000 to 2006, Dell saw inventories in its Austin factory shrink from six days to five to seven hours (Holzman, 2006). While such component gains are impressive, this thesis is primarily concerned with Dell's current finished goods inventory model, so the focus will be restricted as such for the remainder of the text. 1.2.3 New Business Model Despite the efficiency of their in-region manufacturing processes, the changing nature of the PC industry required cost-cutting moves by the late 2000's. As consumer demand surged, Dell began to sell through large retail chains like Wal-Mart and Best Buy. Such contracts often consisted of large bulk orders purchased ahead of time and held by the retailer. In the case of a 50,000+ order for laptops, outsourcing production to a contract manufacturer began to look attractive from a cost and efficiency perspective. Dell's network of regional factories, while flexible and responsive, were not optimized for such large scale production. In 2008, the New York Times reported that Dell had announced they would close their manufacturing facility in Austin by the end of the year (Lohr, 2008). In time, Dell continued to shift production to original design manufacturers (ODMs) located in China. Today, the bulk of laptops sold in the US are manufactured in facilities in Shanghai, Yantian/Zhongshan, and Chengdu. Outsourced manufacturing has significant impacts on the way in which Dell serves its customers' needs. The most efficient process involves building larger volumes of units to a retailers order or forecast, and shipping them to the US via ocean. This works well with the retail model, but doesn't allow for the same degree of customization as had been previously 15 built into Dell's manufacturing operations. In order to meet customer expectations of acceptable delivery times, built to order products must be shipped via air to avoid the lengthy travel time required by ocean freight. In Dell's direct commercial model, this also means the company is required to hold build to stock finished goods inventory in warehouses in the US for eventual distribution to consumers. To address these issues, Dell has developed a new model that tiers products according to their level of customization: build to stock products are defined models that the consumer can select "off the shelf', catalog models are customizable within a range of pre-defined offerings, and custom models are fully customizable platforms tailor built to a consumer's specifications. Within this model Dell continues to push the built to stock offerings as they offer the highest product margin to the company. These offerings also have the most impact on Dell's finished goods inventory holdings, so the analysis of this thesis will be focused on this particular portfolio of products. 1.3 Project Motivation The commoditization of the PC market has existed as a phenomenon for at least the past decade. Eric Bangeman, writing in Ars Technica, an online technology news and information journal, described, "the utter commoditization of the PC market," in an article published in January of 2005. As products become increasingly advanced, even basic models offer every feature a typical consumer needs and often everything they want as well. Attributes which were formerly unique to a brand become generic and thus relatively indistinguishable. Customers no longer pay a premium for these attributes because they are available from all manufacturers. As this commoditization trend continues it becomes increasingly important to streamline operations and achieve the most efficient cost structure possible. The overall consumer demand for customized, built to specification systems is no longer as prevalent, meaning manufacturers can no longer expect to command as much of a price premium for these features. With industry leaders competing to offer products at the lowest possible price, cost reduction becomes an increasingly important component in maintaining product margins. While operational gains can be made throughout the entire supply chain, one key area is inventory management. This thesis examines and analyzes Dell's finished goods inventory model in an effort to achieve greater operational efficiency. 16 When Bangeman addressed the commoditization of the industry, he also stressed Dell's successes in the previous year, stating that they, "held on to the top spot with 17.9 percent of the worldwide PC market," in 2004. Much of that market share was built upon operation excellence. According to AMR Research (now Gartner), Dell ran the 3rd most efficient global supply chain in 2008. Unfortunately, by 2013 they ranked 11th (Gartner, 2013). While these rankings are based on a variety of inputs, one key component is inventory turns. Clearly there is not only a need to streamline operations for the sake of cost, but also for competitiveness. A depiction of Dell's supply chain ranking compared to two industry competitors is given in Figure 2. Gartner Supply Chain Rankings 2007-2013 2006 2008 Axis Title 2010 2012 2014 0 2 4 -- Dell -Competitor 6 A Competitor B 8 10 12 14 Figure 2: Global supply chain rankings from 2007-2013. 1.4 Approach Analyzing the finished goods inventory models for every business line, product type which Dell offers, and region in which Dell operates would be a prohibitive undertaking due to the intensity of the time and labor required. Therefore, a more restricted approach to the problem was taken. The scope of the analysis was narrowed to focus on the finished goods inventory model of Dell's commercial business for the North American region. This portfolio of products includes both laptop and desktop offerings manufactured in both China and Mexico. Under this more defined scope, the following generalized approach was utilized: 17 1. Define current model Before any analysis can be conducted it is important to first capture and understand the existing business process. This was primarily achieved through interviews with relevant members of Dell's business transformation, demand planning, logistics, and inventory management teams. Once the current state was captured and defined, it was used as a benchmark against proposed future models. 2. Collect and analyze relevant data Dell tracks millions of data sets to enable informed business decision making. This provides a wealth of information, but the scale of data quickly becomes unwieldy. To combat this, the entire Dell database is broken into smaller datamarts which contain only that data relevant to a specific organization. This requires the collection and aggregation of data from a variety of locations in order to make cross-functional decisions. Analysis of this data provides insight into potential process improvements. More detail into the data collection and analysis is given in Chapter 3. 3. Formulate alternative model There is a wealth of literature available regarding inventory models and their effects on the supply chain. When developing alternatives to the current process, traditional models were consulted and applied. When necessary, these models were adapted to fit Dell's operational process as much as possible. The intent was not to wildly revamp their existing processes, but introduce a more efficient model which works within the current framework. The development of this model is explained in greater depth in Chapter 3. 4. Compare alternative model with current model The two models are compared from a cost perspective to determine if any benefits are derived through the introduction of new techniques. The comparison is based on current data, but the results are then extrapolated to include other product lines and projected volume growth. The model comparison and associated recommendations are covered in Chapters 4 and 5. 18 1.5 Findings As discussed in the previous section, a subset of Dell's finished goods business was analyzed. A variety of factors contributed to determining which section to more closely scrutinize, but chief among them were relevance to Dell's newest business model and access to and availability of data. Dell's retail channel has operated a finished goods model through third party retailers (Best Buy, Walmart, etc.) for several years, but tiered commercial offerings and their associated inventory impacts are more recent developments. North American merge centers were closely linked to Dell's headquarters in Texas both geographically and by personnel, through the frequent travel of employees between the two. This provided an ease of data access which fostered the development of this project. During the course of this project it was determined that existing inventory holding models provided certain key benefits to the company: they were relatively easy to implement and provided a uniformity across product lines. With these advantages, however, came certain concessions. The uniform process does not fully exploit unique product characteristics which can aid in the construction of a more optimized model. The implementation of a model taking into account such unique characteristics yielded interesting implications to current processes. Of prime importance were inventory volumes and service levels associated with various products. Certain models were carried at higher inventory volumes than required for targeted service levels, while the opposite (low inventory volumes at a corresponding reduction of service levels) was true for others. By applying a new model to the existing product portfolio, service levels were smoothed out and the corresponding inventory volumes were altered as a result. Of note is that while some products were indicated to be carried at lower levels and some products at higher levels under the new model, overall inventory volumes were shown to be reduced, and costs associated with that inventory were reduced as well. The remainder of this thesis will be an exploration into how this model was developed, how it was applied, and what results were indicated through the process. 19 2 2.1 Literature Review Introduction There is a wealth of literature available covering many inventory models, starting with Ford Harris' groundbreaking paper How Many Partsto Make at Once, published in 1913. His economic order quantity (EOQ) model formed the basis for many inventory systems over the previous century, and has provided a rich landscape for further study and expansion. Though the volume of materials published is large, the remainder of this section will examine why companies hold inventory, how product demand can be forecasted and measured, and how this affects commodity supply chain design. 2.2 Inventory Models In 1915, Harris noted there were limits to the optimal quantity of parts that could be produced at one time. Interest tied up in wages, material and overhead sets a maximum limit to the quantity of parts which can be profitably manufactured at one time; "set-up" costs on the job fix the minimum. Experience has shown one manager a way to determine the economic size of lots. (Harris, 1915, pg. 1) This manufacturing model can be directly translated into an inventory model by considering the opposing forces of the fixed costs of ordering inventory with the costs of holding said inventory (Simchi-Levi, Chin, & Bramel, 1997). Simple models deal with single warehouse, single retailer scenarios and deterministic demand, but expanded derivations include multiple warehouses and multiple retailers, stochastic demand, and various time horizons. The EOQ model, and its multiple derivations, are based on a policy of continuous review. You establish a minimum inventory threshold (s) that triggers a replenishment order a predetermined number of items (Q). However, as will be discussed more thoroughly in Chapter 3, the practice of continuously monitoring inventory levels required to implement an (s,Q) model is not always feasible in the context of a large firm's institutional rhythm. Many times decisions are made on a very consistent schedule, so a classification of inventory models based on periodic review is often a better complement. 20 A generalized periodic review model, which will be expanded upon further in Chapter 3, is the base stock model. For this policy, a firm determines an optimal order up to level (S), and orders enough inventory to replenish their current supply back up to that level every review period (R). Unlike the EOQ model, where a company orders the same amount of inventory at varying intervals, in a base policy the company orders varying amounts of inventory at regular, specified intervals. Extensive reviews have been conducted on (R,S) models and their reaction to lead-time uncertainty (Song, 1994), the necessity to rush parts of an order (Groenevelt &Rudi, 2003), and their implications for multi-echelon supply chains (Graves, 1996). In 2000, Roundy and Muckstadt developed a heuristic for computing base stock policies. While their findings that their heuristic is not accurate for products in which the coefficient of variation is greater than two is and interesting result, many of the products analyzed for this thesis demonstrated far more predictable demand behavior. Consequently, the methodology section of this thesis focuses on a single echelon, multi-product model and does not rely on Roundy and Muckstadt's heuristic. 2.3 Methods of Forecasting/measuring forecast accuracy While basic inventory models can be formulated assuming deterministic demand, the models we discuss in this thesis rely upon an assumption of stochastic demand. Often this demand is expressed according to the parameters which define the underlying distribution from which it is drawn. One key assumption we will make in the formulation of our model is that demand is independent and identically distributed and is drawn from the normal distribution. Such an assumption means our demand parameters take the form of a mean demand, y, and a standard deviation of demand, a-. However, while companies must shifts in demand, they attempt to reduce the impact such shifts have on their operations through forecasting techniques. If companies can predict demand variations, they can adjust production and inventory levels accordingly and smooth out the effects of these variations. The extreme example of such a system would be perfect forecasting: knowing with perfect clarity how demand would behave in the future. Under such conditions demand no longer behaves stochastically, it essentially becomes deterministic from the company's point of view. In such a scenario no safety stock or buffer inventory would need to be held, because there are no unpredictable ebbs and flows in 21 demand to protect against. Clearly this is an unrealistic scenario, but it helps highlight the importance of forecasting techniques and methods. In this thesis we are less concerned with how to forecast, and more concerned with using the results of that forecast to our advantage. Stevenson (2012) stresses that, "accurate forecasts are very important for the supply chain. Inaccurate forecasts can lead to shortages and excesses throughout the supply chain." The question then arises of how to best measure the accuracy of a forecast. Hyndman and Koehler (2006) provide a comprehensive survey of various forecast accuracy measurements, and assess the positive and negative attributes of each. Of the various measures they assess (root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), etc.), Kahn (1999) highlights that MAPE is by far the most popular within business and has become almost standard practice. Mean absolute percent error compares the error between the actual value and the forecast value to the actual value itself. This measure is scale independent, which allows for comparing accuracies across various data sets. Dell slightly modifies MAPE by comparing the errors to the forecast, not the actual values; this will be examined in greater depth in Chapter 3.4.2. 2.4 Effects of Under-stocking cost Stevenson's comment regarding shortages and excesses in the supply chain has direct cost implications. Optimistic forecasts which lead to excessive finished goods inventory levels stress the supply chain in multiple ways. Money was invested in producing and transporting an object that is not generating revenue, space is being occupied in a warehouse that could be used to hold more revenue generating products, and the items themselves are often sold at steep discounts, or written off entirely, in order to clear them out of the system. Not only is this capital lost, but so is the opportunity to invest it in other, more profitable ways. Determining the effects of shortages is a more difficult endeavor. While it is tempting to assume the cost of under-stocking is the lost revenue on the unavailable item, the true cost is more elusive. The out of stock item will not be purchased, but it is possible the customer chooses another item they might not have originally, thus offsetting or even overcoming (in the case of a more expensive item) the lost revenue on the original item. Additionally, there is some measure of goodwill which is lost when a desired item is not available. This might be 22 negligible for a small number of instances, but the accumulated effect can be such that consumers might begin to look elsewhere when placing orders in the first place. Dhalla (2008) cites several previous models that assess the effects of shortage costs (Oral et al., 1972; Graves, 2002; and Aksen, 2007). Various techniques are applied, but both Graves and Aksen discuss how difficult it is to measure losses of good will. Dhalla formulates a model which considers a dependence on the number of days late an item is, and then assesses two types of customers. Type 1 customers: those informed their order is delayed after they purchase a system. Type 2 customers: those who choose not to order because the quoted lead times are too long (i.e. a system is unavailable and will not be restocked for some time). This thesis, specifically the analysis devoted to ensuring service levels and fill rates are met, is primarily concerned with avoiding Type 2 customers. Provided there is inventory in the merge center, Type 1 customers are the result of factors outside the scope of this project (incorrect order entries, inaccurate stocking information, issues with national carriers, etc.), and will consequently not be considered. 3 3.1 Methodology Introduction When examining Dell's North American commercial fulfillment model, it is important to understand its context within the rest of the company as a whole. Dell's four key focus areas (EUCS, Enterprise Services, Software, and Support) are tasked with developing, marketing, selling, fulfilling, and support products brought market. Further, the company is divided globally into regions. AMER covers most of North and South America, EMEA has responsibility for Europe, the Middle East, and Africa, and APJ is concerned with Asia, the Pacific, and Japan. While the model described and analyzed in this thesis is primarily concerned with one segment of a global operation, the results can ultimately be scaled and applied to other regions by changing the associated input variables. Figure 3 depicts the generalized supply chain channels for North American products manufactured in China. 23 Ocean replenishment Ar replenishment Figure 3: China - US supply channels. 3.2 Data Collection/Analysis In order to understand the characteristics of products sold through the North American commercial network, two years of data for over 300 products was analyzed. Due to the nature of Dell's data management systems, data was unavailable for products at the finished goods level. Instead, relevant forecast and demand data was captured one level higher. For instance, a stocked finished goods item consists of a specific part list: hard drive type and size, graphics card, operating system, etc. The next higher level in the product hierarchy specifies that the product is a 15" performance laptop, but doesn't include data regarding the specific components mentioned above. Regardless, it was assumed that finished goods products followed similar demand fluctuations as their associated parent. Unsurprisingly, these products demonstrated a wide variety of demand volumes and fluctuations. Each product's forecast distribution parameters were also widely disparate. These forecasts were captured at multiple intervals (one, four, and eight weeks) corresponding to the lead times associated with various transportation methods. These forecasts will be covered in more detail later in this chapter. Actual demand data was assumed to be normally distributed, and each item was graphed according to its coefficient of variation (CoV), which is expressed as the demand standard deviation (0) divided by the demand mean (p) for the period of time measured. 24 CoV =Equation 1: Coefficient of Variation. When displayed graphically (shown in Figure 4), the product CoV's display the classic long tail associated with retail goods. Product Demand Pattern 4.# CoefOKdntOf Vatason Figure 4: North American commercial product demand patterns. The area bounded in green represents products with favorable demand characteristics: the volumes sold are high and variation is low. Products bounded in red are unfavorable to a forecasting and inventory model. Volume is relatively low, but variation is extremely high. The fluctuations in these products demand make them very difficult to forecast accurately and stock at appropriate volumes. This introduces the concept of product differentiation and supply chain segmentation. Replenishment and stocking policies might not be uniform across a product portfolio due to the difference in demand characteristics. This idea will be discussed in more detail in Chapter 3.4. 25 3.3 Process fulfillment characteristics As introduced in Section 2, many companies do not monitor inventory levels and make replenishment decisions on a continuous basis. Dell is no different in this regard. The demand plan is generated monthly, while the item level plan and demand management occur on a weekly basis as highlighted in Figure 5. Effectively this means that decisions regarding order volumes with regards to finished goods inventory are made once a week. This fixes the review period (R) which will be incorporated into the base stock model. Item Le Plan Demand Mgmt. Item Level Plan Demand Mgmt. Item Level Plan Demand Mgmt. Item Level Plan Demand Mgmt. Week 1 Week 2 Week 3 Week 4 Figure 5: Demand management review cycle. In addition to this review period, the various transportation characteristics must be considered. When transporting products from China to the United States, Dell has two primary options: ocean or air. (A third version, delayed air, exists but is not elaborated upon in this thesis.) Manufacturing time is equivalent regardless of the shipping method, but transportation times differ drastically. In the case of an air shipment, goods can be expected at the warehouse three weeks after order entry (two weeks of manufacturing time and one week of transit time). For ocean shipments that interval more than doubles to seven weeks (two weeks of manufacturing time and five weeks of transit time). These variations in lead time drastically affect the amount of inventory needed in the warehouse. Approximate shipping times are highlighted in Figure 6. 26 China Lane to us via air nus netto China Lane V irAi ODM to Port tj -t- via ocean ODM to Sea Pon jAr~ Shutte Port Ocean Sailing Figure 6: China - US shipping times. Adapted from Dell logistics. As with any system of transporting and storing inventory there are various costs associated with each process step. Because Dell does not own the actual manufacturing facilities they essentially buy each product from the ODM. This product cost varies depending on the PC being manufactured and the order quantity. Once the PC is ordered it is either transported via air or ocean to the warehouse in Nashville. Air freight costs exceed ocean costs by a factor of four, which incentivizes high rates of ocean shipping. Dell technically does not own inventory until it reaches the US, so there are vendor managed inventory (VMI) fees associated with transportation as well. Receiving costs at the warehouse are on a per pallet basis, with holding costs assessed on a per unit basis. Depreciation and cost of capital are also factored into the holding cost of a unit. Segrera (2011) cites a Harvard Business Review study by Callioni et al. which indicates that fully assembled PCs depreciate at a rate of 1% per week due to technological advances and component cost declines. Additionally there is the mark down schedule applied to aged inventory. As inventory sits on the warehouse shelf beyond 30, 60, and 90 day intervals its sale price is reduced to reflect waning consumer demand. 3.3.1 Current state process Dell currently manages finished goods inventory according to a periodic review, days of sale inventory (DSI) target model. This model sets a target for the number of DSI on hand at the warehouse at any given time. This target is applied to all products, irrespective of demand characteristics or transportation lead times. The target is calculated by summing all 27 on hand inventory, dividing it by the average forecast for the next two weeks of sales, and then converting the units from weeks to days. DSI target Z on hand inventory [forecastt+1+ forecastt+2 ] day week Equation 2: DSI target model. This ensures uniformity of inventory levels (in terms of DSI, not absolute volumes) and provides ease of management, but cannot be decomposed into various types of inventory. Specifically, it fails to account for product differentiation and variation in transportation times in determining appropriate safety stock levels. This has implications not only on inventory levels, but also fulfillment rates. (A more detailed investigation of this is given in Chapter 3.4.) Each week, system and warehouse inventories are monitored and an order up to level is determined based on the DSI target. This follows the pattern described in Chapter 2.2 of ordering variable amounts of inventory on a fixed cycle. Based on these process characteristics it becomes easy to adopt a base stock model which follows the same cycle. 3.4 Proposed future state Given the current model, an opportunity exists to optimize ordering quantities and holding levels based on various inputs and parameters. The goal is to carry the minimum inventory such that demand is filled and demand fluctuations are buffered against. A generalized base stock model is introduced to determine inventory levels based on demand characteristics and transportation lead times. The following variables are defined: S = order up to level IPt = inventory position in week t R = review period in weeks Lm manufacturing lead time La = lead time of air replenishment LO= lead time of ocean replenishment 28 yi = mean demand f or product i -i = standard deviation of forecast errorfor product i k = safety stock factor Additionally, the following assumptions are made: Demandfor each item follows a normal distribution. Demandfor each item is i.i.d. with respect to time and every other item. Note that manufacturing lead times are the same across the entire product portfolio and lead times only vary according to the mode of transportation chosen. The generalized base stock model then takes the following forms for each transportation mode: Sa = pii[(Lm + La) + R] + ka-iV[(Lm + La) + R] Equation 3: Base stock for air replenishment. So = ij[(Lm + LO) + R] + k-i[(Lm + LO) + R] Equation 4: Base stock for ocean replenishment. The inventory profile of a system of this form is depicted in Figure 7. At t = 0 an amount of inventory Q is ordered equal to S - IPt. After L weeks the inventory arrives and after R weeks the system is reviewed and another amount equal to S - IPt is ordered. 29 S - .-.--..-.. L t 0 R tr t2r Time Figure 7: Inventory profile. Adapted from Graves, 2013. In reality, multiple R periods exist within each L, so you can consider the true profile to be multiple overlapping periods such that nR periods occur before the arrival of inventor ordered at t = 0. The base stock equation is decomposed into three primary components: demand during lead time and review period, safety stock factor, and standard deviation during lead time and review period. The safety stock inventory carried is what protects the system against demand fluctuations, so the concepts of fill rate and forecast error are now introduced. 3.4.1 Service Level A stocked item's fill service level describes, in probabilistic terms, its availability at any given time. This can be divided into two categories: type I service level, which describe the probability that a given item is stocked out, and type II fill rate, which describe the percent of orders that can be immediately fulfilled. Dell's contract with its customers is, in part, based 30 on the expectation of a 95% type II fill rate. That is, if 100 orders are placed for a particular PC, there will be at least 95 PCs in stock at that time. The following variables are defined: f = item fill rate Q= order quantity for product i = S - IPt E [US] = expected units short The general expression is given in Equation 5. E[US] Q Equation 5: Type II fill rate. Calculating a type II service level is somewhat non-intuitive and relies on determining a partial loss expression for the item, G(k). E[US] = a-VpfG(k) Equation 6: Expected units short. From here we need to choose some k such that E[US] = Qj(1 - fl) Equation 7 oTiG (k) Qi Equation 8: Follows from eq. 6. 31 G~)=Qi(1 - fl) Equation 9: Follows from eq. 8 G(k) = fX=k(x - z)cP(x)dx = P(k) - kx(1 - c1(k)) Equation 10: Partial loss function. Equations 6 - 10 demonstrate how the steps required to find k. From this point k is determined by searching across all values in a standard normal distribution table. This k factor is multiplied by the standard deviation of forecast error to determine the optimal safety stock level for a given product J: This standard deviation of forecast error is described more fully in the next section. 3.4.2 Forecast Accuracy and Standard Deviation of Forecast Error As stated in Chapter 3.4, the demand distributions for each product are assumed to be normal and i.i.d. This implies that each product has some constant demand p and some standard deviation o. Figure 8 depicts such a demand profile. 32 0 I Demand Figure 8: Normal demand distribution. However, no company reacts blindly to customer demand. Forecasts for future demand cycles are made in order to smooth the effects of consumer demand on the system. In the ideal case, future demand would be forecast with perfect accuracy. No safety stock would be kept because it is known exactly how many units of each product to carry at any given time. This is obviously an unrealistic scenario, but the closer a forecast is to the actual demand the smaller the effects of variations in that demand are to the system. As discussed in Chapter 2.3, there are a variety of methods for calculating the accuracy of a forecast. Dell slightly modifies the MAPE formula to compare the difference between error and forecast, as shown in Equation 11. forecast accuracy = 1 - | actual - forecast| frcs forecast Equation 11: Forecast accuracy. 33 However, this doesn't capture the required distribution information necessary to calculate safety stock levels. For this, the raw data is manipulated to determine the root mean square error (RMSE), given in Equation 12. The following variables are defined: fAt = forecast for product i in week t di,t= demand for product i in week t R MS= RMSE- E (di,t - fi,t t - Equation 12: Root mean square error. This RMSE is used for the value of a-when calculating inventory levels according to the base stock policy shown in Equations 3 and 4. 3.4.3 Mixed Replenishment Modes When we first introduced the base stock policy we considered the case where all inventory was shipped either via ocean or air. Within these extremes exist all possible combinations of mixing air and ocean replenishment. For instance, Dell might choose to ship 60% of their inventory via ocean to take advantage of the low shipping costs, and 40% of their inventory via air in order to build responsiveness into their supply chain. Simchi-Levi, Clayton, and Raven discuss the concept of balancing efficiency and responsiveness and how it can be applied to a differentiated product portfolio, and the same principles apply in this case. Both Do (2009) and Franken (2012) discuss mixed replenishment models and how they can be optimized. Their solutions depend on a model in which the lead time associated with air component is less than the review period. In our case, the total lead time associated with air includes the manufacturing time, which exceeds our review period of only one week. Additionally, we are interested in investigating a variety of solutions across multiple mixed models, and for these reasons their techniques will not be applied. 34 For the sake of simplicity, the possible ocean and air combinations were defined according to 11 choices, from a 100% ocean solution to a 100% air solution in increments of 10%. Furthermore, each combination was assessed against 5 fill rates: 99%, 98%, 95%, 90%, and 85%. As discussed earlier, Dell's commitment to their customer involves a 95% fill rate, but we explored a larger solution space to weigh the tradeoffs between fill rate and inventory levels, and consequently cost. In order to assess the optimal inventory levels for mixed replenishment modes, a weighted average was taken of Equations 3 and 4. This new model takes the form S = Pa (/-ii((Lm + La) + R)) + kaO'i ((Lm + La) + R)] + Po (iii((Lm + LO) + R)) + k 0 0ai ((Lm + LO) + R)] Equation 13: Mixed replenishment base stock model. Where: Pa = percentage of inventory shipped via air P0 = percentage of inventory shipped via ocean ka = safety stock factor for air replenishment ko = safety stock factor for ocean replenishment Our final inventory model, as expressed in Equation 13, captures the variation between replenishment mode volumes and lead times as well as the particular demand characteristics associated with each product. In Chapter 3.6 we will turn our attention to the techniques used to solve this model. 3.5 Costs associated with inventory Thus far in our model development we have been concerned with determining the appropriate inventory levels based on demand variability/forecast accuracy and manufacturing and transportation lead times. The goal has been to carry the minimum 35 inventory for a specific transportation mode that satisfies demand according to the desired fill rate and buffers against uncertainty. In this section we turn our attention more closely to the costs associated with transporting and holding this inventory. Broadly we can characterize these as purchasing costs, transportation costs, and holding costs. All three depend on the amount of inventory ordered, but they do not scale at the same rate. By understanding these cost relationships, we can better understand how the various inputs into our model affect the overall system efficiency. 3.5.1 Purchase costs As described in Section 3.2, Dell manages their item level plan and the demand associated with it on a weekly basis. Each week they are making the decision to order (or not) more inventory based on the expected demand in future weeks. Because Dell has entered into partnerships with ODMs, and doesn't actually manufacture the systems themselves, they are essentially buy laptops from the ODMs which are manufactured to their specifications. This purchase cost is the cost associated with ordering one system, and varies based on the particular type of system being ordered. (Obviously a high end business laptop with a large screen, touch capabilities, an extremely fast processor, and extensive networking features will cost more than a simpler machine aimed at companies requiring less computing power.) Because the order sizes are (relatively) stable from week to week, we can assume the purchase cost to vary linearly with order size. Unlike components, which can be bought in bulk in advance to take advantage of economies of scale, the cost declines and high turnover of PCs require a weekly replenishment cycle which does not favor large, bulk orders. 3.5.2 Transportation Costs Transporting inventory via air can have significant cost implications, but this is balanced by the flexibility it provides the supply chain. Ocean shipping is extremely cost efficient, but introduces extended lead times and inventory levels. This natural tension provides the context in which transportation costs are described. The cost to ship PCs from China to North America are expressed in a per unit basis. Each PC shipped via ocean incurs a cost of $5, and each PC shipped via air costs $20. These costs varies directly with quantity, and so assume a linear relationship. It is also important to note that while air transportation 36 is four times as expensive as ocean transportation, it also has a three week lead time as opposed to a seven week lead time. Thus, while transportation costs for air are much higher, the associated inventory costs are lower. This tradeoff between efficiency and responsiveness will be discussed further in Chapter 4. 3.5.3 Holding Costs Once PCs have arrived at the Nashville merge center Dell's 3PL provider charges two associated inventory costs. One is to receive inventory, charged on a per PC basis, and one is to hold inventory, charged on a per pallet per week basis. The receiving cost of $1 varies linearly with the number of systems delivered to the merge center in a given week. The holding cost of $4.00 is also linear, but varies with the number of pallets of inventory being held. Laptops are shipped in pallets of 60, and desktops are shipped in pallets of 12. From this we can see that desktops are five times more expensive to hold over the long run than laptops. Two additional holding costs not explicitly associated with the merge center are Dell's cost of capital and depreciation rate. Every time Dell chooses to purchase, transport, and hold inventory they are tying up capital that could be otherwise invested in different opportunities. Dell assigns this cost of capital a value of 10%, essentially determining that if funds were not tied up in inventory they could expect a return of 10% through other means. The depreciation rate is simply the rate at which Dell expects their products to lose value, which they have determined to be 3%. One final cost associated with holding inventory is the markdown cost. Due to both the extremely short lifecycle of many PCs and the volume of space they occupy in the merge center, aged inventory loses value at a rate greater than depreciation alone. Inventory which sits in the merge center for extended periods of time due to forecasts and demand being out of sync, must eventually be disposed of to make room for additional inventory of new products. In order to combat flagging demand (often exacerbated by the introduction of new products), Dell assigns a markdown schedule to each product (shown in Table 1). 37 Discount Rate >60 Days >90 Days >120 Days >150 Days Table 1: Discount schedule. If this continues for too long Dell must eventually turn to a bulk discount retailer in order to eliminate their excess inventory holdings. Such "fire sales" can be an effective way to eliminate inventory from the system, but they represent an almost complete loss to Dell. Based on the above it becomes clear that while transportation costs vary strictly with the amount of inventory ordered, holding costs vary not only with the amount of inventory, but also with its value as well. Ocean and air freight carriers are not concerned with the value of each laptop they are transporting. Their costs are based primarily on size and weight, so the cost to ship a $600 laptop vs. a $2100 laptop is equivalent. However, the $2100 laptop has a far great effect on holding costs than does its less expensive counterpart. While this thesis is not explicitly concerned with determining the optimal product portfolio, it is beneficial to bear in mind the adverse effects incurred by holding high value inventory. To summarize, we define the following costs associated with Dell's finished goods inventory model: Ki = purchase cost of one system i Ta = cost associatedwith shipping one system via air To = cost associatedwith shipping one system via ocean R = cost to receive one system in the merge center H = cost to hold one pallet of systems per week C= cost of capital D = depreciationcost d (t) = discount rate as a function of time 38 The model introduced in Chapter 3.3 and expanded upon in Chapter 3.3.3 determines our optimal order quantities. The above costs are then used to determine those order quantities' financial impact on the system. 3.6 Modeling Techniques A large number of software programs exist to model inventory systems. For the sake of continuity within Dell two simulations were created in Excel. The first considered the long run averages associated with the system under the conditions defined above. This simulation decomposed inventory by pipeline, cycle, and safety stock, but did not offer insight into the behavior of the system on a weekly basis. An event based simulation was also constructed which analyzed the behavior of the system on a week by week basis. The simulation dashboard is shown in Appendix A, and the weekly snapshot is shown in Appendix B. The long run averages calculated by the first simulation was useful in understanding the expected behavior of the system. For the event model, Monte Carlo simulation was employed to explore the possible inventory positions which might exist under a given demand distribution. Five thousand trial runs were conducted in order to assess not only the mean inventory positions, but also the standard deviation around the mean. Because the underlying demand distributions were assumed to be normal, the resulting inventory positions also assumed a normal distribution. While the mean gave an indication as to the expected behavior, the standard deviation provided some insight into the range of inventory positions that could be expected for a given demand distribution. An example of a potential inventory position distribution is given in Figure 9. 39 Average Inventory Position (Merge Center) g0 so 70 s0 2 40 30 20 10 600 900 1200 1500 1600 2100 2400 2700 3000 3300 3600 3900 4200 4500 Figure 9: Monte Carlo output. Though we are primarily concerned with a specific portfolio of products, each with a distinct set of characteristics (replenishment mode, demand distribution, forecast accuracy, price, etc.), solutions were created for all possible conditions. This allowed for a complete exploration of the solution space and fully demonstrated the effects of replenishment mode and forecast accuracy on inventory levels within the system. These interactions as well as the costs associated with them are presented in more detail in Chapter 4. 4 4.1 Results/Recommendations Forecast accuracy impact on inventory As discussed in Chapter3.2.1, the current model relies on a set DSI target when determining how much inventory to order. The proposed base stock model determines inventory order quantities and positions based on system and product characteristics, and allows DSI to be an output of the model. The distinction may be subtle, but it is important: allowing DSI to be an output, as opposed to an input, results in ordering policies which optimize inventory levels for a given fill rate. In essence the fill rate serves as the target, not the DSI level, which ensures Dell's customer commitments can be met. 40 If we assume DSI to be the model's output, then there are three other dimensions which affect this result: forecast accuracy, replenishment mode, and fill rate. By fixing the fill rate it is possible to demonstrate the range of inventory levels associated with varying the replenishment mode for a given forecast accuracy. Figure 10 demonstrates the relationship between DSI and forecast accuracy assuming all inventory is shipped via ocean. 100% Ocean - DSI by FA Fill Rate 99% ------ Target DS 98% -9% -90% Forecast Accuracy Figure 10: DSI levels by forecast accuracy given 100% ocean replenishment. Unsurprisingly, for a given fill rate and replenishment mix, the amount of inventory carried decreases as forecast accuracy increases. By depicting several different fill rates for a particular replenishment mix the tradeoffs between inventory levels and fill rate become more explicit. The target DSI indicated represents the current process target, and can be used to predict what forecast accuracy is necessary to achieve that target under a 95% fill rate. When compared to the existing forecast accuracy for a given product it indicates whether that target is appropriate. If the required forecast accuracy for a given target is lower than the actual forecast accuracy, too much inventory of that particular product is being carried. If the required forecast accuracy for a given target is higher than the actual forecast accuracy, the desired fill rate will not be met. Four choices (either individually, or combined) can be made if this is the case: more inventory can be ordered, more inventory can be shipped via 41 air, a lower fill rate can be accepted, or efforts can be made to increase the accuracy of the forecasting model. All four require cost tradeoffs, and the goal is to determine which decision minimizes the cost impacts. The first two are relatively straight forward. If more inventory is ordered the firm will incur additional order costs, additional transportation costs, and additional holding costs. Furthermore, additional capital will be tied up in the overall value of the inventory which could be put to use elsewhere. Should the decision be made to ship more inventory via the more responsive replenishment mode, the primary cost incurred is the increased transportation cost. While this might seem to be the better option, it greatly hinges on how much more expensive it is to ship via air then ocean. These costs and the tradeoff they present will be examined in more detail in Chapter 4.2. 10% Ocean - DSI by FA Target DS1 Fill Rate 95% Forecast Accuracy Figure 11: DSI levels by forecast accuracy given 10% ocean replenishment. Figure 11 demonstrates the effects on forecast accuracy for a given DSI as more inventory is shipped via air. It may not be immediately apparent, but there is a significant reduction in the required forecast accuracy when only 10% of the inventory is shipped via ocean. This is of particular importance to Dell, because it allows them to set targets for their forecasting models. If a certain forecast accuracy is needed to achieve maximum cost savings by shipping everything via ocean (assuming, of course, they maintain their current DSI target), that provides the benchmark against which their forecasting tools can be measured. 42 The decision to assess transportation mixes from pure ocean to pure air solutions was made in response to the fact that Dell ships the majority of its commercial laptops to North America via air. They have set target goals for higher ocean shipments, so it is instructive to investigate the effects of these goals in the context of their current operations. Assuming Dell maintains their current DSI target policy, the relationship between forecast accuracy and transportation mix can be described. Forecast Accuracy required to achieve target DSI 70% 60% 5,0% 340% W 4 ------ S30% 20% 10% 0% Current ocean mix Target ocean mix Ocan Attainmnt Figure 12: Forecast accuracy required to hit a target DSI for various transportationmixes. Figure 12 demonstrates the increase in forecast accuracy required to maintain their target DSI should they achieve their goal ocean mix. This is not a surprising result: for a given forecast accuracy, safety stock and DSI levels increase as ocean attainment increases due to extended lead times. In order to maintain a consistent DSI level the associated forecast must get better. However, this chart highlights a more intriguing result. We have effectively established an upper bound for the accuracy of our forecasts at a specified DSI target across all replenishment methods. Any product with a forecast accuracy of greater than 58% can be carried at less than the target DSI level, regardless of the replenishment mode. This extends 43 to forecasts for entire portfolios of products, and can be used to identify potential mismatches in inventory volumes. DSI by Ocean % Target DSI ------------------------ Forecast Accuracy 60% Actual Ocean Goal Ocean % Ocean % Figure 13: DSI by ocean transportation for a given forecast accuracy. Figure 13 highlights this mismatch. Dell's aggregate forecast accuracy for their North American build to stock commercial portfolio is 60%. Their target DSI, the volume of inventory carried week to week, can be achieved at ocean attainment levels not only greater than their current transportation mix, but greater than the goal transportation mix as well. Given inventory levels in the Nashville merge center as of October 2013 (the last available data), inventory levels can be reduced by four days under the current transportation mix, and two days under the goal mix. Table 2 outlines these results. 44 FA Ocean % DSI Fill Rate 60% 27% 13 95% FA Ocean % DSI Fill Rate 60% 27% 9 (-4) 95% 60% 55% 7 (-2) 95% Table 2: Inventory mismatch under current conditions. Given the parameters of Dell's current inventory holdings (namely the ocean attainment percentage and forecast accuracy), it is possible to reduce inventory levels in the merge center by four DSI under current conditions, and two DSI under the goal ocean transportation mix. It might seem counterintuitive that Dell's target transportation mix would actually increase inventory levels. However, this increase in holding costs is more than compensated by the decrease in shipping costs. More on this is discussed in the next section. 4.2 Comparison between DSI and base stock methodology The North American smart selection portfolio consists of 22 products, 12 of which are laptop configurations. They primarily consist of offerings from the Latitude line, with several Precision workstations included as well. This portfolio of products provided the test bed against which our recommended inventory model could be tested. Each of the 12 products was analyzed under current forecast accuracy levels and ocean attainment rates. The initial analysis demonstrated that certain products required safety stock volumes which resulted in DSI levels below Dell's target, and that certain products required safety stock volumes which resulted in DSI levels above Dell's current target. Table 3 demonstrates these results (desktop offerings were not included in this analysis). Of the 12 products analyzed, five 45 required safety stock levels which resulted in DSI volumes below Dell's current target. Seven products required DSI's greater than Dell's target (due to unfavorable forecast accuracies), but due to substantial gains made by several of the products the average across all platforms resulted in a decrease of one day of inventory. (This result differs slightly from the analysis conducted of the merge center as a whole due to the fact that the merge center analysis includes products not listed in Dell's smart selection portfolio.) The results at right demonstrate the reduction in inventory which can be achieved under the current transportation mix. On a per product level, some items will cost Dell more than they do under the current system because of the increase in their DSI (remember that for these products targeting a lower DSI the tradeoff is a lower service level and the potential impacts associated with not meeting customer expectations), while the rest of the products result in a cost decrease. A lower DSI results in purchasing few systems (order cost decrease), transporting fewer systems (transportation Product Latitude 3330 Optiplex 3011 AIO (DT) Optiplex 9020 (DT) Optiplex XE2 (DT) Precision R7610 (DT) Precision T1700 (DT) Latitude E3440 Latitude E3540 Latitude E5440 Latitude E5540 Latitude E6440 Latitude E6540 Latitude E7240 Latitude E7440 Optiplex 3020 (DT) Optiplex 7010 (DT) Precision M3800 Precision M4800 Precision M6800 Precision T3610 (DT) Precision T5610 (DT) Precision T7610 (DT) Average DSI+13 -7 -71 4 7 -5 -8 3 3 -9j 8 -1 Table 3: DSI difference between current and proposed model across North American smart selection cost portfolio. decrease), and storing fewer systems (holding cost decrease). Additionally, storing fewer systems reduces the danger of aged inventory and their associated markdowns. As with the DSI levels, these costs can be directly computed and compared to the existing inventory model. In Figure 14 we see the cost per box associated with the current inventory model depicted in red, and the cost per box under the proposed inventory model depicted in blue. Again, this analysis is conducted for the current forecast accuracies, current transportation mix, and current smart selection volumes. 46 Shipping + Holding Cost per Box MBase Stock - 95% FR E Target DSI Latitude 3330 Latitude E3440 Latitude E3540 Latitude E5440 Latitude £5540 Latitude £6440 Latitude £ 6540 Latitude £7240 Latitude £7440 Precision Precision M3800 IM4800 Precision M6800 Product portfolio Figure 14: Shipping and holding costs per box across North American smart selection portfolio. The products outlined in green demonstrate a direct cost savings under adopting a base stock inventory model. Other products incur a cost penalty, but this is only to ensure that Dell meets its committed 95% fill rate. When comparing the current target DSI model and the proposed base stock model, the weighted (by portfolio percentage) cost per box under the base stock model is $.30 cheaper. When applied to current and target smart selection volumes, the $.30 savings per box is not insignificant. Figure 15 shows that smart selection products currently represent about 25% of the total volume sold of the analyzed portfolio. For the first four weeks of quarter four in FY'14, Dell sold 89,250 smart selection units out of this portfolio in North America. This represents a savings of $26,775 under the proposed model. When current sales volumes and smart selection percentages are extended to a 52 week horizon, the yearly savings becomes $278,460. However, Dell expects growth not only in total volume for this portfolio, but also in the percentage of these products that are sold as smart selection offerings. This is in line with global trends, as shown in Figure 16. 47 NA weekly unit volume 100% NORTH AMERICA 357000 80% 60% 40% 20% 0% Q47D Week 40 Week 41 Week 42 Week 43 Week 44 Figure 15: Sales volume and smart selection percentage for analyzed product portfolio. Adapted from Dell internal weekly report. Over the 10 week interval displayed, global smart selection sales as a percent of total volume grew by over 120%. This was during the initial offerings of this portfolio, and growth rates are expected to increase over the life of these products. Dell has set internal targets for each of its regions, with the expectations that these growth rates and volumes are achieved by FY'15. The current smart selection percentage varies by product across the portfolio, but the target percentage is set at 70% across the entire portfolio. Given the current sales volumes and applying the target smart selection percentages, potential savings can be extrapolated under the proposed model. On a 52 week horizon these savings amount to $779,688. Table 4 shows the cost savings under the proposed model for both current and expected growth rates. 48 Sales Mix % Custom BTO -Catalog BTO -Smart Selection 729% 4a.X 61,1% 77 r 31 1% 238% Q2Wk6 Q2Wk7 Q2Wk8 Q2Wk9 Q2Wk1O Q2Wk11 Q2Wk12 Q2Wk13 Q3Wk1 Q3Wk2 Figure 16: Global smart selection sales as a percent of volume. Adapted from Dell global productions. Time Horizon Smart Selection % 52 weeks 52 weeks 25% (current) 70% (target) Savings $278,460.00 $779,688.00 Table 4: Predicted savings under proposed base stock model. These savings represent only a single portfolio of products in one geographical region under current sales volumes and current and predicted smart selection growth. Though the model specifics would necessarily change (lead times and forecast accuracies), and some specifics might change (fill rate), it is possible to apply the generalized model to any finished goods inventory system throughout Dell's global network. On an aggregate level it is feasible to assume that the savings would then grow into several millions of dollars, and a more meaningful conversation about the benefits could be pursued. 49 5 5.1 Conclusion Recommendations Though the DSI target model has certain advantages, namely uniformity and ease of management, Dell should adopt a policy which better differentiates between product characteristics and the impact of transportation decisions. Much of Dell's historical DNA is coded to provide rapid response capabilities to existing demand. However, the consumer side of the business has largely adopted new business models, and the commercial side continues to undergo this transformation. Finished goods inventory becomes a critical component when running a build to forecast, direct to customer model. Care must be taken to efficiently manage this inventory and the costs associated with it. Under this new model Dell can immediately impact their supply chain by holding less inventory for the current conditions. This streamlines the system and reduces cost at all points along the supply chain: ordering, transportation, and storage. No changes need to be made to ocean attainment rates and no investments need to be made in forecast capabilities. (Both these can be advantageous, but impacts can be felt without implementing them.) Further, this policy provides a direct link between inventory levels and fill rates. Dell can confidently make service commitments to its customers knowing that adequate safety stock levels exist for all products. While this policy does introduce a certain level of variability between products, the overall effect is a contraction of inventory and a savings to the company. For products that are currently held at DSI levels in excess of what is required under a 95% fill rate, Dell can take a different approach and immediately begin shipping more via ocean. This will actually result in an increased costs savings, because the difference in shipping costs is so pronounced. The temptation will exist to revert to air shipments when forecasts aren't aligned with sales, but this quick fix should be avoided when possible. Not only are the costs high, but the potential to inject unnecessary turbulence into the system exists. 50 5.2 Opportunities for further work This project is concerned with one aspect of Dell's commercial finished goods replenishment model, but opportunities for follow on work exist in several domains. As fewer and fewer commercial platforms are built to order, forecasts and their accuracy will be increasingly important. Forecasts are made at the component level all the way down to finished goods, and exist for multiple time horizons. A comprehensive study of Dell's forecasting techniques and the interactions between forecasts should be undertaken. So many forecast metrics are created that it can be difficult to communicate in any sort of lingua franca. An understanding of these dependencies can help assist in the creation of such a bridge. From a more mechanical perspective, the forecasting process itself can be analyzed. Forecasting is a rich field with extensive literature and new insights on a regular basis, and this body of knowledge should be exploited. Both forecasting and finished goods replenishment form subsections of Dell's larger supply chain and logistics network. Dell currently operates without a comprehensive enterprise resource planning (ERP) tool, and manages many of its operation decisions through individual modules. Work is underway to determine the feasibility of implementing a large scale ERP, and the impacts this would have on the business as a whole. Many aspects of such an implementation need to be analyzed, from cost to scalability to the interactions what would exist with current systems. A project of this scope could certainly benefit from the additional manpower and resources an LGO internship would bring. Dell faces an ever changing future as a PC manufacturer, and these changes will continue to provide opportunities for further collaboration and study. 5.3 Coda As stated in Chapter 1, this thesis represents merely one link in a chain of collaborative efforts between MIT and Dell. The preceding section enumerates additional projects the author believes would be of benefit to both Dell and the LGO Fellows who might one day work on them. The thread linking one project to the next (aside from the obvious and aforementioned MIT and Dell partnership, and the LGO and LFM alumni at Dell who have mentored multiple interns over the years) is a desire to not only improve the operational practices within the company, but also to engender a culture in which seeking out new ideas 51 and new methods of implementing those ideas is not merely commonplace, but praised and elevated as an act the benefits not only the company but also the individual. In short, it is a declaration that one can always improve, and should always strive to do so. For the intern this takes the form of increased technical knowledge and a greater awareness of the challenges inherent to the competitive business landscape. For the company it is not only the embracement of academic research and the greater implications this research has for its success, but also an acknowledgment that the pursuit of such knowledge is of primal importance to the cultural and organizational health of the company. Each project in this tradition co-mingles academic models and contemporary business practices in an effort to benefit the company, its employees, and its customers; as well as the student and the institution. Such collaborations are of the highest benefit, and serve only to reinforce the partnership between MIT and Dell. This thesis stands as a testament to both those benefits, and the partnership. 52 0. 0. Ocean Lead Time Air Lead Time Review Period [Forecast Variance 1M Forecast Mean Forecast StdDev o Forecast CoV In Forecast Error StdDev Forecast Error CoV Forecast Error Variance 0-u o Cum Forecast Accuracy Cost Hedge upper Hedge lower -Item Transportation Cost (Ocean) Cost (Air) 0L Transportation 0L Receiving Charge < Pallet Charge / day - Pallet Charge / wk - Pallet Size (Ocean) Size (Air) - Pallet -Tnr= 0.1 2500 0.526 69169 62% 1.15 0.85 Transportation Mix Fill Rate (Type I Service) Average Total Inventory Average Inventory Position (Merge Center) Average DSI (Total Inventory) Average DSI (Merge Center) DS1 variance (Total Innventory) DSI variance (Merge Center) Total Demand Total On Time Fill Average Fill Rate Stockout Events Aged Inventory < 30 Days Aged Inventory > 30, <= 60 Days Aged Inventory > 60, <= 90 Days Aged Inventory > 90, <= 120 Days Aged Inventory > 120 Days - - - $ $ $ 98% 4826 1254 66 17 56 82 12792 12792 100% 0 20455 107 0 0 0 Ocean 100% 99% 5006 1434 69 20 57 82 12792 12792 100% 0 21272 107 0 0 0 - $ - - - - - $ $ $ $ - - $ $ $ $ - Transportation Cost (Ocean) Transportation Cost (Air) Receiving Cost Pallet Cost Aged Inventory Discount Cost of Capital Deflation Cost - - $ $ $ $ Total Cost Cost per box 95% 4526 954 62 13 54 77 12792 12528 98% 1 19114 107 0 0 0 Air 0% - 90% 4286 714 59 10 52 73 12792 12240 96% 2 16130 48 0 0 0 - 85% 4120 564 57 8 52 67 12792 11796 92% 4 13397 0 0 0 0 - - - - - - - - - $ $ $ $ $ $ $ $ - - - $ $ $ $ - - $ $ $ $ - - - $ $ - $ $ - - $ $ $ $ $ $ $ $ - - 80% 4000 444 55 7 51 61 12792 11166 87% 7 11409 0 0 0 0 $ $ - - - - - $ $ 0 .4-0 L.a) U CL . 0.00672 0.00951 2.0900 1.9600 99% Fill Rate 100% Ocean 0% Air PLF (z) - Ocean PLF (z) - Air Z value - Ocean Z value - Air Week Base Stock Safetv Stock Order (Ocean) Order (Air) Build Ship (Ocean) Ship (Air) Pipeline Inventory Raw Forecast Actual (hedged) Forecast Start Inventory Arriving Inventory Demand Total Inventory Ending Inventory (Merge Center) DSI (Total Inventory) DSI (Merge Center) Fill Rate Aged Inventory 0 - 1 Week Aged Inventory 1 - 2 Weeks Aged Inventory 2 - 3 Weeks Aged Inventory 3 - 4 Weeks Aged Inventory 4 - 5 Weeks Aged Inventory 5 - 6 Weeks Aged Inventory 6 - 7 Weeks Aged Inventory 7 - 8 Weeks Aged Inventory 8 - 9 Weeks Aged Inventory 9 - 10 Weeks Aged Inventory 10 - 11 Weeks 1560 -8 5694 1560 1560 600 -7 5931 2160 2160 600 -6 6173 2760 1200 1560 540 -5 5662 3300 0 1140 2160 600 -4 5993 3900 0 1140 2760 540 -3 5522 4440 480 0 1140 3300 -2 5306 -1 5543 540 0 1020 3900 0 4920 1 5513 1555 720 0 1020 2880 0 3900 517 517 0 1560 647 4813 913 60 11 100% 913 2 5606 1555 S40 0 1260 2760 0 4020 547 547 913 600 422 5111 1091 66 14 100% 600 491 3 5245 1555 0 0 1260 2700 0 3960 577 577 1091 600 394 5257 1297 69 17 100% 600 206 97 540 165 0 0 4 5798 1555 1020 0 540 2880 0 3420 513 513 1297 540 435 4823 1403 64 19 100% 600 152 0 0 0 5 6166 1555 720 0 1020 2820 0 3840 555 555 1403 600 388 5455 1615 79 23 100% Lrn 7 References Holzner, S. 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