Towards a Consumer-Oriented Supply Chain by Panagiotis Andrianopoulos Master of Business Administration, ALBA Graduate Business School, 2014 Diploma of Advanced Engineering Studies, Mechanical Engineering, NTUA, 2011 and Hector Rafael Perez Wario Bachelor of Science Civil Engineering and Management, Universidad Panamericana, 2008 Bachelor of Science Civil Engineering and Built Environment, Hogeschool Utrecht, 2008 Submitted to the Engineering Systems Division ARCHNES in partial fulfillment of the requirement for the degree of MASSACHMTTS OF Master of Engineering in Logistics ECHNOLAL at the JUL 16 2015 Massachusetts Institute of Technology LIBRARIES June 2015 2015 Panagiotis Andrianopoulos and Hector Rafael Perez Wario. All rights reserved. The authors hereby grant to MIT permissixTo reproduce and to distribute publicly paper and electrpip copi'ethis document in whole or in part. Signature redacted SignatureofAuthor Master o1 gineering in Logistics Program, Engineering Systems Division .............. May 8, 2015 ~.... ofAto.....tro nierngi oitc rorm nierngSsesDvso ~ ~ ~~ ~~ ~ ~~~~~~~~ Signat ure of Author.... ~ ~ ure l/1/" May 8,2015 Certified by.. l~ster of Engineering in Logistics Program, Engineering Systems Division ., - ...................................... rud /I Accepted by.. Siy 6' Dr. Alexis H. Bateman Research AssoCiate Center for Transportation & Logistics Thesis Supervisor IctLU t eUctL U Dr. Yossi Sheffi (F<" Director, Center for Transportation and Logistics Elisha Gray II Professor, Engineering Systems Division Professor, Civil and Environmental Engineering rr Towards a Consumer-Oriented Supply Chain by Panagiotis Andrianopoulos and Hector Rafael Perez Wario Submitted to the Engineering Systems Division On May 8, 2015 in Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Logistics ABSTRACT The current consumer products industry is primarily designed as a customer oriented supply chain; this means that it is designed to fulfill orders from the distribution centers or stores of the retailers. The question posed by retailers and vendors is how might a consumer (not customer) oriented supply chain be defined and designed in a way which retailers and vendors could move towards it in the future? Our methodology consisted of interviews with key stakeholders and industry experts, a literature research, value stream mapping as well as data analysis of historical sales and shipments between a retailer and a vendor that sponsored the project. As a result of our research, we conclude that a consumer oriented supply chain is defined as a supply chain that is triggered by consumer demand data and it implements strong collaboration between the retailer and the vendor, in order to achieve a more efficient response to the consumer needs. A series of interviews with key stakeholders revealed that one of the most important parts of the collaboration is forecasting. Our data analysis depicts that a single, synchronized forecasting of the consumer demand would help both parties operate in a more efficient and collaborative way. As final deliverable we propose a roadmap with short-term and long-term steps necessary to design a consumer oriented supply chain. Thesis Supervisor: Dr. Alexis H. Bateman Title: Research Associate, Center for Transportation & Logistics 2 Acknowledgments We want to express our gratitude to Alexis Bateman, even during difficult and busy times in her life as a young mother, she significantly contributed to this project. Also many thanks to Francisco Jauffred, he helped us to unscramble the challenges of thesis and Chris Caplice for his patience and guidance during the critical steps of this project. We would also like to thank the people from the two sponsor companies that involved in making this thesis possible: Andre, John, Ula, Martin, Sam, Jason and Geri. Special thanks also to our external advisors Larry and Kai. Firstly, I want to thank my thesis partner, Hectorinio, for this major journey. I would also like to thank my father and mentor, Stamatis, for opening my horizons as broadly as to pursue my dream of studying at MIT. Moreover, I want to thank Anna-Maria for her major daily support that helped me tackle all difficulties until the next day. I also want to thank the rest of family, Dimitris, Antonis and Anny, for knowing that they were and would be there for me. Panagiotis MIT has been a remarkable experience in my professional development, and this thesis provides clear evidence of my journey for the last months. I want to thank my thesis mate, Panos, his dedication and stubbornness were key factors in this thesis. I want to thank my family, specially my parents. I dedicate this thesis project to the memory of my beloved uncle Architect Esteban Wario Hernandez, his legacy is a source of continuous inspiration to the community of Guadalajara. Hector 3 Table of Contents 1 Introduction ............................................................................................................................ 1.1 1.2 1.3 1.4 1.5 2 Literature R eview ................................................................................................................. 2.1 2.2 2.3 2.4 2.5 2.6 3 4 M otivation...................................................................................................................................... Sponsor com panies........................................................................................................................ Problem definition......................................................................................................................... Purpose and claim ......................................................................................................................... Report Structure ......................................................................................................................... Introduction................................................................................................................................. Background.................................................................................................................................. Autom atic Replenishm ent System s ....................................................................................... Collaborative Planning ............................................................................................................... O rder M anagem ent..................................................................................................................... Consum er Interaction................................................................................................................. 7 7 8 8 9 10 11 11 11 15 16 17 18 M ethodology.......................................................................................................................... 20 3.1 Data Collection ............................................................................................................................ 3.1.1 Quantitative Data.................................................................................................................... 3.1.2 Qualitative Data...................................................................................................................... 3.2 Data Analysis ............................................................................................................................... 3.2.1 Quantitative Data Analysis................................................................................................. 3.2.2 Qualitative Data Analysis................................................................................................... 21 22 24 25 25 27 R esults, A nalysis, and D iscussion..................................................................................... 33 4.1 AS-IS Process............................................................................................................................... 4.1.1 M ap Construction Process................................................................................................... 4.1.2 AS-IS Value Stream M ap (VM I Process)..............................................................................36 4.1.3 A S-IS Value Stream M ap (Non-VM I Process) ................................................................. 4.2 Connecting the Dots .................................................................................................................... 4.2.1 People ..................................................................................................................................... 4.2.2 Processes ................................................................................................................................ 4.2.3 Technology ............................................................................................................................. 4.3 Forecast Analysis......................................................................................................................... 4.3.1 Analysis of SKU ................................................................................................................... 4.3.2 Analysis of SKU2................................................................................................................... 4.3.3 Analysis of SKU3................................................................................................................... 4.3.4 Analysis of SKU4................................................................................................................... 4.4 TO-BE Scenarios......................................................................................................................... 4.4.1 Hybrid Forecast Replenishm ent Process ............................................................................ 4.4.2 Retailer-Level Forecast Replenishm ent Process ................................................................. 4.4.3 Vendor-Level Forecast Replenishm ent Process................................................................. 4.5 Roadmap towards a Consumer-Oriented Supply Chain ................................................... 4.5.1 Short-term Steps ..................................................................................................................... 4.5.2 Long-term Steps ..................................................................................................................... 33 34 40 43 43 45 49 54 54 56 58 59 61 61 64 66 68 70 71 5 C onclusion ............................................................................................................................. 73 6 R eferences ............................................................................................................................. 75 4 Table of Figures Figure 1.1 Retailers Lose in 59% of Out of Stock Incidences and Vendors in 48% (GMA/FMI)............ 8 Figure 2.1 High Level Communication Map......................................................................................... 14 Figure 2.2 Integrating concepts to build a Consumer-Oriented System ................................................ 19 F igure 3.1 Sp iral technique........................................................................................................................ 21 Figure 4.1 Value Stream Map Canvas Description................................................................................ 35 Figure 4.2 Value Stream Map Symbols Explanation.............................................................................. 36 Figure 4.3 AS-IS Value Stream Map (VMI Process) ............................................................................. 37 Figure 4.4 AS-IS Value Stream Map (Non-VMI Process) .................................................................... 42 Figure 4.5 Integration of People, Process and Technology (Ireland & Crum, 2005)............................. 43 Figure 4.6 Revenues Reinforcing Loops from Replenishment Collaboration (System Dynamics Model) 48 Figure 4.7 JDA Flowcasting (JDA Software Group, 2014)................................................................... 52 Figure 4.8 Oracle Collaborative Planning (Oracle Corporation, 2014)................................................ 53 Figure 4.9 SK U I Forecast Com parison .................................................................................................. 55 Figure 4.10 SK U 2 Forecast Com parison ............................................................................................... 57 Figure 4.11 SK U 3 Forecast Com parison ............................................................................................... 58 Figure 4.12 SK U 4 Forecast Com parison ............................................................................................... 60 Figure 4.13 TO-BE Value Stream Map (Hybrid Forecast Replenishment Process - Flexible Store D ep loym ent Sy stem ) ................................................................................................................................. 63 Figure 4.14 TO-BE Value Stream Map (Hybrid Forecast Replenishment Process - Non-Flexible Store D eploym ent Sy stem ) ................................................................................................................................. 64 Figure 4.15 TO-BE Value Stream Map (Retailer-Level Forecast Replenishment Process).................. 66 Figure 4.16 TO-BE Value Stream Map (Vendor-Level Forecast Replenishment Process).................... 67 Figure 4.17 The Roadmap towards a Consumer Oriented Supply Chain .............................................. 69 List of Tables Table Table Table Table Table Table 4.1 4.2 4.3 4.4 4.5 4.6 Top 20 supply chain management software suppliers (Gartner, 2013)................................... Information Received for Data Analysis ............................................................................... SK U I forecast m etrics ............................................................................................................... SK U 2 Forecast m etrics .............................................................................................................. SK U 3 Forecast m etrics .............................................................................................................. SK U 4 forecast m etrics............................................................................................................... 5 51 54 56 57 59 60 List of Acronyms ARS B2B CAO CPFR CPG CRP CV DC DTS ECR EDI EOQ ERP IT MAPE PO POS QOH RMSE SCP SKU TMS UPC VICS VMI WMS Automatic Replenishment Systems Business-to-Business Consumer Packaged Goods Collaborative Planning Forecasting and Replenishment Consumer Packaged Goods Continuous Replenishment program Coefficient of Variation Distribution Center Direct-To-Store Efficient Consumer Response Electronic Data Interchange Economic Order Quantity Enterprise Resource Planning Information Technologies Mean Absolute Percentage Error Purchase Order Point of Sale Quantity of Hand Root Mean Square Error Supply Chain Planning Stock Keeping Unit Transportation Management System Universal Product Code Voluntary Interindustry Commerce Standards Vendor Management Inventory Warehouse Management System 6 1 Introduction 1.1 Motivation Is a consumer oriented supply chain the future of the supply chain industry? Supply chain managers of different companies around the world raise this question increasingly in discussions. A consumer oriented supply chain is one that utilizes the actual demand of the final consumer to generate feedback to the different stakeholders involved in a particular supply chain. A consumer oriented supply chain opposes the traditional customer oriented supply chain that the majority of companies implement. In a customer oriented supply chain, each stakeholder involved receives feedback only from the next stakeholder in the chain, i.e. the manufacturer of raw materials from the manufacturer of the finished goods, the manufacturer of the finished goods from the retailer and so on. This practice is very popular among companies because it is easier and faster to implement. However, in the 2 1st century the evolution of the supply chain technology and the ubiquitous presence of the Internet create new opportunities in the supply chain industry. These facts create a new territory for collaboration among the different stakeholders of a supply chain and make the vision of consumer oriented supply chains realistic. Supply chain collaboration is of paramount importance for an efficient supply chain. A successful supply chain brings the product to the final consumer in the right place, at the right time and in the most cost efficient way. Out of stock is the indication of a non-consumer oriented supply chain and penalizes significantly both the retailers and the vendors as, according to the Voluntary Interindustry Commerce Standards (VICS) Association (2004), the study of Grocery Manufacturers of America/Food Marketing Institute depict (Figure 1.1). More specifically, the study reveals that in 48% of out of stock incidences, the vendors lose, while the retailers are penalized even more, i.e. in 59% of the incidences. 7 Who loses when an item is out-of-stock? DO Not Purchase Item 1"%Dif"ft loses Substiue - Manufacturer Bra"d (48%) Deiay Purchase 17% loses (59%) - Rtailer* RetailerSubsititute 2D% Buhy ItemT at Another Store 32% Source: GMA/FMI Retail Out-o-Stocks Study Figure 1.1 Retailers Lose in 59% of Out of Stock Incidences and Vendors in 48% (GMA/FMI) 1.2 Sponsor companies This research takes place by the sponsorship of two companies. Company A, from now on mentioned as "the retailer", is a multinational retail company that owns and operates supermarkets in three different continents. Company B, from now on mentioned as "the vendor", is a multinational consumer packaged goods (CPG) company with operations around the globe. The companies have already established good business relations and implemented a vendor managed inventory (VMI) system for the majority (80%) of the products that the vendor sells to the retailer. 1.3 Problem definition Both the retailer and the vendor realize the penalties of out of stock and lost sales. In their traditional customer oriented supply chains, the retailer and the vendor work as single units and invest money and time into technologies and processes that allow them to predict better and 8 respond more efficiently to the demand of their customers. In other words, the distribution centers (DCs) of the vendor struggle to respond to the demand of the retailers' DCs, while the retailers' DCs struggle to respond to the demand of the retailers' stores. Ultimately, the traditional supply chain does not take into direct consideration the actual demand of the final consumer, i.e. the point of sale (POS) data. A number of studies indicate that the use of POS data in the demand planning process of retailers and vendors create more accurate and predictive forecasts. This was confirmed through interviews with Larry Lapide, research affiliate at MIT, and Kai Trepte, senior software engineer at Harvard, who have expertise in this area. However, there is no broad research as far as the interaction and the collaboration between the retailers and the vendors is concerned. Most of the studies only consider improvements that a more responsive planning process has for a retailer or a vendor. Yet, the major problem that we identified is the lack of meaningful communication between retailers and vendors, as far as the replenishment process is concerned. This fact creates a significant obstacle to consumer oriented supply chains. 1.4 Purpose and claim Our research interest focuses on how the retailer and the vendor can collaborate to build a more effective replenishment system with an ultimate goal to create a consumer oriented supply chain. The use of POS data to create forecasts is only a step towards that goal. To take advantage of these - more accurate and responsive - POS forecasts, the retailer and the vendor should establish new business rules and initiate new forms of collaboration in terms of people, processes and technology. Eksoz, Mansouri, & Bourlakis (2014) mention that according to Helms et al. (2000), "collaborative forecasting is the practice in which the knowledge and information that exists internally and externally is brought together into a single, more accurate forecast that has 9 the support of the entire supply chain". This single, synchronized forecast can be the base to build a more responsive and efficient replenishment policy. This project is particularly interesting because it involves both a retailer and a vendor; rarely did a similar research incorporate both the inputs of a retailer and a vendor at the same time. Our methodology consists of interviews with key stakeholders and industry experts, literature research, value stream mapping as well as data analysis of historical sales and shipments between the retailer and the vendor that sponsored the project. The results of our research include the existing process of collaboration between the two companies, depicted in value stream maps, as well as three different scenarios for future collaboration. The three alternative scenarios indicate who will create the forecast and at what level. The scenarios are evaluated based on data collection and forecast accuracy comparison. Based on the scenarios a high level roadmap with long-term and short-term steps is suggested so as to move towards a consumer-oriented supply chain. 1.5 Report Structure In the sections that follow we firstly present our literature review based on which we identified the environment of our research and narrowed our scope (section 2). In section 3 we present the methodology we used in order to conduct our research and perform our analysis. Our analysis and results are included in section 4, while section 5 and 6 presents our conclusions and references respectively. 10 2 Literature Review 2.1 Introduction In the past decade, inventory optimization, strategic replenishment and order management have experienced tremendous advancement. For instance, traditional supply chains, often full of uncertainties and ambiguities, have evolved into collaborative environments, resulting in economic and social benefits. The supply chain between a retailer and a vendor presents a wide range of opportunities due to its complex nature. In particular, this type of supply chain is deeply influenced and indirectly managed by the consumer. 2.2 Background One of the first companies that understood this advantage was P&G (Clark and McKenny, 1995). The Continuous Replenishment program (CRP) changed that entire value of the chain, driving orders based on distribution centers withdrawals and sales data. P&G implemented the approach with several retailers, resulting in dramatic results in both lowering inventories and increasing in-stock at retail. This program provided the foundation for Efficient Consumer Response (ECR) and Collaborative Planning Forecasting and Replenishment (CPFR) programs that are still used today (Taylor, 2004). However we can predict that the benefits harvested from such innovations will come to an end (Bohlen, Beal and Rogers, 1957), therefore the challenge will be to reengineer the process of replenishment itself at its core and reorganize it as one common procedure between the retailer and the vendor. The industry has faced challenges before, the major market developments that make retail challenging started in the 1990s and still are prevalent today, namely high cost pressure, shorter innovation cycles, increasing consumer expectations and globalization (Baumgarten & Wolff, 11 1993). Currently, the market expansion has also brought saturation, especially for the Consumer Packaged Goods (CPG) market. Given the research question, how can we move towards a consumer-oriented supply chain in the CPG market? And what benefits and new challenges will bring to the vendor-retailer relation? This research examines these concerns by analyzing a major CPG vendor and a food retailer. The results of the study will provide a framework for companies in the CPG market aiming to implement this novel approach as a business strategy. The research questions focus on the steps that CPG companies should follow to prepare for change and future industry trends concerning retail replenishment. Currently there are a few relevant academic sources examining the benefits and challenges of a consumer-oriented supply chain. Some studies have described, evaluated and applied consumer-oriented technologies to different industries and with different purposes. Therefore, our literature review is concentrated on collaborative innovations across four different perspectives that together define a consumer-oriented supply chain. The four areas are automatic replenishment systems, collaborative planning, order management, and consumer interaction. It is important to note that these areas focus on minimizing stock outs, the most frequently mentioned cause of frustration for dissatisfied customers in retail (Sterns, Unger L. S., & et al., 1981) and reducing the bullwhip effect. A consumer-oriented supply chain has the motivation to constrain and ultimately reduce the problem of the bullwhip effect. The meaning of the bullwhip effect is well described by Fransoo & Wouters (2000), Lee et al. (1997) and Posey & Bari (2009) as the phenomenon in which the quantity of orders increases as one moves up the supply chain due to the additional safety stock at each stage. Therefore, the quantity of orders of upstream firms is much higher 12 than consumer demand. Simultaneously, the quantity of consumer demand is only known by the retailer (Hohmann & Zelewski, 2011). Lee et al. (1997) state that there are five fundamental causes of bullwhip: demand signaling processing, non-zero lead times, price variations, rationing and gaming, and order batching (Disney & Towill, 2003). Based on preliminary feedback from the sponsor companies, we want to add one more that is crucial. That is the lack of real communication among the different parts of the supply chain. For example, in the worst case, the supplier is not aware that a stock keeping unit (SKU) has been eliminated from a category in the retailer side, until the distributor returns the last big shipment as obsolescent (Holmstrom, Framling, Kaipia, & Saranen, 2002). This lack of communication prevents the supply chain from being responsive to changes. Figure 2.1 depicts the high level processes/communications that take place in the product flow from the vendor manufacturer plants towards the end of the chain or once the consumer takes possession of the product(s). This figure illustrates the complexity involved in the "obvious" process of replenishment / fulfillment, several stakeholders and systems continually monitor the state of the system, triggering the switches to move product downstream, while the information flows upstream, represented with the red arrows or communication flows. 13 ORDER OVERVIEW Stakeholders Infrastructure Systems - p E=* Communication Product flow Figure 2.1 High Level Communication Map The need for better communication has been in the center of many past efforts between retailers and vendors to create a more efficient supply chain. The most common is that of Vendor Managed Inventory (VMI). VMI involves the vendor making the replenishment decision for products supplied to the retailer based on specific inventory and supply chain policies embedded in the contract between the retailer and the vendor. Practically, the vendor will receive logistics information from the retailer (inventory position, service level etc.), create the purchase order and ship the products to the retailer. The VMI process faces a number of challenges in order to be successful. According to Angulo, Nachtmann, & Waller (2004) different incentives and performance measures exist between the vendor and the customer, confidentiality and trust issues arise, technology investments and expenses appear, inventory ownership issues should be resolved, and antitrust regulations apply. Of course, when supply chain members overcome these 14 issues and jointly define the information to be shared in establishing a VMI partnership, the & vendor is faced with the challenge of using that information effectively (Angulo, Nachtmann, Waller, 2004). In the same paper the authors conclude that retailers should not let information inaccuracy slow them down in their VMI implementation (Angulo, Nachtmann, & Waller, 2004). Current trends show the validity of this statement, especially in today's business world where information gathering, even at the Point of Sale (POS) level, is relatively easy with software and hardware collecting and sharing tools. However, it is difficult to agree with the second conclusion of Angulo, Nachtmann, & Waller (2004) that retailers should carefully audit the replenishment processes of VMI suppliers to ensure they will be using shared information in a timely manner, in today's advance technological environments, new software development reduces the need of burdensome auditing. 2.3 Automatic Replenishment Systems The Continuous Replenishment program was the foundation of the Automatic Replenishment Systems (ARS); this technology is one of the most widely applied methods to increase customer responsiveness. However, automation is not a new concept for manufacturers. Kodak and Epicor have used ARS which eventually turned into lean manufacturing back in 1980's. Inventory Replenishment has been a core functional requirement in software systems since 1970 (Broekmeulen & Donselaar, 2005). However, the concepts of automating the replenishment and essentially pulling the product through production without human intervention are revolutionary. As a result, improvements in conventional manufacturing systems, and their respective replenishment, have been the focus of many academic studies. These sources have tried to identify the optimum condition using different methodologies, from the Economic Order of Quantity (EOQ) to simulation models (Silver, Pyke, & Peterson, 1998). However recent 15 studies have coined the Automatic Store Replenishment (ARS) term, an evolution of automation in automobile manufacturing and information integration (Norman Gotz 1999). ARS promises to decrease the number of stock outs while simultaneously reducing store-handling costs. A recent study in ARS analyzes the impacts of the stores and introduces a descriptive model that provides valuable understanding of the differences between manual and automatic systems (Corsten & Angerer, 2007). However this study does not identify the effect on consumer perception and value regarding responsiveness and resilience. Responsiveness is the best way to deliver product directly to the store. An MIT thesis published in 2014 posed the potential of switching the delivery method from a 100% Distribution Center (DC) method to 100% Direct-To-Store (DTS) method (Panditrao & Adiraju, 2014). This study implies an increase in consumer orientation and level of service improvement, although it is focused almost exclusively in the transportation benefits and the reductions of safety stock. In section 4, we discuss the relevance of transportation in the implementation of collaboration practices; in fact the transportation network flexibility can define the ideal processes and strategy. Transportation is a trigger that needs to be considered as part, not purpose, of the future consumer oriented supply chain. 2.4 Collaborative Planning CPFR is by far the most advanced technique that integrates visibility of data through the entire supply chain. Visibility enhances transparency in supply chain and reduces the common bullwhip effect. The CPFR model is not new and although it can provide many benefits, there have been many failed implementations (BUydk6zkan & Vardaloglu, 2012). For the purpose of our study, this perspective gives us an understanding of the past, current and future communication flows and also the barriers generated by any flaw in the system. In the 16 competitive environment of CPG there is a lot of pressure to meet high customer service levels while minimizing the costs. Effective demand planning and collaboration facilitates this balance (Moon, Mentzer, & Thomas Jr., 2000). The value of information among players in a supply chain is something that has not been fully studied in terms of consumer value. Electronic Point of Sales (POS) systems are very valuable technological advancements; these systems capture and transmit data to suppliers using Electronic Data Interchange (EDI). CPFR could combine POS and EDI with the advantage of the internet; trading partners use centralized servers to view and update shared plans and forecasts (Taylor, 2004). Accurate demand forecasts are critical to maintain consumer service levels. The use of CPFR and POS data is clearly exemplified in a study for a 10 ready-to-eat cereal stock-keeping units (SKUs) from 18 regional U.S. grocery distribution centers, this research investigated collaborative forecasting issues and presented a value proposition for the use of shared information (Williams & Waller, 2011). The research demonstrates empirically that the use of shared POS data through the entire supply chain could further improve performance through improved consumer service and reductions of excess inventory. The issue with the empirical analysis is that it is subjective with certain assumptions, and some initial variables are randomly assigned, in addition it doesn't quantify the real consumer improved value. 2.5 Order Management The initial focus of the thesis was on the "orderless supply chain". This concept is so novel that only a small amount of papers have mentioned the concept and with limited applications. After extensive research we found that orderless can be interpreted as a simplification of internal processes with the aim to reduce costs, improve service and 17 consequently increase sales (Angerer & Corsten, 2004). Two authors, Cannella and Ciancimino discussed that the impact of supply chain collaboration is greater than an order simplification or orderless. Order smoothing mitigates the bullwhip effect, but it may have a negative effect on customer service, thus damaging the consumer oriented approach (Cannella & Ciancimino, 2010). This study reshaped our research efforts and proved that the initial approach of an orderless oriented supply chain was not aligned with the consumer focus. 2.6 Consumer Interaction In the last part of our literature review, we examined the processes that suppliers and retailers implement to attract and affect consumer perception to increase demand. A promotion is the main technique to interact and affect consumer behavior. The study by Rajagopal on promotions and simulation buying in retail stores, builds arguments around attractiveness of promotions using POS data, and the effectiveness of consumer service as a tool for gaining a competitive advantage in the retail business environment (Rajagopal, 2008). There is limited literature that combines the complexities of promotions, the value of information sharing, visibility and collaboration with responsiveness of consumer behavior. This study provided insights about collecting POS data and implementing promotions; the main limitation is the geographical dissimilarity because the study is based on the Mexican CPG market. Our research found that there is a limited amount of academic studies that comprehensively address strategies and tools to move towards a consumer-oriented supply chain in the CPG industry. In fact, the consumer-oriented term is not extensively used as the other four terms that we explored in our literature research which are Consumer Interaction, Order Management, Collaborative Planning and Automatic Replenishment. We concluded that if we combine these four terms, we end up with the theoretical scheme of a consumer-oriented supply 18 chain. As depicted in Figure 2.2, the combination of the four main topics of this thesis form the foundations of the future Consumer-Oriented System, in the following section we identify a methodology to integrate these perspectives into an analytical perspective. CONSUMER-ORIENTED SYSTEM Figure 2.2 Integrating concepts to build a Consumer-Oriented System 19 3 Methodology The initial approach is to understand and analyze a single part of the existing supply chain system. The intention is to clearly define and identify the potential opportunities for improvement, and then expand the findings and potential solutions to the rest of the scope. This approach allows us to visualize the relations among different stakeholders and the information flow more clearly and concisely. Some researchers define this methodology as the spiral technique (Boehm, 2000). The main reason to use this approach is essentially because of the ambiguity and wide extension of the problem, since it includes a great proportion of the supply chain between the supplier and the vendor. The initial approach in the spiral method was to select one category of products, one relevant category for both Vendor and Retailer. The findings, explained in the Data Collection part, showed that laundry category is relevant in terms of volume and revenue, both sponsor companies agreed with this, and our conclusions with this category can then be adopted into the rest of the product categories and applied on a broader scale. The spiral technique, described in Figure 3.1, is a comprehensive approach that allowed us to learn with every step we took in our methods. Furthermore, the knowledge gained reinforces and reshapes past and future procedures in our methodology, this means that with every insight, discovery or mistake the mental model evolves and our perceptions change to new conditions, this eventually creates a clear and concise picture of the challenge, as depicted on chapter 4 in Figure 4.3 and Figure 4.4. From the data collection, to the future value stream maps, spiral method has been a proven method of constant success in complex problems. Apart from the spiral method, our methodology consists of two parts: Data Collection and Data Analysis. 20 Figure 3.1 Spiral technique 3.1 Data Collection Quantitative and qualitative data was collected from internal and external sources. The Vendor and Retailer, sponsors of this thesis, use different tools to measure and store data. Moreover, even within companies, sometimes divisions employ different software and techniques. In section 4.1 there is a relevant representation of the different systems, used by Vendor and Retailer. One of the initial fears was the challenge of assessing data from two different parties. However thanks to the development and integration of Information Technologies, the exchange and analysis of data can be a simple process, in chapter 4 we 21 discussed the integrations of technological platforms that companies can leverage to further increase their collaboration. 3.1.1 Quantitative Data Quantitative data were necessary to support the forecast analysis. In this analysis three types of quantitative data were collected and used: forecasting, replenishment and point of sales (POS) data. To simplify the collection and analysis process, the data came from one category of products. A challenge was to identify a representative category, one that could potentially extrapolate the insights and conclusions to the entire portfolio of products. Forecasting Data The vendor and the retailer use different forecasting techniques and systems. Forecast methodologies can vary significantly from one platform to another. It is difficult to assess the effectiveness of their techniques, and it is not part of the scope of this thesis. We assumed that technological platforms share capabilities and functions, and we can rely on this data. Section 4.3 presents the results of this analysis and it is possible to visually compare forecasts from the vendor and the retailer. However these comparisons do not represent a feasible analysis of the effectiveness of the different techniques, but help us understand the importance of the information used to draw those forecasts. The collected forecast data exposed two main differences in the forecast strategies of the sponsor companies. The vendor reports the forecast on a weekly base for a determined horizon; every week the vendor updates the forecast for the subsequent weeks according to their perception of demand and the information sharing with the retailer. On the other hand, the retailer generates reports every working day, Monday to Friday. These 22 reports have a 26 week forecast projection of demand including the current week's demand. The forecast projections are made per Universal Product Code (UPC), i.e. single product. Replenishment Data Replenishment or order fulfillment is the number of items that moved from a distribution center to another a store. The replenishment data indicates the final decision of the number of items to ship based on the forecasts created. Replenishment data show the degree of collaboration, if any, and the responsiveness of the fulfillment system. The vendor and the retailer provided replenishment from the vendor's plant or distribution center to the retailer's DC, and from the retailer's DC to the retailer's stores respectively. The replenishment data is reported on a daily basis including the following information: UPC, Item Description (Primary), Date, Quantity Sold (in the case of the vendor to the retailer), Items Shipped (in the case of the retailer to the stores). POS data The most critical input was the Point of Sales data. This information serves as a comparison tool to measure the performance of the separate forecasts and also gives insights about the replenishment policies and the general behavior of the order and fulfilling management. POS data represents reality, i.e. the consumer preferences. The access to this information is limited and becomes large as we move upstream in the supply chain. Fortunately the retailer stores the POS data using a third party service, this makes it accessible anytime, anywhere. The third party service brings a web-based platform that generates reports based on the criteria established by the user - a vast number of attributes can be selected. For our analysis we exported weekly data with the 23 following attributes: UPC, Item Description, Date range, Total Sales Amount and Total Sales Volume Units. The object of the quantitative study is to focus on the forecast and replenishment, sales volume is critical to determine the performance of these two processes, the other attributes were used for the matching tasks and selection of SKUs. 3.1.2 Qualitative Data To better understand the existing process of replenishment between the retailer and the vendor, a number of interviews were held with key stakeholders. In addition we visited the premises of the retailer (store, DC, Headquarters) in October 2014 with the intention of obtaining a better sense of the operations and the different parts involved in the retailer's supply chain. Our interview schedule included: " Vendor, VMI and non-VMI replenishment " Vendor, promotions and playbooks " Retailer, category management and replenishment " Retailer third party IT, Data capture and extraction " Weekly VMI calls, Retailer and Vendor, VMI coordination and feedback " Bi-weekly joint calls, Retailer and Vendor, project status updates Additionally, we conducted interviews with Larry Lapide, research affiliate at MIT, mentor and opinion leader in the field of supply chain replenishment and Kai Trepte, senior software engineer at Harvard, experienced professional in the field of supply chain collaboration. 24 3.2 Data Analysis In this section we expound the strategy for data collection and quantitative analysis. The intention of this analysis is to retrieve insights of the replenishment process and the overall performance of its participants. 3.2.1 Quantitative Data Analysis The retailer and the vendor follow strict and complex tools and procedures for the forecast and replenishment processes. However, the complexity of the tools doesn't necessary mean better accuracy. In chapter 4, we will discuss technology and the trends that point towards integration and simplification of platforms. In addition to new technologies, a quantitative analysis of existing forecast and replenishment conditions strengthens our main deliverable, the roadmap, and the final conclusions. Segmentation Strategy With thousands of different products, the segmentation and selection of SKUs for analysis becomes a challenge. Selective Inventory Control or ABC analysis is a segmentation strategy that divides SKUs into three different categories according to its volume and monetary value. ABC analysis is similar to the Pareto principle which states that, for many events, roughly 80% of the effects come from 20% of the causes (Lysons & Farrington, 2006). In this case the class A items, which require more managerial attention, represent 20% of the SKUs and bring 80% of the effects. Class B items or the middle share represent 30% of SKUs with 15% of the value. Finally, class C items are nearly 50% of the SKUs with 5% or less of effects. To extrapolate effectively the results, the data was extracted for three class A and one class B products, chosen from the category laundry. 25 Time Horizon Selection Establishing the time horizon is another critical component of the analysis because not all the products have the same life cycle. We received partial data from 2013, full data from 2014 and the beginnings of 2015. During 2013 and 2014 several SKUs where either introduced to the market or discontinued. Additionally, like in any other large database, there are discrepancies and errors, creating additional boundaries for the horizon selection. Some of the initial selected SKUs were not robust enough in terms of data continuity to establish a solid horizon, therefore the horizon analysis was another factor considered in segmentation strategy. After manual verification, the horizon for our study is the 26-week period from December 29, 2013 until June 28, 2014. Comparison Visual comparison is a standard methodology to compare complex phenomena, such as the forecast and replenishment of two collaborative partner companies. For visual comparison we are using standard two-plane graphs that provide a clear and concis C0umparisOn 01 the friecast, replenihmenIt anud POS data. Metrics For data comparison, the use of statistic metrics has proven a consistent and reliable universal method. The Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) are widely used and recognized in the industry. MAPE measures the accuracy of the forecast compared to the POS. The closer a MAPE value is to zero the more accurate the forecast technique. RMSE measures the difference between the predicted and the real values; RMSE is the standard deviation of the sample. The formulas for these metrics are: 26 Future Value Stream Mapping & Roadmap The final objective is to design a future value stream map, which will incorporate the potential improvements mainly in the information flow part. A main part of the future improvement could be the utilization of point of sale (POS) data, which is created in the store cashier scanners, to trigger replenishment. The future value stream map will intend to eliminate any unnecessary existing flows of information as well as replace existing flows with updated processes that will help the companies experience efficiencies and responsiveness in their supply chain. In contrast to the existing value stream map, the future value stream map is built in general terms, this map includes both the changes in the physical, as well as the information flow, a high degree of detail in the future value stream map is not considered as part of this study. After identifying the potential future state, the final part of our methodology presents the modifications and changes that need to take place to reach that future state. These recommendations could be in terms of processes, technology and people, depicted in chapter 4 as Roadmap. 32 n n MAPE = n t=1 RMSE= At - (At - F,)2 n t=1 where At is the POS data, Ft the forecast and n the number of values. In addition to the error metrics, we employed the coefficient of variation (CV). This is a standardized measure of dispersion, defined as the ratio between the standard deviation and the mean of a sample. In our analysis, the coefficient of variation determines the volatility of the demand or POS data. It is commonly represented as a percentage or decimals, the closer the value to zero the less dispersion and variability. The formula for the CV is: - CV = where a is the standard deviation, and t is the average or arithmetic mean. 3.2.2 Qualitative Data Analysis The objective of this thesis is to depict a future consumer oriented supply chain that can in part be achieved by increasing collaboration. However, assessing collaboration is difficult, especially if we only try to understand only with numbers. A qualitative analysis provides the insights and understanding that is difficult to evaluate with quantitative methods. The core components of the qualitative analysis are: " Existing Value Stream Mapping " System Dynamics Representation * Future Value Stream Mapping & Roadmap 27 The Existing Value Stream Mapping is a tool that illustrates the existing process of replenishment between the retailer and the vendor. This process helps visualize, in a meaningful way, the current flows of product and information (Martin & Osterling, 2013). The second tool, the System Dynamics representation, is a systems thinking methodology that enables us, through a causal loop diagram, to depict a holistic view of the information and product flow, and feedback loops created in the system (Sterman, 2000). This approach increases our understanding of the current state and what improvements in terms of processes, technology, and human resources could take place. The third part of the qualitative analysis is the Future Value Stream Mapping. In this section a high level redevelopment of the Existing Value Stream Map depicts the proposed model of replenishment between the retailer and the vendor. This will help both companies understand how their future collaboration could work. In this part we are going also to present the short-term and long-term steps that the companies should follow in a high level in order to shift from the existing to the future state. Existing Value Stream Mapping The use of a value stream map is the most suitable approach to represent the current process of replenishment as well as the future state to incorporate potential improvements. According to Rother & Shook (2003), a value stream consists of the total number of value added and non-value added activities that take place in order for a product to get through the flows of design and production from raw materials into its final consumable form. According to Tabanli & Ertay (2013), Tapping D, Luyster T, Shuker T (2002) consider value stream mapping as a tool that companies utilize in order to create a map of material (physical flow) and information (information flow) of a product or a 28 process. For this thesis a value stream map is a tool that allow us to depict the current process of replenishment between the retailer and the vendor in a clear and structured way. This current picture would be the starting point in order to build the future map. The process of improvement would be both in terms of identifying the waste of non-value added and not necessary activities (muda, i.e. waste), as well as adding new beneficial elements/ communications. Depicting the current and future state map allows us to realize the necessary steps to reach the desired future situation; in other words will help us identify the roadmap of how to move from the current to the future state. The current state map of the information flow would utilize the gathered information and data from the interviews we carried out with different stakeholder of the retailer and the vendor in order to depict in a concise and meaningful way the steps that allow the products to flow from the plants of the vendor to the stores of the retailer. The current state map would include the physical flow of goods as well as the information flow between the different stakeholders. We created two different versions of the current flow map, aligning to the two different processes that the retailer follows. The difference is based on the replenishment practices that the retailer exercises with the supplier. For the majority of the products of the particular supplier, the retailer implements a vendor managed inventory (VMI) process. This means that the purchase orders (PO) are generated by the vendor, not the retailer. The vendor is the one responsible for receiving all the required information, performing the right analysis and fulfilling the replenishment of the retailer's distribution centers (DCs) meeting the contracted service level and inventory level. On the other hand, the existing value stream map of the more traditional replenishment practice is created. This traditional approach is implemented on the rest of 29 the products of the supplier. In this approach the retailer is responsible for gathering all the necessary data, performing inventory analysis and creating the required PO to send to the vendor in order to help the retailer meet its demand efficiently. Consequently, we create two existing value stream map and one depicting the VMI process and one depicting the traditional (non-VMI) process. The next step was to identify the desired future of the replenishment process between the retailer and the vendor. System thinking is a method that can enhance our perception of the situation. System Dynamics Representation This project addresses the interaction of different systems such as replenishment processes and policies, forecasting models, communication and interaction between the retailer and the vendor, and the information flow created by complex integrated and interdependent systems. Systems thinking is an approach to visualize problems holistically. Systems thinking has its roots in General Systems Theory, a work developed by the biologist Ludwig von Bertalanffy in the 1940's, continued by Ross Ashby in the 1950's and extensively developed by Jay Forrester at MIT (Forrester, 2013). Systems thinking is a mental strategy to build models with nonlinear interactions to understand the components of the system in the context of relationships with other components or other systems. System dynamics combines the inevitable change with the notion of time in one single approach, and how the state of our system creates behavior (Sterman, 2000). In this thesis, demand is an uncontrollable force, affected by several constraints. Demand evolves with time and it can be considered a dynamic element. In the law of economics, 30 the supply, price and other controllable factor can affect its behavior and increase its predictability. Every system is affected by a rate of change, which in the system dynamics world is called flow. Some components of the systems can be measured at any time, and this is called a stock. The main differences between these two crucial components of models is that one directly affects the other. The principles of system dynamics can help us to understand the different feedback loops of complex issues. These models are mainly used for simulation analysis and eventually to create or revise policies. This methodology is used to depict the interaction of stakeholders and visually help us identify causal loops that are creating certain behavior. A causal loop is a map that shows the causal links among variables with arrows from a cause to an effect. Causal loop uncovers hidden feedbacks that seem not so obvious to our non-system thinking methodologies. One of the main purposes of this thesis is to create a blueprint or roadmap that depicts the interaction of different stakeholders and to identify room for improvement. System dynamics can provide that level of insight and can be easily understood by nonacademics. There are a minimum number of research studies that employ system dynamics approach in addressing the problem. On the contrary, system dynamics is more a complementary method that creates awareness of the magnitude of a problem and potentially depicts solutions. System Dynamics is a quantitative method, although our intention is not to simulate the model but understand holistically the forecast and replenishment processes. 31 4 Results, Analysis, and Discussion In this section we present the results of our project as well as the analysis and discussion on them. Firstly, we present and elaborate on the existing ("AS-IS Process") value stream map, i.e. the map that describes how the retailer and the vendor collaborate. Then we present the discussion ("Connecting the Dots"), i.e. the results of our literature research that led us to propose the alternative scenarios towards a consumer oriented supply chain. After this we present a descriptive analysis of the forecast comparison ("Forecast Analysis") followed by the discussions on the future value stream maps ("TO-BE Scenarios"). Ultimately, all the previous analysis and synthesis helped us create a path towards a consumer oriented supply chain ("Roadmap Towards a Consumer-Oriented Supply Chain"). 4.1 AS-IS Process A Value Stream Map is a powerful tool to show the condition of business operations. In the context of a consumer oriented supply chain, the value stream map allows us to understand the existing process of replenishment between the retailer and the vendor. This can be done by depicting the existing flow of information and product between the different stakeholders that engage in the replenishment process from both companies. The existing process map, or AS-IS map, shows the crucial interaction among people, process and technologies and helps to identify misconnections, flaws and delays. It also gives visual insights and ideas of possible corrections and improvements to the system. Additionally, the map can function as a strategic management tool, an agreement document not only between the retailer and the vendor, but also among different members of the same organization. 33 4.1.1 Map Construction Process In order to make our value stream map more structured and understandable, we first designed the value stream map canvas (Figure 4.1). The canvas has a meaningful horizontal and vertical area separation: " The horizontal lines create three distinct areas: - The upper area is that of the physical (product) flow from plants to end consumer (black color) * - The middle area is that of the software and computer systems (blue color) - The last area is that of human interaction (red color) The vertical lines (swim lanes) create four distinct areas: - The left swim lane represents the plants and distribution centers (DCs) of the vendor - The next swim lane to the right represents the DCs of the retailer - The next to the right the retail stores - The last one the final consumer Ultimately, by combining the horizontal and vertical lines, the canvas is separated into meaningful areas that intuitively help understand the "position" of the area in the supply chain (vendor, retailer, consumer) and the content of the area (product interaction, software interaction or human interaction). 34 Value StrE am Map Canvas Product Interaction W) C E E E E IA 0 0 A1 C) C 0 U Human~~ Intrcto Figure 4.1 Value Stream Map Canvas Description The symbols that we used to create the value stream map are depicted in Figure 4.2. We utilized boxes to represent key stakeholders and important elements in the physical and information flow. The colors of the boxes represent the content areas that they belong (as described above). We utilized arrows to depict the different flows. The black arrows represent the physical (product) flow, while the blue arrows represent the information flow. 35 Map Symbols Retailer or Vendor Facility Software or camputof ystem Human Interaction a - Physical (Product) Flow Information Flow Figure 4.2 Value Stream Map Symbols Explanation 4.1.2 AS-IS Value Stream Map (VMI Process) Following the spiral technique as described in the methodology section (Section 0) we initiated the construction of the AS-IS value stream map following the replenishment process for the laundry category. The replenishment of the laundry category is fulfilled utilizing the Vendor Managed Inventory (VMI) method. Through interviews with the stakeholders, we understood that for all VMI product categories the process of replenishment is the same. The process is depicted in the following value stream map Figure 4.3. 36 VMI Replenishment Process Retailer Distribution Vendor Distribution Vendor Center Center M AL VMI Po * store pull-s - - - - - - - - - - - - - - - - - - - - - - - - - - --- i ED[ 856 R M PO #2 EA 4M.P Cons ume Pqan Stores store pulls dail E RP #1Tool Tool #t1c in ut #1,;+- CP (Turn &I Promno) Daily PtInventory -- Tool (1week) E nToo! Prm ~-management, pricing,- info VMI Analyst 4weeks) merchandising dpt F merchandising agsnr ong-term (2e forecast VIV Feedbck 0- Planogram #1b Forest AnlystFA edo red flags Anyst (FA) --- - Sales #2 - P 0 An0s exceptions (3w) Store Analyst 0 (3A) ssociate Cashier The replenishment process initiates from the moment a consumer buys a vendor's product in a retailer's store. The transaction is registered by a cash register POS system, modem systems allow the consumer to automatically checkout and pay for products, removing the need of an associate; nevertheless cashiers supervise operations and reliability of the system. At the retail level, a Computer Assisted Ordering (CAO) system is the mainstream technology for replenishment of the stores. CAO is composed of two separate systems, one at the store level and another one at the DC level. The one at the store feeds the DC system with critical inputs that eventually result in a suggested replenishment order. At the store side, Tool #la retrieves POS data from registers every 30 minutes, Tool #Ia provides information for stock, quantity on hand (QOH) and sales. At the DC side ERP #1 gets daily information from Tool #1 a. ERP #1 also needs established planograms from category mangers, sales history, price, promotions and delivery schedules to generate a weekly demand forecast. With the forecast and inventory position, it is possible to determine if replenishment is necessary and with this an order. CAO generates this order and provides access and visibility to the store to confirm the validity of the information and make any changes. If no changes occur the order is transmitted to the DC for picking of the products. At this point it is important to make a product distinction. A product can be characterized as turn (already established product with no current promotion), promotional or new launch. The replenishment process is different among these product categories as far as forecasting is concerned. Tool #1 c is the forecasting software that the retailer utilizes. The main purpose of this software is to create forecasts (for the turn 38 products) and help the forecast analysts of the retailer to forecast (for promotional products, new launches and other exceptions). The main input to the software is the store pulls, i.e. the actual deliveries of products from the retailer's DC to the retailer's stores. Another input to Tool #1 c is the promotional information (this information is communicated by another software called Tool #Ib). The category management, pricing and merchandising departments of the retailer create the promotional information at the first place. Tool #1 c uses the historical demand of the retailer's DCs (orders of the retailer's DCs to the vendor's DCs) and the promotional information to create a threeweek forecast per product (Stock Keeping Unit - SKU). Because the retailer may sell 12,000 SKUs in its stores, the forecasts that Tool #1 c creates cannot be fully reviewed by the forecast analysts of the retailer. The software creates red flags if it realizes significant changes in the forecast of a SKU. The forecast analysts review these red flags as well as the SKUs that are on promotion (around 200 SKUs per week) that period of time and decide for the final forecast (forecast of exceptions). The inventory analysts receive the forecast for exceptions as well as a different set of red flags from Tool #1 c. The inventory analysts will not create a purchase order (PO) in the VMI process. On the contrary, inventory analysts will use the information they receive (forecasts of exceptions and red flags) to resolve any issues that may come up in the weekly collaboration meeting between the retailer and the vendor. The vendor is then responsible to create the PO for the retailer under the VMI system. The vendor utilizes software similar to Tool #Ic that is called Tool #2. Tool #2 receives as an input the daily inventory position of the DCs of the retailer (utilizing the EDI 852 technology). Tool #2 utilizes this information to create the PO for the turn 39 products that the particular vendor sells to the retailer. For the promotional products and the new launches, the vendor occupies a determined to the retailer team of VMI analysts. These VMI analysts receive the promotional information from the retailer and create the PO for the promotional products. The total of the PO (both for turn and promotional products) are sent to the vendor's plants and DCs for fulfillment as well as to the retailer's Tool #lc (via EDI 855) in order for the retailer to have knowledge and be able to resolve any issues that may come up in the weekly collaboration meeting. To sum up, at a high level under VMI replenishment policy, the supply chain of the retailer and the vendor create three forecasts (plus an operational forecasts for the vendor's manufacturing planning). The vendor creates the PO and the retailer reviews the performance of the vendor in weekly meetings. 4.1.3 AS-IS Value Stream Map (Non-VMI Process) Apart from the VMI replenishment policy, the supply chain of the retailer and the vendor implements a non-VMI policy (Figure 4.4). The non-VMI policy is a more traditional approach in which the retailer handles the vendor as a supplier and the vendor handles the retailer as a customer. The non-VMI process is exactly the same as the VMI process described in section 4.1.2 with the difference that the POs are created by the inventory analysts of the retailer, i.e. the vendor receives no more data than the POs to fulfill. The POs for the turn products would again be generated automatically by Tool #1 c and the POs for the exceptions (promotions, red flags, new launches) would be created by the inventory analysts of the retailer. The inventory analysts receive the forecast of exceptions from the forecast analysts and they are those to finalize the POs according to their internal knowledge of the inventory position of the retailer as well as different 40 exogenous factors, such us weather conditions, availability of trucks among others. The inventory analysts are responsible to have the product in the DCs of the retailer on time. On a high level basis, under non-VMI replenishment policy, the supply chain of the retailer and the vendor create again three forecasts (plus an operational forecasts for the vendor's manufacturing planning). The retailer creates the PO and the vendor fulfills the order. 41 Non-VMI Replenishment Process Vendor Retailer Distribution Distribution Center Center MAL cj.~ Tstore PO ED[ 856 ERP #2 PO (no exceptio s) Retailer Stores I I- - pulls - - - - C Consumer - -- - - - - - -t Vendor Plantl store pulls To 1 EiR P it I 17Tl dail in ut I Tool #1a I rD S 0 w M CL rTool I (0 #1b M M 0 0 -- -- - - --- - - -- --- - -0 0 -I - Masters Sales Plan (MSP) Flano.ran Sales orecast (3 days) CL 0 Category Sales FA red flags 0 < (A rD Ci2 Long-term ____________ IA red PO (exceptions) EDI 830 flags IForecast of exceptions (3w) Inventory Analyst (IA) < (0 M 0=3 Forecast Analyst (FA) forecast (120 days) Ln Shelf 3 merchandising Promo info ~1 C 0 0 '0 (D management, pricing, merchandising dpt EDI Analyst C Stor e A na CL n0 3 5 3 LA 0 CL M Store Associate Cas hier 0 CL 4.2 Connecting the Dots In this section we present the discussion, i.e. the results of our literature research that led us to propose the alternative scenarios towards a consumer oriented supply chain (as presented in the following section 0). In order to improve the existing replenishment collaboration, the retailer and the vendor should focus on all three aspects of people, processes and technology. According to Ireland & Crum (2005), companies should not look at the replenishment process initiative as a technology project. In other words, people should be directly connected to the initiative. If people are not engaged, then the equation old processes plus new technology will result in expensive old processes, i.e. Old Processes + New Technology = Expensive Old Processes (Ireland & Crum, 2005). People, processes and technology need to be aligned to the common goal of improving the existing replenishment collaboration (Figure 4.5). People ) People Process Technology Figure 4.5 Integration of People, Process and Technology (Ireland & Crum, 2005) 4.2.1 People People are the most important asset of businesses. According to the interview that we conducted with Kai Trepte, experienced professional in the field of supply chain collaboration, in the improvement of the replenishment process, people are the most crucial element that determine the success or failure of the initiative. The employees of 43 both the retailer and the vendor need to be educated for the change. According to Ireland & Crum (2005), 80 percent of the education/training effort should focus on the new business processes while the remaining 20 percent should focus on familiarizing with the new technology. Another very important aspect of people, apart from training, that affect the success of a collaborative replenishment initiative is the need for executive-level sponsorship. This is an absolute requirement for both the retailer and the vendor. C-level executives of both organizations should be regularly briefed on progress and potential hurdles. Lack of engagement by top management could turn out to be a critical failure point for the initiative (VanDeursen & Mello, 2014). The executives should sponsor the needed changes in culture, organization, incentives/rewards and technology investments (Ireland & Crum, 2005). Trust is another crucial aspect in terms of people. The issue of trust rises not only between the retailer and the vendor but also internally in each company. Between the rctailcr and the vendu Rthe iI Si iII LUstiing the partner to Jo what 1hey promise to o. Internally the different teams may not trust other teams on how they are going to use the partner's data. However, data sharing is mandatory in order to achieve a collaborative replenishment. The reality is that a risk exists both in sharing data and not sharing data (Ireland & Crum, 2005). Sam Walton, founder of Wal-Mart, said "communicate everything you possibly can to your partners. The more they know, the more they will understand. The more they understand, the more they will care. Once they care, there is no stopping them" (Ireland & Crum, 2005). 44 4.2.2 Processes Processes are extremely important in the replenishment collaboration. According to Larry Lapide, opinion leader in the field of supply chain collaboration, well-defined and established processes are mandatory in order to determine the collaboration between the retailers and the vendors. In fact, processes become even more important taking into consideration that the benefits from collaboration can be effectively realized if at least 50 to 60 percent of the retailers and vendors are collaborative partners (Ireland & Crum, 2005). Without specific processes this critical mass of retailers and vendors would be unable to collaborate effectively. The collaboration between retailers and vendors is difficult because their interests are not completely aligned. Vendors are most interested in getting the retailer to commit to binding plans earlier (to cope with long lead-times) and getting access to better demand data to be able to forecast more accurately, while the retailers typically need less accurate forecasts (due to short lead-times) and their main problem is managing store level demand, rather than aggregate demand, adequately (Smaros, 2007). The majority of the discussions we conducted with supply chain professionals and the research we performed for replenishment collaboration included the topic of Collaborative Planning, Forecasting and Replenishment (CPFR). The CPFR was created by industry people in order to address and frame the collaboration between retailers and vendors towards more efficient supply chains. According to VanDeursen & Mello (2014), CPFR is a supply chain strategy in which two or more companies share information in order to monitor, control, and facilitate the overall performance of a supply chain by achieving a smooth flow of product between firms. 45 Coordination of forecasts and promotional events is one key to preventing surprises on the supply side that lead to misunderstandings concerning actual customer demand, and that cause wild fluctuations in replenishment plans at the supplier, i.e. the bullwhip effect (VanDeursen & Mello, 2014). The bullwhip effect in supply chains is the increased order variability as demand moves upstream, away from the final customer (Ali and Boylan, 2010). Under CPFR, the retailer and the vendor agree to execute against a joint sales forecast and a joint order forecast. A joint sales forecast was discussed frequently during conversations with the sponsor companies. They refer to it as "one truth". This means, in other words, that the two companies should have a common picture of how much product will be sold and what promotional activities will occur. According to VanDeursen & Mello (2014), the joint sales forecast is considered to be a commitment on the part of both firms regarding supply and demand actions. The joint forecast should be used to drive production scheduling, distribution planning, and store-activity planning. Exception based interventions into the joint forecast can take place from both companies following promotional activities, or uneuxpCctU actuai UIdmandU. I he two parties would manage these exceptions in collaboration meetings, as described in section 0. New products introduction would also require the collaboration from both parties in order to come up with the joint sales forecast. In our respective interview, Larry Lapide also mentioned the important work of the Voluntary Interindustry Commerce Standards Association (VICS) that created the CPFR process. The CPFR process could be the philosophy of collaboration for the two sponsor companies. According to VICS, the main body of CPFR consists of nine steps (Ireland & Crum, 2005): 46 1. Front-End Arrangement, i.e. a document determining who does what, when and how. 2. Joint Business Plan, i.e. a flowchart of process roles and rules to manage the collaboration process from forecasting to replenishment (exchange of information about strategies, promotions, events among others). 3. Demand Forecast Creation, i.e. a consensus, joint, synchronized forecast of demand that is agreed on by both the retailer and the vendor (the forecast could be in a store level, distribution center level or corporate level). Sharing of the forecast utilizing the technology that is agreed to do so. 4. Identify Item-Level Exceptions to the Demand Forecast, i.e. generation of exceptions by the technology tool and solution by both the retailer and the vendor. 5. Collaborate and Resolve Demand Forecast Exception Items, i.e. adjust the specific item forecasts following the processes defined in step 1. 6. Replenishment Order Forecast Creation, i.e. using the demand forecast and inventory strategies create and share the replenishment forecast. 7. Identify Exceptions to the Order Replenishment Forecast, i.e. develop a list of item level exceptions to review collaboratively between the retailer and the vendor. Exceptions stem from supply and capacity constraints. 8. Collaborate and Resolve Exceptions to the Order Replenishment Forecast, i.e. adjust the specific item replenishment forecasts following the processes defined in step 1. 9. Create the Replenishment Order, i.e. create and communicate replenishment orders. 47 Our research informs this 9 steps process by suggesting that the sponsor companies could implement partially the nine-step model. Additionally, it is important that the retailer and the vendor agree on which items or product categories are more meaningful for them to implement CPFR. Another process that helped us identify the importance of collaborative replenishment was the system dynamics methodology. The high-level system dynamics model that we built is depicted in Figure 4.6. The model consists of two reinforcing loops that depict the strong positive correlation between order effectiveness (stemming from replenishment collaboration) and revenues increase for the sponsor companies. More analytically, as the order effectiveness increases, the replenishment responsiveness increases and therefore the service level increases and the revenues increase. As revenues increase the software investment that lead to demand data accuracy increase and this leads to increased order effectiveness. This is the "information accuracy" loop. Simultaneously, as the order effectiveness increase, the inventory costs decrease and the revenues increase. These are the main elements of the "inventory benefits" loop. Order effectiveness Demanddata accurac + Replenishment responsiveness (RD CR.. Inventory benefits Software + investment + [nventory costs (R) Information accuracy Service level Revenues Figure 4.6 Revenues Reinforcing Loops from Replenishment Collaboration (System Dynamics Model) 48 4.2.3 Technology Communication and interaction, information flow and decision making, vital activities in supply chain collaborative efforts, have been progressively improved thanks to the incorporation of novel technologies, derived of the universal computer adoption and virtual networks in almost every modem corporation. Technologies are bridges that span complex circumstances, connecting people to processes. The ubiquity of the novel information technologies have allowed corporations to lessen some of the burdensome analytical tasks, particularly those related to ordering and replenishment. Although in their continuous effort to integrate state of the art technologies, corporations are facing new challenges of the era of interconnectivity, almost every major retailer captures and stores data at some point, most frequently at the point of sales (POS). Today companies look for ways and strategies to effectively take advantage of their existing information. Some existing technological systems lack the ability to aggregate and manage POS data properly in one single engine; in addition technology systems are not able to translate complex analytics into executable decision, human control is still necessary to discern red flags in the system. Promotions and product introductions are clear examples of how technology depends on human intuition. For the past several decades, the industry has strived to integrate technology in its supply chain. The most known achievement is the Collaborative, Planning, Forecasting and Replenishment system. A technological process, developed more than 20 years ago, CPFR has proven that technology can link consumer demand with replenishment needs. Large-scale retailers applied some of these principles and, as expected, the results of this innovation were different. CPFR was beyond the implementation of rules and 49 technologies; it requires full commitment and human touch. As mentioned by VanDeursen & Mello (2014), partnerships using CPFR used a common standard (extensible markup language, or xml), however actual execution was based on preestablished agreements. In the era of information technologies, new companies have emerged offering a vast range of enterprise software products and solutions. Traditional companies such as Oracle Corporation, SAP AG, IBM, BMC Software, IFS AB, QAD Inc, Microsoft, CA technologies and many more have improved reliability and constant improvement in their products. Most of them have contributed to the development of solid and robust software infrastructure, particularly to the Enterprise Resource Planning (ERP) technologies. ERP is the most accepted and widely used platform; it integrates the entire supply chain processes from financial to manufacturing applications. ERP facilitated the development and integration of new specific tools or suites. New companies have exceled positioning their suites in the market, we can identify companies such as NetSuite, Infor, JDA and Google, but there are many more offering yearly new interesting products. These applications tackle specific problems such as forecasting, Business-to-Business (B2B) communication, replenishment process, data storage, advance planning and scheduling, transportation, and collaboration, among others. Table 4.1 displays the top supply chain companies in 2013 and their different offerings, Supply Chain Planning (SCP), Warehouse Management System (WMS), Manufacturing Execution System (MES) and Transportation Management System (TMS). In the research of supply chain collaboration and consumer oriented supply chain, we solely focus on the forecasting and collaboration applications, although when redesigning the supply chain towards a consumer oriented 50 perspective, it is also important to consider other potential applications. This depends entirely on the current installed software infrastructure and its integration within the different aspects of the organization, such as marketing or finance. Top 20 supply chain management software suppliers I SAP S1721 bilIoin sap.com 2 Oracle $1.453 billon ciace.com 3 JDA $426 jdaoM x 4 Manhattan Associates $160 milifn manh.com x x $131L2 md~ilon opicoron x x miltion ibm-com x 111 million Wor'co" x Softwort 5 Epor 6 IBM $112 on Infor, Gblol 8 RedPrairie 7 9 D"Carw $106 SyuMIW Group Kewill Systems 10 Urit4 10 iciior 2 million x x redprairie.com $96 million $62 millon descoi.mcom 562 milgn wrnt4,co/.w.pysalem $54 gtnexUs.com x US2 mIlion ibaqw x 13 Quwntiq $52 milon quintiq.com x 16 Logility x x x x A x S51 ikon $50 million logilitycom $48 million totvwcom K x It x x x hih__mp__ x x S47 million iisworld com/en x x 19 inapur Genursoft $46 millon S39 mrllon en.inupurcom x kinaxis-com 9 20 Tots Kinaxa x x x __wr__c x _ 18 IFS 17 x x M$ Higlhjump oftwse x www.kowill.com 12 GTNexrs 15 A _x _ 13 mtIion K x Table 4.1 Top 20 supply chain management software suppliers (Gartner, 2013) Recently, we identified three companies that have stood out with their technology advancements in collaboration and integrated forecasting. Oracle, SAP and JDA are pioneers and leaders in the Supply Chain software systems. Their suites JDA@ Flowcasting TM, SAP Supply Chain solutions and Oracle® Collaborative Planning represent an outstanding advancement towards a consumer oriented supply chain. JDA@ flowcasting (Figure 4.7) provides one single truth, one single forecast. Flowcasting enables cooperation and joint planning between vendor and retailer using sell-through forecast, promotions and supply chain planning parameters. It also enables cooperation beyond forecasting, such as promotions, safety stocks, display and presentation stocks, lead times, direct vs. indirect shipments, delivery calendars, lot sizes, 51 etc. JDA flowcasting is deployed via JDA Cloud Services to deliver rapid, sustained return on investment and enhanced organizational agility. According to JDA the main value of the flowcasting is the flexibility and proactive management approach, since flowcasting uses effectively daily POS data the forecast accuracy should be improved. A QJgAjig Company Forecasting Today CEINS 44 44 I~4 Forecast Focst DRP Make to DRP Rwto Stock Forecast - Forecast daOmer -L ~bock to Order L Forecasting - Tomorrow '(AM. CEDIS P42" 01st ft cuoqner (gn n -sic Make to Calculated Replenishmentto Calculated Demand Replenishnertto Calculated Demand "NeverForeastJa Calcujat" ft Q"i Forelt StCan Figure 4.7 JDA Flowcasting (JDA Software Group, 2014) SAP has increased the portfolio of services in supply chain collaboration. New solutions such as Demand Network increases visibility of real time demand to users and 52 suppliers, the Response Network solution improves replenishment efficiency by 50% and up to 20% of inventory reduction, SAP Responsive Network suite allows users to connect directly with partners and plan the push-and-pull-based product replenishment. Oracle's Collaborative Planning (Figure 4.8) is an internet-based collaboration solution that rapidly and significantly improves supply chain performance by providing advance capabilities for collaborative demand, supply, and inventory planning across the supply chain (Oracle Data Sheet). Oracle uses a holistic, e-business planning process that reduces the conventional steps and processes to react quickly to supply chain exceptions. The solution is fully automated and connected to store databases. Trading partners receive proactive notifications when inventory needs to be replaced, according to preestablished inventory levels and planograms policies. Partners confirm the replenishment information in the system and by using the E-business suite, the purchase order is generated automatically, essentially a breakthrough technology with the principles of an Orderless System. Figure 4.8 Oracle Collaborative Planning (Oracle Corporation, 2014) 53 4.3 Forecast Analysis In this section a descriptive analysis of the forecast comparison is presented. As described in the methodology section, we carried out an analysis of four different SKUs in the laundry category. The laundry category is under a VMI process - Figure 4.3 depicts the replenishment map of these items. We pulled data for the first 26 weeks of 2014 and built the respective graphs utilizing the information described in Table 4.2: Information Vendor replenishment Description This is the number of items shipped from the Vendor DCs or manufacturing plants to the Retailer DCs Forecast from the As described in the AS-IS section, currently the retailer generates Retailer at the DC level forecasts at two levels of the Supply Chain, one at the Store level and the other one at the DC level. Since we analyze VMI products, the forecast from the retailer is actually a supplementary and control tool for managing the vendor Forecast from the The retailer has the installed capability to develop a forecast to Retailer Store level replenish the stores Vendor forecast The Vendor generates weekly forecast Point of Sales data This is the comparison point of the replenishment and forecast performance Table 4.2 Information Received for Data Analysis 4.3.1 Analysis of SKU1 The first SKU presented is a class B item. These items are the middle share products -usually 30% of the products fall into this category - and they account for 15% 54 of the Vendor's revenue. Our initial interest in this product stems from the fact that in week 6 the product was re-introduced to the market. As we can see in Figure 4.9, in 2014 this product had a lifecycle of around 17 weeks, from week 6 until week 23, since after that the product was discontinued from production. SKU 1 demand is relatively constant and after week 6 the average is 704 items per week with a Coefficient of Variation (C.V.) of 0.23. Figure 4.9 SKUl Forecast Comparison We can see that Retailer's forecast at the DC level was not interrupted during the first weeks of 2014, even if the product was not on shelf. We can assume that the retailer forecast at the DC level takes into consideration historical DC demand. On the contrary, the retailer forecast at the store level follows closely the consumer demand, and it seems it relies more on new data. Something similar happens to the vendor forecast; it is more responsive and stays close to the POS data. Table 4.3 displays different metrics 55 to assess the performance of the vendor and the retailer. It seems that the forecast accuracy (MAPE, RSME) of the vendor's forecast is better for SKUl. Vendor replenishment displays an interesting behavior. At week 7 the vendor ships a considerable amount of product and builds inventory close to the consumer. In the subsequent weeks the replenishment constantly follows demand. At week 23 there is no more replenishment but demand continues until inventory depletion. Average Std deviation C.V. Retailer DC m Retailer Store Vendor Retailer DC m Retailer Store Vendor 704 159 0.23 0.65 0.33 0.17 651 234 154 Table 4.3 SKUl forecast metrics 4.3.2 Analysis of SKU2 This class A product displays high volatility and complexity in the forecast and replenishment process. A product with these characteristics requires a high level of cooperation. The analysis of SKU2 reveals the communication and information sharing patterns, explained in the AS-IS map, in real action. SKU2 has an average demand of 2,271 items per week with a standard deviation of 1,727 items; the coefficient of variation is 0.76. SKU2 is subjected to seasonality, possibly promotions that increase demand, while competitors' strategies and other events decrease it. In Figure 4.10 we can see this volatility that reaches to a maximum level of nearly 8000 units in week 22, or more than 3 standard deviations above the mean. 56 Figure 4.10 SKU2 Forecast Comparison We notice that the replenishment is conservative most of the time, on week 13 and week 22 the vendor builds inventory near the customer to fulfill what it looks like promotional weeks. As we can expect, the forecast accuracy decreases with this high level of volatility. Table 4.4 displays different performance metrics, the most accurate forecast comes from the vendor with a MAPE of 0.53. SKU2 Average Std deviation C.V. Retailer DC Retailer Store Vendor Retailer DC Retailer Store Vendor 2,271 1,727 0.76 0.64 0.74 0.53 1,643 2,3731,774 Table 4.4 SKU2 Forecast metrics 57 SKU2 reveals the necessity for communication between parties. Week 5 is a clear example of this need as the vendor forecasted around 1000 items while the store forecasted nearly 8 times more and the DC 5 times more. For week 5 the vendor shipped more product following the trends of the retailer. Weeks 7, 11, 13 and 22 display the same behavior. 4.3.3 Analysis of SKU3 SKU3 is considered a class A item and has a low coefficient of variation of 0.4. SKU3 has also seasonality at different time periods and of different magnitude compared to SKU2. In Figure 4.11 we can see that the level of response from the different stakeholders involved is similar, responsive to the demand, except for week 19 when the Retailer DC forecast expected an extraordinary level of sales. Based on the replenishment and store forecast we can assume that this value is an outlier. Without this outlier the MAPE of the DC forecast gets 0.49, a value close to the other forecasts (Table 4.5). Figure 4.11 SKU3 Forecast Comparison 58 Similar to the previous SKUs, the vendor forecast seems to understand and catch more effectively the demand; still the replenishment is not purely following the insights of their forecasts. Clearly the vendor replenished following the retailer's store and DC forecasts recommendations. Weeks 4, 9 and 14 and 20 display this effect, when the vendor ships more product than the forecast insights. Average Std deviation C.V. Retailer DC cc 2,316 930 0.40 0.67 Retailer Store Vendor Retailer DC 0.47 0.42 2,647 Retailer Store Vendor 1,682 1,190 Table 4.5 SKU3 Forecast metrics 4.3.4 Analysis of SKU4 The final chosen item combines constant demand with high turnovers. SKU4 is by far the bestselling item of our analysis. With an average demand of 4,338 items per week and a standard deviation of 1,359, this item seems ideal to finalize the comparison analysis. For this product we notice an increase in forecast performance, as Figure 4.12 depicts. The three different forecasts behave similarly, although the retailer's forecast at the DC level is above the demand during most of the extraordinary weeks. The main difference compared to the previous items is that the retailer's forecast at the store level has a MAPE of 0.2, the lowest error recorded for all four different 59 SKUs, although the Vendor is not far with a MAPE of 0.22. Table 4.6 summarizes the different performance metrics. Figure 4.12 SKU4 Forecast Comparison Average 4,JSs Std deviation 1,359 C.V. 0.31 Retailer DC 0.37 Retailer Store 0.20 Vendor Retailer DC 0.22 4,239 Retailer Store 1,478 Vendor 1,461 Table 4.6 SKU4 forecast metrics The analysis of these 4 different SKUs reveals an interesting arrangement in the forecast and replenishment processes of the sponsor companies: . The forecast from the vendor is relatively constant 60 " The only abrupt changes occur during seasonal periods and possibly promotions " The retailer is generally more responsive in its store-level and DC-level forecasts " The forecast at the DC-level is generally more conservative and predicts high volumes " The forecast at the store-level is more aligned with the demand " During extraordinary events (e.g. promotions, new launches) it seems that the vendor takes more into consideration the input from the retailer than from its own forecasts. 4.4 TO-BE Scenarios The AS-IS value stream maps helped us understand the needs of the retailer and the vendor as well as facilitate our interviews with opinion leaders in the field of planning, forecasting and replenishment. The value stream maps helped us also manage and focus our literature research in the field. Additionally, the data analysis revealed a number of potential future explorations for the companies. By utilizing all these means, we identified opportunities to improve the existing replenishment process and accuracy. Ultimately, three potential solutions can be implemented to effectively leverage the use of POS data, reduce the bullwhip effect, improve replenishment and fulfill aggressive service levels. The three scenarios focus on the reduction of processes, a better utilization of people and an implementation of technologies. The main differentiation factor of the alternative TO-BE scenarios is the forecasting process. The scenarios include: Hybrid Forecast Replenishment Process, Retailer-Level Forecast Replenishment Process and Vendor-Level Forecast Replenishment Process. 4.4.1 Hybrid Forecast Replenishment Process In the AS-IS value stream maps (of the VMI process) we notice that the retailer and the vendor create three different forecasts in order to replenish (store-level, retailer DC-level, vendor DC-level). The Hybrid Forecast Replenishment Process consists of 61 only two separate forecasts, one at the retailer side and one at the vendor side. As a concept, the retailer forecast team would collaborate with the vendor forecast team in weekly meetings in order to reach consensus of the following week forecast and proceed to the purchase order (PO) creation. The Hybrid Forecast Replenishment Process consists of two alternatives based on the flexibility of the retailer's deployment system, i.e. the flexibility of the operations of the retailer to replenishment its stores. This flexibility is defined in terms of the need for fixed delivery schedules, assigned trucks or drivers per route among others. A nonflexible deployment system needs store-level forecasting. This stems from the need to know well in advance the delivery schedules and load per store. A flexible deployment system requires only a retailer DC-level forecasting, since the operations of store replenishment could manage the uncertainty of one-day-in-advance delivery plan creation. The TO-BE value stream map of the Hybrid Forecast Replenishment Process (Flexible Store Deployment System) is depicted at Figure 4.13. The main difference with the AS-IS process is that no forecast is created anymore at the store level. Therefore the Store Analyst box in the TO-BE map is crossed out. The flexible store deployment system allows the retailer to replenish its stores following standard replenishment policies. The Inventory Analyst team would now be responsible to operate these store replenishment policies. As a result, the Store Analyst department would be merged with the Inventory Analyst department. 62 ._|iii;imi O N IM ----auns uuun.."'on''"--11..12liono" nnn-innnn.ananiun 1156 . 211ubll_ ,-i -11, - , 4,2111 -_ -,I -N - -|||||| Hybrid Forecast Replenishment Process (Flexible Store Vnd Vendor Deployment System) n Retailer mecihar o EDI 856 ERoP #2 VM Aiate ass & (Turn Promno)Dal I nnventory Too #1bPangm To #2 - Masters VM (120 days) Deployments Sates storcatonlyteFAs SalesI plan (Msp) stor pMuFlcatls frast (3Cd if n (Iweek) Category fPro mto PO 4 FA red VI Analyst nt-trm t Feedback merchandising p e-o Pro a Shelf i merchandisingr0c be4ag e a t f as Inko (120 days) Sy management pricinge --- ne 0P Str Forecast S of xceptions (3w) Inventory Analyst (1A) s Soeit Figure 4.13 TO-BE Value Stream Map (Hybrid Forecast Replenishment Process - Flexible Store Deployment System) The TO-BE value stream map of the Hybrid Forecast Replenishment Process (Non-Flexible Store Deployment System) is depicted at Figure 4.14. This process is at the retailer DC-level. This is anymore created no forecast AS-IS as uce from the different dearmetwoldb Analyst ht ishForecast nodrt eog) frcstor the reason why the Forecast Analyst box in the TO-BE map is crossed out. The nonflexible store deployment system requires planning well in advance (one week earlier) of the operations for store replenishment (warehousing and trucking). Consequently, the Store Analysts would create a weekly store-level forecast. However, in order to replenish the DCs of the retailer, no extra forecast will be created. The store-level forecasts would be aggregated to form the weekly forecast for each DC (according to the DC that each store belongs). In order to succeed that, the Forecast Analyst department would be merged with the Store Analyst department. 63 Hybrid Forecast Replenishment Process - MAI 11 ~~~ ~ ~ ~ -- - - - Rtie S Rtorlesros Retailer Distribution Center ----- . store pulls - - - - - - - - - - - VenorVendor Distribution Center Plnt (Non-Flexible Store Deployment System) VMI PO ED 856 ERP #2 _____Tool VM_____P Tool #1FraSo #1cd & (Turn Promno) Daily Inventory - Too 1#2 Tool #1b Sale ek) (1w --- PCnora man geme t, p noajhngpoito isaedsn icSnl pt -- I n(etCr Anayst(sA Fleibe tsr Dplymet ysem Forecast RPlesn tP FlWekxmab we selected Slseai sbcuei sese oipeetwt The reason why get Pltis Dpriyngysm 44e Sa S the analysitimea foreca lst) Thiss st Forecast Replenishment Process (ir41)so hre Rtal-evl ntedofhe teore m inraesterepnies(wl e a v n a e ha td evn tugy cr a e senfo r s n s ammeii chllng, ~. herealernedst uneraksmecopoae rendsrctrig ftonle tferxsence loseajr angorecnsum since it tegest 4..f RtorrLeecarsstRpensmetPrcs Tedc fe Analysts -eai-rLee Foreas Re nlset Pr te the collabortinotese ssoFigae 4.5E svenss (Ti osr toa mre cnsuexiriente supplymehant sem)ugs forecasts) aTrs senaorptesnt af themrettier advtag 64 ine teeitne nese foree fo aeven tuaisnl fcrecas wia n be created downstream, i.e. by the retailer. Preferably, the forecast will be created in the retailer DC-level, i.e. partially following the Hybrid Forecast Replenishment Process (Flexible Store Deployment System). The major difference is that in this scenario the vendor will create no forecast at all. The vendor will receive the weekly forecast from the retailer. In the weekly consensus meetings, the vendor would give its input on the forecast and more specifically on the exceptions (promotional products and new launches), since the vendor knows how its products move in other retailers too. By and large, the retailer will create the POs with the critical input of the vendor for the exceptions. The reason why we selected this scenario is because it demands only a single forecast leading to a more synchronized supply chain. The retailer could feel more confident that the vendor will deliver (since the vendor has a real insight in the actual demand) as well as the vendor could better plan its inventory needs and production plan. The weekly consensus meetings are the heart of this scenario since they would facilitate the collaboration between the retailer and the vendor. In order for that collaboration to be as efficient as possible, a technological environment, common for both companies (probably a cloud based IT solution), would help both parties address the exceptions and finalize the forecast that the retailer would have created. The vendor should have access to the forecast at least one day prior to the consensus meeting. The major challenge is that the scenario requires a high degree of trust from the vendor side, since the vendor would not have the consumer demand data to analyze and challenge the base of the retailer's forecast. 65 Retailer-Level Forecast Replenishment Process nm DishionDistriRnte .Jdising - .- MA-L VMA Po st Frcso m h EDI 856 ERP #2 PO (no exceptis) EDI 8A55 dat i pricingon Venangemnt me Tool #1b PCangra Al I Masters Sales F (3C days) Pnetr Plan (MSP)z management, pricing, merchandising dpt EDI Analystde-A ared cromouV l O-BE alue Figure4.15 kags info tream -- - Ctgr Sales merchandisin l ns T m n Pro ess Re m Ma te(Retale r- e e Foreca EL *"rcas days) e20 i o 4..3Vedo-Lve Pot ED1 830 I te lForecast of Pocs Freat i orte exceptions (3w)epenshen flags InVentOry Analyst (1A) forecast---- between----- th reale-ndte-edo.Thssige-oecs threcreatio of-------only -one --Figure 4.15 TO-BE Value Stream Map (Retailer-Level Forecast Replenishment Process) 4.4.3 Vendor-Level Forecast Replenishment Process The Vendor-Level Forecast Replenishment Process (Figure 4.16) also suggests the creation of only one forecast between the retailer and the vendor. This single forecast will be created upstream, i.e. by the vendor. The retailer will create no forecast at all. This is the reason why in the TO-BE value stream map both the Forecast Analyst and Store Analyst departmnents are crossed out. The vendor's VMI Analyst team would create the weekly forecast and send to the retailer. In the weekly consensus meeting the vendor will receive the input of the retailer's analysts concerning any forecast adaptations that may be necessary since the retailer is the one that knows the promotional plan of the competitors' products sold in its stores. This scenario also incorporates the benefits of a single forecast in the supply chain and gives as well insightful visibility to the vendor concerning its products throughout the 66 entire supply chain. In reality, this scenario suggests a real VMI process in which the vendor is responsible for the majority of the decision-making. The scenario does not require new major technological investments since the retailer and the vendor already utilize VMI technologies. In this scenario the consensus meeting is again the main key for success. Therefore, a new technological investment would be meaningful to help facilitate the discussions there. This technological investment would be a forecast sharing tool that will enable the retailer to be informed for the forecasts generated by the vendor at least one day in advance of the consensus meeting. The scenario creates also the important challenge of trust from the retailer side that the vendor would manage fairly the retailer's inventory and fulfill the retailer's service levels. Vendor-Level Forecast Replenishment Process Rtie S Retailer Distribution Center VenorVendor Distribution Plnt Center -b E RP #2 EDi3, EDI 856 VMI Po (Turn Tol#1c dal f ng Too #1a & Cos e Rtores Prm) Demand & I 1inventory -uToo 1#2 T B VuSe #1b Masters Sales Rem Plan (MsP) VMStore 0 (3C days) Promo PO Sales (I week) Promo --- - merchandising dpt fnfo VMVI Analyst Category m ngemenrt, pricingg 4 ek)Shelf Cashier Asoit 8 merchandising Long-term forecast Input0 Feedback rd flagsAsOat Inventory Analyst (1A) Figure 4.16 TO-BE Value Stream Map (Vendor-Level Forecast Replenishment Process) 67 4.5 Roadmap towards a Consumer-Oriented Supply Chain The ultimate goal of this blueprint is to present to the retailer and the vendor a path towards a consumer oriented supply chain. As already described, the way we view a consumer oriented supply chain is the utilization of consumer-demand driven data (POS or store pulls) to generate a collaboration process between the retailer and the vendor in order to achieve a responsive and efficient replenishment. The roadmap towards a consumer oriented supply chain consists of short-term as well as long-term steps and is depicted in Figure 4.17. 68 AS- Executive-level engagement E ~0 Executive-level engagement NO Do not use POS data to forecast NO Establish Goals & KPIs Document & KPIs Document collaboration process Establish Collaboration (VMI, Technologies etc.) Study: Is POS data forecasting more lccurate? YES Use POS data to forecast Study: Is the store eployment syste YES flexible? Hybrid Forecast (Flexible Store Deployment System) Hybrid Forecast (Non-Flexible Store Deployment System) --- ------------------------CL Vendor's E Vendor-Level ------------------------------- Study: hich forecast i better per produc category Retailer's NO Traditional DC Operation Retailer-Level Forecast Forecast 0 POS /data/ Study: Could vendor's mixin enters prepare level orders?stor e> YES Direct Deliveries and/or Retailer's DC Cross-Dock Fans Figure 4.17 The Roadmap towards a Consumer Oriented Supply Chain 69 4.5.1 Short-term Steps The main concept of the short-term steps is that the retailer and the vendor will need to establish a clear vision of future collaboration. This vision will affect the decisions that are about to be made in the short and the long run. Our proposal is that in the short run the retailer and the vendor should target to implement the Hybrid Forecast Replenishment Process as described in section 4.4.1. The Hybrid Forecast is the closest to the existing process of forecasting and replenishment. Organizational and operational changes would not be that radical for both companies and both will be able to realize the benefits of the Hybrid Process. The following table describes all the different elements of the short-term roadmap. Roadmap Element As-Is Establish Goals &KPIs Establish Collaboration (VMl, Technologies etc.) Study: Is POS data forecasting more accurate acre? Description of Element The improvements will start from the AS-IS state as described in section 4.1. The first step is to establish the goals for the improvement process. Top management from the retailer and the vendor must engage. KPIs will also be formed to evaluate the improvements. All decisions will be dumente for future reference. The second step is to establish an initial collaboration. VMI is a very good process that reveals if the retailer and the vendor can effectively collaborate. The retailer and the vendor should also examine the compatibility between their IT systems and identify future technological needs. All findings should be documented for executive level review and future reference. The third step is to perform a study in order to identify if the use of POS data (and/or store pulls) improves the forecast accuracy. This is because the use of those data increases the complexity and the effort to forecast and the companies should be sure that would benefit from it. The proposed study would compare the forecast accuracy of existing forecasts with future POS data driven forecasts for a long period of time. 70 Use POS data to forecast Do not use POS data to forecast tudy: Is the store eployment syste flexible? Hybrid Forecast (Flexible Store Deployment System) If the study reveals better accuracy, the use of POS data (or store pulls) would be suggested for both companies. If the study reveals no better accuracy, the use of DC level data should continue. The forth step is to perform a study in order to identify if the deployment (store replenishment) system is flexible enough so as not to need store level forecasts to plan operations well in advance. The desirable situation is for the retailer to have a flexible deployment system (easiness of truck schedule flips). If the study reveals flexibility, then the retailer and the vendor should implement the Hybrid Forecast Replenishment Process (as described in Figure 4.13). Hybrid Forecast (Non-Flexible Store Deployment System) 4.5.2 If the study does not reveal flexibility, then the retailer and the vendor should implement the Hybrid Forecast Replenishment Process (as described in Figure 4.14). Long-term Steps The Hybrid Process is ideal to progressively move into the future of a consumer driven replenishment between the retailer and the vendor. However, in that future, only the retailer or the vendor would be the one completely responsible to generate the forecast of a product category. The following table describes all the different elements of the longterm roadmap. Description of Element Roadmap Element Study: hich forecast i better per produc category? The fifth step (first in the long-term steps) is to perform a study to identify per product category the company that makes a more accurate forecast in the long run. The proposed study would compare the forecast accuracy of previous forecasts of the retailer and the vendor in different product categories for a long period of time (more than a year). 71 For the product categories that the retailer had a more accurate forecast, the two companies should implement the Retailer-Level Replenishment Process (as described in Figure 4.15). the product categories that the vendor had a more accurate forecast, the two companies should implement the Vendor-Level Replenishment Process (as described in Figure 4.16). The sixth step (second in the long-term steps) is to perform a Retailer-Level Forecast Vendor-Level Forecast 1For Study: Could vendor's mixin centers prepare store- study to identify what requirements the preparation of store level orders? there is a significant return of investment (ROI) for the vendor to offer such a service and if there is real benefit for the retailer. If the study is positive, then the vendor could prepare in its mixing centers shipments ready for direct store deliveries or ready to be cross docked in the retailer's DCs and then shipped to stores together with products from other vendors. This would create a more responsive replenishment. If the study is negative, the supply chain between the retailer Direct Deliveries and/or Retailer's DC Cross-Dock Traditional DC Operation orders in the mixing centers of the vendor creates and if I T and the vendor would continue with its traditional format, i.e. the vendor would ship full truckloads to the retailer and the retailer would pick the store orders and distribute accordingly. 72 5 Conclusion The question posed in this thesis was: How is a consumer oriented supply chain defined and in which way the retailer and the vendor could move towards it in the future? As a result of our research, we conclude that a consumer oriented supply chain consists of two elements. The first one is that a consumer oriented supply chain is triggered, in most of the cases, by consumer demand data (POS or store pulls). The second element is that a consumer oriented supply chain requires a strong collaboration between the retailer and the vendor, so together they can achieve a more efficient response to the consumer demand. This collaboration takes place in terms of people, processes and technology. For the technology aspect there are a lot of software solutions that exist in the market. The biggest challenge is how people in both companies would establish and follow the right processes by utilizing this technology. Based on our research, the forecasting process is a significant element of the desired collaboration. A single, synchronized forecasting of the consumer demand would help both companies operate in a more synchronized and collaborative way. This forecast would be the one and only truth that the companies would believe to plan their operations. Therefore, the ultimate suggestion is that only the retailer or the vendor would create the forecast based on which the replenishment process would take place in each product category. Based on our analysis of forecasts and replenishment shipments of the retailer and the vendor, we can conclude that as forecasts use information closer to the consumer, responsiveness and accuracy are increased. In order to move towards a consumer oriented supply chain we propose a roadmap with short term and long term steps that the companies need to follow. In each major step of the roadmap, a data-driven study needs to take place in order to justify the respective change. These 73 studies are a reference for future projects and research for the two companies. The most crucial studies include in what extend the use of POS data improve the accuracy of the forecasts and which company performs a more accurate forecast per product category. 74 6 References Ali, M. M., & Boylan, J. E. (2010). 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