Flexibility in storage assignment in an e-commerce fulfilment environment

Transcription

1 Eindhoven, August 2013 Flexibility in storage assignment in an e-commerce fulfilment environment by Tessa Brand BSc Industrial Engineering TU/e 2011 Student identity number in partial fulfilment of the requirements for the degree of Master of Science in Operations Management and Logistics Supervisors: dr.ir. R.A.C.M. Broekmeulen, TU/e, OPAC dr.ir. H.P.G. van Ooijen, TU/e, OPAC ir. A.A.C. Koomen, Docdata N.V.

2 TUE. School of Industrial Engineering. Series Master Theses Operations Management and Logistics Subject headings: storage assignment, flexibility, fulfilment, e-commerce ii

3 Abstract This master thesis analyses the effect of flexibility within storage assignment on pick performance and space utilization within a fulfilment environment. Simulation showed that an increased level of flexibility can result in cost savings for both space and labour costs. In addition, it is shown flexibility is especially profitable for an environment characterized by high demand and large assortments. iii

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5 Management summary This research considers a manually operated order fulfilment environment within the e-commerce sector. Recent trends in this sector show a steady growth of the entire e-commerce market and a big increase in the product assortments of online retailers. In combination with seasonality in demand, these trends have their reflection on the fulfilment operations since they put a strain on storage capacity and order pick performance. In this project, flexibility is considered as a solution to deal with these changes. Problem definition The aim of the research is to design a storage strategy, which is able to deal with dynamics in product assortment and demand in order to remain cost effective. The central research assignment is therefore: Design a cost effective storage strategy considering flexibility in storage assignment in order to handle a dynamic product assortment within an e-fulfilment environment? Research design Flexibility is defined as a two dimensional construct with the dimensions compactness and multiple locations, as illustrated by table 1. Compactness is the possibility of storing multiple Stock Keeping Units (SKUs) on one location while the dimension of multiple locations refers to the possibility of storing a single SKU on multiple locations. However, the possible levels of flexibility are restricted by location capacity and base stock levels. Compactness 1 SKU per location Multiple SKUs per location Multiple locations 1 location/aisle per SKU Multiple locations/aisles per SKU Table 1: various levels of flexibility Applying this construct of flexibility to storage assignment results in the four policies which are evaluated in this project: Current storage assignment strategy In which and Compact storage assignment strategy In which and Multiple locations storage assignment strategy In which and Combined storage assignment strategy In which and The performance of these strategies expressed in picking time and space needed. The relation between the storage assignment policies and performance is assessed by the use of simulation. Basic warehouse operations are simulated like inbound activities, incoming customer orders, order batching and picking, in a one block warehouse. Apart from the effect of different levels of flexibility on performance, various scenarios are assessed regarding assortment and demand. Results First of all, a new batching algorithm called the alternative algorithm is introduced. The most important attribute of this algorithm is that it incorporates the possibility of choosing between different locations. Results showed that the alternative batching algorithm always outperforms the Docdata algorithm. Attention is therefore focused on the performance of the Alternative algorithm. For lying locations, locations with a fixed location size, it appeared increasing compactness is beneficial. Figure 1 illustrates the performance in costs per day of the Alternative batching algorithm when the number of locations per SKU (y) is fixed and equal to 1. It is shown both space and labour v

6 Time in minutes Costs per day in euros costs decrease as the number of SKUs per location decrease. As can be seen, space costs decrease as x increases since space costs are linearly related to the number of aisles in use. It seems compactness is of high importance for space costs. In addition, labour costs profit from compact storage as well. Figure 2 shows the composition of time needed per day in minutes for y=2, as the number of SKUs per location increase. It appears the decrease in walking time outweighs the extra time needed for searching up until a certain point. However, the decrease of walking time stagnates while search time is linearly related to the value of x. This points to an optimal value of x at 5 in which total time needed is minimized. After x=5, additional search time exceeds time gains from walk time. Although the biggest cost savings result from increasing x from 1 to 2, it appears that lowest costs for the base scenario are reached at x=4 and y= Costs per day Total costs Labour costs Space costs Number of SKUs per location (x) Figure 1: costs per day in euros for the alternative algorithm, when y=1 Time needed per day Number of SKUs per location (x) Total time Walk time Search time Figure 2: time needed per day for the alternative algorithm, when y=2 For lying location, locations with variable location size, it appeared increasing the number of locations per SKU is beneficial. Figure 3 illustrates the costs per day in euros for varying values of y and x=1. Since space costs are constant, the difference in total costs are caused by changes in labour costs. Although the biggest cost saving results from increasing y from 1 to 2, it is shown that lowest costs are reached at x=1 and y=4. Theoretical contribution Part of the theoretical contribution of the project is the application of flexibility within storage assignment. In this research, a quantitative two dimensional construct of flexibility is developed. In vi

7 Costs per day in euros general, for lying locations the most cost effective storage assignment strategy is the compact strategy. The best performing level of flexibility is reached by increasing compactness (x) up until its optimal value. For hanging locations, the most cost effective storage assignment strategy is the multiple locations strategy. The best performing level of flexibility is reached by increasing the multiple locations dimension (y), as much as possible. In addition, the effect of demand and assortment on the performance of flexibility is evaluated. Scenario analysis on both average order size and the number of orders per day showed that flexibility is extra beneficial when demand increases. Apart from demand, it appeared demand distribution has its impact on the performance of flexibility within storage as well. It appeared flexibility leads to lower total costs when demand is Pareto distributed compared to a uniform distributed demand. Regarding assortment, results showed that increasing flexibility is especially beneficial for a large number of SKUs. For base stock levels it is shown that lower base stock levels lead to a better performance. This is especially the case when flexibility is high, i.e. high number of SKUs per location for lying locations and maximum number of locations per SKU for hanging locations. However, order acceptance should not suffer from lowering base stock levels. In sum, it is shown the most cost effective storage assignment strategies for either lying and hanging locations remain unchanged during changes in assortment and demand. Moreover, it is shown the storage assignment strategies become more profitable when assortment or demand increases. Another theoretical contribution is the alternative batching algorithm constructed in this project. The existing batching algorithm of Docdata does not utilizes the possibility of multiple locations per SKU. In order to benefit from the multiple locations dimension, a batching algorithm that does utilize this possibility is necessary. The alternative batching algorithm provides an algorithm that does take the property of multiple locations into account. 200 Costs per day Total costs Labour costs Space costs Number of locations per SKU (y) Figure 3: costs per day in euros for the alternative algorithm, when y=1 Practical contribution Practical usefulness of the project lies in the possible costs savings that can be achieved by changing the storage assignment strategy. As concluded, compactness of storage is of high importance for lying locations. Therefore, it would be recommendable for these locations to increase the fill rate of locations. For inbound activities, it is thus advisable to store multiple SKUs on a single location. Even though space costs might be fixed in the short run, labour costs can be saved with compact storage as well. For hanging locations, it is advisable to spread SKUs out over the aisles in use rather than store a SKU on a single location. Increasing the number of locations per SKU (y) can improve batches and reduce walking routes. It must be noted however that it may induce extra costs for inbound activities to store SKUs on multiple locations instead of a single location. It goes without saying that the extra vii

8 costs for inbound activities should not exceed the benefit that can be gained from spreading SKUs over multiple locations. In order to benefit most from a flexible storage strategy, a batching algorithm that does utilize the possibility of choosing between different locations is necessary. In this project, the Alternative batching algorithm was introduced. However, this is just one of the possible algorithms that could be used. The dynamic character of assortment and demand makes it important to adapt the storage approach to the changing conditions. This report showed flexibility is especially beneficial for situations in which demand is high and assortment is large. As seen during the case study, it is expected demand will increase in the future, just as assortment size and average order size. The bigger the growth of these variables, the more profitable flexibility within storage seems to be. It is therefore advisable to increase flexibility in order to be better able to handle future scenarios. Based on the case study, it is known that during sales periods, assortments size, the number of orders and average order size increase. In addition, it appears the gap between fast and slow movers increases during these periods. A small part of the assortment experiences high demand. The case study showed that flexibility is more profitable during these periods. Therefore, it is especially profitable to increase the level of flexibility prior to the start of sale periods. Again, this requires a suitable batching algorithm. viii

9 Preface This is it. With finalizing my master project, my student life has come to an end. I experienced my time at university as really enjoyable and educational. It is sometimes said that student life is one of the best periods in your life and to this, I can only agree. Finishing my studies is not something I reached all by myself and therefore, I would like to thank some people. I would like to thank my university supervisors. First of all, I want to thank Rob Broekmeulen for his devoted guidance and support during my project. It was a pleasure to work with him and there was always room for feedback or discussion. I have learned a lot and I really appreciated his helpfulness during the entire project. In addition, I would like to thank Henny van Ooijen for his feedback and advice. The project was performed at Docdata. In this regard, I would like to thank Docdata for providing me the opportunity to perform my research project. Especially, I would like to thank Astrid Koomen, my supervisor of Docdata, for all the time and effort she put into helping and guiding me during the project. I would also like to thank all my colleagues at Docdata for their help, feedback and discussion. Special thanks go to my parents, who enabled me to study and provided the freedom to explore and evolve activities I wanted to pursue besides my studies. But moreover, I want to thank them for their love and support. Furthermore, I would like to thank my boyfriend for all the good times and of course his everlasting mental support. Last but not least I want to thank my friends, the ones I already knew before and the new ones I made during my time at university. These people made my time as a student unforgettable. I enjoyed every piece of it and I would not have missed one single moment. Tessa Brand Eindhoven, July 2013 ix

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11 Contents Abstract... iii Management summary... v Preface... ix 1. Introduction Company description Market characteristics & developments Project scope Report structure Literature review Flexibility Warehousing Picking Batching Storage Process description and modelling Order fulfilment process Replenishment Storage assignment policy Research assignment Research design Design parameters Batching heuristics Docdata heuristic Alternative heuristic Key performance indicators Simulation model Experimental setting Validity & reliability Hypothesis & experimental design Numerical example Scenario analysis Sensitivity analysis Case study Results & discussion Numerical example Lying locations Hanging locations Summary Scenario analysis xi

12 6.2.1 Assortment Demand Summary Sensitivity analysis Layout Batching Cost parameters Summary Case study Docdata Scenario Worst case Medium case Best case Sales period Sales period December Summary Conclusion Theoretical findings Practical recommendations Limitations References Appendix A: List of abbreviations Appendix B: Figures Appendix C: Tables Appendix D: Flowcharts Appendix E: Pseudo code xii

13 1. Introduction This research was performed at DOCDATA N.V. and concerns the order fulfilment activities in the e- commerce sector. Section 1.1 provides a company description. Section 1.2 sketches the market characteristics and developments. The scope of the project is defined in section Company description DOCDATA N.V. consists of two divisions, IAI Industrial Systems and Docdata, as illustrated in Appendix B1. In 2012, the turnover of division Docdata was 142,8 million, which is 24% higher than in 2011, with an average employment of 966 FTE (Docdata annual report 2012). The Docdata division which is a logistic service provider (LSP) and takes care of storage and handling of the products sold online plays an important role as it is the main revenue generator of the company. In the Netherlands, three activities in the field of e-commerce are performed, as described in appendix C1. This research is concerned with Docdata Fulfilment, hereafter referred to as Docdata. In Waalwijk, the Netherlands, multiple warehouses of Docdata are located in which the products of different companies are stored and handled. Docdata performs logistic activities for companies in different sectors, consisting of fashion, consumer electronics, games, books, furniture, CD/DVD/Blu-ray, toys, software, jewellery and finance. The world of e-commerce is changing at a fast rate and Docdata needs to be at the forefront in order to remain successful. This is why Docdata needs to invest continuously in its services ( Docdata Corporate, n.d.). Since Docdata is a service provider to which companies can outsource some activities, it is important to remain an attractive partner. To stay in business, Docdata strives for a good value proposition and cost effectiveness. 1.2 Market characteristics & developments The market of e-commerce has some typical characteristics influencing fulfilment activities. First of all, demand for online retailers looks differently from demand faced by offline retailers. The e- commerce sector is characterized by several demand characteristics, which are summarized below (Tarn et al. 2003). Higher number of order transactions Order sizes tend to be small, one or just a few SKUs per order High probability of significant fluctuation in customer demand Seasonality in customer demand The composition of the product assortment is another typical issue related to e-commerce. In general, the product assortment of an online shop is quite large. This is related to the principle of the long tail, described by Anderson (2006) and illustrated by figure 1.1. The principle is based on a Pareto demand distribution in which typically 20% of the assortment accounts for 80% of the revenue. In general, fast movers are suitable for physical stores, whereas slow movers are more suitable for an online channel. (Agatz et al., 2008, p348). With the long tail, Anderson (2006) highlights the importance of slow movers. Collectively, these low demand products add up and contribute significantly to the total market share of a retailer. Despite attempts of retailers to expand assortment in physical stores by increasing the storage density (Ketzenberg, 2000), the principle of the long tail is especially popular for the e-commerce market. The reason for this is that this sector does not face the limitations of geography and scale that retailers do (Anderson, 2006, p24). Put differently, online channels may achieve strong pooling effects and thereby can afford a broader assortment than physical stores (Agatz et al., 2008, p342). Applying this principle of the long tail to practice is the rationale behind the decision of retailers to broaden their online assortment and thereby increasing the tail. This expansion of the online product assortment is a relatively new and fast growing trend in the e-commerce market. Taking a look into the e-commerce market of the Netherlands, the biggest players clearly follow this trend. RFS Holland Holding, the biggest online retailer of the Netherlands, expanded their online 1

14 jan-10 mrt-10 mei-10 jul-10 sep-10 nov-10 jan-11 mrt-11 mei-11 jul-11 sep-11 nov-11 jan-12 mrt-12 mei-12 jul-12 sep-12 nov-12 # SKUs product assortment as well as Bol.com ( Bol.com opent twee nieuwe speziaalzaken met alles voor huisje, boompje, beestje, 2013) and Albert.nl ( Albert Heijn more than doubles online offering, 2013), respectively the second and third largest retailer of the Netherlands and both owned by Ahold. Figure 1-1: The long tail (source: Anderson, 2006). In addition, the e-commerce market is experiencing a big growth ever since its introduction. Sales of the e-commerce sector have been grown tremendously over the last years (Chen & Dubinsky, 2003). This expansion in sales is attributable to the growing number of businesses and individuals who are able to access the internet (Gunasekeran et al., 2002). Average number of SKUs on stock Figure 1-2: The number of SKUs on stock for one of Docdata s customers, period In sum, e-commerce retailers are facing both dynamic demand and a dynamic product assortment. Not surprisingly, this has its reflection on logistics. Increasing the long tail leads to an increase in the number of Stock Keeping Units (SKUs), defined as an item of stock that is completely specified as to function, style, size, color, and, usually, location (Silver et al., 1998, p32), within the warehouse. This is the case for one of Docdata s customers, which is subject of the case study. Figure 1-2 illustrates the number of SKUs stocked in the warehouse for this customer, measured at the end of each week, considering the period As can be seen, there is a clear trend of an increasing number of SKUs over the years. The average number of SKUs stocked in the warehouse doubled between 2010 and In addition, two peaks can be identified yearly caused by special sales periods. On top of that, the stock levels increased with 0,4 items per SKU between 2010 and Not surprisingly, a growth in both assortment size and base stock levels lead to an increase in space needed for storage. Products in the tail, i.e. the slow movers, have a relatively low demand and are kept in stock for a relatively long period compared to fast movers. Costs for storage are therefore quite 2

15 high compared to costs for handling. Apart from increased space rent costs, a bigger warehouse leads to longer picking tours and thereby increased labour costs. Moreover, dynamics in demand and assortment require flexibility in order to be able to handle these dynamics. Since Docdata is a LSP, it is their responsibility to manage warehousing operations of their customers. One of the challenges for Docdata is to find suitable ways to deal with the changes and dynamics of their customers while maintaining cost effectiveness. 1.3 Project scope The circumstances of the fulfilment process of one of Docdata s customers will be used as a guideline in this project. Within this warehouse environment, there are several interesting aspects which could be suitable for research. Examples are batching, picking and storage. The focus of this project will be on flexibility related to storage assignment. The rationale behind this decision is that expansion of assortment is a relative new trend in the Dutch e-commerce sector, believed to have a big impact on space and labour costs, as discussed in previous section. The effect of flexibility in storage assignment, on both pick performance and space utilization will be examined. The goal of this research is to provide insights in the relation between these aspects. Since it is crucial for LSPs to remain cost effective, the cost aspect is taken into account. Scope of project Goods delivered Storage & handling of goods Goods picked up Suppliers Docdata Carriers Figure 1-3: Flow of goods & actors involved. Figure 1-3 provides an overview of the flow of goods together with the actors involved at Docdata. The boundaries of the project are represented by the dotted line. Since Docdata is a third party LSP, the responsibility of Docdata starts at the moment goods are delivered in Waalwijk and stops when the goods are picked up by the carriers. Together with Docdata, it is agreed to first consider internal flexibility, before involving other parties and considering flexibility throughout the supply chain. The scope of the project will therefore correspond with the responsibilities. In the end, the entire supply chain determines the total output of delivered products. The distribution chain with the lowest output is restrictive for the entire output of the chain. As mentioned, the focus for this project is on the internal processes of Docdata. Flexibility of other links in the chain is out of the scope and therefore, it is assumed that this will not be a restriction to the total system. Since the output during the peak period is determined by the output of Docdata, this is considered to be a realistic assumption. 1.4 Report structure This report is structured as follows. In section 2, relevant literature related to flexibility and storage is discussed. Subsequently, a process description is provided in section 3. Section 4 outlines the research assignment. The research design is described in section 5, while the results and discussions are described in section 6. The case study of Docdata is described in section 7 and finally, the conclusions and recommendations are provided in section 8. 3

16 2. Literature review Given the trend in e-commerce of increasing assortments, dynamic demand and the fact that fulfilment activities in general do not know economies of scale, this raises the question: how can flexibility be used in order to deal with a dynamic product assortment within storage assignment?. This question will be central in this literature review. Flexibility literature is discussed in order to review the existing research regarding this topic. Warehousing literature is discussed to provide insights in basic warehousing constructs and the interaction between these constructs. 2.1 Flexibility A great amount of research has been devoted to the subject of flexibility. Flexibility is important to accommodate changes in the operating environment. (Gupta & Goyal, 1989, p119). According to Sethi and Sethi (1990) flexibility of a system is its adaptability to a wide range of possible environments that it may encounter. A flexible system must be capable of changing in order to deal with a changing environment. (Sethi & Sethi, 1990, p295). Taken together, flexibility can be seen as the ability to cope with a changing environment. Given the scope of the project, an internal view of flexibility is highlighted in this section. Manufacturing flexibility deals with the internal flexibility of a company. Various different definitions of manufacturing flexibility are used in literature. In their research, Swamidass and Newell (1987) consider manufacturing flexibility as helpful in coping with environmental uncertainty (Swamidass & Newell, 1987, p512). The definition of Sethi and Sethi (1990) highly resembles this definition and define flexibility of a system as its adaptability to a wide range of possible environments that it may encounter (Sethi & Sethi, 1990, p295). Just as supply chain flexibility, manufacturing flexibility is concerned as a multi-dimensional construct. A well-known research on manufacturing flexibility is the one of Browne et al. (1984). They categorized flexibility in eight different types: machine, process, product, routing, volume, expansion, operation and production flexibility. The research of Sethi and Sethi (1990) builds on this model of Browne et al. (1984) and add three new types of flexibility to the existing model: material handling, program and market flexibility. Vokurka and O Learly-Kelly (2000) add another four dimensions: automation, labour, delivery and new design flexibility. An overview of all dimensions and their definition is provided by appendix B4. Despite the fact that the research of the authors mentioned in this section build on the same constructs of Browne et al. (1984), there is no consensus about the relationships between the various dimensions of flexibility. Where Browne et al. (1984) describe a more strict and hierarchical relationship, Sethi and Sethi (1990) assume more interrelatedness between the dimensions of flexibility. Although manufacturing flexibility is seen as inter-firm focused, some research consider exogenous factors. Vokurka and O Learly-Kelly (2000) discuss both the direct role of the exogenous factors strategy, organizational attributes, technology and environmental factors on manufacturing flexibility, and their role as moderating variable in the relationship between manufacturing flexibility and firm performance. D Souza and Williams (2000) make a distinction between externally and internally driven dimensions of manufacturing flexibility, in which externally driven dimensions are employed for meeting market needs. Labelled as exogenous or not, it appears the content of the dimensions differ not that much from the ones summarized in appendix B4. Research considered so far is fairly qualitative since the amount of research with a qualitative approach is considerable compared to the amount of research with a quantitative viewpoint. Although D Souza and Williams (2000) attempted to convert the qualitative constructs of flexibility into measurable dimensions, qualitative research on flexibility is scarce. An example of a more quantitative approach is the work of Jordan and Graves (1995). In their research on process flexibility, they consider decisions on which products are to be built at which plants (Jordan & Graves, 1995, p577). Within certain boundaries, two points of flexibility are defined, with dedicated production concerned as no flexibility, and total flexibility as each plant is able to build all products. Within these boundaries, different levels of flexibility are defined by the number of links between products and plants. The principle of chaining, defined as a group of products and plants which are all connected, directly or indirectly, by product assignment decisions (Jordan & Graves, 1995, p580) turned out to 4

17 yield most of the benefits of total flexibility. Although quantitative research in the area of flexibility is limited, this research shows that it is possible to quantity flexibility. In addition, the implications of the results of Jordan & Graves (1995) can be of high relevance for production environments. This example reveals the possibilities and usefulness quantitative research can offer for literature. The research of Jordan and Graves (1995) can thus function as an inspiration or starting point to further explore flexibility issues from a quantitative perspective. Although multiple authors attempted to construct a clear and unambiguous qualitative or quantitative definition of flexibility, it does not seem one of these definitions have maintained in literature. It can thus be concluded that there is no generally accepted or general definition for flexibility. It appears flexibility is used as an umbrella concept, without a clearly delineated definition. This shows that flexibility is apparently seen as a subjective construct, which can be defined by using different components. Furthermore, a gap in literature can be detected in the area of fulfilment research. Although in literature fulfilment is often approached as a special type of manufacturing, fulfilment in e-commerce has its unique characteristics, as discussed in section 1.2. Further research on flexibility in the area of order fulfilment can bring new insights. 2.2 Warehousing In this section, literature regarding warehousing is considered, since storage is not something which can be studied in isolation. Aspects like picking and batching can influence the effect of storage on performance. Decisions made at the various levels are strongly interdependent. For example, a certain layout of storage assignment may perform well for certain routing strategies, but poorly for others. (De Koster et al., 2007, p489). Therefore these topics will be covered to illustrate their conjunction with storage. In addition, a typical e-fulfilment environment is characterized by piece-picking activities (Tarn et al. 2003). Bulk storage and broken case picking are not very common in such an environment. The literature considered subsequently is therefore not focused on storage assignment for bulk and broken case picking storage Picking According to De Koster et al. (2007), over 80% of all order-picking systems in Western Europe are manually employed, low-level, picker-to-parts order picking systems. Therefore, subsequent literature will mainly be focused on manually employed, picker-to-parts order picking systems. An important aspect of picking, related to storage assignment and performance, is routing. According to Roodbergen (2001), routing policies can be organized according to two categories: optimal algorithms and heuristics. Optimal algorithms determine the shortest route of all possibilities. Heuristics are used to determine a feasible route, which is not necessarily the shortest one. Roodbergen (2001) describes different routing heuristics, which are illustrated in appendix B5. Apart from routing, the specific picking policy plays an important role in the picking process as well. According to De Koster et al. (2007), there are basically two main strategies: single order picking and order batching. As the name implies, orders are picked individually using single order picking. This method is used when orders are considerable large. Order batching is used when orders are small and combines a set of orders in one single picking tour. The principle of batching will be considered in more detail in subsequent section. In case orders are batched, the products must be sorted by order as well. This can be done during the picking process, denoted as a sort while pick method (Roodbergen & Vis, 2006). Sorting can also be performed separated from picking, and if then performed after picking has been finished Batching Since order sizes tend to be small in the e-commerce sector (Tarn et al., 2003), batching is often used within e-fulfillment. Batch picking, is designed to reduce the average travel time per order by sharing a pick tour with other orders (Choe & Sharp, 1991). In general, batching methods can be divided into two groups: time window batching and proximity batching. Time window batching means that each order is assigned to a batch, based on the time window in which the order has arrived. Orders arriving 5

18 during the same time interval, or time window, are grouped as a batch and processed simultaneously. Using proximity batching, each order is assigned to a batch, based on proximity of its location to the locations of other orders. In conjunction with calculating proximities, a sequencing rule for visiting the different locations must be assumed. Within proximity batching, there are two types of solution procedures (De Koster et al., 2007): savings algorithm and seed algorithm. Savings algorithms are based on the algorithm for the vehicle routing problem, as described by Clarke and Wright (1964), in which small tours are combined into larger tours. Seed algorithms constructing batches in two phases: seed selection and order congruency. Seed selection defines the order in which orders are batched while order congruency determines which order is added to the current batch. For both seed and savings algorithms, De Koster et al. (1999b) describe several guidelines Storage A storage assignment method is a set of rules which can be used to assign products to storage locations. (De Koster et al., 2007, p490). Goetschalckx and Ratliff (1990) distinguish two major classes of storage policies: dedicated and shared storage. Using dedicated storage results in a situation in which a particular storage location is reserved for one SKU during the entire planning horizon. In a shared storage method, it is allowed to store different SKUs successively. The article of De Koster et al. (2007) provides an overview of several types of storage assignment: Random storage: The location of an incoming item is selected randomly from all eligible empty locations with equal probability. Closest open location storage: The location of an incoming item is the first empty location that is encountered by the employee. Dedicated storage: The location of an incoming item is reserved and fixed. Each SKU has its own dedicated location. Full turnover storage: The location of an incoming item is determined by the turnover of the item. SKUs with the highest sales rates are located at the easiest accessible locations. Class based storage: The location of an incoming item is determined by the class to which the item is assigned. Classes are made based on demand measurements of SKUs, and are assigned to a dedicated area in the warehouse. Family grouping: The location of an incoming item is determined by the relation with other items. SKUs with related demand are located in the same area. Within class based storage, another layout related aspect influencing pick performance is the storage implementation strategy. The storage implementation strategy is the specific way in which classes are assigned to the available storage locations. Petersen et al. (2004) consider four different storage implementation strategies, as illustrated by appendix B6. De Koster et al. (2007) consider two storage implementation strategies, as illustrated in appendix B7. Although the location of the depot is different, the within aisle strategy of appendix B7 corresponds to appendix B6. The across aisle storage strategy of appendix B7 is just another variation of the same storage principle. In addition to these storage implementation strategies, there are several ways to reduce the total storage space needed. De Koster et al. (1999a) mention three methods: using pallet storage, improve efficiency of use of storage locations and improve cooperation and coordination with suppliers. The second method can be executed in various ways, like storing multiple SKUs per location (De Koster et al., 1999a) or subdividing normal location into multiple compartments (Van den Berg & Zijm, 1999). In an automated environment, compact storage systems are considered more often, like in the research of De Koster et al. (2008). A substantial amount of research has been devoted to evaluate the performance of various storage assignment methods. Appendix C1 provides an overview of important research articles. The research of Hausman, et al. (1976), focused on optimal storage assignment in warehouses using stacker cranes, revealed that travel times can be reduced by using class based storage rather than the closest open location policy. Supplementary to this, the research of Graves et al. (1977), considering a similar situation, shows that class based storage policies requires more rack locations, and thereby more warehouse space, than a random storage policy. The findings of Choe and Sharp (1991) confirm these 6

19 findings and argue that random storage results in high space utilization and increased travel time. Dedicated storage on the other hand, appears to result in underutilization of space and big savings in travel time. The findings of Petersen and Aase (2004) also confirm these findings for a manual warehouse environment. They concluded that the same holds for both class based storage and volume based storage. However, both methods require periodic relocation of SKUs to keep up with changing demand and increase picker congestion within aisles in which the most popular SKUs are stored. The study of Petersen et al. (2004) on the effect of class based storage and volume based storage revealed that volume based storage performs slightly better than class based storage. However, the performance gap between class based storage and volume based storage decreases as the number of storage classes increases (Petersen et al., 2004, p542). The findings of all this research point in the same direction. Therefore, it can be concluded that for both manual and automated operated warehouses, class based storage and volume based storage requires more rack locations, has therefore a lower space utilization but requires less travel times compared to random storage. The applicability of class based and volume base storage methods for manual operated warehouses can be questioned, since relocation is a time consuming and expensive activity in such environments. In addition, both methods require appropriate and up to date demand information. Goetschalckx and Ratliff (1990) nuance previous findings by stating that the amount of space reduction resulting from shared policies depends on the balance of input and output. This balance is taken into account since their research considers the time needed for both storage and retrieval in an automated picker-to-parts environment. The simulation study showed that unit load duration of stay outperforms average product turnover. In contrast, the conclusions of Kulturel et al. (1999) point in the opposite direction. This research considers a comparable environment and uses both storage and retrieval time as a performance measure as well. The difference, however, is the way in which the duration of stay method is applied in the model. Kulturel et al. (1999) consider a 2-class shared storage system for duration of stay where Goetschalckx and Ratliff (1990) consider a strictly duration of stay storage method without any classes. It appeared the duration of stay method is very sensitive for the use of classification. In addition, the size of the storage zones is of high importance and care must be taken on this point when comparing results for different storage methods. Again, the applicability can be questioned of both turnover based and duration of stay based methods for the same reasons as for class based and volume base storage methods. Especially since the success of turnover based and duration of stay based methods depend highly on the number of categories and thereby the precision and availability of demand and product information. Although most research shares roughly the same conclusions, it appeared that the specific way in which a method is executed is of high importance to the results. Therefore, the profitability of specific storage methods may vary for different situations. In addition, some methods put high requirements on the information systems. Given the contradicting conclusions of for example Goetschalckx and Ratliff (1990) and Kulturel et al. (1999), it is hard to generalize the findings of various researches. Special attention must be paid to the specific situation in which the storage method is applied. Given the large amount of research that has been performed for evaluating different storage methods, it seems there is not much space for providing new insights. However, existing research assume a single SKU is stored on solely one location. We found no literature in which the possibility of storing single SKUs on multiple locations is concerned. This provides room for a contribution to existing literature. In addition, little attention has been paid to the interaction between the methods to reduce storage space and pick performance. Given the strong growth of the e-commerce sector and the trend of growing product assortments, this is a relevant topic to research. Lastly, what is missing is the integration between flexibility on the one hand and storage assignment on the other hand. Given the specific characteristics and developments of the e-commerce sector as described in section 1.2, fulfilment activities in this sector differ greatly from fulfilment activities in other sectors. Since the e-commerce market is fairly dynamic, combining flexibility and storage assignment provides an opportunity to bring new insights for literature. 7

20 3. Process description and modelling In this section, the entire process of order fulfilment is described and modelled, in order to improve understanding of the current situation at Docdata and to explore possibilities for the future. The process description is based on the order fulfilment process of one of Docdata s customers. It is believed that the resulting process model is representative for the fulfilment process of a LSP in general. 3.1 Order fulfilment process The order fulfilment process is driven by the online orders, received by the webshop of the LSP s customer 24 hours per day. For a LSP, the inventory control is out of scope and it is assumed that each online order can be fulfilled with the on-hand inventory in the warehouse (no backlog). This correspond with only accepting orders for SKU s which have sufficient inventory. Several times a day, an order run is carried out. During an order run, all orders that are placed in the web shop since the last order run are retrieved. By the use of order runs, some sort of time window batching is applied. This time window batching is necessary due to one of the service level agreements the LSP has agreed upon with the customer. Directly after an order run is carried out, batches are created to reduce the average travel time per order by sharing a pick tour with other orders (Choe & Sharp, 1991). A batch is thus a combination of orders, which are picked together in one picking tour. In their study, Gademann and Van de Velde (2005) proved that the batching of orders is an NP hard problem. This implies that the batching problem of this research is NP hard as well. Using heuristics is therefore justified. The input of a batching heuristic consists of all the orders in the order run and the current storage locations of the SKU s in the warehouse. Since this research investigates flexibility in storage assignment, the two different heuristics for batching will be tested. A manually operated warehouse is assumed at the LSP, with a picker-to-parts order picking system in which operators are guided by a scanner. Only individual items/cases are picked and are loaded on the picker cart during the picking process. Given the high number of picking locations per aisle after the application of the batching heuristic, a traversal walking strategy can be used, which is defined as any aisle containing at least one pick is traversed entirely. Aisles without picks are not entered. (De Koster et al., 2007, p199). This strategy is also denoted as the S-shape method. An overview of the outbound part of the fulfilment process is given in figure 3.1. Incoming customer orders Order run Batching Picking Figure 3-1: Fulfilment process 3.2 Replenishment The total fulfilment process consists of two sub processes: the outbound process described above and the inbound process that is responsible for replenishment of the inventory. Although the outbound and inbound processes sometimes overlap each other, this happens rarely. Therefore, it is assumed that the inbound activities are finished before the outbound activities are started and thereby outbound and inbound can be approached as two separated sub processes. It is assumed that the inbound process starts at the beginning of the working day, while the outbound process starts at the end of the afternoon. 8

21 The inbound process is driven by the replenishment of the inventory. Since the inventory control is out of scope for the LSP, it is assumed that all picked items are replenished. This resembles a periodic review, base stock (R,S) policy with zero lead-time. In this policy, stock levels are checked periodically with a fixed interval R. If the stock level has dropped below base stock level S, the amount needed to restore this level back to S is ordered. This strategy is considered because of its simplicity to model. It is assumed that the periodic review period R is equal to 1 day. The adoption of this policy means that the replenishment quantities are equal to what is picking during that day. The products are delivered before the inbound processes start, such that the inventory levels for all products are restored to their reorder levels. 3.3 Storage assignment policy All incoming products need to be assigned to a location within the warehouse. A distinction is made between two different types of storage locations: lying locations and hanging locations. Lying locations are locations with a fixed location size. This means location size is independent of the number of items that are stored on the location. Lying locations are often used in practice to store various types of products. For the hanging location type it is assumed that locations have a variable size in order to keep the amount of space needed to store all products constant. The assumption of variable location size might seem unusual. However it is very common to use hanging locations in practice, especially for storing apparel. A storage assignment policy is a set of rules to determine the assignment of products to a location. As seen during the literature study, several storage assignment policies are common: - Random storage - Closest open location storage - Dedicated storage - Full turnover storage - Class based storage - Family grouping The four latter methods rely on product and sales information. Given the fact that assortments for online retailers change quite frequently, at least four times a year with the change of the season, this information is often not available. These methods are therefore not suitable for this project. This leaves random storage and closest open location storage as possible assignment strategies. However the random storage policy will only work in a computer-controlled environment. If order pickers can choose the location for storage themselves, this would probably result in a system known as closest open location storage (De Koster et al., 2007, p491). This implies that the random storage method only exists in theory. A closest open location strategy better resembles a practical situation, which was also encountered at Docdata. It is therefore chosen to focus on the closest open location strategy for storage assignment. To recall, the definition of a closest open location strategy is as follows: the location of an incoming item is the first empty location that is encountered by the employee. Within the closest open location storage assignment method, several options can be considered as well. (1, 1) One SKU per location and single location per SKU: this is the basic closest open location policy. Each SKU has a single location in the warehouse and each location can store one SKU at a time (but multiple items, depending on the capacity of the location). (x, 1) Multiple (x) SKU per location and single location per SKU: in this option it is allowed that different SKUs are stored on the same location, restricted by the capacity of the location. (1, y) One SKU per location and multiple (y) locations per SKU: in this option it is allowed to use alternative storage locations for a SKU. (x, y) Multiple (x) SKU per location and multiple (y) locations per SKU: this option combines both flexibility measures. Increasing the number of locations per SKU creates the possibility of choosing between different locations to visit for picking. It can be imagined that by having multiple locations, the construction of 9

22 batches can be improved and walking routes can be reduced. On the other hand, increasing the number of locations per SKU increases the required number of locations and therefore the space needed for storage, which in turn results in higher space and labour costs. Given these contradicting expectations regarding costs, it is interesting to research the effects of storing a single SKU on multiple locations. Storing a single SKU on multiple locations is regarded as a dimension of flexibility that is researched in this project. Another dimension of storage flexibility is the number of different SKUs per location. As mentioned previously, the big increase in assortment leads to an increase in space needed and thereby in higher space and labour costs. Allowing multiple SKUs per location leads to compacter storage and can provide a good solution for this issue. Allowing multiple SKUs on a single location can increase the number of items stored on one location, i.e. increase occupation rates of locations. An occupation rate of a location is defined as the number of items stored on a location divided by the number of items that can be stored on a location. Compacter storage can result in lower space costs by using less space for the same assortment. In addition, walking routes can be shortened which saves labour costs. However, increasing the number of SKUs per location introduces time needed for searching, which increases labour costs. Although it is not sure what the effect on labour costs is, allowing multiple SKUs to be stored on a single location can provide a solution to the volume problem introduced by growing assortments. The dimension of compactness is therefore a dimension of flexibility that is researched in this project as well. In the last option, the dimensions multiple locations and compactness are combined. As mentioned, only increasing the number of locations per SKU is expected to lead to an increase in space needed. The dimension of compactness can counter this effect by reducing the space needed for storage. A combination might bring the best of both worlds, by reducing total space needed with compactness and improving batching by introducing multiple locations. This possibility is therefore taken into account in the research as well. By introducing storage of multiple SKUs on one location, some additional time is needed for searching during the order fulfilment process. It is assumed the number of search actions depend on the number of SKUs that are present at the location. Although in practice order pickers might know which SKU they are searching for by information provided by the scanner, this is not the case for all operators. For simplicity, it is assumed productivity of order pickers is equal for all operators. Therefore, it is assumed operators do not know which SKU they are looking for. It is assumed they randomly grab and scan a SKU in order to check if it is the correct one. If this is not the case, they grab and scan another SKU until they find the right one. The expected number of search actions can thus be determined by approaching this searching process as a hyper geometric distribution. In this section, the fulfilment process of a LSP active in the e-commerce sector is described and moddeled. It is argued that both the batching heuristic and the storage assignment policy can have an effect on the operating costs of the LSP. 10

23 4. Research assignment LSPs in the e-commerce sector have to deal with increasing assortments of their customers and to stay competitive at the same time. This raises the question how the increase in assortment volume can be managed. On top of that, seasonality in demand requires flexibility within the operation in order to deal with these dynamics. Therefore, the aim of the research is to design a storage strategy, which is able to deal with dynamics in product assortment and demand in order to remain cost effective. The literature review of section 2 revealed a gap in existing literature regarding flexibility in the area of fulfilment research. What is missing is the integration between flexibility on the one hand and storage assignment on the other hand. The main question of this research is therefore defined as follows: How can flexibility be used in order to deal with a dynamic product assortment within storage assignment? Since a clear quantitative construct of flexibility is missing in existing literature, the first sub question is focused on the definition of flexibility. 1. What are different levels of flexibility within storage assignment? Given the importance of cost effectiveness for LSPs, the cost and benefits of flexibility need to be defined as well. 2. What are the benefits related to these levels of flexibility? 3. What are the costs related to these levels of flexibility? Subsequently, the construct of flexibility is applied to a warehouse environment. In order to provide basic insights, a numerical example is used to which the construct of flexibility is applied. 4. What is the most cost effective level of flexibility for a numerical example? Section 1 showed that assortments in the e-commerce sector can be quite dynamic. Besides the change in assortment because of seasonality, assortment size is subject to change as well. In addition, demand is characterized by strong seasonality. It is thus interesting to take a look at different scenarios regarding assortment and demand. 5. How does this level change in response to different scenarios regarding assortment? 6. How does this level change in response to different scenarios regarding demand? Finally, it can be useful to translate the theoretical findings to a practice. A case study based on one of the customers of Docdata is used to illustrate the practical usefulness of flexibility within storage assignment. 7. How should flexibility within storage assignment be applied to the case of Docdata? Summarizing, the project assignment is as follows. Design a cost effective storage strategy considering flexibility in storage assignment in order to handle a dynamic product assortment within an e-fulfilment environment? 11

24 5. Research design Based on the process described in section 3, a model is constructed which is used to research the question posed in section 4. In this section, the first the design parameters are introduced, which answers subquestion 1. Next, the key performance indicators are defined that are needed to answer subquestion 2 and 3. The batching heuristics are described in subsection 5.3. The simulation model and experimental setting are given in subsection 5.4. Section 5.5 introduces the hypothesis and the experimental design. In order to provide basic insights, a numerical example is defined to which the construct of flexibility is applied. The order fulfilment process at a LSP is a stochastic system, since the LSP has to deal with stochastic orders. Since finding an optimal solution is not possible for a stochastic system, the effect of several storage assignment strategies on costs are researched using simulation. Simulation is used because of its common use in literature regarding warehouse performance. In addition, a simulation model offers the possibility to include realistic aspects of the process, in contrast to a strict mathematical analysis which requires simplification. Since dynamics and seasonality in demand and assortment are crucial aspects in this research, preference is given to simulation as research method, in which these aspects can be taken into account. Based on the process model introduced in section 3, it is postulated that at the start of each order run, all online orders are known as well as the storage locations of all SKUs under the given storage assignment strategy. Since the order batching problem that is part of the fulfilment process is NP-hard, the two batching heuristics are tested as well. The following notation is used in this section. v Length of the time horizon [days] Working day with Length of an aisle, measured from the centre of one cross aisle to the centre of the other cross aisle [m] Distance between two adjacent aisles [m] Walking speed [m/s] Time needed for 1 search action in seconds Total set of batches during the time horizon in simulation run j Number of unique aisles visited on the pick tour of batch with Highest aisle number visited on the pick tour of batch with Total number of pick orders of batch with Number of SKU s in batch with Number of different SKUs present on the selected location of SKU of batch with Maximum number of aisles in use during time horizon in simulation run j Total average costs [ /day] Labour costs [ /s] Space costs [ /aisle.day] Total time needed for walking in simulation run j [s] Total time needed for searching in simulation run j [s] 5.1 Design parameters In this project, the parameters of the storage assignment strategy are the decision variables for which the best values that result in minimum expected costs need to be find. Given the research assignment, the goal of this research is to investigate flexibility in storage assignment. Two different dimensions of flexibility are used in this project to apply flexibility to storage. Inspired by the research of Jordan and Graves (1995), a definition of storage flexibility is constructed. They define different levels of flexibility based on the number of links between the production plants on the one hand and the products to be produced on the other hand. Production plants can be used to produce a number of products and products can be produced in a number of production plants. Translating this to storage, 12

25 storage locations can be used to store a number of SKUs, and SKUs can be stored in a number of storage locations. Flexibility in storage can thus be seen as a combination of two different dimensions: compactness and multiple locations. Starting with the first dimension compactness, flexibility is solely related to the number of items that can be stored on one location. For this dimension, the following options are identified. Option A: Locations are suitable to store multiple items of only one SKU Option B: Locations are suitable to store multiple items of multiple SKUs, with being the number of different SKUs that can be stored on one location One new storage assignment strategy is thus to increase the value of from 1 to larger values. This parameter x is denoted as the compact storage assignment parameter. The second dimension, multiple locations, is solely related to the number of locations on which one SKU is stored. Looking only at this dimension, flexibility can be seen as a continuum with two extremes: no flexibility and full flexibility. No flexibility: all items of a SKU are stored on one location, at a time. The location of a SKU can change over time, since locations are not dedicated to one product. Full flexibility: all items of a SKU are stored on locations, at a time. One SKU will thus be spread out and stored on multiple locations. In the most extreme form of full flexibility, each SKU is stored on each location. However, storing each SKU on each location is not desirable from a practical perspective. Given the relative large assortment in a typical e-commerce environment, it is not possible to store each SKU on each location and thus store the entire assortment on each location. In this study, a traversal routing method is assumed. Full flexibility in this project can thus be reached by storing each SKU within each aisle. Therefore, this definition will be used for full flexibility in this project. However, the maximum number of allocated aisles of one SKU is limited by the number of items of that SKU stocked within the warehouse. Between the two extremes of no flexibility and full flexibility of the dimension multiple locations, intermediate levels of flexibility can be defined to further specify flexibility. In line with the method of Jordan and Graves (1995), the subsequent level of flexibility after no flexibility, can be reached by adding one more aisle to the number of aisles in which one SKU is stored. Several subsequent intermediate levels of flexibility can be defined according to this principle, with being the number of different aisles used to store one SKU and being the total number of aisles within the warehouse. These levels are visualized in appendix B8. Another new storage assignment strategy is thus to increase the value of from 1 to larger values. The parameter y is denoted as the multiple locations storage assignment parameter. Combining the dimensions multiple locations and compactness is a possibility as well. This is denoted as the combined storage assignment strategy. Flexibility is hereafter seen as a combination of the two parameters. Combining these two dimensions results in the options presented in table 5.1, presenting respectively the number of SKUs per location ( ) and the number of locations per SKU ( ). For this new construct of flexibility, the situation described by (1,1) in table 5.1 can be seen as the lower bound of flexibility, or no flexibility. The situation in which y is equal to the base stock level, and x is equal to the location capacity, can be seen as the upper bound of flexibility, or full flexibility. Referring back to sub question 1, What are different levels of flexibility within storage assignment?, the answer is provided by table 5.1. Compactness 1 SKU per location Multiple SKUs per location Multiple locations 1 location/aisle per SKU Multiple locations/aisles per SKU Table 5-1: various levels of flexibility 13

26 Applying this new construct of flexibility to storage assignment leads to four storage assignment strategies that are evaluated in this project. Current storage assignment strategy, in which and Compact storage assignment strategy, in which and Multiple locations storage assignment strategy, in which and Combined storage assignment strategy, in which and 5.2 Batching heuristics In this research, the two different batching heuristics are investigated. The first, called the Docdata heuristic, is based on the current procedure at Docdata. The second, alternative heuristic is developed to take better advantage of the multiple storage locations (y>1). A batching procedure has as input a set of online orders which belong to a order run and the location of the SKU s in the warehouse at the start of the order run. The output is a set of batch pick locations, the number of aisles visited and the highest aisle number visited. In both heuristics a seed algorithm is used. Research of De Koster et al. (1999b) showed that a seed algorithm performs best in combination with a traversal routing method while a savings algorithm is better suitable when using the largest gap routing strategy. Since a traversal routing strategy is assumed, it is chosen to use a seed algorithm. Next, the heuristics use order congruency to add orders to the seed orders. The adding of orders continues until the limit of number of items within one batch is reached. This is a practical restriction since picker carts have a limited capacity. In addition, customer orders are kept intact and are not split in different batches for both methods Docdata heuristic This algorithm is based on a seed algorithm, and thus constructs batches in two phases: seed selection and order congruency. Both seed selection and order congruency are based on the first come, first serve (FCFS) rule. In case of multiple locations of a SKU, a selection rule is used which selects the location which is closest to the I/O punt, i.e. the lowest location number. In other words, a location with a low aisle number will have priority above one with a higher aisle number. In summary, the following rules are applied: o Seed selection: FCFS o Order congruency: FCFS o Location selection: proximity to I/O point Reflecting on the Docdata algorithm, a few comments can be made. First of all, the use of the FCFS policy can be criticized. Although from a customer perspective it might seem fair to process orders according to the time they are placed in the web shop, it is of no practical use. Docdata has agreed to process all orders placed in the web shop before a certain cut-off time, and have them ready to get picked up by the carrier at a certain time later that day. The FCFS rule is thus of no special or practical use. In addition, significant time savings can be achieved by changing a FCFS batching rule for other simple order batching rules, according to De Koster et al. (1999b). This provides a reason to change the order of seed selection and order congruency. Regarding the location selection rule, a drawback is that locations within the picking route are not taken into account. It would shorten walking routes if the location is selected closest to the existing walking route. This is a reason to change the current clustering algorithm. Despite the large amount of research related to batching algorithms, literature regarding a situation in which single SKUs are stored on multiple locations is lacking. Based on these drawbacks, a new algorithm is developed for this project, which is denoted as the Alternative algorithm Alternative heuristic This algorithm is an alternative version of the Docdata algorithm and is based on a seed algorithm as well. For each SKU, the centroid value is calculated. For all orders, the average of all centroid values of the SKUs are determined. Both seed order selection and order congruency is based on this average 14

27 value, in which the order with the lowest value is selected. For all SKUs, the location which adds the least distance to the walking route is selected. Orders are batched until the limit of number of items within one batch is reached. In addition, customer orders are kept intact and are not split in different batches for both methods. In summary, the following rules are applied: o Seed selection: order with the lowest centroid value o Order congruency: order with the lowest centroid value o Location selection: location which adds the least extra distance to the walking route Both seed selection and order congruency is based on the average centroid value of the orders. By using the centroid value, orders are batched such that the average locations of SKUs are close to each other. This method is chosen to introduce batching based on proximities between pick locations. The decision to base location selection on the extra distance added to the walking route is used to reduce the length of pick tours. 5.3 Key performance indicators In order to evaluate the different settings of the storage assignment parameters, the cost and benefits need to be examined. As noted in section 2.2.3, an extensive amount of research has been devoted to evaluate various storage assignment methods. This evaluation is based on the trade-off between space utilization and pick performance. In line with existing literature, performance will be assessed using the following Key Performance Indicators (KPI s) as output variables: Pick performance; this will be assessed by the amount of time used for walking and searching Space utilization; this will be assessed by the amount of aisles in use These output variables depend on the simulation run, since each run has a different demand stream. The total time needed for walking is assumed to be linear to the travel distance. The time needed for walking can be divided in the time needed to move within the aisles and the time needed to move between the aisles. The traversal picking strategy, described in the process model, assumes that if an aisle is entered, it is walked entirely. The number of unique aisles visited in a picking tour of one batch is therefore multiplied by the length of an aisle. In case the unique number of aisles is an odd number, the last aisle is travelled two times in order to end in the correct cross aisle, at the level of the I/O point. To incorporate this situation, the number of visited aisles are first divided by two, rounded up to the first integer and multiplied by two again. The distance travelled between the aisles depends on the aisle within the pick tour furthest from the I/O point. The total time needed for walking in simulation run j is described by. (( ) ( )) (1) The values for the number of unique aisles visited on the pick tour of batch ( ) and the highest aisle number ( ) depend on the way in which batching is organized. The total time needed for searching depends on the expected number of search actions for each item to pick. This, in turn, depends on the number of orders, the number of items of the orders, the location which is visited for each item and the number of different SKUs on this location. The expected number of search actions can be determined by approaching this searching process as a hyper geometric distribution. An example is used to illustrate this distribution. Assume item of order needs to be picked which is located on a location which stores 4 different SKUs ( ). The probability that the first item that is grabbed is the correct one is equal to, because all SKUs have an equal probability of being grabbed. The probability that two search actions are needed is equal to, because the first search action must result in the incorrect SKU (probability of ) and the second search action among the remaining SKUs must result in the correct SKU (probability of ). Proceeding, the probability that three search actions are needed is equal to 15

28 and the probability that four search actions are needed is equal to. The probability of needing one, two, three or four search actions is thus equal to one divided by the number of SKUs present on the location ( ). The formula for the total time needed for searching during simulation run j is expressed by ( ) (2) As can be seen, the expected number of search actions per item is reduced by one, as only the additional search time is considered. In the original situation (x=1 and y=1), the expected search actions are equal to one, since there is only one SKU located at each location. Because only the additional search time is considered in this research, the number of expected search actions per item is reduced by one. Finally, the total time needed is divided by the number of working days that are considered in the time horizon to get the average time needed for searching per working day. The total number of square meters in use is assumed to be linear with the number of aisles in use. Space costs therefore depend on the maximum number of aisles that are used for storage on a day. Space costs are often fixed in the short run, and cannot be varied on a daily basis. Costs for space rent are therefore based on the maximum number of aisles in use during the entire time horizon that is considered during simulation run j. The trade-off between space utilization and pick performance will be expressed in costs. This is done because the two KPI s cannot be compared directly. Converting performance to costs makes it possible to evaluate an assignment strategy in total, i.e. on both aspects simultaneously. In addition, costs aspects are taken into account because of its relevance for practice. Labour costs depend on the time needed for walking and the time needed for searching. The total cost function consists of costs for labour and costs for space. A simulation with horizon, is repeated for times. Total average daily costs is then the average value of all repetitions j. (3) ( ( ) ) (4) In sum, these KPI s and cost drivers will be used to assess the benefits and costs related to the different levels of flexibility as defined in previous section. 5.4 Simulation model In this section, the simulation model is introduced, which is based on the process model described in section 3. Second, the validity and reliability of the model are discussed Experimental setting The processes that are modelled are based on the process description of section 3. Design choices made for the simulation model are based on this discussion. In this section, a summary of the design choices are given. Layout The simulation model considers a one-block warehouse as illustrated in appendix B9. A few parameters are assigned a value, like the length of an aisle, the distance between two adjacent aisles, 16

29 the number of locations within an aisle and the maximum number of items per locations. Furthermore, it is assumed that: - All aisles are identical - Aisles can be accessed from both sides and can be travelled in both directions - No bulk storage is used - All storage locations are identical - A storage location is suitable to store all types of SKUs Replenishment Products are ordered at the end of the day and are delivered the next morning before inbound activities start. The products ordered are equal to what is demanded during that day. This resembles a (R,S) policy, with and. In the base scenario, the base stock levels S are assumed to be equal to 3, since this resembles the average stock levels in practice. Storage assignment For the initial settings, all SKUs need to be assigned to locations. This process is described by the flowchart in appendix D1 and pseudo code in appendix E1. The process of inbound activities is described by the flowchart as presented in appendix D2 and the pseudo code as presented in appendix E2. In addition, it is chosen that: - Storage assignment and inbound activities are based on the closest open location method - The level of compactness ( ) is assigned a value - The level of multiple locations ( ) is assigned a value - Multiple locations of one SKU cannot be located within the same aisle - Storage assignment and inbound activities are finished before outbound starts Incoming customer orders The arrival of demand is assumed to be distributed according to a Poisson distribution, with an average number of order arrivals per day. The Poisson distribution is assumed because it is a common distribution used in literature to model order arrivals. The arrival process is of importance because Docdata works with order runs. In short, the use of order runs creates some sort of time window batching. The demand among the assortment has a distribution as well. Historical data does not provide sufficient information to determine this distribution during different periods of the year. Therefore, two common distributions are considered in this project: Uniform distribution All SKUs have an equal probability of being demanded. This distribution is assumed because it a simple and clear distribution. In addition, a uniform distribution makes verification of the model possible. Two-level distribution As noted in section 1.2, the principle of the long tail is grounded in a Pareto distributed demand. Based on this, a two-level distribution is applied in the project. SKUs are divided into two groups: fast movers and slow movers. The group of fast movers consist of a minority of the SKUs which accounts for a majority of the total sales. The group of slow movers consist of the rest of the SKUs and accounts for the rest of the total sales. In a typical Pareto distribution, the group of fast movers consist of 20% of all SKUs, which account for 80% of the sales. In a two level distribution, the size of the fast movers group can be assigned a value, just as the amount of sales this group accounts for. This two-level distribution is considered since it resembles reality as discussed in section 1.2. Order size is assumed to be distributed uniformly and dicreet, since it only possible to order discrete products. If the average order size is equal to 2.5, the order size of a random order can be equal to 1, 2, 3 or 4 items with equal probability. Order run Five times a day, an order run is carried out. Appendix D3 and E3 describe respectively the flow chart and pseudo code for the order run. 17

30 Batching Two different batching algorithms are used: the Docdata algorithm and the alternative algorithm. For both algorithms, the batching process is an iterative process consisting of two steps: seed selection and order congruency. This process is illustrated by the flow chart in appendix D4 and the pseudo code in appendix E4. The Docdata algorithm is described by the flowchart in appendices D5 and D6, and pseudo codes in appendices E5 and E6. The alternative algorithm is displayed in appendices D7, D8, E7 and E8. The minimum number of items within one batch is set to 15. Picking - A traversal walking strategy is used - Search time is hyper geometric distributed - All order pickers are identical and have the same productivity Validity & reliability Validity is checked in several ways. Both during the model construction and afterwards, individual modules are checked manually to verify whether they are doing what was intended. In addition, the validity of the entire model is checked by simulating certain situations which can easily be verified. For example, the situation in which the entire assortment can be stored in one aisle, for different values of y. Reliability is checked by determining the run length and the number of repetitions. First, the run length is based on the regular interval an assortment in changed. In the retail sector, assortments often change together with the change of the seasons four times a year. A period of 3 months (65 working days) is thereby the maximum time horizon to consider. Taking a run length of this maximum time horizon of 65 days, variance cannot be further reduced. It is therefore chosen to use a run length of 65 working days and simulate an entire season. To further improve reliability, each simulation will be replicated 10 times. The simulation model is written in Visual Basic for Applications (VBA) and simulation runs are executed in Microsoft Access. On average, the simulation model takes 10 minutes to perform 10 replications of a simulation run of 65 working days, for x=1, y=1 using the Docdata algorithm, with a 95% confidence interval of [ 158,2; 159,0] (Law & Kelton, 1982). 5.5 Hypothesis & experimental design The assignment of this research is to design a cost effective storage strategy considering flexibility in storage assignment in order to handle a dynamic product assortment within an e-fulfilment environment. In addition, few sub questions are formulated. The hypotheses formulated in this section are constructed to assess first the effect of the flexibility dimensions on several KPI s and second to help answer sub question 4. The scenario analysis is constructed to provide answers to sub questions 5 and Numerical example First of all, the effect of various flexible storage assignment strategies on the output variables and total costs are researched. With hypothesis I, II and III, the effect of the flexibility dimensions on operations are researched. I. Increasing compactness (x) a. will lead to a decrease in space in use b. will lead to a decrease in walking time c. will lead to an increase in searching time Starting with the first hypothesis (Ia), it is expected that increasing the number of SKUs per location leads to an increase in fill rates of the locations, i.e. more items are stored on one location. This leads 18

31 to a decrease in the number of locations needed for storage, and thereby the number of aisles needed. This decrease in space in use is expected to lead to shorter walking routes and thereby to a decrease in walking time (Ib). However, increasing the number of SKUs per location results in additional time needed for searching (Ic). II. Increasing multiple locations (y) a. will lead to an increase in space in use b. will lead to a decrease in walking time c. will have no effect on searching time Increasing the number of locations per SKU is expected to lead to an increase in space needed (IIa). Despite this, it is expected to lead to a decrease in walking times (IIb) as well. The possibility to choose between different locations can lead to an improvement of batches. Given the increase in space needed for storage, the picking route may be located further from the I/O point. However, the distance travelled between the first and last pick might be reduced. In total, it is expected to reduce walking distances and thereby walking time. Increasing the dimension multiple locations is expected to have no effect on search time (IIc), since the number of SKUs per location is unaltered. III. Increasing compactness (x) and multiple locations (y) a. will have no effect on space in use b. will lead to a decrease in walking time c. will lead to an increase in searching time Increasing both compactness and multiple locations is expected to have no effect on space in use (IIIa). However, this hypothesis only holds when both x and y are increased with the same rate, e.g. from x=1, y=1 to x=2, y=2. The increase in space needed when increasing y from 1 to 2, is compensated by allowing more SKUs to be stored on one location when increasing x from 1 to 2. Increasing both x and y is expected to lead to a decrease in walking time (IIIb). This decrease is expected to be a result of improved batches that can be achieved by increasing the number of locations per SKU. Total searching time is expected to increase, as a result of an increase in the number of SKUs per locations (IIIc). Hypotheses I, II and III are tested with a numerical example, which is illustrated in appendix C3. Settings and proportions of the numerical example are inspired by the case. Time and cost parameters are based on industry standards and are displayed in respectively appendixes C4 and C5. For the numerical example, the performance of various values for x and y are researched. Among the different storage strategies, the most cost effective strategy is determined Scenario analysis Second, the effect of dynamics regarding assortment and demand are researched. As noted in section 1, the e-commerce market is characterized by dynamic assortments and seasonality in demand. Therefore, a scenario analysis is performed in order to assess the effect of changes in assortment and demand on the performance of the different storage assignment strategies. This section will therefore help in providing answers to sub questions 5 and 6, how does the most cost effective storage strategy for a numerical example change in response to different scenarios regarding assortment and demand?. In the scenario analysis, several demand and assortment parameters are changed one by one while keeping other parameters constant, to research their individual impact on the KPI s and costs. For all parameters that are researched, the effect of different values of the parameter on the performance are considered, for both the best performing storage strategy and the current storage strategy. The effect of the assortment on performance is evaluated on two aspects: assortment size and base stock levels. Assortment size is researched because of the trend of growing assortments of online retailers as mentioned in section 1.2. Increasing the number of SKUs in the assortment leads to even bigger warehouses, and thereby increases the need for a different storage assignment approach. It is 19

32 therefore interesting to research the effect of assortment size on the performance. Looking at the expectations, on the one hand, a bigger assortment leads to a bigger warehouse, which results in longer walking routes and higher labour costs. On the other hand, it is expected bigger cost savings in space costs can be attained by increasing compactness because of a scale effect. This in turn would lead to time savings because walking routes can be shortened. These conflicting expectations make it difficult to formulate hypothesis for assortment size. The reason to consider base stock levels is that this parameter influence space needed for storage as well. Lowering base stock levels is expected to lead to a decrease in space costs, since less items need to be stored. Increasing base stock levels is expected to improve batches, since a single SKU can be stored on more locations. This could lead to shorter walking routes and thereby a decrease in labour costs. Given these two contradicting expectations, it is not clear what the effect of different base stock levels on total costs is. Demand is evaluated on three factors: average order size, the number of orders per day and demand distribution. Section 1.2 showed that seasonality in demand is very common in the e-commerce sector. The effect of average order size and the number of orders per day on the performance is therefore considered in the scenario analysis. Increasing total demand, either by average order size or the number of orders per day, contains ambivalent expectations as well. On the one hand, an increase in demand is expected to have a scale effect. Without adjusting base stock levels, space costs remain constant and only labour costs increase as demand increases. On the other hand, it might be the case that increasing the number of locations per SKU leads to improved batches when demand is relatively low. It can be imagined that the first batches of the day have very short routes because SKUs are located on multiple locations. However, if demand is high, the routes of the last batches of the day might be longer. The number of locations to choose from have been decreased and the central spots might have been taken by the earlier batches. Increasing demand might thus lead to longer walking routes and thereby higher labour costs. Apart from demand size, demand distribution is considered in the scenario analysis as well. In section 1.2, it is shown that demand can differ much between different SKUs. Fast movers do have a very high demand compared to slow movers. It is interesting to research how flexibility in storage performs when demand is Pareto distributed. Throughout the numerical example, it is assumed demand is distributed uniformly. However, it is more common that demand is distributed according to a Pareto distribution, especially for online retailers offering a long tail of products. A two-level distribution is considered to approach a more realistic environment. What the effect of a two-level demand distribution on performance is, is ambiguous as well. For the fast movers group, increasing the number of locations per SKU might lead to increased labour costs. Instead of visiting one location to pick all the items of one SKU, it might be the case that order pickers must visit multiple locations because there are not sufficient items on stock on one single location. This increases walking distance and thereby labour costs. For slow movers, the principle of multiple locations might be especially interesting because a slow mover can easily be added to a walking route without adding much extra walking distance. In sum, it appeared expectations regarding assortment and demand are contradictory, which makes it difficult to formulate clear hypothesis. The scenario analysis is thus used to explore the effect of assortment and demand on space in use, walk time, search time and total costs Sensitivity analysis In addition, sensitivity of the model to certain factors is tested. The factors considered in the sensitivity analysis are layout, batching decisions and costs. For the layout, the performance difference for a warehouse with many short aisles versus one with few long aisles is considered. Sensitivity of the model to batching decisions is evaluated on the number of order runs and batch sizes. In addition, sensitivity to cost factors is regarded as well. The goal of the sensitivity analysis is to verify the robustness of the results to the chosen parameters Case study In section 7, a case study based on the case of Docdata is performed. With this case, the practical usefulness is illustrated of the theoretical findings in section 6. Section 7 is focused on the application of the storage assignment policies to a practical situation. Three possible different scenarios of

33 are regarded, in order to map the possible cost savings for the future. In addition, several special sales periods are considered, in which assortment and demand characteristics are subject to big changes. During these sales periods, the effect of changes in assortment and demand on performance are considered simultaneously. It is also shown what the possible cost savings could be during these periods. For the case study, parameters are adjusted to the situation at Docdata. For confidential reasons, the values of these parameters are not displayed and results are expressed in percentages. For simplicity, it is assumed the warehouse layout is identical to the layout of the numerical example. 21

34 6. Results & discussion In this section, the results of the numerical example, the scenario analysis and sensitivity analysis are presented. 6.1 Numerical example The numerical example is used to research the hypothesis. In this section the effect of different storage assignment strategies on space in use, walk time, search time and total costs is considered. For the numerical example, the performance of various values for x and y are researched. Among the different storage strategies, the best performing strategy is determined. A distinction is made between two different types of storage locations: lying locations and hanging locations Lying locations For the first set of simulations, lying locations are considered. First, the effect of various storage strategies on space in use is considered. Table 6.1 illustrates the number of aisles in use for various values of x and y. For both the Docdata and alternative batching algorithm, the amount of space in use is equal, since the method to assign locations to SKUs is equal. As can be seen, some cells are blank. When y=1, x cannot have a bigger value than 4, given the base stock level of 3 and location capacity of 12 items. A blank cell thus indicate that a combination of values for x and y is not possible. Number of SKUs Number of locations per SKU (y) per location (x) Table 6-1: the maximum number of aisles in use Taking x=1, y=1 as a starting point, the number of aisles in use is equal to 10. In this situation, each location in use stores all 3 items of one single SKU. Table 6.1 shows that increasing x results in a decrease in the number of aisles in use and confirms hypothesis Ia. This result is not a surprise, since all locations have a capacity to store 12 items on which only 3 items are stored. Increasing x in this situation results in an increase in the fill rate of the locations because 6 items can be stored per location instead of 3. Less locations and thereby less aisles are needed to store the entire assortment. As can be seen, the biggest decrease results when changing x=1 to x=2. When increasing x from 1 to 2, fill rates of the locations are doubled and therefore this results in half of the aisles in use. When increasing x from 2 to 3 and from 3 to 4, fill rates are increased with respectively 50% and 33%. It can thus be concluded that the biggest decrease in aisles in use results from increasing x from 1 to 2. Further increasing x results in a decrease as well, up until a certain point after which it stagnates and the number of aisles in use cannot be further reduced. For the numerical example, this minimum value for the number of aisles in use 3. When y is equal to 1, x can have a maximum value of 4. On each location, 3 items of 4 SKUs can be stored. Given that the assortment size is and S=3, each aisle with 2000 locations can store 8000 SKUs. In total 2,5 aisles are needed to store the entire assortment. It is assumed it is not possible to rent half aisles so the minimum number of aisles needed for y=1 is equal to 3. Given S=3, the maximum value of y is 3. The minimum number of aisles for y=3 cannot be reduced further than y, since all SKUs need to be located in y different aisles. In sum, the minimum number of aisles in use is equal to 3 for the numerical example. Increasing x leads to a decrease in the number of aisles needed until this minimum value has been reached. Looking at the number of locations per SKU, it appears increasing y leads to an increase in aisles in use, just as expected and 22

35 Time in minutes stated in hypothesis IIa. Increasing y from 1 to 2 or 3 leads to a proportional increase in the number of aisles needed. Instead of storing a single SKU on one location, it is stored on two or three locations, which results in a doubling or tripling of the number of locations needed. Increasing x and y simultaneously is expected to have no effect on the number of aisles in use, as stated in hypothesis IIIa. Comparing x=1, y=1 to x=2, y=2 confirms this expectation. Comparing x=1, y=1 to x=3, y=3 does not confirm this expectation. The same holds for comparing x=2, y=1 to x=4, y=2 and x=6, y=3. Although the numbers do not differ much, they do not show an unambiguous picture. Appendix C6 shows the time needed for walking and for searching in minutes for respectively the Docdata and the alternative algorithm for lying locations. Comparing the two batching algorithms shows an average time saving of 28,3 minutes using the alternative algorithm instead of the Docdata algorithm. Of these 28,3 minutes, 23,6 minutes result from time savings in walk time and 4,6 minutes from time savings in search time. The time savings in walk time show that the alternative algorithm performs better than the Docdata one. It appears the batching rules of the alternative algorithm provide improved batches, i.e. batches with shorter walking routes Time needed per day Number of SKUs per location (x) Total time Walk time Search time Figure 6-1: time needed per day for the alternative algorithm when y=2 Figure 6.1 provides an overview of the time needed per day for the alternative algorithm when the number of locations per SKU is equal to 2 (y=2). It is chosen to consider this value for y, because x has a bigger maximum value when y=2 then when y=1. As can be seen, walk time is decreasing when the value of x is increasing, which confirms hypothesis Ib. The biggest decrease results when x increases from 1 to 2. After x=5, walk time seems to become stable. These results correspond to the number of aisles in use, displayed in table 6.1. This suggests time needed for walking is closely correlated to the number of aisles in use. Looking at the tables in appendix C6, it is shown walk time increases steadily when x=1 and y is increasing. Hypothesis IIb is thereby not confirmed. In contrast, instead of shortening walking routes, increasing the number of locations per SKU seems to increase walking routes. The finding does however strengthens the suggestion that time needed for walking is related to the number of aisles in use. Considering time needed for searching, a steady increase is shown when x increases, which confirms hypothesis Ic. This is not a surprise since search time is modelled as a hyper geometric distribution, depending on the number of SKUs present at a location. Increasing y should therefore have no effect on search actions. This is confirmed by the numbers in appendix C6, which confirms hypothesis IIc. The increase in search time when x increases is out weighted by the decrease in walk time for low values of x. After a certain point, time savings in walk time are smaller than time gains in search time. It appears the value for x has an optimal value at which total time needed is lowest. In this numerical example, this value of x is 5 when y=2. Figure 6.2 provides a bar chart displaying the time needed per day for three different settings, with settings (1): x=1, y=1, (2): x=2, y=2 and (3): x=3, y=3. The reason these values are chosen is that x and y are increased with equal values. If for example x=2, y=1 and x=4, y=2 are considered, the results might be 23

36 Time in minutes biased because of the possible strong effect of x compared to y. It is therefore chosen to increase x and y with equal steps. Setting 1 can be seen as the least flexible setting, while setting 3 can be considered the most flexible setting. As can be seen, increasing both x and y results in a decrease in walking time and an increase in searching time. These findings confirm the expectations IIIb and IIIc of section Time needed per day Total time Search time Walk time Figure 6-2: time needed per day, for the alternative algorithm, (1) x=1, y=1, (2) x=2, y=2, (3) x=3, y=3 Table 6.2 and 6.3 show the total costs per day in euros for respectively the Docdata and alternative batching algorithm. Although for some settings of x and y the total costs per day are equal for the two algorithms, the Docdata algorithm never outperforms the alternative algorithm. On average, the alternative algorithm saves 8,7 euro per day compared to the Docdata algorithm. Given that for each setting of x and y, the number of aisles in use are equal for both algorithms as mentioned previously, space costs are equal as well. Cost savings thus result from savings in labour costs, as could be expected based on the numbers in appendix C6, presenting the time needed for walking and searching. This is again suggesting that the batching rules of the alternative algorithm result in batches with shorter walking routes. Given that total costs is the sum of space costs and labour costs, the changes in total costs as presented in table 6.2 and 6.3 are the direct result of changes in the number of aisles in use and the total time needed per day as discussed previously in this section. Number of SKUs Number of locations per SKU (y) Number of SKUs Number of locations per SKU (y) per location (x) per location (x) ,6 236,9 317, ,5 225,0 304,6 2 97,5 136,1 176,1 2 88,7 127,0 167,2 3 78,6 109,1 143,6 3 75,3 105,4 138,1 4 75,0 97,6 120,9 4 68,4 90,7 114,6 5 68,9 88,6 5 68,6 86,4 6 73,7 93,2 6 73,3 90,8 7 96,2 97,9 7 76,2 95, ,5 102,3 8 77,7 98, ,9 105,5 9 78,7 101, ,1 83, ,2 83,1 Table 6-2: total costs per day in euros, for the Docdata Table 6-3: total costs per day in euros, for the alternative algorithm algorithm Although the alternative algorithm slightly outperforms the Docdata algorithm, the performance of both algorithms show overall similarity. For a given value of x, it appears increasing y solely leads to an increase in costs. The situation in which x=10, y=3 for the Docdata algorithm seems to be an exception to this. The low number of aisles in use for this setting seems to be the explanation for this low value. In general, it seems increasing y for lying locations does not bring any benefits at all. In 24

37 Costs in euros contrast, it is shown increasing x does bring cost savings. Figure 6.3 displays the cost structure for the alternative algorithm when y=1 for different values of x. As can be seen, labour costs account on average for about 62,8% of the total costs for these values of x and y. In this model, only variable labour activities needed for picking are considered. For both picking and other activities several aspects are not taken into account. For picking, activities like accelerating, slowing down, searching for the right location, collecting the pick list and picker cart are not taken into account. Other activities are left out of the model as well, just as activities like inbound, sorting and packing. Despite the simplification of the model, the number of 62,8% does not differ much from the standard in literature of picking activities accounting for 55% of all warehouse expenses (De Koster et al., 2007). Biggest cost savings are attainted when x is increased from 1 to 2, since this causes the biggest savings in both space costs and labour costs. Further increasing x results in additional costs savings as well, albeit somewhat smaller. Looking at tables 6.2 and 6.3, it appears it is not profitable to increase x to high values, because the number of aisles in use become stable and search time keeps on increasing. Costs per day Number of SKUs per location (x) Total costs Labour costs Space costs Figure 6-3: costs per day in euros for the alternative algorithm, when y=1 Looking at the best performing values in tables 6.2 and 6.3, which are indicated with the green cells, the Docdata algorithm appears to perform most cost effective when x=5, y=2 while the alternative algorithm performs best when x=4, y=1. Although labour costs are lowest when x=5, y=2 for both batching algorithms, space costs are lowest for both algorithms when x=4, y=1. So far, the Docdata algorithm seems to result in longer walking routes and thereby higher labour costs compared to the alternative algorithm. Labour costs have thus a relative big influence on total costs for the Docdata algorithm compared to the alternative algorithm. This might be the reason that for the Docdata algorithm, the best performing settings are equal to the settings in which labour costs are minimized. For the alternative algorithm, labour costs have a relatively lower contribution to total costs, which may be the reason that the best performing settings are equal to the settings in which space costs are minimized. Comparing the performance of the best performing settings, the alternative algorithm results in a small cost saving of 1,8 euros per day compared to the Docdata with 95% confidence intervals of respectively [ 68,2; 68,2] and [ 68,7; 69,1] (Law & Kelton, 1982). In sum, the most cost effective storage strategy turns out to be the compact storage assignment strategy, using the alternative batching algorithm Hanging locations In order to get a better understanding of the effect of the number of locations per SKU, factor y, a second set of simulation runs is performed. For these runs, hanging locations are considered. For the hanging locations, location size of x=1, y=1 is based on location size for lying locations when x=1, y=1. This results in a constant number of 10 aisles in use for the numerical example of hanging locations. Hypotheses Ia, IIa and IIIa are therefore not taken into account. 25

38 Time in minutes Appendix C7 shows the time needed for walking and for searching in minutes per day for respectively the Docdata and the alternative algorithm for hanging locations. An average time savings of 39,8 minutes is achieved by using the alternative algorithm instead of the Docdata algorithm. Of these 39,8 minutes, 34,9 minutes result from time savings in walk time and 4,9 minutes from time savings in search time. This confirms the finding of previous section that the alternative batching algorithm improves the construction of batches and thereby shortens walking routes compared to the Docdata algorithm. Taking a look at the time needed for walking with a fixed value for y, the values remain relatively stable, which contradicts hypothesis Ib. When changing values for x, both the number of aisles in use and the number of locations per SKU remain constant. It appears that those two factors are influencing time needed for walking. For y=1 and y=3, walk time is constant for different values of x. For y=2, these values are fluctuating somewhat more. The difference between y=2 and y=1 or y=3, is that for y=2, the number of items per locations can fluctuate. Given that S=3 and y=2, locations store either 1 or 2 items per SKU. However, it is still not exactly clear where these fluctuations in walking time come from. Considering search time, the numbers in appendix C7 displaying the search times for hanging locations closely resembles the numbers in appendix C6 illustrating the search times for lying locations. Increasing x still leads to a constant increase in search time, since search time depends on the number of SKUs present at a location. This confirms hypothesis Ic. Increasing y has no effect on search time, which confirms hypothesis IIc. These findings resemble the ones for lying locations. In sum, it appears not to be profitable to increase x for hanging locations. As opposed to lying locations, increasing the number of SKUs per location does not lead to a reduction of the number of aisles in use, since total space needed for storage is constant. Increasing x leads thus solely to an increase in search times Time needed per day The number locations per SKU (y) Docdata Alternative Figure 6-4: total time needed per day for both batching algorithms, when x=1 Figure 6.4 shows the total time needed per day, for both algorithms when x=1 and various values of y, expressed in minutes per day. As can be seen, changing the number of locations per SKU for a fixed value of x leads to reduction in walking time. This finding confirms hypothesis IIb, in contrast to the findings of lying locations. It appears increasing y leads to a decrease in walking time, if the number of aisles in use remain unaffected. Given that x=1, searching time is 0 and the total time needed per day is equal to the time needed for walking. For both algorithms, it is shown the biggest decrease in total time needed per day results from increasing y from 1 to 2. While increasing y from 1 to 2, the number of locations per SKU are doubled. This means not only the number of possible locations to choose from have been doubled, also the number of SKUs closely located to the I/O point have been doubled. Since the Docdata batching algorithm does not take into account the possibility of choosing between multiple locations, the reduction when increasing y from 1 to 2 is probably related to the latter effect. In principle, at y=1 all 10 aisles were needed to store at least one item all SKUs. Increasing y to 3, only one third of the aisles are needed to store at least one item of all SKUs. This can lead to a shortening of walking routes. Increasing y from 1 to 2 leads to about half of the space 26

39 Time in minutes needed to store at least one item of all SKUs, while increasing y from 1 to 3 leads to one third of the space needed. Increasing y from 2 to 3 thus leads to an additional decrease in space of, ( ). Logically, savings are biggest when increasing y from 1 to 2. Further increasing y leads to additional but smaller costs savings. Considering the difference in performance between the two batching algorithms shows surprisingly a decrease as y increases. Given that the alternative algorithm takes the possibility of choosing between multiple locations into account, it would be logical that the performance would improve as the number of options to choose from increases. A decrease in difference between the two batching algorithms is thus opposite to what could be expected. A possible explanation might be the number of aisles needed to store at least one item of all SKUs. As mentioned, increasing y leads to a smaller number of aisles needed to store at least one item of all SKUs. The area in which an entire batch can be picked is thus smaller, which leads to shorter walking routes. This in turn might lead to a smaller absolute difference in performance of the batching algorithms. However overall the alternative algorithm performs better than the Docdata algorithm. In addition, increasing y leads to a decrease in walking time and thereby total time for both batching algorithms. Figure 6.5 provides a bar chart displaying the time needed per day for three different settings. The same settings are chosen as for figure 6.2, with settings (1): x=1, y=1, (2): x=2, y=2 and (3): x=3, y=3. Again, increasing both x and y results in a decrease in walking time and an increase in searching time, which confirms hypotheses IIIb and IIIc of section Since the number of aisles in use are kept constant, hypothesis IIIa can again not be confirmed. 600 Time needed per day Total time Search time Walk time Figure 6-5: time needed per day, for the alternative algorithm, with (1) being x=1, y=1, (2) being x=2, y=2, (3) being x=3, y=3 Table 6.4 and 6.5 show the total costs per day in euros for respectively the Docdata and alternative batching algorithm. As can be seen, the alternative algorithm always outperforms the Docdata algorithm, with an average cost saving of 12,3 euros per day. Since the number of aisles in use are equal for all values of x and y, these costs savings result solely from cost savings in labour costs. The alternative batching algorithm seems again to construct batches with shorter walking routes than the Docdata algorithm. For a given value of y, it appears increasing x solely leads to an increase in costs. It seems increasing x for hanging locations does not bring any benefits at all. In contrast, increasing y does bring benefits in the form of labour cost savings. Number of SKUs Number of locations per SKU (y) Number of SKUs Number of locations per SKU (y) per location (x) per location (x) ,6 131,2 114, ,5 122,1 108, ,5 136,1 118, ,3 127,0 113, ,3 138,5 123, ,1 129,5 118,0 27

40 Costs per day in euros 4 173,1 145,7 128, ,0 136,6 122, ,6 133, ,5 127, ,4 137, ,3 132, ,7 142, ,4 136, ,5 147, ,1 141, ,0 151, ,8 146, ,6 156, ,2 151,1 Table 6-4: total costs per day in euros, for the Docdata Table 6-5: total costs per day in euros, for the alternative algorithm algorithm Figure 6.6 displays the cost structure for the alternative algorithm when x=1 for different values of y. As can be seen, labour costs account on average for about 45,7% of the total costs for hanging locations, for these values of x and y. This is lower than the 62,8% of lying locations. However, comparing these values is not a fair comparison. First of all, different values for x and y are displayed in figure 6.3 and 6.6. In addition, the number of locations per aisle is constant for lying locations while it is different for hanging locations. For lying locations, space costs are decreasing for increasing values of x because less aisles are needed for storage. For hanging locations, this is not the case because space costs are stable. This explains why space costs are a relatively large part of total costs for hanging locations compared to lying locations. 200 Costs per day Total costs Labour costs Space costs Number of locations per SKU (y) Figure 6-6: costs per day in euros for the alternative algorithm, when y=1 As could be expected based on the numbers in appendix C7, biggest costs savings are attained when y is increased from 1 to 2. It seems further increasing y results in additional costs savings, although savings are decreasing as y gets larger. Given these results, it is not a surprise that the best performing values, which are indicated by the green cells in tables 6.4 and 6.5, turn out to be x=1, y=3 for both batching algorithms. For this setting, the alternative algorithm saves 5,6 euros per day compared to the Docdata algorithm, with 95% confidence intervals of respectively [ 108,3; 108,8] and [ 113,9; 114,3] (Law & Kelton, 1982). It can be concluded that the most cost effective storage strategy for hanging locations is the multiple locations storage assignment strategy Summary The results of section and revealed information regarding the hypothesis of section Appendix C8 provides a schematic overview of all hypothesis and whether they are confirmed (C) or not (-). The most important findings are summarized below. Starting with the factor compactness, it appeared that increasing the number of SKUs per location leads to a big decrease in the number of aisles in use for lying locations. Hypothesis Ia can thus be confirmed for lying locations. The principle of hanging locations is based on a constant number of aisles. Hypothesis Ia does therefore not apply for hanging locations. In addition, it is shown increasing compactness leads not only to costs savings in space costs but also to a decrease in time needed for 28

41 walking, for lying locations. For hanging locations, time needed for walking is stable for different values of x. Hypothesis Ib can thus be confirmed for lying locations and cannot be confirmed for hanging locations. Hypothesis Ic is confirmed for both lying and hanging locations. However, search time is modelled based on the number of SKUs per location so this finding is a logical result of this modelling design choice. The factor multiple locations turned out to lead to an increase in space in use for lying locations, which confirms hypothesis IIa. For hypothesis IIb, contradicting conclusions are found. For lying locations, increasing the number of locations per SKU leads to an increase in walking time. In contrast, increasing y leads to a decrease in walking time for hanging locations. The difference can be explained by the number of aisles in use. For lying locations, the number of aisles in use increases which lengthens walking routes. For hanging locations, this is not the case and increasing y leads to a reduction in walking routes. Hypothesis IIb can thus not be confirmed for lying locations but can be confirmed for hanging locations. Hypothesis IIc can be confirmed for both location types. Results showed that the effect of increasing both x and y on space in use is not ambiguous. Hypothesis IIIa can thus not be confirmed for lying locations. For hanging locations, space in use is constant so hypothesis IIIa can be confirmed for hanging locations. For both lying and hanging locations, increasing x and y does lead to a decrease in both walk and search time. Hypothesis IIIb and IIIc can thus be confirmed. Referring back to the sub questions, sub question 2,3 and 4 can now be answered. Sub questions 2 What are the benefits related to these levels of flexibility? and 3 What are the costs related to these levels of flexibility? can be answered as follows. The benefits related to compactness (x) leads to a decrease in space in use and a decrease in walking time for lying locations. On the other hand, compactness (x) leads to an increase in searching time, for both lying and hanging locations. Increasing multiple locations (y) leads to a decrease in walking time for hanging locations. The disadvantage of multiple locations (y) is an increase in space in use and in walking time for lying locations. Sub question 3 What is the most cost effective level of flexibility for a numerical example?, can be answered as well. For the numerical example, the most cost effective storage strategy for lying locations is to increase compactness (x), while for hanging location it is most cost effective to increase multiple locations (y). Although it appears that increasing both x and y leads to benefits in both space in use and time needed, the most cost effective storage strategies turn out not to be the combined storage assignment strategy. For lying locations, the compact storage strategy turned out to be most cost effective at x=4, y=1. For hanging locations, the multiple locations storage assignment strategy appeared to be the most cost effective strategy at x=1, y=3. It chosen to focus on these settings during the scenario and sensitivity analysis, together with the current storage strategy of x=1, y=1, in order to display the difference in costs between these three strategies. In addition, it is found that the alternative batching algorithm results in savings in total time needed per day, compared to the Docdata algorithm. The alternative algorithm seems to construct batches with shorter walking routes. Therefore, it is chosen to focus on this algorithm in the scenario and sensitivity analysis in sections 6.2 and Scenario analysis In the scenario analysis the effect of dynamics regarding assortment and demand are researched. In section the effect of the assortment on performance is considered. In section demand is evaluated Assortment In the model, assortment has two parameters: assortment size and base stock levels. The effect of these aspects of the assortment on costs is evaluated. Assortment size 29

42 Costs per day in euros First, the assortment size is considered. For the base situation, the number of SKUs is set equal to In the scenario analysis, the number of SKUs in the assortment is ranging from to SKUs, with steps of Figure 6.7 displays the cost structure for both hanging and lying locations using the current storage assignment strategy, i.e. x=1, y=1. As can be seen, both labour and space costs increase linearly as the number of SKUs increases. Given that it is not allowed to store multiple SKUs on one location (x=1), increasing the assortment with 2000 SKUs leads directly to an additional aisle in use and thereby an increase in space costs. A larger warehouse leads in turn to longer walking routes and thus higher labour costs. Although both space and labour costs are increasing linearly, space costs increase at a faster rate than labour costs. The reason for this might be that space costs are a direct result of increased SKUs. Labour costs depend on walking routes which in turn depend on warehouse size and are thus an indirect result of increased SKUs. 250 Costs per day Total costs Labour costs Space costs Number of SKUs Figure 6-7: costs per day in euros for varying assortment size, lying & hanging locations, x=1, y=1, alternative algorithm Figure 6.8 shows the cost structure for lying locations for x=4, y=1. Interestingly, space costs for x=4, y=1 appear not to be linearly related to assortment size. This is opposed to what can be expected based on figure 6.7, which shows a linear relationship. In figure 6.8, costs increase more stepwise, especially space costs. The same cost structure is shown in figure 6.9 for hanging locations at x=1, y=3. The structure for space costs in figure 6.8 and 6.9 is explained by the fill rate of the aisles. With fill rate of an aisle, the number of items stored within an aisle divided by the number of items that can be stored within an aisle is denoted. For lying locations, an aisle contains 2000 locations on which 4 different SKUs can be stored. One aisle has thus the capacity to store 8000 different SKUs. For SKUs, two aisles are filled entirely storing SKUs while one aisle stores the remaining 4000 SKUs. Aisle number three has a fill rate of 50% ( ). Increasing the assortment with 2000 SKUs to SKUs would increase the fill rate of the third aisle to 75%, but the number of aisles needed in total remains three. Increasing the number of SKUs to SKUs would require an additional aisle, aisle number four. This causes the increase in space costs as shown in figure 6.8. As can be seen, the interval in which space costs increases is equal to the capacity of one aisle, 8000 SKUs. For hanging locations, the number of locations within one aisle is equal to 6680, so each aisle can store 6680 SKUs. Figure 6.9 shows that the interval of rising space costs is subsequently 6000 and 8000 SKUs. Since 6680 is not a multiple value of 2000, the interval in which the number of aisles increase can differ somewhat between 6000 (3*2000 SKUs) and 8000 (4*2000 SKUs). This corresponds to the findings in figure 6.9. Labour costs follow the pattern of space costs but increase more evenly. This is because the number of aisles in use increases the walking distances of batches. Labour costs are thus an indirect result of an increase in the number of SKU, while space costs are a direct result. 30

43 Costs in euros Costs in euros Costs per day Total costs Labour costs Space costs Number of SKUs Figure 6-8: costs per day for varying assortment, lying locations, x=4, y=1, alternative algorithm Costs per day Total costs Labour costs Space costs Number of SKUs Figure 6-9: costs per day for varying assortment, hanging locations, x=1, y=3, alternative algorithm Figure 6.8 and 6.9 show that costs are increasing as assortment size increases. However, the costs per SKU is decreasing as assortment size increases. Figure 6.10 shows the costs per SKU for lying and hanging locations when x=1, y=1, for lying locations when x=4, y=1 and for hanging locations when x=1, y=3. The situation in which x=1, y=1 shows a steady decrease in costs per SKU. Despite the alternating shape of the line for lying locations when x=4, y=1 and hanging locations when x=1, y=3 a decreasing trend line can be identified. For all levels of flexibility and types of locations, it appears total costs are increasing but costs per item are decreasing. Therefore, it seems an efficiency of scale is present for assortment size. However this is not really the case. As mentioned, the scenario analysis is executed ceteris paribus, so demand is remained unchanged. It can therefore be assumed revenues remain constant while total costs are increasing which point to an inefficiency of scale effect. Figure 6.11 illustrates the cost savings for the best performing settings of both lying and hanging locations compared to a situation in which x=1, y=1. Again, despite the alternating shape of the lines, an increase in cost savings can be identified when the number of SKUs increases. An increased level of flexibility appears to result in cost savings compared to the situation in which x=1, y=1. As the assortment size increases, cost savings increase as well. Having flexibility thus turns out to be even more beneficial for an increased assortment size. 31

44 Savings per day in euros Costs in euros 0,009 0,008 0,007 0,006 0,005 0,004 0,003 0,002 0,001 0 Costs per SKU lying/hanging, x=1,y=1 lying, x=4,y=1 hanging, x=1,y=3 Number of SKUs Figure 6-10: costs per SKU in euros for varying assortment, lying & hanging locations, alternative algorithm Savings per day lying, x=4,y=1 hanging, x=1,y=3 Number of SKUs Figure 6-11: total savings per day in euros for varying assortment, lying & hanging locations, alternative algorithm Base stock levels Second, base stock levels are researched. Base stock levels of 2, 3, 4 and 5 items per SKU are considered. For lying locations, simulations are run for the situation in which y=1 and for different levels of x. For hanging locations, simulations are run for the situation in which x=1 for different levels of y. This is chosen because it is shown in previous section that the most cost effective storage assignment strategy is to solely increase x or y for respectively lying and hanging locations. Figure 6.12 presents the costs per day for lying locations and y=1, for the situation in which S=2. As can be seen, daily costs decrease as the number of SKUs per location increases. This corresponds to what can be expected based on the findings of section in which S=4. In addition, it can be noted that solely the line for S=2 is displayed in figure The reason for this is that it appeared that increasing S does not have an effect on the total costs. This can be illustrated by the example of x=1, y=1 and S=2. Capacity of a location is sufficient to store 12 items but only has fill rate of, because it is only used to store 2 items. Increasing S from 2 to 3 would result in an increase of the fill rate of the location to, but it does not affect the number of locations needed. Space costs remain thus constant, just as labour costs. Further increasing S would further increase the fill rate but leave the number of aisles in use unaffected, up until S=12. The line of for example S=4 would thus be exactly similar to the one of S=2. The only difference between the various S levels is the maximum value of x. For S=2, x can have a maximum value of 6, given the location capacity of 12 items per location. For S=4, the maximum value of x is equal to 3. The line of S=4 thus ends after x=4. In sum, the line of different S 32

45 Costs in euros Costs in euros levels is exactly similar to the one of S=2, however, the length of the line is restricted by the possible values of x, which in turn depend on location capacity. Increasing base stock levels do not directly result in a change in costs, however, lower costs can be attained at higher values for x and thus lower values for S. Costs per day Number of SKUs per location (x) S=2 Figure 6-12: costs per day for varying base stock levels, lying locations, y=1, alternative algorithm Costs per day Number of locations per SKU (y) S=2 S=3 S=4 S=5 Figure 6-13: costs per day for varying base stock levels, hanging locations, x=1, alternative algorithm For hanging locations, simulations are run for the situation in which x=1 for different values of y. Figure 6.13 displays the relationship between the total costs per day and different values of the base stock level. Looking at the distance between the different lines, it is shown raising the level of S leads to an increase in total costs. Location size depends on the number of items stored on the location. Increasing S leads thus directly to an increase in space costs. Taking a look at the shape of the lines, it appears increasing y leads for all S levels to a decrease in costs. This points to a decrease in labour costs, since total space and thereby space costs are constant for a given level of S. It seems increasing the base stock levels leads to batches with shorter walking routes and thereby lower labour costs as y increases. Figure 6.14 provides an overview of the time needed per day for varying base stock levels in order to provide more insight in labour costs. As can be seen, for each value of y, raising S levels lead to an increase in time needed. As mentioned, increasing S levels logically leads to an increase in space in use. This increase in space results in an increase in time needed per day because of longer pick routes. Not only space costs but also labour costs increase when the S increases. In addition, it is shown that 33

46 Time in minutes increasing the number of locations per SKU leads for all different values of S to a decrease in time needed. This confirms the finding that increasing y leads to shorter walking routes and lower costs. For the situations in which S is bigger than 3, the original performance can be compared to the performance in which y=3. Both for S=4 and S=5, the situation of x=1 and y=3 results in higher costs compared to S=3. Time needed per day has increased as illustrated in figure 6.13, just as space needed. Lowering S leads on the other hand to a better performance although y cannot have a value of 3. In general, new best performing settings for each level of S turn out to be the settings in which y has its maximum value. In addition, it seems lowering levels of S leads to a decrease in costs. It must be noted that lowering S levels have an impact on order acceptance as well. This is not taken into account in this project Time needed per day Number of locations per SKU (y) S=2 S=3 S=4 S=5 Figure 6-14: total time needed per day in minutes for varying base stock levels, hanging locations, x=1, alternative algorithm Demand Demand is evaluated on three factors: average order size, number of orders per day and demand distribution. Average order size For demand, first the average order size is considered. Average order size is varied from 1 to 3 with steps of 0,5. Figure 6.15 shows the relation between the total costs per day and varying average order size, for the best performing settings of x and y for both lying and hanging locations. Since the assortment remains unchanged, space costs do not change. Labour costs, in contrast, are affected by average order size. As average order size increases, labour costs and thus total costs are increasing linearly as shown in figure As order size increases, less customer orders fit in one batch and more batches need to be constructed. This increases total time needed for walking and searching and thereby increases labour costs. Only hanging locations for x=1, y=3 show an exception to linearity with a small kink around an average order size of 2,5. It is not clear what is causing this kink. It is interesting to look at the difference in performance for no flexibility, x=1, y=1, and the best performing levels of flexibility, x=4, y=1 for lying locations and x=1, y=3 for hanging locations. The difference between the lines of lying and hanging locations for x=1, y=1 and the other lines indicate the cost savings that can be reached when increasing flexibility. As can be seen, the lines of the best performing values for lying and hanging locations increase less steep than the one for x=1, y=1. Because the number of batches and thereby the number of pick tours are increasing at the same rate for all three lines, cost savings must result from the length of the pick tours. Apparently, both the dimensions compactness for lying locations and the multiple locations for hanging locations reduce walking routes. As average order size and thus the number of batches increases, savings resulting from 34

47 Costs in euros Costs in euros shorter walking routes are increasing. It can thus be concluded that an increased level of flexibility becomes more profitable when order size increases. 200 Costs per day lying/hanging, x=1,y=1 lying, x=4,y=1 hanging, x=1,y=3 0 1,5 2 2,5 3 3,5 Average order size Figure 6-15: total costs per day for varying order size, lying & hanging locations, alternative algorithm 0,25 0,20 Costs per item 0,15 0,10 0,05 lying/hanging, x=1,y=1 lying, x=4,y=1 hanging, x=1,y=3 0,00 1,5 2 2,5 3 3,5 Average order size Figure 6-16: costs per item for varying order size, lying & hanging locations, alternative algorithm Figure 6.16 displays the costs per item for varying order size, for both lying and hanging locations. As can be seen, cost per item is steadily decreasing when the average order size increases. Since the increase in labour costs is a small contribution to the total costs, the results point to an efficiency of scale. In addition, it can be expected that additional demand results in additional revenues, which would strengthen the scale effect. It must be noted, however, that an increase in total demand normally would affect stock levels as well. As shown in section 6.2.1, increasing base stock levels lead to an increase in both space and labour costs. Number of orders per day Second, the number of orders per day are considered. The number of average orders per day are varied between 75 and 675 items per day with intervals of 150 items. Figure 6.17 displays the costs per day for varying number of orders per day, for both lying and hanging locations. Figure 6.17 highly resembles figure 6.15, displaying the same results for varying average order size. Total costs are increasing linearly as the number of orders increase, and the increase is more steeply for x=1, y=1 than for settings with more flexibility, i.e. x=4, y=1 for lying locations and y=3, x=1 for hanging locations. As the number of orders per day increase, the number of batches and thus picking tours increase as well. Increasing flexibility results in shorter walking routes and thereby higher cost savings. Increasing 35

48 Costs in euros Costs in euros flexibility is thus especially profitable when the average number of orders per day are high. However, considering the results for the average order size, it seems total costs react the same to an increase in demand, regardless whether this is coming from average order size or number of orders per day Costs per day lying/hanging, x=1,y=1 lying, x=4,y=1 hanging, y=3,x= Orders per day Figure 6-17: costs per day for varying orders per day, lying & hanging locations, alternative algorithm Figure 6.18 displays the costs per item for varying number of orders per day for both lying and hanging locations. Again, an efficiency of scale effect is found when the number of orders per day are increasing. In this model, a certain assortment size and base stock level is chosen, regardless of the demand. The number of aisles in use and thereby space costs are thus constant for different sizes of demand. This is why the biggest drop in costs per item are achieved when the number of orders increase for low number of orders per day. The increase in labour costs resulting from an increase in demand is relatively small compared to space costs. When the number of orders per day are higher, costs per item are still decreasing, however at a slower rate. 0,5 0,4 Costs per item 0,3 0,2 0,1 lying/hanging, x=1,y=1 lying, x=4,y=1 hanging, y=3,x=1 0, Orders per day Figure 6-18: costs per item for varying orders per day, lying & hanging locations, alternative algorithm Comparing figure 6.18 to 6.16, the figures seem to look different at first sight. However, taking a closer look shows that scales are different for both figures. Considering the number of items demanded per day, the scale of figure 6.16 covers 564 to 1315 items per day, while figure 6.18 covers 188 to 1688 items per day. As mentioned, it seems total costs react the same to an increase in demand, regardless whether this increase in demand is coming from average order size or number of orders per day. It might be interesting to see if this is true. Therefore, the data from figure 6.16 and 6.18 are integrated with a scale covering the overlapping area between 564 and 1315 items per day. Figure

49 Costs in euros displays the costs per items for varying demand for both lying and hanging locations. The smooth curve of the line suggests that the effect on costs is similar for varying values of both average order size and the number of orders per day. As with the average order size, it is shown again for the number of orders per day that total costs increase slowly compared to the growth in demand. A scale effect seems to exist for an increase in demand in total, disregarding where this growth is exactly coming from. In addition, it seems that there is no advantage of batching big or small orders, since changes in costs as a result of average order size are similar to the ones resulting from the number of orders per day. Again, the number of orders per day would normally affect base stock levels just as for the average order size. 0,25 Costs per item 0,2 0,15 0,1 0,05 0 lying/hanging, x=1,y=1 lying, x=4,y=1 hanging, x=1,y=3 Figure 6-19: costs per item for varying demand per day, lying & hanging locations, alternative algorithm Demand distribution So far, demand is assumed to be uniformly distributed. However, a Pareto distribution is more common in practice, as discussed previously. In order to test the performance, a simulation was run with a group of slow movers consisting of 20% of the assortment and accounting for 80% of the sales. For this experiment, fast movers have a base stock level of S=12 which is four times bigger than the ones of slow movers, with S=4. Given the location capacity of 12, not all combinations of values for x and y are possible anymore. x y y x ,5 172,5 243, ,5 88,9 89,3 2 93,7 129,0 2 93,7 94, ,7 3 98,9 Table 6-6: total costs per day in euros, Pareto demand, lying locations, alternative algorithm Number of items demanded per day 37 Table 6-7: total costs per day in euros, Pareto demand, hanging locations, alternative algorithm Tables 6.12 and 6.13 display the total costs per day for respectively lying and hanging locations, for a Pareto distributed demand. For lying locations, it is not possible to increase x when y=1. Therefore, it is not possible to compare the initial best performing value directly to the best performing value for a uniform distribution. What can be compared is the performance of the situation x=1, y=1 to the performance of x=2, y=2, and x=3, y=3. However, the numbers do not provide an unambiguous picture of the effect of increasing x and y on total costs. It is not clear what causes these numbers, so no clear conclusions can be drawn for demand distribution of lying locations. For hanging locations, a comparison is easier to make. Recall that the best performing settings (x=1, y=3) for uniform demand resulted in a total costs of 108,5 euros per day. Comparing this to the costs of a Pareto demand for x=1, y=3, results in cost savings of 19,3 euros per day for a Pareto demand compared to a uniform demand. With 95% confidence intervals of [ 89,1; 89,4] for a Pareto demand

50 and [ 108,3; 108,8] for a uniform demand, the settings x=1, y=3 perform better for demand that is Pareto distributed (Law & Kelton, 1982). Appendix C9 shows the labour costs and space costs for both lying and hanging locations with Pareto demand. Comparing these numbers to the cost structures shown in section and 6.1.2, it can be seen that space costs remain unchanged. Although for lying locations this resembles reality, for hanging locations this is not the case. For fast movers, location sizes should be bigger than for slow movers, since more items per SKU are stored for fast movers. However, this is not taken into account in the model. It could thus be expected that in practice, space costs for hanging locations would be higher for a Pareto demand compared to a uniform demand. In this model, space costs remain unchanged so cost savings shown in table 6.13 are the result of changes in labour costs. Appendix C10 shows the total costs per day in euros for both lying and hanging locations using the Docdata algorithm. Although costs for the alternative algorithm are lower, big cost savings are also attained when the Docdata algorithm is used. Apparently, it is in general less costly to deal with a Pareto distributed demand compared to a uniform distributed demand. An explanation might be found in the number of picks per batch. For the fast movers of a Pareto distributed demand, demand is relatively high. The possibility that one SKU is demanded multiple times within one batch is therefore bigger than for a uniform distributed demand. This lowers the number of picks per tour and thereby the length of the walking route. Lower labour costs could thus be the result of a different demand distribution. However, demand distribution is normally not something that can be influenced. It is especially interesting to look at the possible savings that can be reached by increasing the level of flexibility from x=1, y=1 to x=1, y=3 for hanging locations for either types of demand distributions. For a uniform distributed demand, savings of 38,9 euros per day can be reached when increasing x=1, y=1 to x=1, y=3, as mentioned in section For a Pareto distributed demand, savings are equal to 13,3 euros per day when changing y=1 to y=3 and even 13,6 euros per day when changing to y=2. However, it is not clear why lower costs are reached when y=2 instead of y=3. Although cost savings are higher for a uniformly distributed demand compared to a Pareto demand, total costs are lower when demand is Pareto distributed compared to uniform distributed demand Summary In this section, different scenarios regarding assortment and demand are considered. First it is shown that an increase in assortment size results in an increase in both space and labour costs. For a situation without flexibility, x=1, y=1, the increase is linearly related to the number of SKUs in the assortment. For both lying locations at x=4, y=1, and hanging locations at x=1, y=3, the increase in costs is more stepwise. The fill rate of the aisles are causing a stepwise increase in space and labour costs. Although total costs are increasing, the costs per SKU are decreasing. In addition, it is shown that cost savings resulting from an increased level of flexibility are increasing as assortment size increases, both for lying or hanging locations. Besides assortment size, base stock levels are considered as well. For lying locations it is shown that changing the S level has no effect on total costs. However, the maximum value of x is restricted by the base stock level. Increasing base stock levels do not directly result in a change in costs, however, lower costs can be attained at higher values for x and thus lower values for S. For hanging locations, it is shown raising the level of S leads to an increase in space costs. Besides, it is shown increasing y leads for all S levels to a decrease in labour costs. In general, new best performing settings for each level of S turn out to be the settings in which x has its optimal value for lying locations and y has its maximum value for hanging locations. These findings answer sub question 5 how does this level change in response to different scenarios regarding assortment?. In sum, the most cost effective storage assignment strategies remain the same for different values for the number of SKUs and base stock levels. Regarding demand, it is shown that an increase in both average order size and the number of orders per day leads to an increase in total costs but to a decrease in costs per item. A scale effect is thus shown for total demand. For demand distribution, it is found that total costs are lower when demand is Pareto distributed compared to uniform distributed demand. However, the model does not take everything into account regarding space effects. It is also found that cost savings resulting from increased flexibility are smaller for a Pareto distributed demand compared to a uniformly distributed 38

51 demand. These findings answer sub question 6 How does this level change in response to different scenarios regarding demand?. Again, the most cost effective storage assignment strategies remain the same. 6.3 Sensitivity analysis A sensitivity analysis is performed to test the sensitivity of the model to certain parameters. Three categories of parameters are considered: layout, batching and costs Layout For layout, the warehouse proportions are considered. Two extreme situations are evaluated. One extreme is a warehouse with few long aisles of 100m, the other extreme is a warehouse with many short aisles of 10m. Warehouse proportions First, the costs for both lying and hanging locations are considered for x=1, y=1. Appendix C11 displays the time needed per day in minutes, the number of aisles in use and the cost structure for lying and hanging locations when x=1, y=1. As can be seen, the warehouse with long aisles needs only 3 aisles to store all SKUs, as opposed to the 30 aisles that are needed in the warehouse with short aisles. The warehouse with many short aisles has lower labour costs, while space costs are equal. It is not a surprise for space costs to be equal, because the total number of locations and thereby total space needed for storage remains the same. The lower labour costs for the warehouse with short aisles is caused by a shortening in walking routes. For the warehouse with long aisles, visiting one aisle results in travelling of the entire warehouse, compared to for the warehouse with short aisles. In the least favourable situation for the warehouse with long aisles, all three aisles need to be visited and the entire warehouse is travelled. For the warehouse with short aisles, the most unfavourable situation results in 15 aisles to be visited, which is only half of the number of aisles in the warehouse. This difference is displayed in the labour costs. In sum, for the situation without flexibility, total costs are lower for the warehouse with many short aisles than the one with few long aisles. Similar results are found in appendix C12 displaying the same results for lying locations when x=4, y=1 and in appendix C13 displaying the results for hanging locations when x=1, y=3. For both lying and hanging locations, costs are lower for the warehouse with many short aisles compared to the warehouse with few long aisles. For lying locations, space costs are not equal. Probably, the fill rate of the aisle is not equal to 100% for the warehouse with one long aisle. This explains why the warehouse with many short aisles uses 8 aisles, which is not a plural of 1. Space costs are thus not equal. Although space costs are not similar for lying locations, labour costs are always lower for the warehouse with short aisles. The difference in total costs between x=1, y=1 and x=4, y=1 for lying locations is similar, while the difference between x=1, y=1 and x=1, y=3 for hanging locations is much smaller. As mentioned previously, the difference in labour costs depends on the length of the picking tours. For hanging locations with increased flexibility of x=1, y=3, one item of each SKUs is stored in each of the three aisles. The probability of visiting all three aisles during one picking tour is thus much smaller than it was for x=1, y=1. This explains why the difference is smaller for x=1, y=3 than for x=1, y=1. In sum, total costs are lower for warehouses with many short aisles than for warehouses with few long aisles Batching Sensitivity of the model to batching decisions is evaluated. First the number of order runs and second the batch size is considered. Order runs The number of order runs per day are varied between 3 and 7 with an interval of 1. Appendix C14 displays the total costs for various number of order runs per day for both lying and hanging locations. The results show that performance can be slightly improved by lowering the number of order runs per day. Decreasing the number of order runs leads to an increase in the number of orders that are retrieved and batched during that order run. This increase in the number of orders that need to be 39

52 batched leads to a better composition of batches, i.e. batches with shorter walking routes. However, the row displaying the situation in which x=1, y=1 shows that this is common for batching in general, and not specific for flexibility in storage. Batch size Batch size in this project is considered as a number indicating some sort of limit of the batches. Customer orders are added to a batch until the limit is reached. Batch size is varied between 5 and 25 with intervals of 5. Appendix B10 displays the total costs per day in euros for various batch size, for both lying and hanging locations. It can be seen that total costs per day decrease as batch size increases. Increasing batch size reduces the number of batches that need to be made. For each picking tour, a distance need to be travelled until the first pick. When the number of batches decrease, this distance is travelled less frequently. In addition, bigger batches may lead to a better composition of the batches as well, i.e. batches with a relatively shorter walking routes. In sum, bigger batch size results in lower costs. Again, cost savings resulting from bigger batch sizes is common for batching in general, as is shown by the line indicating x=1, y= Cost parameters Last part of the sensitivity analysis considers the cost parameters for labour and space costs. Labour costs For the numerical example, it is assumed costs for an hour of labour is equal to 18,5. For the sensitivity analysis this value is varied between 16,5 and 20,5 with steps of 1,0. Appendix B11 presents the total costs per day for both lying and hanging locations. Not surprisingly, total costs are increasing when labour costs are increasing. In addition, total savings when changing from x=1, y=1 to x=4, y=1 for lying locations are increasing as labour costs increase. Comparing the best performing settings for lying and hanging locations, cost savings for lying locations are bigger because savings in space costs contribute to total savings as well. In sum, increasing flexibility within storage assignment becomes more profitable when labour costs are rising. Space costs For the numerical example, it is assumed costs for renting one pallet place of is equal to 70,0. For the sensitivity analysis this value is varied between 50,0 and 90,0 with steps of 10,0. Appendix B12 displays the total costs per day both lying and hanging locations. As can be seen, both total costs and total savings increase as space costs increase for lying locations. For hanging locations, total savings remain constant since the number of aisles in use are constant is well. Increasing flexibility is thus more profitable when space costs increase for lying locations Summary During the sensitivity analysis, parameters regarding layout, batching decisions and cost parameters are considered. For layout, it is shown that total costs are lower for the warehouse with many short aisles compared to the one with few long aisles. This is true for both lying and hanging locations regardless the level of flexibility. In addition, it is shown that a lower number of order runs per day leads to a small cost saving compared to higher number of order runs per day. For batch size, the opposite effect is found. Bigger batch sizes lead to lower total costs. Both order runs and batch size are general scale effects and are unrelated to flexibility. For cost parameters, it is found increasing flexibility within storage assignment becomes more profitable when labour costs are rising. In addition, increasing flexibility becomes more profitable for lying locations when space costs are rising. 40

53 Costs per day 7. Case study Docdata The case study concerns the warehouse operations of one of Docdata s customers. The central question of this research is especially interesting for this case since demand is characterized by big seasonality around two special sale periods and the season holidays in December. As shown by the numerical example, it can be expected that increased flexibility is beneficial for a growth in product assortment, growth in orders per day and growth in order size. This case study is used to illustrate the cost savings that can be achieved by introducing a more flexible storage assignment strategy. For confidential reasons, cost savings are expressed in percentages. However, it must be noted that the model considers only few of all labour activities, as mentioned previously. In this model, only variable labour activities needed for picking are considered. For both picking and other activities several activities are not taken into account. Cost savings presented in this section are thus higher than in practice. 7.1 Scenario 2014 For scenario-2014, three different cases are researched. The medium case is based on the growth between 2011 to Worst and best case scenarios are respectively downgraded and upgraded revisions of the medium case. Before going into more details, it is verified whether the most cost effective storage assignment strategies as identified in previous section apply to the case as well. Figure 7.1 provides an overview of the total costs per day for the three different scenarios, for lying locations. As expected, figure 7.1 shows lowest costs are attained when increasing x to a higher value, 4 in this case. It is shown the compact storage assignment strategy with x=4, y=1 is also the most cost effective strategy for the case, for lying locations. Total costs per day Worst case Medium case Best case Number of SKUs per location (x) Figure 7-1: total costs per day for varying values of x, for three different scenarios, lying locations, alternative algorithm Figure 7.2 shows the total costs per day for the three different scenarios, for hanging locations. As can be seen, increasing y to its maximum value, 3 in this case, results in lowest total costs per day. This is according to what could be expected based on the results of section 6. It appears the multiple locations storage assignment strategy with x=1, y=3 is also the most cost effective strategy for the case, for hanging locations. Based on these results, focus is put on the difference between the current storage assignment strategy and the compact and multiple locations storage strategy. In addition, the difference between the Docdata batching algorithm and the alternative batching algorithm is taken into account as well. Results for the different cases are provided in subsequent sections. 41

54 Costs per day Total costs per day Worst case Medium case Best case Number of locations per SKU (y) Figure 7-2: total costs per day for varying values of y, for three different scenarios, hanging locations, alternative algorithm Worst case For the worst case, it is expected SKUs experience a growth of 12,5%, orders are expected to grow with 15% and average order size is expected to grow with 0,5. Table 7.1 provides insights in the cost savings that can be achieved by changing storage assignment strategy and batching algorithm. After the header row, the first row of the table shows the current storage assignment strategy, x=1, y=1, using the Docdata batching algorithm. Results for this situation are equal for both lying (L) and hanging (H) locations. As can be seen, cost savings are equal to zero since this is the situation to which all other options are compared. The second row, for example, differs from the first row because the alternative batching algorithm is used instead of the Docdata algorithm. As can be seen, this results in a total cost saving of 5,7%, resulting from savings of 9,2% in labour costs. Solely changing the storage strategy for lying locations results in a cost saving of 5,7% as can be seen. Increasing the value of x from 1 to 4 for lying locations results in a cost saving of 55,9%. Increasing both the value of x and changing the batching algorithm results in even higher cost savings of 58,7%. As can be seen the additional cost savings resulting from the alternative batching algorithm become smaller, which corresponds to the findings of section 6.1. However, changing both flexibility and the batching algorithm results in the highest cost savings for lying locations. The same is true for hanging locations. Increasing y from 1 to 3 and using the alternative batching algorithm results in total cost savings of 31,3%. In sum, highest cost savings are achieved when both the batching algorithm and the storage assignment strategy are changed. Location type Batching algorithm x y Savings labour costs Savings space costs Savings total costs L & H Docdata 1 1 0,0% 0,0% 0,0% L & H Alternative 1 1 9,2% 0,0% 5,7% Lying Docdata ,3% 75,0% 55,9% Lying Alternative ,9% 75,0% 58,7% Hanging Docdata ,3% 0,0% 28,3% Hanging Alternative ,9% 0,0% 31,1% Table 7-1: cost savings in % for various levels of flexibility for the worst case, lying & hanging locations, Docdata & alternative algorithm Medium case The number of SKUs for the medium case is expected to be equal to 25%. Order growth is expected to be 30% and average order size is expected to grow with 1,0. Table 7.2 displays the same data as table 7.1 of previous section, but with the numbers for the medium case. Similar results are shown for the medium case as for the worst case. So far, it can be concluded that increasing flexibility and changing the batching algorithm becomes more profitable as the assortment, the number of orders and average order size are increasing. 42

55 Location type Batching algorithm x y Savings labour costs Savings space costs Savings total costs L & H Docdata 1 1 0,0% 0,0% 0,0% L & H Alternative 1 1 7,3% 0,0% 5,0% Lying Docdata ,1% 69,2% 54,2% Lying Alternative ,9% 69,2% 55,4% Hanging Docdata ,9% 0,0% 31,9% Hanging Alternative ,6% 0,0% 34,4% Table 7-2: cost savings in % for various levels of flexibility for the medium case, lying & hanging locations, Docdata & alternative algorithm Best case In the best case, it is expected to have an assortment growth of 37,5%, an order growth of 45% and an expected growth of average order size of 1,5. Table 7.3 displays the same information as table 7.1 and 7.2, but shows the numbers for the best case. As could be expected, cost savings are again comparable for the worst and medium case. This confirms the finding in section that increasing flexibility and changing the batching algorithm becomes more profitable as assortment size, the number of orders and average order size are increasing. Location type Batching algorithm x y Savings labour costs Savings space costs Savings total costs L & H Docdata 1 1 0,0% 0,0% 0,0% L & H Alternative 1 1 6,2% 0,0% 4,5% Lying Docdata ,2% 71,4% 55,3% Lying Alternative ,5% 71,4% 56,3% Hanging Docdata ,6% 0,0% 31,6% Hanging Alternative ,6% 0,0% 34,5% Table 7-3: cost savings in % for various levels of flexibility for the best case, lying & hanging locations, Docdata & alternative algorithm 7.2 Sales period 1 The first sales period is a [confidental] sales period. A simulation run considers [confidental] working days and is repeated 10 times. Demand is assumed to be Pareto distributed with a fast movers group consisting of 25% of the assortment, generating 75% of the sales. For fast movers, base stock levels are three times as high as for slow movers. The number of SKUs increase with 38,6%, the number of customer orders increase with a factor of 2,7 and average order size is equal to 3. Table 7.4 displays total cost savings per day for various levels of flexibility compared to the original situation in which x=1, y=1 and the Docdata algorithm is used. As can be seen, solely switching from batching algorithm results in a cost saving of 11,5% on labour costs. Solely changing the storage strategy for lying locations from y=1 to y=3 leads to a cost saving of 38,8%, resulting from both savings in labour and space costs. For hanging locations a cost savings of 36,5% can be achieved. Although savings resulting from switching the batching algorithm become smaller, highest cost savings are achieved when both the batching algorithm and the storage assignment strategy is changed. Location type Batching algorithm x y Savings labour costs Savings space costs Savings total costs L & H Docdata 1 1 0,0% 0,0% 0,0% L & H Alternative ,5% 0,0% 8,0% Lying Docdata ,6% 57,1% 38,8% Lying Alternative ,4% 57,1% 40,8% Hanging Docdata ,8% 0,0% 36,5% Hanging Alternative ,0% 0,0% 40,1% Table 7-4: cost savings in % for various levels of flexibility in sales period 1, lying & hanging locations, Docdata & alternative algorithm 43

56 For sales period 1, it is confirmed highest cost savings are attained when switching to the alternative algorithm, using the compact storage assignment strategy for lying locations and the multiple locations storage assignment strategy for hanging locations. 7.3 Sales period 2 The second sales period lasts [confidental] days. A simulation run considers thus [confidental] working days and is repeated 10 times. Demand is assumed to be Pareto distributed with a fast movers group consisting of 25% of the assortment, generating 75% of the sales. For fast movers, base stock levels are three times as high as for slow movers. Assortment increases with 35%, the number of orders increases with a factor of 14,1 and average order size is equal to 4. Table 7.5 displays total cost savings per day for various levels of flexibility compared to the original situation in which x=1, y=1 and the Docdata algorithm is used. As can be seen, solely switching from batching algorithm results in a cost saving of 7,9% of total costs. These savings are even increasing for flexibility within hanging locations. Given the relative high demand, much time is needed for picking. Apparently the multiple locations strategy provides the batching algorithm to construct batches with shorter walking routes when demand is high. In sum, highest cost savings are achieved when using the compact storage assignment strategy for lying locations, and the multiple locations storage assignment strategy for hanging locations in combination with the alternative algorithm. For sales period 2, it is thus advisable to apply these storage assignment strategies in combination with the alternative algorithm. Location type Batching algorithm x y Savings labour costs Savings space costs Savings total costs L & H Docdata 1 1 0,0% 0,0% 0,0% L & H Alternative 1 1 8,4% 0,0% 7,9% Lying Docdata ,4% 57,1% 32,1% Lying Alternative ,9% 57,1% 34,4% Hanging Docdata ,6% 0,0% 34,3% Hanging Alternative ,5% 0,0% 45,4% Table 7-5: cost savings in % for various levels of flexibility in sales period 2, lying & hanging locations, Docdata & alternative algorithm 7.4 December The last big peak in demand takes place in December. A simulation run considers 25 working days, and is repeated 10 times. It is assumed demand is uniform distributed. During this period, assortment grows with 30%, demand with factor 2,3 and average order size is equal to 2,5. Table 7.4 displays total cost savings per day for various levels of flexibility compared to the original situation in which x=1, y=1 and the Docdata algorithm is used. Again, cost savings are achieved when changing either the batching algorithm or the storage assignment strategy. However, biggest cost savings are attained when changing the batching algorithm and the storage assignment strategy simultaneously. Location type Batching algorithm x y Savings labour costs Savings space costs Savings total costs L & H Docdata 1 1 0,0% 0,0% 0,0% L & H Alternative ,4% 0,0% 9,1% Lying Docdata ,1% 69,2% 53,0% Lying Alternative ,4% 69,2% 55,4% Hanging Docdata ,8% 0,0% 33,7% Hanging Alternative ,7% 0,0% 38,7% Table 7-6: cost savings in % for various levels of flexibility in December, lying & hanging locations, Docdata & alternative algorithm 7.5 Summary The case study was intended to illustrate the practical usefulness of the theoretical findings in section 6. The case study illustrated the cost savings that can be achieved by introducing a more flexible storage assignment strategy. 44

57 The results of the case study confirm the findings of section 6. The most cost effective storage assignment strategy for lying locations is the compact storage assignment strategy with an optimal value for x. For hanging locations, the most cost effective storage strategy is the multiple locations storage assignment strategy, with a maximum value for y. In addition, the case study showed that cost savings can be achieved by incorporating flexibility within storage assignment. It is shown costs savings resulting both from space and labour costs can be attainted. Although in practice space costs might be fixed in the short run, savings in space costs can be achieved in the long run. In contrast, savings in labour costs can be achieved immediately. Moreover, it is shown cost savings are increasing for various growth scenarios for 2014, the special sale periods and the December period. Again, it must be noted that the model considers only few of all labour activities, and cost savings presented in this section are thus higher than in reality. Given these results, sub question 7 can be answered how should flexibility within storage assignment be applied to the case of Docdata?. Concrete, Docdata should use the compact storage assignment strategy with optimal values for x for lying locations. For hanging locations, Docdata should introduce the multiple locations storage assignment strategy with a maximum values for y. Moreover, it is advisable to adjust the storage assignment strategy to benefit even more in the future and during periods with deviating demand and assortment. 45

58 8. Conclusion 8.1 Theoretical findings The main question of the project is: how can flexibility be used in order to deal with a dynamic product assortment?. Six sub questions were formulated in order to find the answer to this main question. Part of the theoretical contribution of the project is the application of flexibility within storage assignment. In this research, a quantitative two dimensional construct of flexibility is developed. The first dimension of flexibility is compactness (x), defined as the number of SKUs that can be stored at one location. This dimension of flexibility can increase the fill rate of lying locations. Not only space costs but also labour costs decrease steeply with the number of aisles in use. Benefits are thus cost savings in both space and labour costs. Even though increasing the number of SKUs per location increases search time, this is offset by time savings from walking for low values of x. After a certain point, the increase in search time is bigger than cost savings in walking time. Further increasing x therefore results in higher cost. For hanging locations, the benefit of compactness does not apply since total space needed is constant. Increasing the number of SKUs per location leads solely to an increase in searching time and is therefore not beneficial for hanging locations. The second dimension of flexibility is multiple locations (y), which is defined as the number of locations per SKU. The benefit of this dimension is that it can decrease walking time for hanging locations. By increasing the number of locations per SKU, the composition of batches can be improved which reduces walking time. A benefit of this level of flexibility is that it can save labour costs. For lying locations however, this does not apply. Increasing solely the number of locations per SKU leads to a big increase in space needed, which results in higher space and labour costs. These results answer sub question 1 until 4. In this project, it is found assortment has its influence on the performance of flexibility. Results show that an increase in assortment size results in an increase in both space and labour costs. For a situation without flexibility, x=1, y=1, the increase is linearly related to the number of SKUs in the assortment. For both lying locations when x=4, y=1, and hanging locations when x=1, y=3, the increase in costs is more stepwise. The fill rate of the aisles are causing a stepwise increase in space and labour costs. Although total costs are increasing, the costs per SKU are decreasing. In addition, it is shown that cost savings resulting from an increased level of flexibility are increasing as assortment size increases, both for lying or hanging locations. Besides assortment size, base stock levels are considered as well. For lying locations it is shown that changing the S level has no effect on total costs. However, the maximum value of x is restricted by the base stock level. Increasing base stock levels do not directly result in a change in costs, however, lower costs can be attained at higher values for x and thus lower values for S. It must be noted that order acceptance should not suffer from lowering base stock levels. For hanging locations, it is shown raising the level of S leads to an increase in space costs. Besides, it is shown increasing y leads for all S levels to a decrease in labour costs. In general, new best performing settings for each level of S turn out to be the settings in which x has its optimal value for lying locations and y has its maximum value for hanging locations. These findings answer sub question 5 How does this level change in response to different scenarios regarding assortment?. In sum, the most cost effective storage assignment strategies remain the same for different values for the number of SKUs and base stock levels. It is found demand has its influence on the performance of flexibility as well. It is shown that an increase in both average order size and the number of orders per day leads to an increase in total costs, but to a decrease in costs per item. A scale effect is thus shown for total demand. For demand distribution, it is found that total costs are lower when demand is Pareto distributed compared to uniform distributed demand. However, the model does not take everything into account regarding space effects. It is also found that cost savings resulting from increased flexibility are bigger for a uniformly distributed demand compared to a Pareto distributed demand. These findings answer sub question 6 How does this level change in response to different scenarios regarding demand?. Again, the most cost effective storage assignment strategies remain the same. 46

59 Another theoretical contribution is the alternative batching algorithm constructed in this project. The existing batching algorithm of Docdata does not utilizes the possibility of multiple locations per SKU. In order to benefit from the multiple locations dimension, a batching algorithm that does utilize this possibility is necessary. The alternative batching algorithm provides a batching algorithm that does take the property of multiple locations into account. Finally, what does this mean for main question and research assignment? The assignment design a cost effective storage strategy considering flexibility in storage assignment in order to handle a dynamic product assortment within an e-fulfilment environment. was formulated in order to answer the main question. To recall, a storage assignment strategy is a set of rules to determine the assignment of products. In general, for lying locations, the best performing level of flexibility is reached when the compact storage assignment strategy is used. For hanging locations, the best performing level of flexibility is reached when the multiple locations storage assignment strategy is used. In sum, the most cost effective storage assignment strategy consists of two rules: For lying locations, use the compact storage assignment strategy For hanging locations, use the multiple locations storage assignment strategy It is found the most cost effective storage assignment strategy applied to the case of Docdata as well, which answers sub question Practical recommendations Practical usefulness of the project lies in the possible costs savings that can be achieved by changing the storage assignment strategy. As concluded, compactness of storage is of high importance. Therefore, it would be recommendable for lying locations to increase the fill rate of locations. For the inbound activities, it is thus advisable to store as multiple SKUs on a single location. It is shown that an increase of x from 1 to 2 leads to biggest cost savings, so it would be advisable to increase x to 2. Even though space costs might be fixed in the short run, labour costs can be saved with compact storage as well. For hanging locations, it is advisable to spread SKUs out over the aisles in use rather than store a SKU on a single location. Increasing the number of locations per SKU can improve batches and reduce walking routes. It must be noted however that it may induce extra costs for inbound activities to store SKUs on multiple locations instead of on a single location. It goes without saying that the extra costs for inbound activities should not exceed the benefits that can be achieved from spreading SKUs over multiple locations. An example of how these extra costs can be limited is by changing the sorting process of incoming goods. At Docdata, incoming goods are checked and sorted before the inbound process starts. One possibility is to modify the sorting process in such a way that incoming SKUs are spread across different trolleys. With a closest open location inbound strategy, using different trolleys and starting the inbound process at different locations within the warehouse increases spreading of SKUs without drastic changes in the process. Another possibility which does not require big changes in the process is the inbound process of returned goods. Instead of trying to store SKUs on a single location, allow operators to store returned goods at location in other aisles within the warehouse. The dynamic character of assortment and demand makes it important to adapt the approach to the changing conditions. This report showed flexibility is especially beneficial for situations in which demand is high. As seen during the case study, it is expected demand will increase in the future, just as assortment size and average order size. The bigger the growth of these numbers, the more profitable flexibility within storage seems to be. It is therefore advisable to increase flexibility in order to be better able to handle future scenarios. The same is true for periods with deviating demand and assortment. Based on the case study, it is known that during sales periods assortments size, the number of orders and average order size increase. In addition, it appears the gap between fast and slow movers increases during sales periods. During these periods, a small part of the assortment experiences high demand. The study showed that flexibility is more profitable when demand is high and the assortment is big. Therefore, it is advisable to increase the level of flexibility prior to the start of sale periods. 47

60 An important additional requirement of the strategy is a suitable batching algorithm. In this project, the alternative batching algorithm was introduced. However, another batching algorithm using the property of multiple locations could be suitable as well. In addition, it is recommended to test the strategy with historical data of a specific environment before implementing it. The simulation model of this study used a few random generators. Historical demand and storage data can provide insights in the costs and benefits of a specific fulfilment environment. A preliminary study can show if the new strategy would indeed have performed better than the original one. 8.3 Limitations Few limitations of the study must be noted. First of all, an important limitation is the interrelatedness of various warehousing activities. Decisions made at the various levels are strongly interdependent. (De Koster et al., 2007, p489). This quote highlights the main weakness of the project. The model doesn t consider time needed for inbound activities. It might be possible that time needed for inbound activities increases so much that it out weights the benefit of reduced picking time. In addition, time needed for sorting is not taken into account as well. Moreover, care must be taken for applying results to other situations if aspects like batching and sorting are organized differently from the process considered in this project. One should thus be cautious for generalizing results. Although it is assumed that the principle is applicable in general, the project is carried out for specific warehouse characteristics, like the traversal picking strategy. Caution is desirable because of the strong interaction between different processes. Further research to the interaction between for example inbound and storage processes are desirable to provide more insight. Related to previous point, is the limitation of the number of batching methods that are considered in the research. The batching methods regarded are limited to the one used at Docdata and the alternative method. Since the Docdata algorithm does not utilize the possibility of choosing between different locations, another batching algorithm was needed that does consider this. However, the alternative method used in this project could be replaced by another batching method which uses this attribute as well. Constructing a suitable batching algorithm could be subject of a new study to further investigate the effect of multiple locations per SKU on the performance. Another limitation of this project is the limited scope. This effect is twofold. First of all, the study considers labour costs and space costs. However, for labour costs only specific picking activities are considered. Other activities are not taken into account, since they are assumed to be constant. Examples are inbound activities, sorting, time needed for searching locations and putting items in the picker cart etc. The cost savings in the case study are presented in percentages, however, the numbers presented are thus higher than in reality. Second, it might be interesting to broaden the scope and engage broader aspects in the study. For example, it is not known upfront what the demand will be during a special sale period. However, it might be interesting to involve viewer statistics of the web shop prior to the start of a sale period, in order to construct a forecast of the demand, which in turn can be used to determine storage assignment. Another example is to take the re-order strategy into account. The decision about which SKUs and the amount of items to hold on stock are outside the scope of this project. However, it might be interesting to take these issues into account as well. Despite the specific case study that is performed for one of the customers of Docdata, proportions of the base scenario rely on this environment as well. Although the situation of Docdata is assumed to be common in e-commerce, specific characteristics vary from company to company. As mentioned in the recommendations, it is advisable to adjust tests to a specific environment before it is implemented. A more practical concern is the deviation between theory and practice. One example is the way in which orders are picked in practice. The software uses a specific walking strategy in order to determine the way in which batches are constructed and the way in which the route is determined. 48

61 However, this walking strategy may deviate from the walking strategy used in practice, because of the autonomy of the operator. For example, at Docdata it is assumed order picking is done according to a traversal strategy. However, in practice, the exact picking strategy depends on the individual strategy of the order picker case. Order pickers work with trolleys on which the items are loaded. Instead of taking the trolley along all the way, trolleys are often parked somewhere in the main aisle. Some order pickers visit a few aisles without their trolley, gather some items, carry them their selves and drop them off at the trolley. This picking strategy corresponds partly to a return strategy as described by Roodbergen (2001). The autonomy of the picker makes it possible to deviate from the intended walking strategy. This makes it extremely difficult to model the walking strategy used in practice and adjust the batching process and routing to this. 49

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64 Docdata Corporate. (n.d.) retrieved on March 13, 2013, from Persberichten. (n.d.) Retrieved February 25, 2013, from 52

65 jan-10 mrt-10 mei-10 jul-10 sep-10 nov-10 jan-11 mrt-11 mei-11 jul-11 sep-11 nov-11 jan-12 mrt-12 mei-12 jul-12 sep-12 nov-12 # SKUs Appendix A: List of abbreviations FCFS I/O point KPI LSP (R,S) strategy SKU VBA first-come, first-served, is a sequential rule in which the sequence of the orders to be processed is determined by the time an order is placed in the web shop Input/Output point Key Performance Indicator Logistic Service Provider Inventory management strategy in which the stock levels are checked periodically, period R, and are supplemented to a certain level, level S Stock Keeping Unit Visual Basic for Applications, programming language Appendix B: Figures 1. Organization chart DOCDATA N.V. 2. Average number of SKUs on stock The number of SKUs on stock for one of Docdata s customers, period

66 Scope of project Goods delivered Storage & handling of goods Goods picked up Suppliers Docdata Carriers Flow of goods & actors involved 4. Flexibility dimensions and definitions, Vokurka & O Learly-Kelly (2000) 5. 54

67 Routing policies for a layout of one block, Roodbergen (2001) 6. Storage implementation strategies, Petersen, Aase & Heiser (2004) 7. 55

68 Two common storage implementation strategies, De Koster et al. (2007) 8. No flexibility Level 1: Each SKU is stored on one location Full flexibility Level y: Each item will be stored on y locations, within y different aisles Level n: Each SKU is stored within each aisle Various levels of multiple locations flexibility 9. Visualization of the warehouse model