Rodenburg: picking the right order

Master thesis

“Picking the right order: Combining traditional retailing with online retailing�

University of Groningen, Faculty Economics and Business Msc Ba, specialization Supply Chain Management Dirk Rodenburg, s1612379 Supervisor: Dr. Zhu

Abstract This article discusses an order batching strategy for combining orders from online and traditional retailing in order to increase the performance in the order picking process. Decreasing the time needed to pick the items releases a part of the pressure on other processes. Especially with the online retailing where the amount of 24-hours delivery orders is increasing. In order to have sufficient time for completing the 24-hours online orders, the remaining orders of online retailing and the traditional retailing should be processed as soon as possible. The solution in this article is suitable for parallel aisle warehouses with no cross-aisles. An existing batching algorithm is used in order to combine both online and traditional orders. With this batching algorithm the total distance per batch is decreased, and therefore total time needed to pick all the items in a batch is decreased.

Word Count: 8,785 2

Table of Contents Preface

4

1

Introduction

5

2

Theoretical Background

7

2.1

Order picking process

8

2.2

Order batching problem

9

2.3

Traditional retailing

11

2.4

Online Retailing

11

3

Methodology

14

4

Results

17

4.1

Data analysis

17

4.2

Possible improvements

21

4.3

Simulation

22

4.4

Results simulation

24

5

Discussion

25

6

Conclusion and future research

28

7

References

30

Appendices

33

Appendix A

33

Appendix B

34

3

Preface This master thesis is the final part in completing my Master program Supply Chain Management (SCM) at the Rijksuniversiteit Groningen. The following research has been conducted at CB-logistics and I would like to thank some people who helped me during this project.

First, I would like to thank my supervisor at CB-Logistics, Anoek Peters, and my manager at CB-Logistics, Dave Anakotta, for giving me the flexibility and trust in order to complete this research. Without their advice and recommendations during this challenging project the results would not have been the same. Especially I would like to thank Anoek for his aid in the data collection, with his knowledge I was able to gather the right data relatively quick.

Secondly, I would like to thank my supervisor Dr. Stuart Zhu for his constructive feedback during the execution of this project. Not only about the content of this research but also concerning the structure of this thesis.

Thirdly, I would like to thank my parents and my girlfriend for their support and patience during this project. In addition, I would like to thank my parents for allowing me to use their car to travel from home to Culemborg and Groningen.

Finally, I would like to thank my brother for reviewing this thesis and giving feedback for completing my thesis.

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1 Introduction The current recession is forcing organizations to cut their costs and improve productivity in order to survive and remain competitive. Improving the efficiency in processes increases the total productivity of that process. In warehousing, up to 60% of the labour activities is consumed by the order picking process (de Koster et al. 1999). Order picking is defined as “the process of retrieving products from storage (or buffer areas) in response to specific customer requests� (de Koster et al. 2007: 481). Due to the emergence of online retailing, the number of customer orders increases whereas the quantity per order decreases (Gong & De Koster 2008). Picking order simultaneously (in batches) can increase the efficiency of the order picking process (Ruben & Jacobs 1999). It might be cheaper and faster to pick two (or more) orders together in one cycle compared to picking them all separately. The increasing pressure for efficiency and the emergence of online retailing are the drivers for this research.

The order picking process is either very capital intensive when automated or very labour intensive when done manually, therefore professionals consider order picking as the main area that is the most suitable for productivity improvements (de Koster et al., 2007). One way of doing so is batch picking, where a number of customer orders are combined to form a batch and the batch is picked in one sequence, in order to decrease the mean travel time per order (Won & Olafsson 2005). Several authors have developed algorithms to develop the (near) optimal batch size under specific conditions, for traditional manufacturers and distributors (Won & Olafsson 2005; Gademann & Van de Velde 2005; Van Nieuwenhuyse & de Koster 2009; Rabta & Reiner 2012). Beside these algorithms more recent literature focuses on the order picking problems experienced by the emerging online retailers. As Gong & de Koster (2008) mention online retailers 5

face additional requirements customers expect from them. The increasing popularity of 24-hours delivery (Gong & De Koster 2008) requires short and accurate delivery, which results in a shorter lead time and a complex logistics process. Because those online orders keep arriving during the day, online retailers are unable to plan their entire order picking at the beginning of each day.

Adding up to the planning problem postponement of the deadline of ordering, up to 11 p.m., is putting pressure on the order picking process.

While literature has addressed both batching problems (those of both traditional and online retailing) separately, however, to the best knowledge of the author, no literature has addressed the batching problem perceived by manufacturers/distributors who expanded their business with online retailing. Combining the traditional distribution to retailers and the online retailing to endconsumers enables the benefit of having one central warehouse for one supply chain, whereas with two separated organizations there is a need for two separated warehouses (Xia & Zhang 2010).

A case study is done at CB-logistics, the book distributor for bookstores in The Netherlands which is also responsible for the picking and packaging of the online book-orders via Bol.com and various other online retailers. With the increasing use of online retailing the company receives more 24-hours delivery orders which puts the picking process under pressure. CB-Logistics faces difficulties in the order picking process combining the orders of bookstores and those of individual customers. This research tries to search for more economies of scale by studying the optimal picking batch size and composition for CB-logistics, while minimizing the total time needed for the order picking process. Existing literature on both the traditional retailing and online retailing will be used in this research. The research question used in this research therefore is: “What is

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the optimal order picking batch size and batch composition when combining traditional retailing and online retailing?� With batch composition I refer to the kind of orders included in a batch, traditional or online orders. The proposed model will be tested against the currently used method of batching orders for improvements. The contribution of this research will be theoretical on the one hand, instead of picking both orders in separated batches combining orders in batches might improve efficiency when using one algorithm. On the other hand there will be a practical contribution for managers of existing warehouses as well. When they have the intention of expanding their activities to online retailing, providing them with insights on how to organize their batching strategies can assist in making strategic choices.

The paper is organized as follows. In chapter 2 a thorough literature is provided on the available literature on order batching, online retailing, and scheduling. The proposed model will be presented in chapter 3, and the case data will be explained in chapter 4. After explaining the case the results will be presented and discussed in chapter 5. In chapter 6 the conclusions will be given and finally suggestions for further research are given.

2 Theoretical Background In order to get a good understanding of what the order picking process entails, first an overview on literature about the order picking process is given. After establishing a deep understanding of the order picking process, the sequence of orders which need to be picked in this process is mentioned. This is also referred to as the batching problem. Next traditional retailing and online retailing are considered in separate paragraphs. At the end of both

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paragraphs the influences of traditional retailing and online retailing on batching strategies are mentioned to show the differences between both ways of retailing.

2.1 Order picking process The order picking process consist of several steps. It contains the scheduling of customer orders, assigning stock to orders, releasing the orders, picking the items from storage and the disposal of the picked items (de Koster et al. 2007). Order picking systems can be distinguished in two major types: parts-to-picker and picker-to-parts (Gong & De Koster 2008; Bozer & Kile 2008).

In parts-to-picker systems the products are automatically brought to the pick location (depot) (de Koster et al. 2007), whereas in picker-to-parts the order picker walks or drives through the aisles to pick the products from the shelves. Picker-to-parts is the most common of the two types (de Koster et al. 2007), therefore the aim of this research is at this type of order picking. Picker-toparts can be divided in three basic variants: Picking by order, picking by article and wave picking (de Koster et al. 2007). Picking by order means each order is picked individually. Picking by article has many variants with the two most well-known being sort-while-pick and pick-and-sort. Sortwhile-pick involves picking several orders simultaneously with each order picked in a separate bin and the pick-and-sort involves first picking all the items of the orders and these items are later sorted per individual order (Gademann & Van de Velde 2005). Picking by article refers to batch picking, which will be elaborated on in the next section. Finally, wave picking is the variant where several batches are picked simultaneously by a number of pickers with each picker picking one batch (Gademann et al. 2001). Another distinction which has to be made is the difference between conventional manual order picking and automated picking. In manual order picking, the pickers go through the aisles and manually pick the items. This can be done with the aid of a 8

picking cart or a vehicle (Petersen & Aase 2004). Automated picking is an automated storage and retrieval system (AS/RS) which is capable of handling items without the interference of an operator (Roodbergen & Vis 2009). This research is aimed at conventional manual picking which still is applied in the majority of picking systems within warehouses worldwide (de Koster et al.

2007).

The emergence of online retailing caused the number of items per order to decrease beneath the unit-load. Instead of retailers ordering higher quantities of an article, a unit-load, end-consumers order only one single article. This resulted not only in lower order quantities of the online orders, but it caused the order quantities of traditional retailers to decrease as well (Bozer & Kile 2008). Therefore the warehouse operation capacity can be insufficient to pick all customer orders individually (Le-Duc & de Koster 2007). In order to be able to pick all the items requested in orders, individual orders are grouped together in a pick route to gain an increase in labor efficiency (Gibson & Sharp 1992; Potts & Kovalyov 2000). The question how to group the individual orders is concerned as the Order Batching Problem (OBP) in literature.

2.2 Order batching problem As mentioned earlier orders are grouped together to gain efficiency in the order picking process. Batching orders is part of the planning of the order picking process (Gu et al. 2007). The sequence in which orders need to be assigned to batches is concerned as the Order Batching Problem. Research has resulted it in two dominant types of batching heuristic in order to solve the OBP, seed algorithms and time saving algorithms (de Koster et al. 1999). Both algorithms will be discussed briefly below.

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Seed algorithm construct batches in two sequential phases: Seed selection rule and order congruency rule (Henn et al. 2010). The first order which is selected for a batch is called the seed (Elsayed & Stern 1983). There are several seed selection rules, for example a random order (Gibson & Sharp 1992) or the order with the highest amount of items (Pan & Liu 1995). Ho and Tseng (2006) give a nice overview of existing seed selection rules. Once the seed is selected the order congruency rule determines which orders are added to the batch based on their characteristics. These congruency rules could be a random rule or smallest number of additional picking locations rule. For an overview of all congruency rules the author refers the list of accompanying order selection rules in Ho and Tseng (2006).

Time saving algorithms use a different approach and have multiple variants; CWright algorithm (Clarke & Wright 1964), EQUAL algorithm and the SL (small and large) algorithm (Elsayed & Unal 1989). To get an understanding of a time saving algorithm the basic variant of the Clarke and Wright (1964) algorithm is explained next. First, the time savings of all the combinations of two orders (pairs) are calculated, taking the maximum capacity of the picking cart or vehicle into account. The pairs are sorted based on their savings in a decreasing way, and the pair with the highest amount of saving is selected. If two combinations both have the highest savings, one is picked randomly. The next step consists of three possibilities. (1) if both orders have not yet been assigned to a batch and there is sufficient capacity with the order picker, both orders are included in a new batch. (2) If one of the two orders is included in an existing batch (because it provided higher savings in combination with another order) and the volume of the other order fits in the remaining capacity of this batch, add the other order to the batch. If the unassigned order does not fit into the batch proceed to the last step. (3) Both of the orders are

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included in an existing batch, proceed to the last step. This last step is selecting the next order combination, until all orders are assigned to batches. For a more extensive review on time saving algorithms the author refers to Elsayed and Unal (1989) and de Koster et al. (1999).

Traditional retailing and online retailing have been mentioned a few times already. A distinction between these two and their differences will be made in the following part.

2.3 Traditional retailing In the traditional retailing retailers order their products at the wholesaler. Customers visit the retailer in order to get the products they need (consumer-to-products). Consumers are able to touch and feel the product before they purchase the product. When the product is bought the consumer can take the purchased items, assuming the items are on stock, back home (Avery et al. 2012). The retailer orders products at the wholesaler/warehouse, which results in the wholesaler/warehouse receiving few but large orders (Le-Duc & de Koster 2007). These orders will be delivered on pre-determined days during the week at the retailer (Alba et al. 1997). With these orders known up-front deterministic models where developed for batching the orders, in order to make the picking process more efficient (Gademann & Van de Velde 2005).

2.4 Online Retailing Online retailing has increased the opportunity for customers to buy their products without having to go to a store. With the existence of catalog and phone sales consumers were already enabled to buy products without going to a store (Nickels 1973), but the emergence of online retailing significantly enhanced this (Swaminathan & Tayur 2007). This new way of retailing resulted

in

new

opportunities

for

existing

retailers,

opportunities

for

existing 11

wholesalers/manufacturers, the emergence of internet retailers with no physical assets and the emergence of online retailers with physical assets (de Koster 2003; Chen et al. 2011), resulting in an significant increase in internet sales (Xia & Zhang 2010) and increasing competition in online retailing (Wruck et al. 2012).

Online retailing differs from traditional retailing in a way that the “last mile� of the entire distribution within a supply chain is done by the online retailer rather than by the customer (Swaminathan & Tayur 2007). This implies the delivery of the products at the consumers’ homes rather than the consumer going to the store to buy the product and take it home. According to Gong and de Koster (2008) fast response is critical for online retailers in order to stay competitive.

Fast delivery promises (order today before 11.00 p.m., delivery tomorrow), together with the shift from few-but-large orders to many-but-small orders (Le-Duc & de Koster 2007), puts the lead time under pressure (Henn 2012).

Another complex factor is the moment the orders are received. As mentioned before with traditional retailing the orders for a specific day are known in the morning and a schedule can be made for that day. With online retailing with 24-hours delivery orders arrive during the day. The extension of deadline for ordering makes it impossible for online retailers to wait until all orders are received and start picking at that moment. This order arrival and release is called online or stochastic (Gong & De Koster 2008). In these stochastic order arrivals two different ways of time window batching can be distinguished: Variable Time Window Batching (VTWB) and Fixed Time Window Batching (FTWB) (Van Nieuwenhuyse & de Koster 2009). In VTWB a batch is formed if predetermined parameters are reached (number of orders or number of items for example). In

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FTWB one or more batches are formed on predetermined (fixed) time intervals with all the available orders. For the purpose of this research only FTWB is taken into consideration.

Research dedicated to online retailing predominantly focus on the additional revenue it might offer. These studies are mainly focusing on the marketing aspect of online retailing. Less research has been dedicated to the operations concerned with online retailing, and these studies are mainly focusing on inventory ownership (see for instance Netessine & Rudi 2006) and physical distribution (de Koster 2003; Kull et al. 2007). The research that does exist on order picking concerning online retailing is focusing on the online retailer only (Gong & De Koster 2008), and does not look in to the combination of both ways of retailing.

This research tries to fill this gap, by looking into the possibilities of combining batching strategies of both traditional retailing and online retailing. Whereas in current literature batching strategies are developed for either online retailing or traditional retailing, no research has been done on developing one strategy which combines the orders of both ways of retailing. In a case study, where traditional retailing is combined with online retailing, first the different batching strategies of traditional and online retailing are applied in order to look for differences in performance considering the time needed for the order picking process. After comparing the results for both batching strategies, possibilities for combining the batching strategies of traditional and online retailing are studied. A potential new strategy will be tested and the results will be compared to the results of the batching strategies of traditional and online retailing.

Positive results could lead to combined warehouses for both traditional and online retailing which could lead to economies of scale.

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3 Methodology As mentioned before, an explorative case study will be done in this research. The decision to execute a case study is because of the explorative nature of this research. An in-depth case study is an useful method of research in an explorative research (Karlsson 2009). In this research a single case will be studied in order to do an extensive research on the possibilities. According to Eisenhardt (1989) the case selection is an important aspect of case study. The case will be drawn from the population, therefore determining the population is crucial in order to select a representative case. For this research the population consists of organizations which are active in both the traditional and online retailing. CB-Logistics, the book-distributor of The Netherlands, has been selected as case. The warehouse of CB-Logistics provides bookstores (traditional retailing) as well as online customers (online retailing) with books. The majority of the orders from bookstores are known at the first scheduling moment which is at 01.20 a.m., the remaining orders from retail are emergency orders which arrive during the day and need to be delivered at the bookstores the next day. The online orders can be separated in two different groups, the 48-hours delivery orders and the 24-hours delivery orders. The first group of orders are also known at the first scheduling moment, like the majority of the retail orders. The other part of the online orders, the 24-hours orders, are received via online retailing during the entire day until 11.15 p.m. The deadline for order completion of the online orders is at 1 a.m. because at that time the packages, which have to be delivered next day, are collected. In order to have as much time as possible to complete all the incoming online orders during the day, the 24-hours orders, it is beneficial for the company to fulfill all the orders known at the first scheduling moment as soon as possible.

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The process under consideration in this research is the order picking process, as depicted in Figure 1. Picking and sorting is done by pick-and-sort; all the books of a batch are picked and put in to crates. Full crates are put on a conveyor belt which transports the crates to an automated sorter which sorts the books per order. In front of the sorter is a buffer, where all the crates of one batch are grouped together.

Receiving books

Storage in Bulk area

Replenishment of picking area

Order picking

Buffer

Sorting

Shipment

Figure 1: Processes at CB-Logistics

After the sorter (nearly) finishes the previous batch, a new batch is assigned to the sorter, provided that the batch is complete.

The unit of analysis in this case study is the order picking process within CB-Logistics. Data on the order picking process is gathered directly from the company database. This data includes data about batch composition (which orders are included in the batch), the quantity of items in a batch, locations of the items in that batch and the time needed to pick the items. First, data of one day, 2nd December 2013, consisting of 40 batches will be used to analyze the current batching strategy of the company. The results of this analysis will initiate the application of an existing batching strategies in an attempt to increase performance of the order picking process.

At CB-Logistics the order picking is done in two separate warehouses. One warehouse consists of multiple parallel aisles, with each aisle having its own truck, to aid in the picking, and its own output point. The other warehouse, the Fast-Mover (FM) warehouse consists of books with high demand figures which are stored over several zones. This research does not take this second warehouse into consideration and is focused on the first warehouse because it is this 15

warehouse where productivity losses are experienced. The warehouse under consideration consists of 24 parallel aisles, with no cross aisles. Each aisle has got his own truck which can go horizontal as well as vertical through the aisle. It is not possible to go to another aisle with a truck, when books need to picked from a different aisle as where the picker is at the moment, he or she needs to switch trucks. A picture of a truck is displayed in appendix A. The orders from bookstores are grouped in to batches at 01.20 a.m. together with the online orders without 24h delivery. These batches are processed in a specific order. During the day there are several fixed time window batching moments. At each time window the current number of orders are grouped into one or more batches. After completing a batch it is automatically scheduled after the pre-determined batches. In order to generate as much time as possible for the batches generated during the day, efficient order batching is needed for the first batches.

Some assumptions are made in order to conduct a proper research. First, the assumption is made that all the needed books requested by orders are in stock and on a pick location. Including replenishment would be beyond the scope of this research. Second, the books are stored in a class-based storage. Based on their recent demand the books are divided in five classes, with A being the class with the most frequent demand, and E the class with the lowest demand.

Furthermore an article is stored in more than one location in the warehouse. Finally, in this case no setup times are considered, since these are negligible.

The batch size and batch composition are the two decision variables. The current batching strategy separates the online orders from the traditional orders and assigns the individual orders to batches afterwards. By first analyzing the current batching strategy possible time savings will

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be identified. After establishing the possible time savings, a batching strategy with combining both traditional and online retailing orders is suggested and a small simulation is made to verify the proposed solution. The expected result will be a strategy which has the highest time savings in the order picking process. As mentioned earlier the contribution of this research will be theoretical on the one hand, the knowledge of combining both warehouses which leads to economies of scale via the use of one algorithm. On the other hand there will be a practical contribution for managing existing warehouses with the intention of expanding their activities to online retailing.

4 Results In this section first an overview of the current batching method, where the orders from traditional and online retailing are separated, used as the case company CB Logistics, is given and the current performance of this method is calculated and analyzed. Based on currently known strategies a seed algorithm is applied to the data obtained from the case. After simulation the data is compared to the current performance and possible improvements are given for combining the orders from both traditional and online retailing.

4.1 Data analysis For the measurement of the current performance only the data of the batches formed in the night are taken into consideration. Because batches which are formed during the day consist of orders which are placed during the day they and are not known in advance. Therefore they cannot be used in the attempt to decrease to time needed for picking the books. For the 2nd of December 40 batches were created in order to fulfill all demand. Since data of 8 batches was 17

incomplete this research is based on 32 batches in total. The indicators of the data are displayed in Table 1.

Number of batches Total number of books Number of orders Number of books from warehouse under consideration Time needed to pick all the books (hours) Average time per book (sec)

32 132,251 3,851 74,959 153.04 7.35

Table 1: Data indicators

The calculation of the average time per book was done per aisle per batch. Therefore we have 32 (batches) times 24 (aisles) = 768 measurements of average time per book. This lead to an average of 7.35 second which is needed to pick a book. In Table 2 the ten best performing combinations of batch number and aisle number are displayed. The performance is determined by the average time per book which was needed to pick all the books in that specific aisle of a batch, which is depicted in the last column of the table. Before analyzing these figures it is a given fact that in the current situation each batch needs books from all the 24 aisles. Therefore for every batch all the trucks need to go through the aisle and pick the books required from that aisle.

Total time Time per Batch Aisle # locations # books Books/locations Distance (sec) book (sec) 18 4 5 45 9 78 158,00 3,511111 18 11 6 31 5,166667 128 114,00 3,677419 18 3 5 27 5,4 90 100,00 3,703704 14 10 119 298 2,504202 160 1107,00 3,714765 14 24 111 246 2,216216 162 914,00 3,715447 11 5 60 146 2,433333 158 547,00 3,746575 12 11 107 203 1,897196 162 789,00 3,8867 11 2 68 144 2,117647 152 588,00 4,083333 12 10 134 293 2,186567 160 1218,00 4,156997 36 2 102 226 2,215686 162 967,00 4,278761 Table 2: Best performing combinations of batches and aisles

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Table 2 tells us that aisles 4, 11 and 3 from batch 18 all have a low number of books and very few locations, therefore having a high number of books per location. These aisles perform the best since they require the least amount of seconds per book. This can be explained by the orders in batch 18. The orders in this batch are from education institutions and they require a higher quantities per book. The other best performing aisles have a lower number of books per location, ranging from 1.9 books per aisle to 2.5 books per aisle. These aisles contain a higher number of books compared to the first three aisles. Despite their difference in number of books and number of books per location their performance is somewhat similar. The explanation of this could be the distance which is covered by the truck. A truck initially has a fixed route it follows within an aisle, this route is the S-shaped route which is displayed in Figure 2 on the left side. In this predetermined route the picker travels three times to the back of the aisle and returns to the front, with a vertical adjustment at each turning point at the beginning or the end of the aisle.

Underlying reason for this route is the output point, which is at the front of each aisle. With this route the picker is enabled to unload full boxes with books at three moments during a route through one aisle, which determines the capacity per truck per aisle. The turning point of a truck within an aisle does not need to be at the end of an aisle. If the last books which has to be picked on a particular height is located halfway down the aisle, the truck can return to the beginning of the aisle right after it has picked the book which was the furthest away. This is shown in Figure 2 on the right side, where the route in the middle does not reaches to the end of the aisle. With

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Figure 2: S-shaped route through an aisle

fewer books to be picked in an aisle the chance a truck does not need to travel the whole distance becomes bigger and therefore the total distance in an aisle will be less. Besides this, the predetermined route also generates a maximum distance which can be covered by a truck within an aisle. When this maximum distance has been reached, any additional books within this aisle will not add any distance to the route because its location is already on the route. Adding additional books to this aisle will decrease the average distance and therefore time needed per book for this aisle.

A regression analysis was carried out based on the performance data per aisle and gace the following significant results at a 95% confidence interval. The number of books, the number of locations and the distance covered within an aisle explains almost 75% of the variance in the total time which was needed to pick all the books in this data sample. The formula which was created via the regression analysis is: Time needed to pick = - 136 + 4,42 locations + 2,44 books + 2,5382 distance The results of the regression analysis can be found in appendix B.

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4.2 Possible improvements After interpreting the results of the different batches possible room for improvement was identified. As shown with the regression analysis the number of books, number of locations and the total distance determine 75% of the total time needed to pick all the items in one aisle. In order to improve the performance, the average time per book should decrease. In order to make an improvement in the total performance during a day, the number of locations that have to be visited should decrease or the total distance should decrease. Since the number of books per batch on average cannot be increased due capacity of the machine used in the next process, another method of batching the orders is considered to achieve an improvement in performance.

The first option for improving performance is decreasing the number of locations while remaining the same number of books. This will lead to a higher pick quantity per location. As can been seen in Figure 3 the average time per book needed to pick decreases when the number which have to picked per locations increases.

Figure 3:Average time per book per quantity

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Increasing the number of books per location is possible when the orders with similar books are grouped together in a batch. The second option for improving performance is decreasing the total distance covered. Because of the earlier mentioned fixed route through an aisle there is a maximum distance which can covered per aisle per batch. Considering that in the current situation every batch needs books from every aisle, 24 trucks take the route through their aisle.

By grouping orders based on the aisle numbers from where their product need to be picked, the number of aisles which have to be addressed per batch could be reduced. Decreasing the number of aisles per batch will decrease the number of routes which have to be taken by the trucks, leading to a decrease in total distance covered per batch. For this research a simulation based on all data is out of scope, therefore a small scale simulation will be executed. The first option for improving performance would require a total simulation due to the characteristics of the data (Many different titles and orders). Therefore for this research a small scale simulation for the second option has been conducted.

4.3 Simulation In order to calculate the possible benefits of batching the orders based on the item locations a small simulation has been executed. Due to the limitation of this study only a small scale simulation will be executed, instead of u full scale simulation based on the total dataset gathered from the case. In the simulation 30 orders of traditional retailing have been selected and 30 online orders have been selected from the data. With these orders two scenarios are simulated. First, the orders are treated separately and therefore treated as two separated batches. In the second scenario the distinction between online and traditional orders is not used and the orders are combined in one pool of data. Based on an Ho & Tseng (2006) a seed algorithm is used to assign 22

the 60 orders are into two different batches. The algorithm which is used consists of two rules, first the seed-order selection rule, which is followed by the order congruency rule. The seed-order selection rule is the Smallest Number of Picking aisles (SNPA), and the order congruency rule used in tis simulation is the Smallest Number of Additional Picking Aisles (SNAPA) rule.

1 Identify all the items in an order 2 Determine the number of aisles per order 3 List orders based in the number of items per order 4 Pick the first order with the smallest number of picking aisles as seed-order 5

IF multiple orders have the smallest number of aisles

6

pick the order with the earliest timestamp to be the seed-order of the batch

7 Calculate the number of additional aisles of the remaining orders 8 List remaining orders based on the number of additional aisles in a descending way 9 Add order with the smallest number of additional aisles to the batch 10

IF multiple orders have the smallest number of additional aisles

11

Add the order with the highest total number of aisles to the batch

12

IF the total number of aisles is the same

13

Add the order with the earliest timestamp to the batch

14 Count number of orders in the batch 15

IF number of orders < maximum capacity then return to step 6

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IF number of orders = maximum capacity then Terminate batch and return to step 1

17 When all order have been assigned to batches terminate and give output with batches

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The algorithm was applied to the 60 orders which were randomly chosen, and any differences between the two scenarios were calculated. Since the number of orders per batch is not similar to the case data it was not possible to calculate the amount of time needed to pick the books. Because no book was ordered by more than one customer the number of locations and books are similar in both scenarios. Therefore the difference between both scenarios can only be in the distance which has to be covered by the truck. In order to identify the possible improvement the focus remains on the distance covered by the truck.

4.4 Results simulation The simulation contains two scenarios. The first scenario is similar to the current batching strategy used at the case company. In this first scenario, where the traditional and online orders are separated in two different batches, all the aisles need to be addressed in both batches. In two batches 48 aisles had to be visited to complete all orders. Taking a look at scenario two, where the algorithm described in the previous section was applied, the orders are assigned to batches based on the number of aisles per order. In this scenario, the first batch needs to be address only 19 aisles, which is displayed in Table 3. The savings can be calculated by using the following formula: (total aisles scenario 1 â&#x20AC;&#x201C; total aisles scenario 2) / total aisles scenario 1 With assigning order based on the number of aisles a saving of 10.4% on the number of aisles that need to be addressed can be reached.

scenario 1 scenario 2

Number of Number of aisles Total number of aisles Batch 1 batch 2 aisles 24 24 48 19 24 43

Table 3: Number of aisles per batch

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Taking a look at the total distance which need to be covered by the trucks there is also a decrease in distance for the second scenario compared to the first. The distance covered per batch per scenario is displayed in Table 4. The saving in the total distance covered is calculated with the following formula: (total distance scenario 1 â&#x20AC;&#x201C; total distance scenario 2) / total distance scenario 1 The savings in total distance in scenario 2 is less than compared to the savings on the number of aisles, but still is a 248 meters or 5.6% saving can be reached with the other batching strategy.

scenario 1 scenario 2

Distance covered in Distance covered in Total distance batch 1 in meters batch 2 in meters in meters 2,270 2,138 4,408 1,863 2,297 4,160

Table 4: Distance covered per batch in meters

5 Discussion As shown in the previous section times can be saved by combining the order of traditional retailing and online retailing orders. This was expected since the batching method had proven already to be more efficient in a warehouse which only serves traditional orders. Other improvements were also considered, like increasing the number of orders per batch, therefore increasing the number of books per batch and the number of books per aisle per batch. In this case this was not possible since all the books from a batch are sorted by an automated sorter after the picking process. At this sorter each order is assigned to one or two exits, depending on the size of the order. The number of exits is therefore the limiting factor in the number of orders which can be assigned to one batch. In order to increase the number of books per aisle a different batching algorithm should be applied based on the location of the books in the warehouse.

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No previous research has been conducted concerning the combination of online and retail orders. As the simulation showed, even when only two batches are considered there could be a 5.6 % saving on distance (in meters). In the simulation the saving on distance occurs in the first batch, the batch where the new algorithm was applied. The second batch shows a slight increase in distance compared to the second batch in the first scenario. The underlying reason for this is that the last batch consists of all the remaining orders which are assigned to that batch. Therefore there was no need to apply the algorithm to this second batch since only 30 orders remained, which was a full batch This second batch is concerned to be the rest batch.

Answering the research question: â&#x20AC;&#x153;What is the optimal order picking batch size and batch composition when combining traditional retailing and online retailing?â&#x20AC;? does not results in a ratio of online orders and traditional orders in a batch. The optimal size of a batch is equal to the maximum capacity of all picking trucks combined. Since this number cannot be reached for the trucks of all the aisles, because of the limitation in number of orders by the automated sorter, the number of aisles needs to be reduced in order to approach the maximum number of items per truck. The composition of the batch, the kind of orders which are included in the batch, is dependent of the items in those orders. Based on the aisles where the items are stored, batches are composed which lead to a 5,6% decrease in distance which has to be covered by the trucks.

This 5.6% saving is just based on a small scale simulation, only 60 orders were used in the simulation, which is very small compared to the 3,851 orders from the total data set. Even with this small number of orders distance and time savings, and therefore the benefits, occurred. The total savings and benefits of the company could be even greater when all the orders of that day were considered. When all the orders available were used, there is a bigger pool of orders. After

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selecting the seed-order, the chance to find an order with similar books, or orders with books from similar aisles becomes even greater. The savings would become even greater when the pool of orders would increase. In this small scale simulation the last batch, with all the remaining orders and therefore not generated with the use of the algorithm, is fifty percent of the total production that day, causing the total savings to decrease. Considering the thirty-two batches from the data set, having one rest batch in that case would have less impact on the savings.

The 5.6% saving on distance does not seems much but when we keep in mind that the order picking process can take up to 60% of the labour activities in warehouses (de Koster et al. 1999) this could mean cost and time savings for companies. In order to put the 5.6% saving in perspective, for the company taken in this case a saving of five percent of the total time used at the order picking process, would mean a time saving of seven and a half hour per day, saving almost the costs of one employee in the picking process. These numbers are based on a saving on the total time, the savings from this study are in distance covered, which influences the total time needed. The savings only apply to the first scheduling moment a day, which is very early in the morning and where all the orders are known at the moment the batches en schedules are made.

On the one hand, since the simulation has been conducted on a small scale and the casespecific limitations of the processes did not have any influence on the results, the outcome of this study is generalizable to other companies. On the other hand, the reason why this particular simulation was conducted was the limitation of a next process, the number of exits at the automatic sorter limits the number of orders per batch. In this case it was not possible to adjust anything in this process, in other cases this might be possible. In other cases other improvements

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might suite better to their processes. One condition is required for other companies to use the results of this study, the orders from traditional retailing and online retailing have to be picked from the same warehouse. The results of this study can be taken into consideration when other companies experience efficiency losses in the order picking process, although all other options should be taken into consideration.

6 Conclusion and future research With the emergence of the 24-hours orders in online retailing, warehouses experience an increasing pressure on their lead time because of shorter delivery times. In order to enable warehouses to meet these demands, the remaining orders from online retailing and the order from traditional retailers should be processed as quick as possible. Since the order picking process consumes the majority of the labour activities in a warehouse, time savings in this process will release some pressure of the total process.

In this research the order picking process is studied and possible improvements are identified and studied. A Case study was conducted at CB-logistics, which is the main book distributor in the Netherlands. At this company orders from traditional and online retailing are separated and subsequently grouped in batches in order to increase the efficiency in the order picking process. Via data analysis the three main variables which influence the total time needed to pick the items were identified: the number of books per aisle, the number of locations which are addressed per aisle, and the distance covered per aisle. Decreasing the total distance covered was established as being the variable with the highest potential of savings total time needed to pick the books. Since this could only be achieved by the assignment of orders, a different batching 28

strategy was suggested. A small scale simulation was conducted were orders were assigned to batches based on the item location of the ordered books.

This research shows that combining online orders with traditional retail orders can lead to time and cost savings in the order picking process. The total distance covered within an aisle can at least be decreased by 5.6 % as shown with the simulation. In this small scale simulation only 60 orders were used, a full scale simulation based on the 3,851 probably would lead to more savings. Assigning orders to batches based on the item location provides time benefits to a company which both serves online customers as traditional retailing stores. I therefore recommend CB-logistics to apply the suggested batching algorithm in order to increase the efficiency in the order picking process.

This research has its limitation and the first one is the simulation which has been executed on a small scale and in a single scale. Future research should do simulations on the full data of a case, and look for similar cases to do the same study. The second limitation is the fact that this study was performed in a parallel aisled warehouse with man-on-board trucks used to pick all the books. Research need to be done whether this also applies to other warehouses with different processes.

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Appendices Appendix A Here is a picture displayed of an aisle in the warehouse under study. On both sides you can see the yellow boxes where the books are stored, and below the yellow boxes are smaller locations where the books with lower demand are stored.

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Appendix B A regression analysis gave the following regression equation with a 95% confidence interval: Time needed to pick = - 136 + 4,42 locations + 2,44 books + 2,5382 distance

With an adjusted r-squared of 73,8% almost three quarter of the variance is explained with these three variables.

Predictor Constant # locations # books Distance

Coef SE Coef T P -135,88 45,62 -2,79 0,003 4,4236 0,4494 9,84 0,000 2,4401 0,2253 10,83 0,000 2,5382 0,3222 7,88 0,000

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