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

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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

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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

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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

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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 end-

consumers 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-to-

parts 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. Sort-

while-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

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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

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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.

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

Receiving books

Storage in Bulk area

Replenishment of picking area

Order picking

Sorting ShipmentBuffer

Figure 1: Processes at CB-Logistics

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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

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

in Table 1.

Number of batches 32

Total number of books 132,251

Number of orders 3,851

Number of books from warehouse under consideration 74,959

Time needed to pick all the books (hours) 153.04

Average time per book (sec) 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.

Batch Aisle # locations # books Books/locations Distance Total time (sec)

Time per 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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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.

Figure 2: S-shaped route through an aisle

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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

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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

16 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

24

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 – 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.

Number of aisles Batch 1

Number of aisles batch 2

Total number of aisles

scenario 1 24 24 48

scenario 2 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 – 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.

Distance covered in batch 1 in meters

Distance covered in batch 2 in meters

Total distance in meters

scenario 1 2,270 2,138 4,408

scenario 2 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.

26

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: “What is the optimal order picking batch size and batch

composition when combining traditional retailing and online retailing?” 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

27

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 case-

specific 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

29

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.

30

7 References

Alba, J. et al., 1997. Interactive Home Shopping : Consumer , Retailer , and Manufacturer Incentives to Participate in Electronic. Journal of Marketing, 61(July), pp.38–53.

Avery, J. et al., 2012. Adding Bricks to Clicks : Predicting the Patterns of Cross-Channel Elasticities Over Time. Journal of Marketing, 76(May), pp.96–111.

Bozer, Y. a. & Kile, J.W., 2008. Order batching in walk-and-pick order picking systems. International Journal of Production Research, 46(7), pp.1887–1909.

Chen, J. et al., 2011. Optimal inventory and admission policies for drop-shipping retailers serving in-store and online customers. IIE Transactions, 43, pp.332–347.

Clarke, G. & Wright, J.W., 1964. SCHEDULING OF VEHICLES FROM A CENTRAL. Operations Research, 12(4), pp.568–581.

Eisenhardt, K.M., 1989. Building Theories from Case Study Research. Academy of management review, 14(4), pp.532–550.

Elsayed, E.A. & Stern, R.G., 1983. Computerized algorithms for order processing in automated warehousing systems. International Journal of Production Research, 21(4), pp.579–586.

Elsayed, E.A. & Unal, O.I., 1989. Order batching algorithms and travel-time estimation for automated storage/retrieval systems. International Journal of Production Research, 27(7), pp.1097–1114.

Gademann, A.J.R.M., Van den Berg, J.P. & Van der Hoff, H.H., 2001. Copyright ©2001. All Rights Reserved . IIE Transactions, 33, pp.385–398.

Gademann, N. & Van de Velde, S., 2005. Order batching to minimize total travel time in a parallel-aisle warehouse. IIE Transactions, 37(1), pp.63–75.

Gibson, D.R. & Sharp, G.P., 1992. Order batching procedures. European Journal of Operational Research, 58, pp.57–67.

Gong, Y. & De Koster, R., 2008. A polling-based dynamic order picking system for online retailers. IIE Transactions, 40(11), pp.1070–1082.

Gu, J., Goetschalckx, M. & McGinnis, L.F., 2007. Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), pp.1–21.

Henn, S., 2012. Algorithms for on-line order batching in an order picking warehouse. Computers & Operations Research, 39(11), pp.2549–2563.

Henn, S. et al., 2010. Metaheuristics for the Order Batchin Problem in Manual Order Picking Systems. Business Research, 3(1), pp.82–105.

31

Ho, Y. & Tseng, Y., 2006. A study on order-batching methods of order-picking in a distribution centre with two cross-aisles. International Journal of Production Research, 44(17), pp.3391–3417.

Karlsson, C., 2009. Researching Operations Management, New York: Routledge.

De Koster, R., 2003. Distribution Strategies for Online Retailers. IEEE Transactions on engineering management, 50(4), pp.448–457.

De Koster, R., Le-Duc, T. & Roodbergen, K.J., 2007. Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), pp.481–501.

De Koster, R., Van der Poort, E.S. & Wolters, M., 1999. Effcient orderbatching methods in warehouses. International Journal of Production Research, 37(1), pp.1479–1504.

Kull, T.J., Boyer, K. & Calantone, R., 2007. Last-mile supply chain efficiency : an analysis of learning curves in online ordering. International Journal of Operations & Production Management, 27(4), pp.409–434.

Le-Duc, T. & de Koster, R., 2007. Travel time estimation and order batching in a 2-block warehouse. European Journal of Operational Research, 176(1), pp.374–388.

Netessine, S. & Rudi, N., 2006. Supply Chain Choice on the Internet. Management Science, 52(6), pp.844–864.

Nickels, W.G., 1973. Central Distribution Facilities Challenge Traditional Retailers. Journal of Retailing, 49(1), pp.45–50.

Van Nieuwenhuyse, I. & de Koster, R., 2009. Evaluating order throughput time in 2-block warehouses with time window batching. International Journal of Production Economics, 121(2), pp.654–664.

Pan, C.H. & Liu, S.Y., 1995. A Comparative Study of Order Batching Algorithms. Omega International Journal of Management Science, 23(6), pp.691–700.

Petersen, C.G. & Aase, G., 2004. A comparison of picking, storage, and routing policies in manual order picking. International Journal of Production Economics, 92(1), pp.11–19.

Potts, C.N. & Kovalyov, M.Y., 2000. Scheduling with batching: A review. European Journal of Operational Research, 120(2), pp.228–249.

Rabta, B. & Reiner, G., 2012. Batch sizes optimisation by means of queueing network decomposition and genetic algorithm. International Journal of Production Research, 50(10), pp.2720–2731.

Roodbergen, K.J. & Vis, I.F. a., 2009. A survey of literature on automated storage and retrieval systems. European Journal of Operational Research, 194(2), pp.343–362.

Ruben, R. a. & Jacobs, F.R., 1999. Batch Construction Heuristics and Storage Assignment Strategies for Walk/Ride and Pick Systems. Management Science, 45(4), pp.575–596.

32

Swaminathan, J. & Tayur, S.R., 2007. Models for Supply Chains in E-Business. Management Science, 49(10), pp.1387–1406.

Won, J. & Olafsson, S., 2005. Joint order batching and order picking in warehouse operations. International Journal of Production Research, 43(7), pp.1427–1442.

Wruck, S., Vis, I.F.A. & Boter, J., 2012. Time-restricted batching models and solution approaches for integrated forward and return product flow handling in warehouses. Journal of the Operational Research Society, 64(10), pp.1505–1516.

Xia, Y. & Zhang, G.P., 2010. The Impact of the Online Channel on Retailers ’ Performances : An Empirical Evaluation. Decision Sciences, 41(3), pp.517–547.

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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 Coef SE Coef T P

Constant -135,88 45,62 -2,79 0,003

# locations 4,4236 0,4494 9,84 0,000

# books 2,4401 0,2253 10,83 0,000

Distance 2,5382 0,3222 7,88 0,000