Inventory Allocation for an Omnichannel Strategy

218 Pages·2015·6.05 MB·English

Inventory Allocation for an
Omnichannel Strategy
A STUDY ON THE REQUIREMENTS OF THE OMNICHANNEL STRATEGY AND THE EFFECT OF CHANGING THE
INVENTORY ALLOCATION TO IMPROVE THE PERFORMANCE OF RETAILERS, IN TERMS OF COSTS AND
FULFILLMENT OF INCREASED CONSUMERS’ EXPECTATIONS
Evelien Tielbeek 1511068

THESIS COMMITTEE:
PROF. DR. BERT VAN WEE | TU DELFT · TPM
DR. IR. DRS. ALEXANDER DE HAAN | TU DELFT · TPM
IR. M.B. DUINKERKEN | TU DELFT · 3ME
M.-J. GROOTENBOER |DELOITTE CONSULTING· MANAGER STRATEGY & OPERATIONS
1
Preface
This report is part of a master thesis project for the master Transport, Infrastructure & Logistics (TIL) at Delft University
of Technology. I made the conscious choice of performing this thesis at a company in order to gain some
experience of working in a consulting firm. After a seminar at Deloitte, I came in contact with Marlies de Boer. This
contact resulted an interview for Deloitte to do a thesis project. I enjoyed working at Deloitte and I am grateful for
their openness, honesty and enthusiasm.

At the first contact in June with Deloitte, the following research question was proposed: “What are the key
challenges per consumer business sub-segment (consumer electronics, fashion, food & beverages) concerning
the implementation of an omnichannel strategy?” I favored the multidisciplinary character of the omnichannel
strategy and the complexity. Therefore I decided to start in September 2014 and I have researched the following
question in the first three months: “What is omnichannel strategy? What are the challenges, both mentioned in
literature and at Deloitte?” In these first three months I discovered that inventory allocation is a big challenge,
which is not conducted well by many companies and not been researched a lot. So after the first months I
specialized on the inventory allocation for applying of the omnichannel strategy. At the midterm meeting Mark-
Jan Grootenboer (my supervisor of Deloitte) commented: “It is not the final goal to implement the findings of this
research into real life tomorrow. We are interested in the insights”. It is not the goal of this thesis to develop a tool
that is able to make operational and tactical decisions, but to gain insight in how inventory allocation strategies
in an omnichannel environment can be improved by delivering methods and insights. What are important factors
and what are not?

Readers that have a little amount of time, are advised to consider the summary, the conclusion and the
recommendations. Readers that have an interest in omnichannel strategy and the requirements, are advised to
read chapter 2 and chapter 3. Readers that have an interest in applications of discrete event simulation are
advised to read chapter 4.

I would like to thank everyone who was involved and has supported me during the accomplishment of my thesis.
First of all I would like to express my gratitude towards Mark-Jan Grootenboer and Sander Feenstra, who have
supervised me in the entire process. They have helped me to scope and define my research for a large part. They
were always open for questions and I felt supported throughout the process. I would also thank Laurens Nolet for
the data and the all information on Company X China.
I owe many thanks to my assessment committee for their time, ideas, guidance, directions and support. First of all,
Alexander de Haan who have supervised me in my entire thesis, for the interesting conversations, his guidance
and positive attitude. I would also like to thank Mark Duinkerken, for his openness, his different views and his
patience. I would like to thank Bert van Wee, for the time for an extra meeting, his structured approach and his
clear directions. I am very grateful and inspired how in such a short time frame he was able to capture the main
findings and how we were able to write a clear story line.
I would like to thank all my roommates at the Zoutmanstraat for their support and how they have always took
time to discuss my difficulties and listen to me. I would also like to thank Marjon, Frits and Robert for supporting me
when I needed it the most. I would like to thank Robert for his patience, his flexibility and for making me laugh
when I had a hard time. Also, my family has also been a huge inspiration and support to me.
Besides the direct guidance there were many others who supported me not only for the past month, but during
my years as a student in Delft. I want to thank all my friends and fellow students for the time we have spent
together, their interest and their support.

2
Definitions
All definitions used in this thesis will be explained in the foot notes when they are first used. On top of that, definitions that
are used often will be explained below.

The supply chain includes all activities concerned with planning, coordinating and controlling material, parts and
finished goods from supplier to consumer (Stevens, 1989).

In Supply Chain Management (SCM) material and information flows are managed both within and between facilities
(Thomas & Griffith, 1996).

Channel: The generic digital platform or device a product or service can be viewed and bought via, e.g. smartphone,
mobile, laptop etc. (webcredible, 2012).

A client of Deloitte (a company) will be referred to as a customer while the person who consumes the product and is the
client of the client of Deloitte will be referred to as consumer.

PC: Personal Communication: Refers to quotes out of interviews.

System: A very broad term used to describe a set of related components that together work toward some purpose. A
system might be something as simple as a waiting line at an automated teller machine (ATM), or as complex as a
complete airport or a worldwide distribution network. In any such system, be it existing or merely contemplated, it’s
natural and sometimes even essential to understand how it will behave and perform under various configurations and
circumstances (Kelton et al., 2011).

Inventory pooling: is about which inventory appointed to whom.

Dynamic allocation: is the process which defines how to decide where an item goes to

3PL: A 3PL (Third Party Logistics) is an outsourced provider that manages all or a significant part of an organization’s
logistics requirements and performs transformation, locating and sometimes product consolidation activities (Maxwell,
2015).

CLC is Central Logistic Center, the main warehouse of for example, Company X China.

Account is a retailer.

DC is a Distribution Center. A Distribution Center is a facility that is usually smaller than a firm’s main warehouse and is
used for receipt, temporary storage and redistribution of goods according to the consumer orders as they are received.
Also called branch warehouse or distribution warehouse (BusinessDictionary.com, 2015)

Product is a product in the model. It has a unique product ID and other attributes.

PI: Performance indicator: A quantifiable measure to compare performance in terms of meeting strategic or operational
goals. The most important Performance Indicators are the KPI’s: Key Performance Indicators.

Product fulfillment: The demand is all products that are desired at specific moments of time. A consumer wishes to have
one or more products and if available, the product is sold. Product fulfillment is the proportion of products that are sold
divided by the demand, which is the total number of product that could have been sold with 100% availability.

3
Summary
Goal of the research
Nowadays, retailers are faced by two challenges: an increase of competition and higher expectations of the
consumers. This has been driven by five macro trends: globalization, technology, sustainability & safety, recession
and prosperity. These two challenges have led to an increased pressure on retailers. To remain successful, retailers
have to fulfill the increased consumers’ expectations without increasing their cost in an increasingly competitive
landscape.

In order to deal with these challenges, several retailers have developed a strategy. This is called the omnichannel
strategy. This strategy combines store and non-stores channels and enables each channel to serve the consumer
throughout their entire purchase journey with a seamless experience. More and more companies are applying
and/or researching the strategy, which indicates the potential of the strategy.

However, Deloitte (2014) reports that retailers who execute the omnichannel strategy often face unsatisfactory
results, such as high operational costs and low extra revenues (PC: M.J. Grootenboer, S. Feenstra). The aim of this
research is to find out what causes these unsatisfactory results and to identify possible strategies to obtain
satisfactory results.

The omnichannel strategy is considered as a complex and vague strategy, for which no clear definition exits. For
example, S. Mobasser (PC, 2014) refers to omnichannel as a fuzzy marketing-word without any clear definition or
outline and thus not able to work with. Therefore, the first step in analyzing the cause of unsatisfactory results is to
define and describe the omnichannel strategy and to give an overview of the reasons why retailers apply the
omnichannel strategy.

Secondly, it is believed that the unsatisfactory results are caused by the fact that retailers do not meet
requirements of the omnichannel strategy (PC: M. J. Grootenboer, 2015). In accordance with that, Chaturvedi,
Martich, Ruwadi and Ulker (2013) state that retailers either have to fulfill a transformative set of requirements to
compete in the new landscape, or they will struggle to survive. Therefore, requirements will be determined for the
omnichannel strategy and ranked on importance.

Thirdly, it will be assessed if satisfactory results are reached when these requirements are met, in terms of lower
costs and higher fulfillment of increased consumers’ expectations. Because of time constraints, only the most
important requirement will be investigated. The most important requirement is the inventory allocation strategy.

Thus, the goal of the thesis is to gain insights in the requirements of the
omnichannel strategy and the effect of these requirements on the
performance of retailers in terms of costs and fulfillment of increased
consumers’ expectations.

Three research questions have been formulated to reach this goal:
1. What is the omnichannel strategy and why do retailers apply the omnichannel strategy?
2. What are the requirements for the omnichannel strategy and how important are these requirements?
3. What is the effect of the changes in the required inventory allocation strategies on the performance of
the retailer?

The first question will be answered by a literature study and consultation of Deloitte supply chain consultants. The
answer of the second question is based on literature, previous research and consultation of Deloitte supply chain
consultants. The third question will be investigated using a case study, which uses the data of Company X in
China. Only one case study is used, because it would be too time consuming to use multiple. Company X China
has been selected as case study for multiple reasons, such as data availability and the fact that the current
inventory allocation strategy is not aligned with the omnichannel strategy but there is a wish to do so. A discrete
event simulation model will be created to test the effect of changes in the required inventory allocation strategies
on the performance of the retailer.

The three questions will be separately described below and the summary will end with the recommendations.

4
The omnichannel strategy and the reasons for application
The following macro trends were identified:
- Technical Changes: Changes and innovation in the digital landscape have led to an explosion of
channels and information;
- Prosperity & Productivity: Consumers are expecting more and companies are producing more;
- Economic changes: Focus on cost reduction due to the recession in 2008, while IT expenses are growing;
- Sustainability: Sustainability developments led to increased amount of requirements which resulted in the
need for more cost reduction;
- Globalization: An increased network has led to a broader geographical market and thus an ability to
grow, but also more information and more competition.
As a result of these macro trends, the following two changes in retailers’ landscape can be distinguished:
- The consumer has changed and expects more. The consumer has become more knowledgeable,
empowered, collaborative, diverse, interactive, dynamic and demanding
- The retailers are operating in an increasingly competitive environment
The omnichannel strategy has been developed to overcome these changes in the retailers’ landscape.
While there are many definitions, this thesis will use the following definition:

Definition: Omnichannel can be defined as a synchronized operating model in which all of the retailers’ channels
are aligned and present a single face to the consumer, along with one consistent way of doing business (Carroll
and Guzman, 2013)

The omnichannel strategy has been developed as lines between the many channels are blurring and retailers
wish to create a consistent experience throughout all channels. The omnichannel strategy is different than the
multichannel strategy, because it goes along with one consistent day of doing business, which means that the
back-office is aligned over the different channels.

Currently, the omnichannel strategy is recommended by both literature and companies and many advantages
have been mentioned in literature and at Deloitte. It does not only increase revenue, but it also improves business
performance through increased margin and reduced inventory. The following advantages have been identified:
- It increases brand awareness;
- It leads to more efficiency in inventory management;
- It results in a better positioning of products throughout the channels;
- It leads to a greater scope for innovation;
- It better fulfills expectations of the consumers (such as availability and comfort) and thus improves
consumer loyalty;
- It is focused on profitable consumers;
- It unlocks new markets.
Requirements for the omnichannel strategy
This thesis collected requirements out of literature, previous research and consultation of supply chain consultants
and summarized these into the following ten requirements.
1. A proper inventory allocation strategy: the ability to (re)allocate inventory based on consumer
requirements at all nodes fast, cheap and seamless;
2. A proper inventory visibility: single real time view on inventory at all nodes anytime;
3. Good return logistics: logistics of returns are integrated across all channels and have low costs and high
service;
4. The ability to apply a cross channel fulfillment strategy;
5. An increased availability of products and reduced delivery time;
6. A single brand identity and consistent product information;
7. Aligned technology: Technology that is properly functioning for all systems to enhance the integration;
8. Strong analytics that lead to insights: advanced supply chain analytics provide insight into profitability, to
support informed decision making;
9. A well-grounded chosen assortment for each channel based on the channel characteristics and the
ability to amend this if required;
10. A clear vision supported by the entire organization and all people.
The above requirements have been ranked on importance. This is done by asking two questions in a survey to 18
supply chain consultants:
1. Is this requirement important for retailers to operate in the current retailers’ landscape?
2. Are retailers currently struggling with this requirement?
5
Four experts have been interviewed to find out which requirements are a must have and which requirements are
nice to have (PC, S. Feenstra, F. Cornelissen, L. Nolet and M.J. Grootenboer, 2015). The top three requirements of
the above list were indicated as a must have according to these four experts.

Inventory allocation strategy is the most important requirement according to this research. This requirement is
about where to position the inventory and about the rules which decide to which channel, to which consumer
and at what time a product is appointed to. Two challenging questions that retailers are currently struggling with
are:
1. Should the inventory be placed central or decentral?
2. Should the inventory between the digital and store channel be shared or non-shared?
The effect of these two questions of the inventory strategy are analyzed on the performance of the retailer, in
terms of supply chain costs and fulfillment of consumers’ expectations.

What is the effect of the changes in the required inventory allocation strategies
on the performance of the retailer?
A case study will be conducted to investigate whether adjustments within the inventory strategy increase the
performance of the retailer in terms of supply chain costs and fulfillment of consumers’ expectations. These
adjustments of the inventory allocation strategy concern the position which can be central or decentral and the
inventory which can be shared or non-shared over the channels.

First, a case study is selected according to these criteria:
a) Availability of data. In other words, the company gave Deloitte access to its data;
b) The retailer has the inventory positioned both central and not shared;
c) The retailer has the wish to apply the omnichannel strategy and is open to change the inventory
allocation if that would improve the performance of the retailer;
d) The retailer has the ability to position the inventory decentral and share the inventory between channels.
In order to change the inventory allocation, the retailer has to own the supply chain and should own
stores. The sharing of inventories cannot be investigated if the retailer, for example a purely internet
based store, has no stores.
For the case study, Company X is selected as it meets the above criteria. The inline channel and digital channel
will be further investigated. These two channels are chosen because of the availability of data and the possibility
to share the inventory between these two channels. A representative group of 19 product types are chosen. In
total 20.624 products can be sold, of which 1.526 at the digital channel and 19.098 at the inline channel. The
supply chain network from the Central Logistic Center (CLC) to the consumers of the digital channel and 26 inline
stores are investigated. Both a solution with one Distribution Centre (DC) and a solution with three DCs have been
modeled.

In order to test the effect of the two adjustments, four solutions are tested, as presented in Figure 1 and Table 1.

Central Decentral
NonShared inventory Current Solution Decentral Solution
Shared inventory Shared Solution Omnichannel Solution
TABLE 1: FOUR SOLUTIONS
Current solution Decentral solution
Two extra solutions are also researched:
- The shared at CLC solution: In this
solution the inventory is shared at the
CLC instead of the Digital DC
- The omnichannel extra replenishment
solution: In the omnichannel solution Digital DC
consumers who purchase at the digital
channel and stores are divided into
three areas. This is done based on the
nearest RDC and they are supplied by
this RDC. In this solution, the digital Shared solution Omnichannel solution
channel and stores are replenished by
the other 2 RDCs if the nearest RDC is
out of stock. This results in a higher
product fulfillment, but also higher costs.

6 FIGURE 1: VISUALIZATION OF THE SOLUTIONS
The effects of the solutions are measured by the Key Performance Indicators (KPI’s). The KPI’s are factors that
indicate the increase of the performance of the retailer to deal better with the competitive landscape (by
lowering costs) and fulfill the increased expectations of consumers (by increasing product fulfillment).
There are four KPI’s:
- Product fulfillment at digital channel if all products are accepted
- Product fulfillment at digital channel if products with a lead time of more than 2 days are rejected
- Product fulfillment at inline channel
- Logistic Cost per Product
Taxes, economic situation, weather and other events are external factors that may affect the KPI’s, but these are
left out of scope.

The model is based on some important assumptions and simplifications, which are:
- Constant size and price of all products;
- Consumers may choose for pick-up-at-store but this does not affect the model or the results;
- Availability does affect consumer behavior in the future;
- Transport costs are constant, independent of transshipment size or position in the supply chain;
- Not all costs are included. Only inventory, handling and transport costs are included from the moment
products are at the CLC;
- Indicative quantities are used;
- Marketing strategy, such as an promotional activity, is not included;
- Returns are not included;
- Special events, such as accidents, are not included;
- Consumer sensitivity is not included;
- Consumer response on availability is indicative;
- Replenishment rules are indicative.

The model is a dynamic model with deterministic and stochastic elements. The model is implemented in a
discrete event simulation model. A discrete event simulation model is suitable for a complex and stochastic
problem with discrete elements, in which the goal is to analyze inventories and other phenomena which depend
on uncertainty such as transportation times (Persson & Olhager, 2002). Simio is the software package that has
been used. A strength of Simio is that it is relatively easy to adapt the number of distribution centers or stores and
it is easy to follow the process and to overview the network. Simio facilitates the creation of the simulation model
by putting consecutive process blocks of the system in the software, which an entity (an order or a product) can
pass. The flow of the entities is animated when the model is executed. Benefits of the animation are that both the
operation and the results are being visualized, which gives the opportunity for clients to gain a better
understanding of the system and also for the modeler and the client to verify and validate the working of the
model (Verbraeck, 2014).

The model is verified. However, for three parameters different estimations are mentioned by experts (PC: F.
Cornelissen, S. Feenstra, 2015). These three parameters are forecast accuracy, percentage of the products that is
placed at the stores at the start of the season and replenishment rules. Several models are run with varying values
for these three parameters and the value that resulted in the most positive score on the KPI’s is chosen.
Although the lead time is slightly longer and the supply chain costs are slightly higher than in reality, the model is
valid for the purpose of this research.

The values of the KPI’s for the solutions compared to the current solution are shown in Figure 2. The case study
leads to the following insights:
- The current situation and decentral solution have the most positive scores on the KPIs. The other solutions
are less favorable. In general, the current solution should be recommended if product fulfillment at
digital channel is important and the decentral solution should be recommended if product fulfillment at
the inline channel or at the digital channel within two days is important.
- Decentralizing leads to a slightly higher product fulfillment for the inline channel and the digital channel
with a maximum lead time of 2 days. However, product fulfillment of the digital channel decreases.
- The sharing of inventories over the channels leads to higher costs and lower product fulfillment of the
digital channel if a maximum lead time of 2 days is set.
- The sharing of inventories of DC’s which are close to each other leads to lower logistics cost per product.
- An omnichannel inventory allocation strategy is very expensive. It increase the digital product fulfillment
significantly with a maximum of 2 days (+36%) in the case study, but the product fulfillment of the inline
channel and digital channel decreases. According to the profit calculations in the case study, the
benefits are not worth the extra costs. However, the effect of the increase of the product fulfillment at
the digital lead time with a maximum lead time of 2 days may result in extra demand in the future. Effect
7
of product fulfillment or service level on future behavior of consumers has not been included in the
model.
- Enablement of replenishment by other RDC than the nearest RDC leads to higher costs and lower
product fulfillment at the digital channel and at the digital channel with a maximum lead time of 2 days.

If profit was chosen as KPI, results would not have been different. Reasons to choose profit as KPI are that it covers
both the costs and benefits of the solution, and that it facilitates to compare the solutions in one sight. However, it
is based on to many assumptions to provide valuable insights for the retailers, which is the goal of this case study.

Translation of this case study to other retailers should be made carefully, because only one case study has been
conducted with many assumptions and simplifications. It is expected that decentralization may lead to more
flexibility and will become more important as consumers are expecting a lower lead time. A full transformation to
an omnichannel inventory allocation strategy results in increased complexity and costs in the case study. It is
expected that this is also true for other retailers because the main factors leading to increased complexity and
costs are also present at other retailers.

RESULTS CASE STUDY
Logistic cost per product [dollar]
Product fulfillment at inline channel
Product fulfillment at digital channel with lead time <2 days
Product fulfillment at digital channel
% %
832 642 %8
0
2
%
%001 %001 %001 %001 %201 % % %001 %101 %811 %0 %39 631 %29 %99 %201 % % %09 %801 %69
6 8 8 6 8
6 6 6 6

FIGURE 2: RESULTS OF THE CASE STUDY

Recommendations
Recommendations for retailers
It is recommended for retailers to execute the omnichannel strategy, as it results in many benefits. In this thesis it
may seem the omnichannel strategy is either fully executed or not at all. However there are many degrees of
executing of the omnichannel strategy in reality. Nowadays, almost all retailers execute the omnichannel strategy
in a degree, such as offering pick-up-in-store after buying a product online. A step by step implementation of the
omnichannel strategy is a way which results in the benefits of the omnichannel strategy, but without the costs and
complexity that are the result of the full execution of the omnichannel strategy.

An important point to take into consideration is that it will become more difficult to define and measure the
omnichannel strategy, since channels are blurring. A maturity model, for example the maturity model presented
8
by Obert, van der Pol, van Rhijn, Pigeaud, Schilthuis and Stol (2014), is a way of measuring the degree of the
execution of the omnichannel strategy.

An example of a first step of the implementation of the omnichannel strategy would be to outsource the supply
chain to a mature digital company, or a third-party logistics provider. This will lead to a high service level while
costs are low since these companies have economies of scale.

Recommendations for Company X
The conclusion that can be drawn out of the case study is that a full transformation to an omnichannel inventory
allocation strategy results in more complexity, costs and risks. Merging of the CLC and digital DC is not allowed
because of administrative and tax reasons.

The following steps are recommended for Company X:
1. Decentralize the digital channel to lower lead time at the digital channel, since it is expected that a low
lead time will become the standard in the market.
2. Look for opportunities to share the inventory of these Regional Distribution Centers with other channels,
such as the inline channel
3. It is recommended to research what the most important wishes and expectations of the consumers are,
and how these can be fulfilled cost effectively.

Recommendations for future research on the omnichannel strategy
It is clear that the omnichannel strategy is worth to investigate further in the future, given the increased amount of
attention for the omnichannel strategy and the varying results retailers get when applying the omnichannel
strategy.

This thesis did not explicitly research the effect of the omnichannel strategy on the consumers’ satisfaction and
profit of the retailer. The increasing amount of attention for omnichannel strategy could be an indication that the
omnichannel strategy has the desired impact on fulfillment of the increased consumers’ expectations. Other
strategies may also be optimal to increase the performance of the retailer. Thus, a first recommendation includes
the research on the most important consumers’ wishes and expectations and the effect the omnichannel
strategy has upon this.

Two ways to improve the research conducted in this thesis are the following. First, input for the list of requirements
and the rankings has been based solely on consultation of the supply chain consultants of Deloitte and literature
study. It is recommended to consult retailers and other experts as well. These interviews may result in the removal
or enclosing of requirements or a different ranking. Second, only two changes of inventory allocation strategies
have been included. As other changes may be equally or even more important, it is recommended to research
those as well.

A suggestion for future research would be to validate and further improve the list of requirements and the
associated importance ranking presented in this thesis.. This list of requirements can be mapped on impact and
effort to meet the requirements. Requirements that have a low effort should be researched and met by retailers
as soon as possible, since they may lead to a positive effect on the short term. Requirements with a high impact
and with an effort which is not too high, should be researched and met by retailers.

Recommendation for future models
The model that is created in this thesis can easily be adjusted for other countries and retailers. However, it s also
time-consuming to construct and run and may become unstable for a large number of products. A discrete
event simulation model is a proper tool to support decision making and for explorative analysis, in particular for
fashionable products without any information on past sales. The model is not appropriate for exact forecasting of
product allocation. If the demand of a product can be reasonably predicted, it is recommended to use
mathematical modeling to improve forecast accuracy and product allocation.

Two types of models are recommended for future research. If a retailers wish to model the product allocation
over the channels for a longer time period and the sales are known of can be predicted with a low uncertainty,
astrategic network optimizer, LLamasoft is recommended. If the goal is to gain insights in inventory allocation
strategies with an uncertain daily demand, a discrete event simulation model is recommended. Many software
packages can be used, based on the experience of the modeler. An example of a proper software package is
Delphi.

9