Netherlands Defence Academy, Breda, The Netherlands, and
M.B.M. de Koster and A. Smidts
Rotterdam School of Management,
Erasmus University, Rotterdam, The Netherlands
Purpose – The purpose of this paper is to investigate how warehouse management, understood as a
cluster of planning and control decisions and procedures, is organized and driven by task complexity
(TC) and market dynamics (MD).
Design/methodology/approach – A multi-variable conceptual model is developed based on the
literature and tested among 215 warehouses using a survey.
Findings – The results suggest that TC and MD are the main drivers of warehouse management,
measured by planning extensiveness (PE), decision rules complexity, and control sophistication.
Differences between production and distribution warehouses are found with respect to the relationship
between assortment changes and PE. Furthermore, TC appears to be a main driver of the speciﬁcity of
the warehouse management (information) system (WMS).
Research limitations/implications – This paper is based on 215 warehouses in The Netherlands
and Flanders (Belgium); future research may test the model on a different sample. More research
should be conducted to further validate the measures of the core dimensions of warehouse
Practical implications – Different levels of TC and MD characterize warehouses. Such a
characterization is a ﬁrst step in determining generic warehouse functionalities and helping managers
to decide on the best software for their warehouse operations.
Originality/value – The paper deﬁnes the core dimensions of warehouse management, makes them
measurable, tests them and assesses how these drivers impact speciﬁcity of WMS. The paper shows
that PE in production warehouses is driven by different variables than in distribution centers.
Keywords Logistics, Survey, Conceptual framework, Empirical, Information management,
Paper type Research paper
The warehouse is today playing a more vital role than it ever has in the success
(or failure) of businesses (Frazelle, 2002). Warehouses play a critical intermediate role
between supply chain members, affecting both supply chain costs and service (Kiefer
and Novack, 1999). In an attempt to rationalize supply chain processes and to manage
them more efﬁciently, many companies have set up centralized production and
warehouse facilities over the last decades (HIDC/BCI, 2001). This has resulted in larger
warehouses responsible for the distribution to a greater diversity of more demanding
The current issue and full text archive of this journal is available at
The authors gratefully acknowledge Michael Murtin, Emile Bouwman, Sanne van Rhenen,
Wessel Koning and Sebatiaan Ruys for their hard work in collecting data (via the telephone) for
Received 14 December 2011
Revised 30 March 2012
14 June 2012
Accepted 17 September 2012
International Journal of Operations &
Vol. 33 No. 9, 2013
qEmerald Group Publishing Limited
customers in a vaster region and, consequently, with more complex internal logistic
processes (see the survey of ELA/AT Kearney, 2005).
As a consequence, managing complex warehouses effectively and efﬁciently has
become a challenging task. An important question therefore is how warehouse
management, as a cluster of planning and control decisions and procedures, is organized
in order to meet today’s challenges. Warehouse management encompasses the control
and optimization of complex warehouse and distribution processes (Ten Hompel and
Schmidt, 2006), and it depends on the tasks to be performed and on the market the
warehouse operates in. In the area of production management, it is commonly accepted
that the produced volume and product variety (i.e. task complexity) and the rate of
change of the external environment (i.e. market dynamics) are the main drivers of the
planning and control structure (Bertrand et al., 1990; De Toni and Panizzolo, 1997; Hatch,
1997; Peterson and Silver, 1979; Van Assen, 2005). However, systematic research into the
drivers of the warehouse planning and control structure seems to be lacking.
Rouwenhorst et al. (2000) and Gu et al. (2007, 2010) conclude in their reviews of
warehouse management literature that analysis-oriented research on isolated
subproblems is dominant in the current warehouse management literature. This
study takes the ﬁrst step in exploring the drivers of warehouse management.
Warehouse processes that need to be planned and controlled include: inbound ﬂow
handling, product-to-location assignment, product storage, order-to-stock location
allocation, order batching and release, order picking, packing, value-added logistics
activities, and shipment (Ackerman and La Londe, 1980; Frazelle, 2002). Particularly
storage and order picking are complex, often labor-intensive processes that determine
warehouse performance to a large part.
Our ﬁrst contribution is to deﬁne and make measurable the core characteristics of
warehouse management. To the best of our knowledge, there is currently hardly any
literature that clearly captures warehouse management as a coherent whole. Because
this study is just a ﬁrst step in exploring the dimensions of warehouse management,
we consider warehouse management at a high level of aggregation. We propose and
deﬁne the constructs necessary to do this, focusing on both planning and
control activities within the warehouse and on the decision rules used to schedule
and optimize the inbound, storage, and retrieval processes in the warehouse.
Second, we establish and test the drivers of warehouse management. Following the
literature on production systems (Van Assen, 2005), we distinguish task complexity and
market dynamics as the main drivers of the warehouse planning and control activities
and of the decision rules used. Task complexity measures the depth and breadth of
the tasks a warehouse has to perform and is internally oriented. Market dynamics
measures the rate of change of the external environment in which a warehouse operates.
In general, we expect that a more complex warehouse task results in more complex
decision rules for scheduling and optimizing inbound, storage, and outbound activities.
Warehouse planning and control depends both on the complexity of the warehouse task
and on the dynamics of the market. We test these propositions in our study.
Our third contribution relates to a speciﬁc aspect of warehouse management: the
warehouse management (information) system (WMS). Information systems play a
signiﬁcant role in managing complex processes (LeBlanc, 2000). We assess the degree
to which the drivers of warehouse management impact the speciﬁcity of the
information system. Establishing the dependency of the WMS’s speciﬁcity on these
drivers may help managers to decide on the most effective information system to suit
the warehouse management’s needs. Since many standard warehouse management
software systems are available on the market (Loudin, 1998; Randal, 1999), selecting
one can be a difﬁcult task.
We carried out a survey to test our theory and collected data on 215 warehouses and
distribution centers in The Netherlands and Flanders (Belgium). Warehousing is of
particular importance to The Netherlands and Flanders due to their geographical
location with deep-sea ports (Rotterdam and Antwerp), major European air-cargo hubs
(Schiphol and Zaventem), and direct connections via water (barge and short-sea), road,
and rail to a large part of industrial Europe. More than half of all European Distribution
Centers (EDCs) are located in this region (BCI, 1997; HIDC/BCI, 2001; Kuipers, 1999). The
level of performance of warehousing operations in the region is generally high,
represented in high labor productivity and low error rates (OECD, 2006). High costs of
land and labor, and increasing labor shortages are forcing companies to invest in people
and automation systems with a relatively long-term investment horizon.
The organization of this paper is as follows. In Sections 2 and 3, we develop our
constructs and formulate our hypotheses. In Section 4, we describe our research
methodology, including the sample selection and data collection approaches and the
measures of the constructs. Section 5 describes the results of the data analysis and
hypotheses testing. Section 6 concludes the paper.
2. General framework for warehouse management
2.1 Warehouse management
Analogous to production management (Bertrand et al., 1990), the objective of warehouse
management is to efﬁciently and effectively coordinate all warehouse processes and
activities (Harmon, 1993; Tompkins et al., 2003). Warehouse management includes all
planning and control procedures to operate the warehouse. Planning and control is
concerned with managing the ongoing activities of the operations so as to
satisfy customer demand (Slack et al., 2001). The main purpose of planning and
control is to ensure that operations run effectively and produce products and services as
they should (Slack et al., 2001). Whereas planning involves deciding what should be done
and how, control is the process of ensuring that the desired output (plan) is obtained
(Anthony and Young, 1984; Van Goor et al., 2003). Planning is therefore proactive and
control is reactive. Together, plans and controls regulate outputs. Within planning, we
distinguish a tactical and an operational level. At the tactical decision level, warehouses
draw up plans to make efﬁcient use of resources and to fulﬁll market demand. However,
due to the highly dynamic environment, for many warehouses the tactical planning
horizon is only days or weeks rather than months. At the operational level, decision rules
are used to sequence, schedule, and optimize planned activities (Slack et al., 2001).
In the literature, the dimensions of warehouse management have not yet been
explored. Based on the above literature and interviews with experts, we propose to
broadly deﬁne warehouse management as a combination of the planning and control
systems and the decision rules used for inbound, storage, and outbound ﬂows. We now
consider each aspect in more detail.
Tactical planning system. A plan is a formalization of what is intended to happen at
some time in the future (Slack et al., 2001). Plans or norms specify a desired output
(expressed in quantity, quality, cost, timing, etc.) at some future time. The most
important tactical issues in warehouses include: stock planning, storage-location
assignment planning, transport planning, and capacity (personnel and equipment)
planning. For a comprehensive overview of these and other planning processes, see
Van den Berg (1999) and Van den Berg and Zijm (1999).
Stock planning decides which products are kept in storage in what quantities, and
determines when shipments arrive. Intelligent stock planning may reduce warehousing
costs. Storage location planning decides the location types (for example, shelf, pallet,
high-bay or block-stack) and the zones within these storage areas where the products
will be stored. An effective storage-location assignment plan may reduce the need for
space and the mean travel time for storage/retrieval and order picking. Capacity
planning and transport planning determine the required personnel, equipment, and
Making such tactical plans is time consuming and should only be done if they lead
to performance improvements. Therefore, the number of tactical plans may vary
per warehouse. We deﬁne the construct planning extensiveness by the number of
tactical plans a warehouse explicitly draws up.
Inbound, storage and outbound decision rules. Tactical plans determine which
products arrive in what quantities, where these should be stored and how much
personnel, equipment, and transport is needed to process the products and orders.
Tactical plans deﬁne a framework for the operational planning level (Van den Berg,
1999). Operational decisions typically deal with the sequencing, scheduling, and
routing of order picking and storage/retrieval operations. There are many different
rules that can help operations make these decisions and improve performance
(Chen et al., 2010; Rouwenhorst et al., 2000). For an overview of decision rules focusing
on storage and order picking, see De Koster et al. (2007) or Wa
The complexity of the decision rules implemented in a warehouse differs
per warehouse. Complexity is a well-deﬁned construct in management literature (Alter,
1999; Hatch, 1997; Huber, 1984). The complexity of a system depends on the number of
differentiated components of the system, the number of interacting components, and
the nature of the interaction between components (Alter, 1999). We therefore deﬁne the
construct decision rules complexity as the number of different types of decision rules
used for inbound, storage, and outbound activities and the perceived complexity of
Control system. When plans are implemented, things do not always happen as
expected (Slack et al., 2001). For example, customers may change their minds about
what they want and when they want it, suppliers may not deliver on time, machines
may fail, inventory records may be inaccurate, and staff may be absent due to sickness.
Control is the process of coping with these changes (Slack et al., 2001). It relates to the
feedback and corrective action function of the management system. By monitoring
what actually happens and making the necessary changes, control makes the
adjustments which allow operations to achieve the objectives that were laid down in
the plan (Slack et al., 2001).
Thus, monitoring, analyzing, reporting and intervening are core functions of the
control system. For this purpose, information about the progress and realization of the
plans is essential (Anthony and Young, 1984). The time to respond to deviations and
changes is limited. Therefore, in this study, we consider the speed at which data
is transformed into information by the control system as the most important
dimension of control. Accurate and timely information on the shop ﬂoor is essential
to control operations.
Information can be recorded and presented on paper, online, or in real-time if
radio-frequency technology is used with mobile terminals and scanners, or with voice
response. As the speed of the transformation of data into information increases, the
control system becomes more sophisticated. However, the degree of sophistication of the
control system must be justiﬁed economically, i.e. the higher accuracy and the lower
stock-outs must outweigh the extra costs of the control system. As a consequence, the
sophistication of the control system differs per warehouse. We deﬁne the construct
control sophistication as the speed of transforming data into information used by
warehouse employees to decide and act upon.
Based on the above-mentioned dimensions of warehouse management, we deﬁne
warehouse management as a combination of planning extensiveness, decision rules
complexity, and control sophistication.
2.2 Warehouse management (information) system
In most warehouses, information systems support warehouse management. Such
information systems can be either built speciﬁcally for a warehouse (tailor-made) or
bought off-the-shelf (standard software package). Software is primarily focused on
broad or speciﬁc functionality (Lynch, 1985). A software product with broad
functionality supports a large number of different processes in an organization (e.g. an
ERP system). Although ERP systems can be conﬁgured to the customer’s processes, the
ﬁne-tuning is complex (Somers and Nelson, 2003) and conﬁguring the system involves
making compromises and has its limits (Davenport, 1998). Software products with
speciﬁc functionality support a smaller number of processes in an organization but with
more intensity (e.g. WMS). The speciﬁcity of the information system will differ per
warehouse. We deﬁne the construct information system speciﬁcity by distinguishin g six
different types of information systems with an ascending degree of speciﬁcity.
2.3 Drivers of warehouse management
The basic premise of our study is that the best approach to warehouse management
depends on the speciﬁc characteristics of the warehouse. Traditional contingency
theorists such as Burns and Stalker (1961) and Lawrence and Lorsch (1967) suggest
that effectiveness derives from structuring an administrative arrangement appropriate
to the nature of an organization’s environment. In the operations management literature,
it is also a generally accepted assumption that the environmental context inﬂuences
the appropriateness of the organization of the planning and control system (Sousa and
Voss, 2008). Van Goor et al. (2003) state that the characteristics of the market, the
products, and the processes determine the way supply chains are managed; and
according to Fisher (1997) and Lee (2002), the choice of a supply chain control depe nds on
the product type and the predictability of the market.
The need to consider the environmental context is obvious and widely accepted in
literature. With regard to warehouse management, the environment refers to the
immediate operating environment that is beyond the control of management in the short
run. Therefore, we decided to concentrate on narrowly deﬁned parts of the environment
rather than overall industry parameters. Analogous to Van Assen (2005), in this study,
the warehouse operations environment consists of the external warehouse environment
(i.e. the market) and the internal warehouse system.
The core concept that captures the effects of the organization’s environment on its
performance is uncertainty (Thompson, 1967). Researchers in organizational theory
identify two major dimensions of uncertainty: complexity and dynamism (Duncan, 1972;
Hatch, 1997; Miller and Friesen, 1983; Premkumar and Zailani, 2005). Complexity refers
to the number and diversity of the elements in an environment (Hatch, 1997). The
warehouse system can be characterized by the products (SKUs) that have to be stored
and picked, the processes to store and pick these products, and the orders that request
the delivery of these products. Complexity increases as the number and diversity of
SKUs, order lines, and processes increase. In general, if the number of SKUs increases,
more storage space (often different type and control logic) will be needed and more
products have to be registered and managed in the warehouse information system. Some
warehouses have a greater number and/or variety of processes and some of these
activities are labor intensive and have substantial impact on order throughput time. The
number of order lines is a good indicator for the total amount of work in order picking
and thereby for the total amount of work to be done in the warehouse. In general,
the majority of warehouse work is in order picking (Drury, 1988). We conceptualize
environmental complexity with regard to warehouse management as the complexity of
the task a warehouse has to perform. Thus, we deﬁne the construct task complexity by:
.the number of different products (SKUs) handled in the warehouse;
.the number and variety of the processes carried out by the warehouse; and
.the number of order lines processed by the warehouse per day.
Task complexity affects warehouse management through the comprehensibility of the
work to be done.
Environmental dynamism is characterized by the rate of change and technology
innovation in the industry as well as the uncertainty or unpredictability of the actions of
competitors and customers (Burns and Stalker, 1961; Lawrence and Lorsch, 1967;
Thompson, 1967). At the level of the immediate operating environment, warehouse
management interacts directly with customers. Other factors relevant to the warehouse’s
goal setting, such as competitors, suppliers, government, technology, economy, and labor
have a less direct inﬂuence on warehouse management and, for this reason, are not
considered in this study. We conceptualize environmental dynamism with regard to
warehouse management as the dynamism in the customer market. We deﬁne the
construct market dynamics by:
.the unpredictability of market demand; and
.the rate of change in the taste and preference among customers.
Unpredictability of market demand refers to the difﬁculty of forecasting customer
behavior (Khandwalla, 1977). It is difﬁcult to know just how customers will react to
very new products and services, and to anticipate the various problems that might
occur. The rate of change in customers’ preferences refers to the turbulence of the
market. Warehouses that operate in more turbulent markets are likely to have to
continually modify their products and services in order to satisfy customers’ changing
preferences. A dynamic market also manifests itself in frequent assortment changes.
Market dynamics affects warehouse management through the predictability of
the work to be done.
The consideration of task complexity and market dynamics provides a sound
starting point to study the drivers of warehouse management. Our full research model
is shown in Figure 1. Task complexity and market dynamics are expected to strongly
affect how warehouse management is organized. The organization of warehouse
management takes shape in three dimensions (planning extensiveness, decision rules
complexity, control sophistication), and is reﬂected in warehouse management’s
information system speciﬁcity.
It should be noted that warehouse management is not solely determined by these
two main drivers. In reality, many other elements must be in place for warehouse
management to be successful. These include an educated and well-trained workforce,
appropriate alliances with customers and suppliers, well-designed strategic planning
processes, well-designed lay-out and systems, well-designed work processes, etc. Task
complexity and market dynamics were selected for this study because they play a key
role in designing and managing warehouse systems.
To our knowledge, the relationship between the dependent constructs planning
extensiveness, decision rules complexity, control sophistication, and information
system speciﬁcity and the independent constructs task complexity and market
dynamics has not been addressed in the literature.
The task a warehouse has to perform is complex if the number of SKUs, process
diversity, and number of daily order lines are high. As the complexity of a system
Number of SKUs
Number of plans
Decision Rules Complexity
Number of order lines
Speed of transformation of information
Number of different types of decision rules
Nature of decision rules
• Demand Unpredictability
Information System Specificity
increases, managing the system becomes harder (Van Assen, 2005). Each resource has
to be aligned to perform the warehousing activities. This can be done efﬁciently only if
the resources and the relationships between these resources are coordinated in a timely,
complete, and reliable fashion (Van Assen, 2005). This means that tactical and
operational planning is necessary. The ﬁrst hypothesis of our study is:
H1. The more complex the warehouse task, the more extensive the planning.
It can be expected that the decision rules (i.e. operational plans) used to schedule and
optimize activities in the warehouse will be complex when the task is complex. For
example, a large number of SKUs generally implies that many of them will need
different storage and order picking logic and conditions (think of size, weight, physical
condition, packaging, and product carriers like totes or pallets), all laid down in
decision rules. If the number of SKUs, process diversity, and number of order lines is
small, we expect the decision rules to be simple. The second hypothesis of our study is:
H2. The more complex the warehouse task, the more complex the decision rules.
If the warehouse task is complex, the organizational structure can be adapted to deal
with this (Espejo and Watt, 1988), and management may delegate responsibilities
downward in the organization to reduce complexity (Mintzberg, 1983). However, the
lower levels of the organization have to justify their decisions by regularly reporting
progress and results to management. We therefore expect a more complex warehouse
task to require more comprehensive and thus, more sophisticated instruments to
control the task. The third hypothesis of our study is:
H3. The more complex the warehouse task, the more sophisticated the control system.
Based on a sample of 20 production and distribution warehouses, Faber et al. (2002) found
that complex warehouses (measured by the number of order lines processed per day and
the number of active SKUs), use tailor-made software solutions whereas simple
warehouses use standard software solutions to support warehouse management. Hence
we expect that if the warehouse task is complex, a standard software solution will
be difﬁcult to ﬁnd because the situation is too speciﬁc. In such situations, only a tailor-made,
speciﬁc solution or a standard, speciﬁc software package with substantial customization
will be effective. A simple warehouse task can be supported by standard, broad solutions or
even by no automated information system. The fourth hypothesis of our study is:
H4. The more complex the warehouse task, the more speciﬁc the functionality of
the information system.
Major characteristics of a dynamic market are demand unpredictability (DU) and
frequent assortment changes. Extensive tactical planning is not effective in a highly
dynamic environment because plans have to change constantly. Short-term planning
or coordination by feedback (Perrow, 1967), i.e. negotiated alterations in the nature or
sequence of tasks, would be more effective in this situation. In contrast, if the market is
stable, extensive planning is advisable in order to make efﬁcient use of warehouse
resources (personnel, machines, transport, and stock locations) and to minimize stock.
These arguments lead to the ﬁfth hypothesis of this study:
H5. The more dynamic the market of a warehouse, the less extensive the planning.
As decision rules are internally oriented, externally driven unpredictability of demand
and assortment changes will have much less inﬂuence on the complexity of decision
rules. Thus, we do not propose an inﬂuence of market dynamics on decision rules
Some operations are reasonably predictable and usually run according to plan.
In these situations, the need for control is minimal (Slack et al., 2001). Dynamism affects
the reliability of the information and the assumptions that are used in planning. The
higher the rate of change, the more momentarily available information is (Van Assen,
2005). The internal reporting system has to process and deliver information rapidly to
keep up with changes. Also, in a dynamic market, online information exchange with
partners (suppliers and/or customers) in the supply and demand chain is needed to
respond to market changes. The sixth hypothesis of our study is, therefore:
H6. The more dynamic the market of a warehouse, the more sophisticated the
The H6 of this study are shown in Figure 2. All hypotheses are directional, which
means the relationships are positive or negative. We carried out a survey to test our
4.1 Data collection and sample
Data were collected by means of a survey among warehouse managers. The ﬁnal
questionnaire consisted of 55 questions divided into ten sections (general data,
warehouse type, assortment, order lines, processes, market situation, planning, decision
rules, control, and information systems). The questionnaire was extensively pre-tested
for clarity and for assessing the length of the interview. A face-to-face pretest with two
warehouse managers of different warehouses indicated that we had to improve the
wording of a number of questions and response categories. Later on, we tested the
Decision Rules Complexity
Information System Specificity
improved questionnaire by telephone on two other warehouse managers representing
two more warehouses. This led to some minor changes in the questionnaire.
A single database on warehouses in The Netherlands does not exist. We aimed at a
complete representation of medium and large warehouses. We constructed a database
by cooperating with HIDC, the Holland International Distribution Council, who owns a
database of all multinational warehouse operations in The Netherlands (HIDC/BCI,
2001). We extended the database with the membership lists of well-known logistics
associations in The Netherlands (www.NDL.nl, www.FENEX.nl, IMCC and VLM) and
Flanders (Belgium) (www.VIL.be, www.warehouseandlogisitcs.com and www.bruca
rgo.be). All, except two of these lists, were available on the internet. The remaining two
were disclosed at our request. The database was further completed with recent lists of
attendees of workshops and conferences on warehouse management. We targeted
warehouses representing various industry sectors, with different positions in the
supply chain (varying from production-related warehouses, i.e. storing not only
ﬁnished products but also raw materials and components – to wholesale and retail
warehouses), and different outsourcing relations. After eliminating duplicate listings
and incomplete addresses, our sample population consisted of 765 warehouses, which
provides good coverage of the medium and larger warehouses in The Netherlands and
Flanders. These warehouses were approached in two waves.
The ﬁrst wave of 250 warehouses gathered from the HIDC database and lists of
attendees of workshops and conferences was contacted by telephone and asked to
collaborate. Obtaining answers to the questions was an elaborate job. For one thing, the
targeted managers were very busy, and a single contact sometimes required about three
to four calls to complete the questionnaire (several calls were required to ensure that the
managers had prepared the questionnaire which contained many questions on factual
data). 101 warehouses were willing to participate. The most often-heard argument for
non-response was that the questionnaire was too time consuming.
Due to the rather small absolute sample size of the ﬁrst wave, in the second wave, we
approached 515 warehouses, based on the membership lists of logistics associations in
The Netherlands and Flanders, by sending a cover letter and the questionnaire by e-mail.
Two steps were taken to increase response. First, the survey instrument and process
were made as user friendly as possible by using various media, i.e. a web-based
questionnaire on the internet, an electronic questionnaire, and a hardcopy. Second, we
approached all non-responding addressees a second time by e-mail. The most
often-heard argument for non-response was lack of time. In the second wave,
114 warehouses ﬁlled in the questionnaire. If deemed necessary, follow-up calls were
made to verify responses, solve ambiguities, or to ask the respondent to check objective
data. After carefully checking the responses of both waves for completeness and
eligibility, 215 completed questionnaires could be used for this study, representing a
response rate of 28 percent. Given the low response rates for surveys in the logistics
industry in general (Muilerman, 2001), this response rate is good.
Because the respondents represent an organization, they must be knowledgeable about
the main constructs (Huber and Power, 1985). We requested a logistics or warehousing
executive, preferably the warehouse manager, to complete the questionnaire. The
warehouse manager is probably one of the few people with sufﬁcient knowledge about the
data in our questionnaire. For this reason, we had to use a single respondent. Table I shows
that we were quite successful in contacting the preferred informant: 85 percent of the
respondents are senior warehouse managers and 15 percent are logistics staff members.
Table I also shows other demographic data such as warehouse types, general industry
classiﬁcation, and number of warehouse employees.
Table I shows that our sample covers a wide variety of industries, from automotive
to retail, and it represents all types of warehouses. The average number of full-time
direct employees is 78, the average number of stored SKUs per warehouse in our
sample is 14,000, and the average number of shipped order lines is over 10,000 per day.
Although our research is limited to The Netherlands and Flanders, warehousing
practices in these regions are not different from elsewhere in Western Europe. In fact,
many companies run multiple similar facilities elsewhere in Western Europe
(Quak and De Koster, 2007). Overall, we conclude that the response is large enough
and sufﬁciently diverse to draw meaningful conclusions for medium and large
Keller et al. (2002) provide a directory of all multi-item scales published in leading
logistics journals from 1961 to 2000. Most scales for the constructs included in this study
were not available in the literature. Hence, we developed our own measures for these
constructs. An important issue in this regard is whether constructs are considered
reﬂective or formative (Diamantopoulos and Winklhofer, 2001). The choice of
warehouse % Sector %
10.2 Automotive 5.6 ,10 14.0
58.1 Healthcare 3.3 11-20 15.8
31.7 Pharmaceutical 3.3 21-30 13.0
Defence/police 4.2 31-50 13.5
Foodretail 5.1 51-70 10.7
7.4 71-100 10.2
11.2 101-160 11.2
Industrial products 18.1 161-250 6.0
28.4 251-370 1.9
Public warehouses 13.5 371-520 2.3
Total 100 Total 100.0 Total 100.0 Total 100.0
a formative versus a reﬂective speciﬁcation depends on the causal priority between the
measurable items and the latent variable ( Jarvis et al., 2003).
In operationalizing the constructs of our study, we closely followed the
recommendations of Churchill (1979) and particularly Rossiter (2002), who updates
and elaborates the Churchill paradigm of scale development. Each construct was deﬁned
and four experts in the domain of warehouse management (two academics and two
warehouse managers) provided speciﬁc items to measure these constructs. After several
discussion rounds, our experts concluded that all constructs in our study are formative.
Fundamental to formative constructs is that all items of the construct contribute to the
construct; omitting an item is omitting a part of the construct. Therefore, we kept items
in our construct even if they did not correlate strongly with other items in that particular
construct. The resulting indicators per construct are presented and discussed below.
A compilation of the constructs and their measures is summarized in Appendix.
Task complexity is measured by summing up the standardized scores of:
.Number of SKUs (TCa). This variable is measured as the log of the number of
SKUs since the effect of the number of SKUs on task complexity is expected to be
skewed (Faber et al., 2002; De Koster and Balk, 2008).
.Process diversity (TCb). Following Faber et al. (2002) and Schoenherr et al. (2010),
this variable ismeasured by both the number ofspecial processes and the number of
modes in which processes can be carried out in the warehouse. Instead of measuring
the number of all the different processes, we only measure the number of special
processes. Nearly all warehouses perform processes such as receiving, storage,
internal replenishment from bulk to pick areas, order picking, and shipping, but
some warehouses also perform special processes. Examples include product
repacking, return handling, customs clearance, cycle counting for stock integrity,
cross-docking, and value adding activities. We measure the number of special
processes on a binary scale with ﬁve special processes as a split value; ﬁve is a fairly
large value since most warehouses have at least some special processes. Warehouse
processes can be carried out in different modes. For example, storing products in a
shelf area differs substantially from storing products in a pallet area with regard to
procedures, product carriers, and material-handling equipment used. The same is
true for order picking in such areas. Complexity is driven by the average number of
modes in which processes are carried out. We measure this average number of
modes on a binary scale with three as a split value; three is a relatively small value,
justiﬁed by a low average number of modes per process in most warehouses.
.Number of order lines (TCc). This variable is measured as the log of the number
of order lines since the effect of the number of order lines on task complexity is
expected to be skewed (Faber et al., 2002).
To measure market dynamics (MD), we used Miller and Friesen’s (1983) perceptual
measures for dynamism. Here, market dynamics is measured as the sum of the
standardized scores of:
.Demand unpredictability (DU). This variable is measured as the sum score of
three questions asking for perceived demand predictability in the very short, in
the short, and in the long-run, each measured on a three-point scale (predictable,
limited predictability, and difﬁcult to predict).
.Assortment changes (AC). The frequency and amount of assortment change is
measured on a three-point scale (hardly, to a limited extent, and to a great extent).
Warehouse management structure is measured by three constructs:
(1) Planning extensiveness (PE). We measure this construct by the number of
tactical plans that are explicitly drawn up in the warehouse (ranging from 0 to a
maximum of four plans).
(2) Decision rules complexity (DC). We measure the number of different types of
decision rules (DCa) by counting activities that are systematically executed
using decision rules. We distinguished 12 activities (see Appendix for the full
list). We measure the nature of the decision rules (DCb) by asking the
respondent’s opinion on the perceived complexity of both inbound and
outbound rules, each measured on a three-point scale (ranging from simple to
complex). The score for decision rules complexity is obtained by summing the
standardized scores of both parts (DCa, DCb).
(3) Control sophistication (CS). This construct is measured by two indicators: the
sophistication of the internal reporting system and the online information
exchange with partners (using, e.g. EDI). The sophistication of the reporting
system is the degree to which data in a warehouse are recorded and monitored
online (CS1, CS2), and the reaction time to unforeseen situations (CS3). The
aspects are all measured on a binary scale. Online information exchange
(scheduling information, plans, orders, etc.) with business partners is the degree
of online information sharing with suppliers and customers, respectively, on a
yes/no scale (CS4, CS5). The total score is the sum of all aspects of the reporting
system and online information exchange with partners.
Information system speciﬁcity (IS). This is measured by distinguishing six different
types of information systems with an ascending degree of speciﬁcity, ranging from no
automated system to a tailor-made system (see Appendix for precise levels).
At the construct level, we assume all subdimensions to contribute equally to a
To assess the relationship between warehouse characteristics and warehouse
management, we performed a series of regression analyses. The correlations between
the variables speciﬁed in Table II are the input for the regressions. The results are shown
in Table III. The regressions were conducted both at the construct level (see results on
upper part of Table III) and at the subdimension level (results on lower part of Table III).
At the subdimension level, we conducted stepwise regression among the ﬁve
independent variables and we present results only for variables for which p,0.10
(two-tailed). We chose a higher p-value because of the exploratory character of this
study. We also tested for interaction effects between the main constructs. These turned
out to be not signiﬁcant for any of the dimensions of warehouse management.
H1 predicted a positive relationship between task complexity and planning
extensiveness, whereas H5 predicted a negative relationship between market dynamics
and planning extensiveness. The results in Table III conﬁrm a signiﬁcant and positive
effect of task complexity (standardized regression coefﬁcient beta ¼0.29, p,0.001)
TC Tca TCb TCc MD DU AC PE DC CS IS
Task complexity (TC) 1.00
Log no. of SKUs (TCa) 0.81 *1.00
Process diversity (TCb) 0.66 *0.33 *1.00
Log no. of order lines (TCc) 0.72 *0.45 *0.12 1.00
Market dynamics (MD) 0.08 0.09 ** 0.14 *** 20.04 1.00
Demand unpredictability (DU) 20.17 *** 20.05 0.03 20.34*0.70 *1.00
Assortment changes (AC) 0.29 *0.18 ** 0.16 *** 0.28 *0.70 *20.03 1.00
Planning extensiveness (PE) 0.28 *0.07 0.18 ** 0.38 *20.10 20.27 *0.12 1.00
Decision rules complexity (DC) 0.44 *0.28 *0.28 *0.39 *20.01 20.14 *** 0.12 0.35 *1.00
Control sophistication (CS) 0.21 ** 0.18 ** 0.14 *** 0.14 *** 0.07 0.00 0.09 0.11 0.27 *1.00
Information system speciﬁcity (IS) 0.33 *0.18 ** 0.22 *0.35 *0.08 20.03 0.14 *** 0.18 ** 0.23 *0.23 *1.00
n212 215 215 212 213 214 214 215 215 212 213
Min. 22.55 1.3 2 1.18 21.86 3 1 0 21.66 5 0
Max. 2.02 5.41 4 5.3 2.03 9 3 4 2.42 10 5
Mean 0 3.63 2.98 3.16 0 5.74 2.48 2.63 0 7.66 2.71
SD 1 0.74 0.8 0.92 1 1.74 0.54 1.22 1 1.48 1.8
Note: Signiﬁcant at: *p,0.001, **
and a negative effect of market dynamics (b¼20.14, p,0.038). Table III shows that
task complexity and market dynamics explain approximately 10 percent of the variance
in planning extensiveness. However, the regression analysis in the lower part of Table III
shows that 21 percent of the variance in planning extensiveness is explained by the
subdimensions. Especially “number of order lines” has a strong positive effect on
A detailed examination of the relationship between market dynamics and planning
extensiveness indicates that the two subdimensions of market dynamics appear to work
in opposite directions. As expected, the subdimension “demand unpredictability” has a
signiﬁcant negative effect on planning extensiveness (correlation ¼20.27, p,0.001,
Table II), which means that if demand is more difﬁcult to predict, planning will be less
extensive. Contrary to our expectation, the subdimension “assortment changes” appears
to affect planning extensiveness marginally positively (correlation ¼0.12, p,0.074,
Table II). This means that if the assortment of the warehouse changes often, tactical
planning will be more extensive. This result seems to suggest that changes in the
assortment of a warehouse are predictable. Our initial expectation was based on theories
that mainly focus on production situations in which changes in the assortment are
considered unpredictable thus increasing uncertainty (Fisher, 1997). Indeed, frequent
product changes may be rather unpredictable in a production environment.
Construct level analysis
complexity (TC) 0.29 4.41 0.000 0.44 7.00 0.000 0.20 2.96 0.003 0.33 5.00 0.000
dynamics (MD) 20.14 22.08 0.038 20.05 20.71 0.477 0.04 0.54 0.587 0.04 0.60 0.553
0.10 0.19 0.04 0.11
n210 210 207 208
Log no. of
SKUs (TCa) 20.17 22.29 0.023 0.19 2.75 0.007
diversity (TCb) 0.18 2.70 0.007 0.23 3.69 0.000 0.17 2.55 0.011
Log no. of order
lines (TCc) 0.38 5.13 0.000 0.37 5.91 0.000 0.33 5.10 0.000
(DU) 20.16 22.37 0.019
0.21 0.21 0.04 0.15
n210 210 207 208
However, when information about product changes and new products is exchanged
between production and distribution, a distribution center should be able to predict the
changes in the assortment. This even encourages distribution centers to put more effort
into planning to cope with these changes.
Support for this interpretation can be found when comparing the production
warehouses and the distribution centers in our sample with respect to the effect of the
subdimension “assortment changes” on planning extensiveness. Production warehouses
store and distribute raw materials, semi-ﬁnished and ﬁnished products in a production
environment. Distribution centers store products between point-of-production and
point-of-consumption and are located close to the products’ markets. In other words,
production warehouses are located upstream, whereas distribution centers are located
downstream in the supply-and-demand chain. We therefore expect a negative effect of
negative effect of assortment changes in production warehouses. Although we have to be
cautious since the number of production warehouses is relatively small in our sample
(n¼22), the results of a separate regression (Table IV) for production warehouses showa
signiﬁcant negative effect (
¼20.45, p,0.027). This means that the more the
assortment changes, the less extensive is the planning. Changes in the assortment are
difﬁcult to predict and therefore difﬁcult to plan for in production warehouses.
In distribution centers (n¼193), no signiﬁcant effect of assortment changes on planning
extensiveness shows up in Table IV because the effects of the other variables, especially
“number of order lines” are more dominant. The results of correlation analyses (Table V)
Production warehouses Distribution centers
Planning extensiveness (PE) Planning extensiveness (PE)
Construct level analysis
Task complexity (TC) 20.12 20.64 0.530 0.32 4.65 0.000
Market dynamics (MD) 20.60 23.20 0.005 20.10 21.40 0.162
Task complexity (TC)
No. of SKUs (TCa) 20.14 21.85 0.066
Process diversity (TCb) 0.18 2.60 0.010
No. of order lines (TCc) 0.40 5.07 0.000
Market dynamics (MD)
Demand unpredictability (DU) 20.46 22.48 0.023 20.15 22.08 0.039
Assortment changes (AC) 20.45 22.42 0.027
Production warehouses Distribution centers
Planning extensiveness (PE) Planning extensiveness (PE)
Assortment changes (AC) 20.38 0.083 22 0.18 0.015 192
changes” and planning
show a signiﬁcant positive correlation (correlation ¼0.18, p,0.015)) between
“assortment changes” and planning extensiveness for distribution centers, and a
negative correlation (correlation ¼20.38, p,0.083) for production warehouses. Our
results indicate that because distribution centers are located downstream in the supply
chain, assortment changes can be predicted and planned.
Table IV shows, in addition, that market dynamics drives planning extensiveness
¼20.60, p,0.005) in production warehouses, and that task complexity drives
planning extensiveness (
¼0.32, p,0.001) in distribution centers. Distribution centers
appear to process signiﬁcantly more order lines (mean ¼11,715, SD ¼27,585) than
production warehouses (mean ¼881, SD ¼766) (t¼3.47, p,0.001), which explains the
stronger effect of task complexity on planning extensiveness in distribution warehouses.
H2 predicted a positive relationship between task complexity and decision rules
complexity. Table III shows that 19 percent of the variance in decision rules complexity
is explained by task complexity. The results in Table III conﬁrm a signiﬁcant and
positive effect of task complexity (
¼0.44, p,0.001): the more complex the
warehouse task, the more complex are the decision rules. As expected, market
dynamics has no signiﬁcant effect on decision rules complexity ( p,0.477).
H3 and H6 predicted an effect of both task complexity and market dynamics on control
sophistication. The results in Table III conﬁrm H3 (
¼0.20, p,0.003) and rejects H6
¼0.04, p,0.587). A more complex warehouse task requires more sophisticated
control, while a more dynamic market does not affect control. Only 4 percent of the
variance in control sophistication can be explained by task complexity. We conclude that
task complexity and market dynamics do not explain the variances in control
sophistication; other factors may play a role, and further research is needed here.
H4 predicted that warehouses with a complex task need a more customized and
tailor-made information system. Table III shows that the information system
speciﬁcity is indeed signiﬁcantly and positively affected by task complexity (
p,0.001). 11 percent of the variance in information system speciﬁcity is explained by
task complexity. We conclude that a more speciﬁc and customized information system
is required once a warehouse task becomes more complex. As expected, market
dynamics has no effect on information system speciﬁctiy.
We tested for several control variables, such as industry sector and respondent’s
position, by adding variables to the subdimension regression of Table III, applying a
Chow (F-change) test for increase in explained variance. We only found signiﬁcant
effects of industry sector for three warehousing dimensions, i.e. decision rules
complexity (F-change ¼2.72, p,0.007), control sophistication (F-change ¼2.11,
p,0.036), and information system speciﬁcity (F-change ¼3.11, p,0.002). No effect
of industry sector was found for planning extensiveness ( p.0.24).
A more detailed analysis reveals that information system speciﬁcity is signiﬁcantly
higher than average for the sector public warehouses ( p,0.001). A possible
explanation for why public warehouses use signiﬁcantly more speciﬁc information
systems (72 percent have implemented a WMS) could be that logistic-service providers
serve multiple clients in such warehouses (on average 4.4 within a single facility.
De Koster and Warffemius (2005) which all require speciﬁc processes. Such a diversity of
processes is, in general, not sufﬁciently supported by generic warehouse management
systems. This could also explain the signiﬁcantly higher-than-average decision rules
complexity in public warehouses ( p,0.013). In addition, decision rules complexity
is signiﬁcantly lower ( p,0.018) than average for the healthcare/pharmaceutical sector.
This might be due to a smaller-than-average diversity in outbound processes. In this
sector, all shipments are in small quantities, picked from storage systems, ﬁt for piece
picking. A greater diversity can be often observed in the dedicated systems in other
industry sectors, since next to piece picking, box and pallet picking also takes place.
Finally, control sophistication is signiﬁcantly lower for defence/police warehouses
(p,0.002) and for industrial products warehouses ( p,0.038). For defence/police
warehouses, especially online information exchange with business partners is
particularly low. This could be explained by the important role of security and data
protection in such warehouses. Warehouses with industrial products are located
upstream in the supply chain. As lead times generally increase upstream in supply
chains, this implies that such warehouses have a longer planning horizon and do not
have to respond in real-time to changes and unforeseen situations.
To summarize, we ﬁnd clear support for H1,H2,H4,andH5, but weak support for
H3, and no support for H6 (Figure 3). For H1 and H5, we found different effects for
production warehouses and distribution centers. H1 is supported by distribution
centers, but not by production warehouses; and H5 is supported by production
warehouses, but not by distribution centers. With regard to market dynamics, the
subdimension “demand unpredictability” behaves as expected, but the effect of the
subdimension “assortment changes” seems to be different in distribution centers than
in production warehouses. The variable “assortment changes” does not seem to be a
characteristic of a dynamic market in distribution centers. Importantly, most
subdimensions of the independent constructs have signiﬁcant effects and differ in size.
This indicates the importance of measuring the constructs in a detailed manner and
analyzing the subdimensions separately.
6. Conclusions and future research
In this paper, we proposed and deﬁned warehouse management by three constructs:
planning extensiveness, decision rules complexity, and control sophistication.
(in distribution centers)
Decision Rules Complexity
(in production warehouses)
Market Dynamics (Information) System
Information System Specificity
We developed a model linking the two main drivers of warehouse management,
task complexity and market dynamics, and tested this model in a large sample of
warehouses. In the process, we developed new measures for most constructs.
We empirically ﬁnd that warehouse management is largely driven by task
complexity and to a much lesser extent by market dynamics. Our results show that
the more complex the warehouse task is, the more extensive is the planning and the more
complex are the decision rules. Furthermore, a complex warehouse task leads to a more
sophisticated control system. Our H5 is weakly supported by the data. This is mainly
due to the role of the subdimension “assortment changes” in the market dynamics
construct. Assortment changes appear to have a different effect in distribution centers
than in production warehouses. Whereas the subdimension “demand unpredictability”
indeed leads to less extensive planning, frequent assortment changes lead to more
extensive planning in distribution centers. In hindsight, this makes sense as changing
assortments is regular business (think of regular promotions, seasonal products) in
many distribution centers, and stock and location plans have to anticipate this.
Furthermore, we ﬁnd that distribution centers process signiﬁcantly more order lines
per day than production warehouses. This phenomenon, together with the opposite
effect of “assortment changes”, explains why planning extensiveness is driven by
market dynamics in production warehouses and by task complexity in distribution
centers. Our H6 is not supported by the data. Apparently, only task complexity plays a
We expected to ﬁnd the choice of the WMS (measured by information system
speciﬁcity) to be driven by task complexity. Indeed, the data conﬁrmour H4 that the more
complex the warehouse task is, the more speciﬁc is the functionality of the information
system. Other factors than TC obviously play a role in the WMS choice. For example,
logistics service providers do not base their WMS choice on a single warehouse but on the
various warehouses they operate. The organization of warehouse management is
strongly related to the speciﬁcity of the information system: all three dimensions
(planning extensiveness, decision rules complexity, and control sophistication) correlate
positively with the speciﬁcity of the software system. This was to be expected as generic
software systems do not sufﬁciently support complex requirements.
In this research, we contributed to the study of warehouse management by
developing new measures for most constructs. We provided an extensive and detailed
operationalization of each construct and its subdimensions. Most of these measures
were developed using objective facts about the warehouse and the insights and
observations of expert informants, usually the senior warehouse manager.
Future research should be conducted to further validate the measures. Our
operationalization could be used as a starting point for developing measures in similar
contexts such as transshipment terminals and cross-dock operations. Further research
could also test whether the current operationalizations (dimensions and
subdimensions) are general enough to be applied outside of the warehouse context,
for instance, in production.
Our research focused on warehousing in The Netherlands and Flanders. It would be
interesting to test our hypotheses in warehouses in non-Western countries. A priori, we
do not expect signiﬁcantly different results. In addition, it might be interesting to extend
the sample with more production warehouses to more robustly test for differences
between production warehouses and distribution centers. In our study, we still ﬁnd
a substantial variability in responses to task complexity and market dynamics.
In follow-up research, in-depth case studies might provide additional insight into the
implementation of the subdimensions of warehouse management.
Our proposed operationalization provides means to systematically study other
aspects of warehousing. Most notably, how warehouse management affects warehouse
performance. In particular, it would be interesting to research whether a (mis)match
between the independent variables, task complexity and market dynamics, and the
way warehouse management is organized impacts warehouse performance. Such a
study would complement current studies on warehouse performance (De Koster and
Balk, 2008; De Koster and Warffemius, 2005; Hackman et al., 2001) that did not test
drivers. Detailed case studies could provide more insight into the motivations and
reasons for apparent mismatches. Such reasons might be diverse. Think of warehouses
with physical constraints (e.g. space shortage, outdated systems, poor layout) that have
not been able to timely adapt to changes in the environment or tasks. Furthermore, it
would be interesting to study the impact of differences in WMS and the choice of WMS
Finally, this study could help warehouse managers to benchmark their
warehouse against the independent constructs developed in this paper. Knowledge
of scores on these indicators can help managers effectively organize warehouse
management and assist them with their choice of WMS; in particular, whether a
standard system will do, or whether a system with speciﬁc functionalities will be more
Ackerman, K.B. and La Londe, B.J. (1980), “Making warehousing more efﬁcient”, Harvard
Business Review, Vol. 58 No. 2, pp. 94-102.
Alter, S. (1999), Information Systems: A Management Perspective, 3rd ed., Addison-Wesley,
Anthony, R.N. and Young, D.W. (1984), Management Control in Non-proﬁt Organizations, 3rd
ed., R.D. Irwin, Homewood, IL.
BCI (1997), Europese distributie en waardetoevoeging door buitenlandse bedrijven (European
Distribution and Value Added Logistics by Foreign Companies), BCI, Nijmegen, pp. 39-54,
Bertrand, J.W.M., Wortmann, J.C. and Wijngaard, J. (1990), Production Control: A Structural and
Design Oriented Approach, Elsevier, Amsterdam.
Burns, T. and Stalker, G.M. (1961), The Management of Innovation, Tavistock, London.
Chen, C.-M., Gong, Y., De Koster, M.B.M. and Van Nunen, J.A.E.E. (2010), “A ﬂexible evaluative
framework for order picking systems”, Production and Operations Management, Vol. 19
No. 1, pp. 70-82.
Churchill, G.A. (1979), “A paradigm for developing better measures of marketing constructs”,
Journal of Marketing Research, Vol. 16 No. 1, pp. 64-73.
Davenport, T.H. (1998), “Putting the enterprise into the enterprise system”, Harvard Business
Review, Vol. 16 No. 4, pp. 121-131.
De Koster, M.B.M. and Balk, B. (2008), “Benchmarking and monitoring international
warehouse operations in Europe”, Production and Operations Management, Vol. 17
No. 2, pp. 1-10.
De Koster, M.B.M. and Warffemius, P.M.J. (2005), “American, Asian and third-party international
warehouse operations in Europe: a performance comparison”, International Journal of
Operations & Production Management, Vol. 25 No. 8, pp. 762-780.
De Koster, M.B.M., Le-Duc, T. and Roodbergen, K.J. (2007), “Design and control of warehouse
order picking: a literature review”, European Journal of Operational Research, Vol. 182
No. 2, pp. 481-501.
De Toni, A. and Panizzolo, R. (1997), “Repetitive manufacturing planning and control systems:
a framework for analysis”, Production Planning and Control, Vol. 8 No. 5, pp. 500-508.
Diamantopoulos, A. and Winklhofer, H.M. (2001), “Index construction with formative indicators:
an alternative to scale development”, Journal ofMarketing Research, Vol. 38 No. 2, pp. 269-277.
Drury, J. (1988), “Towards more efﬁcient order picking”, IMM Monograph 1, The Institute of
Materials Management, Cranﬁeld.
Duncan, R. (1972), “Characteristics of organizational environments and perceived environmental
uncertainty”, Administrative Science Quarterly, Vol. 17 No. 3, pp. 313-327.
ELA/AT Kearney (2005), Excellence in Logistics 2004: Differentiation for Performance, ELA,
Espejo, R. and Watt, J. (1988), “Information management, organization and managerial
effectiveness”, The Journal of the Operational Research Society, Vol. 39 No. 1, pp. 7-14.
Faber, N., De Koster, M.B.M. and Van de Velde, S.L. (2002), “Linking warehouse complexity to
warehouse planning and control structure: an exploratory study of the use of warehouse
management information systems”, International Journal of Physical Distribution
& Logistics Management, Vol. 32 No. 5, pp. 381-395.
Fisher, M.L. (1997), “What is the right supply chain for your product?”, Harvard Business Review,
Vol. 75 No. 2, pp. 105-116.
Frazelle, E.H. (2002), World-Class Warehousing and Material Handling, McGraw-Hill, New York, NY.
Gu, J., Goetschalckx, M. and McGinnis, L.F. (2007), “Research on warehouse operation:
a comprehensive review”, European Journal of Operational Research, Vol. 177 No. 1,
Gu, J., Goetschalckx, M. and McGinnis, L.F. (2010), “Research on warehouse design and
performance evaluation: a comprehensive review”, European Journal of Operational
Research, Vol. 203 No. 3, pp. 539-549.
Hackman, S.T., Frazelle, E.H., Grifﬁn, P.M., Grifﬁn, S.O. and Vlasta, D.A. (2001), “Benchmarking
warehousing and distribution operations: an input-output approach”, Journal of
Productivity Analysis, Vol. 16 No. 1, pp. 79-100.
Harmon, R.L. (1993), Reinventing the Warehouse, Word-Class Distribution Logistics,
The Free Press, New York, NY.
Hatch, M.J. (1997), Organization Theory: Modern Symbolic and Postmodern Perspectives, Oxford
University Press, New York, NY.
HIDC/BCI (2001), From EDC to ELC, Holland International Distribution Council, The Hague.
Huber, G.P. (1984), “The nature and design of post-industrial organizations”, Management
Science, Vol. 30 No. 8, pp. 928-951.
Huber, G.P. and Power, D.J. (1985), “Retrospective reports of strategic-level managers: guidelines
for increasing their accuracy”, Strategic Management Journal, Vol. 6 No. 2, pp. 171-180.
Jarvis, C.B., MacKenzie, S.B. and Podsakoff, P.M. (2003), “A critical review of construct indicators
and measurement model misspeciﬁcation in marketing and consumer research”, Journal of
Consumer Research, Vol. 30 No. 3, pp. 199-218.
Keller, S.B., Savitskie, K., Stank, T.P., Lynch, D.F. and Ellinger, A.E. (2002), “A summary and
analysis of multi-item scales used in logistics research”, Journal of Business Logistics,
Vol. 23 No. 2, pp. 703-729.
Khandwalla, P.N. (1977), The Design of Organizations, Harcourt Brace Jovanovich, New York, NY.
Kiefer, A.W. and Novack, R.A. (1999), “An empirical analysis of warehouse measurement systems
in the context of supply chain implementation”, TransportationJournal, Vol. 38 No. 3, pp. 18-27.
Kuipers, B. (1999), “Lie
`ge, Tongeren, Genk or the Maasvlakte: what’s the difference? Spatial
behaviour of European Distribution Centres”, in Rodenburg, R.H.J. and Kruse, A.L. (Eds),
Vervoerslogistieke Werkdagen 1999, Connekt, Delft, pp. 433-445.
Lawrence, P.R. and Lorsch, J.W. (1967), Organization and Environment, Harvard Business
Graduate School Press, Boston, MA.
LeBlanc, L.J. (2000), “The impact of information technology on operations management”,
International Journal of Technology Management, Vol. 20 Nos 3/4, pp. 243-251.
Lee, H.L. (2002), “Aligning supply chain strategies with product uncertainties”, California
Management Review, Vol. 44 No. 3, pp. 105-119.
Loudin, A. (1998), “Pick a winner”, Warehousing Management, Vol. 5 No. 11, p. 34.
Lynch, R.K. (1985), “Nine pitfalls in implementing packaged applications software”, The Journal
of Information Systems Management, Vol. 2 No. 2, pp. 88-94.
Miller, D. and Friesen, P.H. (1983), “Strategy making and environment: the third link”, Strategic
Management Journal, Vol. 4 No. 3, pp. 221-235.
Mintzberg, H. (1983), Structure in Fives: Designing Effective Organizations, Prentice-Hall,
Englewood Cliffs, NJ.
Muilerman, G. (2001), “Time based logistics: an analysis of the relevance, causes and impacts”,
published PhD thesis, Delft University of Technology, Delft.
OECD (2006), OECD Estimates of Labour Productivity for 2005, September, available at:
Perrow, C. (1967), “A framework for the comparative analysis of organizations”, American
Sociological Review, Vol. 32 No. 2, pp. 194-208.
Peterson, R. and Silver, E.A. (1979), Decision Systems for Inventory Management and Production
Planning, 2nd ed., Wiley, New York, NY.
Premkumar, R. and Zailani, S. (2005), “Supply chain integration and performance: US versus
East Asian companies”, Supply Chain Management: An International Journal, Vol. 10
No. 5, pp. 379-393.
Quak, H.J. and De Koster, M.B.M. (2007), “Exploring retailers’ sensitivity to local sustainability
policies”, Journal of Operations Management, Vol. 25 No. 6, pp. 1103-1122.
Randall, S. (1999), “The value of WMS”, Modern Materials Handling, Vol. 54 No. 7, pp. 50-52.
Rossiter, J.R. (2002), “The C-OAR-SE procedure for scale development in marketing”,
International Journal of Research in Marketing, Vol. 19 No. 4, pp. 305-335.
Rouwenhorst, B., Reuter, B., Stockrahm, V., Van Houtum, G.J., Mantel, R.J. and Zijm, W.H.M.
(2000), “Warehouse design and control: framework and literature review”, European
Journal of Operations Research, Vol. 122 No. 3, pp. 515-533.
Schoenherr, T., Hilpet, D., Soni, A.K., Venkataramanan, M.A. and Mabert, V.A. (2010), “Enterprise
systems complexity and its antecedents: a grounded-theory approach”, International Journal
of Operations and Production Management, Vol. 30 No. 6, pp. 639-668.
Slack, N., Chambers, S. and Johnston, R. (2001), Operations Management, 3rd ed., Prentice-Hall,
Somers, T.M. and Nelson, K.G. (2003), “The impact of strategy and integration mechanisms on
enterprise system value: empirical evidence from manufacturing ﬁrms”, European Journal
of Operational Research, Vol. 146 No. 2, pp. 315-338.
Sousa, R. and Voss, C.A. (2008), “Contingency research in operations management practices”,
Journal of Operations Management, Vol. 26 No. 2, pp. 697-713.
Ten Hompel, M. and Schmidt, T. (2006), Warehouse Management: Automation and Organisation
of Warehouse and Order Picking Systems, Springer, Berlin.
Thompson, J.D. (1967), Organization in Action, McGraw-Hill, New York, NY.
Tompkins, J.A., White, J.A., Bozer, Y.A. and Tanchoco, J.M.A. (2003), Facilities Planning, Wiley,
New York, NY.
Van Assen, M.F. (2005), “Empirical studies in discrete parts manufacturing management”,
published PhD thesis, RSM Erasmus University, Rotterdam.
Van den Berg, J.P. (1999), “A literature survey on planning and control of warehousing systems”,
IIE Transactions, Vol. 31 No. 8, pp. 751-762.
Van den Berg, J.P. and Zijm, W.H.M. (1999), “Models for warehouse management: classiﬁcation
and examples”, International Journal of Production Economics, Vol. 59 Nos 1/3, pp. 519-528.
Van Goor, A.R., Ploos van Amstel, M.J. and Ploos van Amstel, W. (2003), European Distribution
and Supply Chain Logistics, Stenfert Kroese, Groningen.
¨scher, G. (2004), “Order picking: a survey of planning problems and methods”, in Dyckhoff, H.,
Lackes, R. and Reese, J. (Eds), Supply Chain Management and Reverse Logistics, Springer,
Berlin, pp. 323-347.
About the authors
N. Faber is an Associate Professor of Logistics at The Netherlands Defense Academy, and a
Doctoral Researcher at the Rotterdam School of Management, Erasmus University. Her research
interests are warehousing, defense logistics and supply chain management. N. Faber is the
corresponding author and can be contacted at: firstname.lastname@example.org
M.B.M. de Koster is Professor of Logistics and Operations Management at the Rotterdam
School of Management, Erasmus University. His research interests are warehousing, material
handling, retail operations, and behavioral operations. He is (co-) author/editor of eight books and
over 130 papers in books and journals including Journal of Operations Management,
Transportation Science,Production and Operations Management, and International Journal of
Operations and Production Management. He is a member of the ELA R&D committee, of the
BVL Scientiﬁc Advisory Board, Chairman of the Material Handling Forum, and in the editorial
review boards of seven journals.
A. Smidts is Professor of Marketing Research at the Rotterdam School of Management,
Erasmus University and Director of the Erasmus Center for Neuroeconomics. He was Scientiﬁc
Director of the Erasmus Research Institute of Management (ERIM) and Dean of Research of the
Rotterdam School of Management from 2004 to 2011. His research interests include behavioral
decision making and persuasive communication. He is currently working in the area of decision
neuroscience and neuromarketing. His research has been published in journals such as
Management Science, Academy of Management Journal, Marketing Science, International
Journal of Research in Marketing, Journal of Consumer Research, Journal of Consumer
Psychology, Social Cognitive & Affective Neuroscience, Psychological Science, Journal of
Neuroscience and Neuron.
To purchase reprints of this article please e-mail: email@example.com
Or visit our web site for further details: www.emeraldinsight.com/reprints
instruction Response categories Computation
TC Task complexity Sum (TCa, TCb, TCc)
TCa Log number of SKUs What is the average number of
(open) Log (number of SKUs)
TCb Process diversity Sum (TCb1,TCb2)
TCb1 Number of special processes Check off process if applicable Quality control
Count number of processes
If count is ﬁve or less TCb1 ¼1;
if count is more than ﬁve
Internal product transportations
Value added logistics
Other special processes
TCb2 Average number of modes What is the average number of
modes in which processes can be
Three or less
More than 3
If average number of modes per
process is three or less
TCb2 ¼1; if more than three
TCc Log number of order lines What is the average number of
order lines per day?
(open) Log (number of order lines)
MD Market dynamics Sum (DU, AC)
DU Demand unpredictability Sum (DU1, DU2, DU3)
DU1 DU long-term How predictable is the total
number of order lines for long-
term (half a year-one year)?
Predictable to a limited extent
Difﬁcult to predict
If demand is predictable
DU1 ¼1; if predictable to a
limited extent DU1 ¼2; if
difﬁcult to predict DU1 ¼3
DU2 DU short-term How predictable is the total
number per product/product
group for short-term?
Predictable to a limited extent
Difﬁcult to predict
If demand is predictable
DU2 ¼1; if predictable to a
limited extent DU2 ¼2; if
difﬁcult to predict DU2 ¼3
Summary of measures
instruction Response categories Computation
DU3 DU very short-term (one day) How predictable is the total
number of order lines per day for
very short-term (one day-one
Predictable to a limited extent
Difﬁcult to predict
If demand is predictable
DU3 ¼1; if predictable to a
limited extent DU3 ¼2; if
difﬁcult to predict DU3 ¼3
AC Assortment changes Is the assortment liable to
To a limited extent
To a great extent
If assortment hardly changes
AC ¼1; if AC to a limited extent
AC ¼2; if AC to a great extent
PE Planning extensiveness Check off plans Stock planning
Storage location planning
Count number of plans checked
DC Decision rules complexity Sum (Dca, DCb)
DCa Number of activities explicitly
using decision rules
Check off activities using
Allocate dock doors to inbound
transport units (e.g. trucks);
Count number of activities
Allocate capacity (personnel and
equipment) to inbound transport
Allocate inbound products to
storage locations or cross-
Allocate inbound products and
storage locations to inbound
routes (optimal routes);
Allocate inbound routes to
Cluster orders to be shipped to
Cluster customer orders to
instruction Response categories Computation
Settle the executing start point
Allocate pick order lines to
Allocate pick order lines and
storage locations to pick routes
Allocate pick routes to available
Allocate dock doors to outbound
DCb Perceived complexity of
Sum (DCb1, DCb2)
DCb1 Perceived complexity of
inbound decision rules
How complex are the inbound
Not simple, not complex
If decision rules are simple
DCb1 ¼1; if decision rules are
not simple and not complex
DCb1 ¼2; if decision rules are
complex DCb1 ¼3
DCb2 Perceived complexity of
outbound decision rules
How complex are the outbound
Not simple, not complex
If decision rules are simple
DCb2 ¼1; if decision rules are
not simple and not complex
DCb2 ¼2; if decision rules are
complex DCb2 ¼3
CS Control sophistication Sum (CS1, CS2, CS3, CS4, CS5)
CS1 Operational data registration Is operational data generally
registered real time and online?
If registration is not real time
and online CS1 ¼1; if
registration is real time and
online CS1 ¼2
instruction Response categories Computation
CS2 Operational information
Is information for operational
control generally available real
time and online?
If information is not real time
and not available online
CS2 ¼1; if information is real
time and available online
CS3 Reaction to unforeseen
What is the reaction to
Takes some time
If reaction takes some time
CS3 ¼1; if reaction is immediate
CS4 Operational information from
Is operational information from
suppliers (e.g. stock levels)
If supplier information is not
available online CS4 ¼1; if
supplier information is available
online CS4 ¼2
CS5 Operational information from
Is operational information from
customers (e.g. point-of-sale
information) received online?
If customer information is not
received online CS5 ¼1; if
customer information is received
online CS5 ¼2
IS Information system speciﬁcity Check off information system
type used to manage warehouse
No automated information
Standard ERP with substantial
Standard WMS with substantial
0 – no automated information
system, 1 – standard ERP, 2 –
standard ERP with substantial
customization, 3 – standard
WMS, 4 – standard WMS with
substantial customization, 5 –
tailor-made system (increasing
level of speciﬁcity)
All variables are standardized before summing