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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 management. 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.
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Organizing warehouse
N. Faber
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,
Operations planning
Paper type Research paper
1. Introduction
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
this study.
Received 14 December 2011
Revised 30 March 2012
14 June 2012
Accepted 17 September 2012
International Journal of Operations &
Production Management
Vol. 33 No. 9, 2013
pp. 1230-1256
qEmerald Group Publishing Limited
DOI 10.1108/IJOPM-12-2011-0471
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
transport capacities.
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
¨scher (2004).
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
these rules.
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.
3. Hypotheses
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
Figure 1.
Research model
Warehouse Management
Task Complexity
Number of SKUs
Planning Extensiveness
Number of plans
Decision Rules Complexity
Market Dynamics
Process Diversity
Number of order lines
Control Sophistication
Speed of transformation of information
Number of different types of decision rules
Nature of decision rules
• Demand Unpredictability
Assortment Changes
Warehouse Management
(Information) System
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
control system.
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. Method
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
Figure 2.
Warehouse Management
Task Complexity
Planning Extensiveness
Decision Rules Complexity
H2 +
Control Sophistication
Warehouse Management
(Information) System
Market Dynamics
Information System Specificity
H4 +
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 (,, IMCC and VLM) and
Flanders (Belgium) (, and www.bruca 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
4.2 Measures
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
position %
Type of
warehouse % Sector %
Number of
employees %
85.1 Production
10.2 Automotive 5.6 ,10 14.0
14.9 Distribution
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
Agricultural products/
food products
7.4 71-100 10.2
Information and
11.2 101-160 11.2
Industrial products 18.1 161-250 6.0
Other products
(mainly consumer
28.4 251-370 1.9
Public warehouses 13.5 371-520 2.3
520-700 1.4
Total 100 Total 100.0 Total 100.0 Total 100.0
Mean 78
SD 106
Note: n¼215
Table I.
Sample description
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
5. Results
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)
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, **
p,0.01, ***
Table II.
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
planning extensiveness.
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.
extensiveness (PE)
Decision rules
complexity (DC)
system specificity
tpB tp
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
Subdimension analysis
complexity (TC)
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
dynamics (MD)
(DU) 20.16 22.37 0.019
changes (AC)
0.21 0.21 0.04 0.15
n210 210 207 208
Table III.
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
0.39 0.11
n21 189
Subdimension analysis
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
0.38 0.22
n21 189
Table IV.
Regression production
warehouses vs
distribution centers
Production warehouses Distribution centers
Planning extensiveness (PE) Planning extensiveness (PE)
Assortment changes (AC) 20.38 0.083 22 0.18 0.015 192
Table V.
Correlation “assortment
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.
Figure 3.
Warehouse Management
Task Complexity
Planning Extensiveness
(in distribution centers)
Decision Rules Complexity
H2 +
Planning Extensiveness
(in production warehouses)
Control Sophistication
Warehouse Management
H3 +
Market Dynamics (Information) System
Information System Specificity
H4 +
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
dominant role.
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
on performance.
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
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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:
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.
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Variable Description
Accompanying question/
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
Return handling
Count number of processes
checked off
If count is five or less TCb1 ¼1;
if count is more than five
TCb1 ¼2
Product repacking
Cycle counting
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
carried out?
Three or less
More than 3
If average number of modes per
process is three or less
TCb2 ¼1; if more than three
TCb2 ¼2
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
Table AI.
Summary of measures
Variable Description
Accompanying question/
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
AC ¼3
PE Planning extensiveness Check off plans Stock planning
Storage location planning
Capacity planning
Transport 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
decision rules
Allocate dock doors to inbound
transport units (e.g. trucks);
Count number of activities
checked off
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
available capacities;
Cluster orders to be shipped to
transport units;
Cluster customer orders to
Table AI.
Variable Description
Accompanying question/
instruction Response categories Computation
Settle the executing start point
per batch;
Allocate pick order lines to
storage locations;
Allocate pick order lines and
storage locations to pick routes
(optimal routes);
Allocate pick routes to available
Allocate dock doors to outbound
transport units
DCb Perceived complexity of
decision rules
Sum (DCb1, DCb2)
DCb1 Perceived complexity of
inbound decision rules
How complex are the inbound
decision rules?
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
decision rules?
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
Table AI.
Variable Description
Accompanying question/
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
CS2 ¼2
CS3 Reaction to unforeseen
What is the reaction to
unforeseen situations?
Takes some time
If reaction takes some time
CS3 ¼1; if reaction is immediate
CS3 ¼2
CS4 Operational information from
Is operational information from
suppliers (e.g. stock levels)
available online?
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
Standard ERP with substantial
Standard WMS
Standard WMS with substantial
Tailor-made system
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
Table AI.
... As described by Rouwenhorst et al. (2000, p. 515), "[T]he efficiency and effectiveness in any distribution network . . . is largely determined by the operation of the nodes in such a network, i.e. the warehouses." For example, recent studies have highlighted the role of omnichannel warehouses to meet customers' expectations on shorter lead times (same-day delivery) while offering a variation of delivery options (clickand-collect, pick-up points, home delivery, etc.) and cutting the total logistics costs (Faber et al., 2013;H€ ubner et al., 2016). In comparison with other types of distribution warehouses, omnichannel warehouses must effectively combine different types of flows, especially the significantly different order and material flows for store replenishment and online customers (Larke et al., 2018;Kembro and Norrman, 2020). ...
... In parallel, Faber et al. (2013Faber et al. ( , 2018 study the contextual factors that influence the planning and control aspects and the selection of warehouse management systems (WMSs). They consider two groups of factors representing the external warehouse environment (i.e. the market) and the internal warehouse system. ...
... not handling store replenishment). The study indicates multiple interdependencies between contextual factors, which are structured in three levels based on categories submitted by Hassan et al. (2015) and Faber et al. (2013Faber et al. ( , 2018: external factors (e.g. customer requirements, product and order characteristics and volume handled), internal corporate factors (the warehouse role, major suppliers and last-mile strategy) and internal warehouse factors (picking method, shipping route optimization). ...
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Purpose Recent studies have highlighted the importance of adopting a contingency approach to configuring omnichannel warehouses. Nonetheless, research on how various contextual factors influence the selection of warehouse configuration is scarce. This study fills this knowledge gap by exploring how and why certain configurations fit in different omnichannel contexts. Design/methodology/approach A case study is conducted with six leading Swedish omnichannel retailers. Focusing on outbound warehouse configurations, data are collected through interviews, on-site observations, and secondary sources. A multistep analysis is made, including both pattern matching and explanation building. Findings The qualitative analysis reveals 16 contextual factors, of which assortment range, requested online order fulfillment times, goods size and total transactions are the most influential. The study shows how contextual factors create different challenges, thereby influencing the choice of the configurations. In addition to market dynamics and task complexity, the study describes four categories of the factors and related challenges that are particularly important in omnichannels: speed, space, economies of scale and tied-up capital. Research limitations/implications The findings highlight the importance of understanding context and imply that multiple challenges may require trade-offs when selecting configurations, for example, regarding what storage, processes and resources to integrate or separate. To confirm, extend, challenge and further operationalize the ideas and observations put forward in this paper, an agenda with future research issues is given for this accelerating, contemporary phenomenon. Practical implications Managers could leverage the frameworks proposed for the contextual profiling of their current and future positions. The frameworks provide support for understanding the important challenges and potential trade-offs and developing aligned configurations. Originality/value This study is original in the way it provides in-depth, case study findings about contextual factors and their influence on omnichannel warehouse configuration.
... Warehouse management has been a critical component of logistics and supply chain management owing to its significant influence on overall time and labor costs (Mahroof, 2019). Key challenges in warehouse management include space constraints, workforce shortages, poor layouts, and outdated IS (Faber, De Koster, & Smidts, 2013). These issues are exacerbated in fulfillment centers, which tend to process a large number of small packages and a wide assortment of items, while facing tight delivery schedules (e.g., next-day or even same-day) and highly volatile demands (e.g., due to seasonal sales; Boysen, de Koster, & Weidinger, 2019). ...
... Similarly, research studies have observed issues around upskilling employees to work with AI, fitting AI with existing processes, and coordinating the AI augmented workforce including e-commerce fulfillment centers (Min, 2010). As a key component of logistics and supply chain management (Aziz, Razak, Yaacob, Hussin, & Razmin, 2016), warehouse management is "a combination of the planning and control systems and the decision rules used for inbound, storage, and outbound flows" (Faber, et al., 2013(Faber, et al., , p. 1232, to support "process-oriented businesses centered on managing the flow of material and abstract resources, between a point of origin and point of destination" (Mahroof, 2019, p. 177). With a focus on coordinating the activities related to goods and orders, warehouse management is inherently an information-intensive process (Davarzani & Norrman, 2015) and human-centered process that demands a skilled human workforce (Faber, De Koster, & Van de Velde, 2002). ...
... The resource orchestration perspective is also appropriate for studying ecommerce fulfillment centers in that warehouses have been considered as a combination of processes and resources (Karagiannaki, et al., 2011). It has been argued that the performance of warehouse management depends on whether resources are orchestrated in a timely, complete, and reliable fashion (Faber, et al., 2013). ...
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Despite heightened interest, integrating artificial intelligence (AI) into businesses remains challenging. Recent surveys show that up to 85 % of AI initiatives ultimately fail to deliver on their promises. Studies on successful AI applications that could provide invaluable lessons for organizations embarking on their AI journey are still lacking. Therefore, this study aims to understand how AI technology, people, and processes should be managed to successfully create value. Building on the resource orchestration perspective, this study analyzes the successful applications of AI at Alibaba's e-commerce fulfillment center. The findings indicate that the key AI resources include data, AI algorithms, and robots. These resources must be orchestrated (e.g., coordinated, leveraged, deployed) to work with other related resources, such as warehouse facilities and existing information systems, to generate strong AI capabilities. The key AI capabilities generated include forecasting, planning, and learning. More importantly, AI capabilities are not independent – they interact and coevolve with human capabilities to create business value in terms of efficiency (e.g., space optimization, labor productivity) and effectiveness (e.g., error reduction). The implications of understanding these social informatics of AI for research and practice are discussed.
... Single command cycles start and return at the P&D point after a storage or a retrieval action [1,3,6,7,9]. Consequently, given a storage location to connect, the total length of the Int J Adv Manuf Technol single command cycle is two times the distance to connect the P&D point to the storage location. ...
... Dual command cycles join a storage and a retrieval action within the same warehouse access. They are made of a single command cycle plus the so-called travel between, connecting the drop-off to the next pickup position [1,3,6,7,9]. ...
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Non-traditional warehouses rise as effective solutions to shorten the travelled distances to store and retrieve unit loads, adding aisles crossing the parallel racks. Multiple warehouse configurations are proposed by the literature discussing the enhancements toward standard layouts. In previous contributions, the authors introduced the diagonal cross-aisle model, concluding about its positive impact on the handling performances under single command operations. This paper extends the previous works, integrating dual command operations, through an original analytic model supporting the design of non-traditional warehouses with a couple of symmetric straight diagonal cross-aisles and random storage assignment strategy. The closed-form expressions to compute the expected cycle travel distances are provided, optimising the aisle position. An industrial case study applies the model, getting distance savings ranging from 11 to 17%, compared to standard layout and further considering the loss of storage space due to the presence of the additional aisles.
... For this type of business, it will be important for the products to be picked according to the type of packaging and weight, which describes stacking resistance. It is to avoid the necessity to rearrange products placed on the order picking truck in a sequence, where small items can be damaged by heavier and larger items located above [16,17]. Therefore, by distributing products only according to their demand, completion according to the type of packaging and weight would cause a number of problems -it would be necessary to cross the warehouse several times to revisit the places in the same area [2]. ...
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Proper planning of a warehouse layout and the product allocation in it, constitute major challenges for companies. In the paper, the new approach for the classification of the problem is presented. Authors used real picking data from the Warehouse Management System (WMS) from peak season from September to January. Artificial Neural Network (ANN) and automatic clustering by using Calinski-Harabasz criterion were used to develop a new classification approach. Based on the picking list the clients' orders were prepared and analyzed. These orders were used as input data to ANN and clustering. In this paper, three variants were analyzed: the reference representing the current state, variant with product relocation by using ANN, and the variant with relocation by using automatic clustering. In the research over 380000 picks for almost 1600 locations were used. In the paper, the architecture of the system module for solving the PAP problem is presented. Presented research proved that using multi-criterion clustering can increase the efficiency of the order picking process.
... The warehouse environment is also dynamic because operational conditions could easily change, thus changing the decision-making characteristics, i.e., the relative importance of each objective may well change over time. The dynamism and complexity of this environment demand that decision-makers (DMs) make strategic decisions more carefully as time goes by (Faber et al. 2013). ...
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Choosing an order picking strategy is one of the most important decisions related to warehouse management. Making this decision properly can lead to high standards of efficiency, since order picking represents more than a half of a wholesale and retail organization’s operational costs and consumes a huge amount of the resources allocated to warehouse labor. Moreover, some productivity and service-oriented objectives related to order picking are sometimes conflicting, and require managers’ preferences to be considered, thus making the decision problem multi-objective and complex. We put forward a multicriteria decision model based on the ELECTRE III method that supports how to choose an order picking strategy. It takes managers’ preferences into consideration and integrates all the core elements for assessing how picking is being performed. Results showed that the model is able to identify the strategy that yields the best compromise between the objectives of productivity and the service-oriented ones, and that this strategy also represents the organization’s aims.
... In fact, warehousing is integrally involved in four distinct supply chain processes, namely, sourcing/inbound logistics, processing/manufacturing, outbound distribution and reverse logistics (returns, recycling, etc.) Harrington (1998). Faber et al. (2013) defined warehouse management as a combination of planning, decision-making and controlling inbound, storage and outbound flows. Figure 1 illustrates the generic warehouse activities that are performed to some degree in all warehousing operations as it is possible that one or more of them do not exist in a given facility or are combined with other activities (Gourdin, 2006). ...
Purpose The literature on warehouse performance assessments is mainly focussed on the efficiency and effectiveness of an action or activity due to customer demand and tailored fulfilment, with less attention being given to the performance measurement of each function of the warehouse and its overall productivity. Therefore, this study was aimed at revising the key warehouse performance metrics to a set of productivity measurement indicators that can be adopted internationally for benchmarking productivity performance. Design/methodology/approach A literature review and semi-structured survey questionnaire were used for this study. The importance of warehouse productivity performance was reviewed to revamp the measurement indicators. Through the use of a directed content analysis and descriptive analysis, an extensive study was carried out to analyze existing warehouse productivity indicators. Findings The findings of this study provide comprehensive references for practitioners and academicians for improving the classification of productivity measurements from existing key performance metrics for warehousing. Also, this paper highlights the warehouse resources related to the respective warehouse operation activities. Research limitations/implications The study was limited to productivity performance indicators adapted from Staudt et al. (2015). Furthermore, the samples for this study comprised Malaysian academicians and practitioners in the related field. The findings can be adapted on a global scale as this study implemented general warehouse operation processes. Originality/value Consequently, the contributions of this study are that it provides relevant benchmarks for key productivity performance indicators in the warehousing sector that has worldwide applicability and the developed model provides a conceptual platform from which further theoretical and empirical developments can be carried out.
... Warehouse management is a combination of planning, monitoring, and application which was guided by task complexity (TC) and market dynamics (MD). The inputs of inventory were maintained using JAVA web application and MySQL database in [9]. Information sharing is the vital part of a hassle-free supply chain [10] The Objectives of this research is (1) to design a warehouse with RFID in entry and exit gate and (2) to design a firebase android application for retailer, warehouse manager and manufacturer. ...
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... The basic components of AS/RSs are storage racks, I/O locations, and S/R machines or automated stacker cranes with computerized control to store and retrieve unit loads without human interference. The effective and efficient management of a warehouse has become more and more a challenging task (Faber et al., 2013) and a lever for competitive advantage (Choy et al., 2014) in global markets wherein companies attempt to minimize activities that do not add value to products, such as inventory management, warehousing (Voordijk, 2010), transport, and material handling. Fundamental to any warehouse strategy and design is the minimization of costs while achieving the desired level of customer service (Moynihan & Padmanabhan, 2006). ...
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A critical element in the evolution of a fundamental body of knowledge in marketing, as well as for improved marketing practice, is the development of better measures of the variables with which marketers work. In this article an approach is outlined by which this goal can be achieved and portions of the approach are illustrated in terms of a job satisfaction measure.
Supply Chain Management (SCM) is one of the most popular management concepts to impact business and the logistics concept in the 1990s. Problems facing the concept of SCM include (1) the lack of research on what it means to practice SCM, (2) how to implement a SCM program, and (3) how to measure the performance of a supply chain. A major contributing factor to these problems is defining which processes are managed in a supply chain and which firms, or intermediaries, are included in a supply chain. This research will focus on the warehousing component of the supply chain process and, in particular, on how firms measure the performance of their warehouse (intermediary) operations. Two types of firms will be used for the analysis: those implementing a supply chain orientation and those that are not. The empirical analysis will offer a comparison between common warehouse performance measurements for SCM-oriented firms and non-SCM-oriented firms and provide insight into the relationship between managers' perceptions of warehouse measurement effectiveness and the degree of SCM sophistication.
Twenty-two decision groups in three manufacturing and three research and development organizations are studied to identify the characteristics of the environment that contribute to decision unit members experiencing uncertainty in decision making. Two dimensions of the environment are identified. The simple-complex dimension is defined as the number of factors taken into consideration in decision making. The static-dynamic dimension is viewed as the degree to which these factors in the decision unit's environment remain basically the same over time or are in a continual process of change. Results indicate that individuals in decision units with dynamic-complex environments experience the greatest amount of uncertainty in decision making. The data also indicate that the static-dynamic dimension of the environment is a more important contributor to uncertainty than the simple-complex dimension.
Today's supply chain managers have been bombarded with a wide variety of the socalled leading-edge supply chain strategies. New terminologies and initiatives are being developed constantly. However not all these initiatives or strategies are appropriate for all firms. Companies need to first understand the uncertainties faced by the demand and supply of its products and then try to match these uncertainties with the right supply chain strategies. Based on an analysis of the uncertainties of supply and demand faced by the firm, this article develops a framework that can assist managers in developing the right supply chain strategy for their products.
We develop an input-output model of a warehouse system to assess operational efficiency. Our model simultaneously accounts for all of the critical resources (labor, space, storage and handling equipment) and the different workload requirements (broken case, full case and pallet picking, storage and order accumulation) of a warehouse. We collected extensive data on 57 warehouse and distribution facilities from a variety of industries, including auto parts, dental and office supplies, electronics, fine papers, hardware, health care, industrial packaging, mail order apparel, office machines, photographic supplies, and wholesale drugs, and used the model to assess and compare their efficiencies. We offer 3 conclusions based on a statistical analysis of the operating efficiencies obtained from several models: Smaller warehouses tend to be more efficient than larger warehouses. Warehouses using lower levels of automation tend to be more efficient. This association is more pronounced in small firms. Unionization is not negatively associated with efficiency and in fact may actually contribute to higher efficiency.
Manufacturing firms are bound by the conditions of their environment. They have to fulfil the demands of various stakeholders, such as customers, suppliers, government, financiers, and employees) that provide them the resources to perform their operations. The effectiveness of a manufacturing firm depends on the fit between the manufacturing system and the level of uncertainty in its environment. There must be consistency between numerous manufacturing dimensions, such as operations strategy, process choice, level of decentralization, production planning and control strategies, concepts, and tools, and the characteristics of the dominant Product/Market/Technology (PMT) combinations of the manufacturing firm. This book focuses on empirical studies that address the impact of PMT-uncertainty on various operations management issues in Dutch discrete parts manufacturing firms. It includes an in-depth exploratory longitudinal case study at Urenco Aerospace, a second-tier supplier in the aerospace industry, and two independent surveys (i.e., questionnaire-based research) among Dutch discrete parts manufacturers. The central research question of this book is, ‘what is the impact of PMT-uncertainty on important discrete parts manufacturing management issues, such as the strategic focus on competitive priorities, the dominant type of manufacturing layout, the decentrality of the locus of production planning and control, the use of various production planning and control tools, and line management’s attention for social issues like individual competence management?’ In addition, we extensively focus on organizational and innovation-related characteristics that affect the adoption of an Advanced Planning and Scheduling system within the discrete parts manufacturing industry.