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A proposed maturity model to improve warehouse performance
Article  in  International Journal of Productivity and Performance Management · May 2021
DOI: 10.1108/IJPPM-01-2021-0043
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A proposed maturity model to
improve warehouse performance
Loay Salhieh and Waed Alswaer
German Jordanian University, Amman, Jordan
Abstract
Purpose – The purpose of this paper is to propose a maturity model to improve warehouse performance.
Design/methodology/approach – This paper will follow De Bruin et al’s (2005) suggested six relevant
phases: scope, design, populate, test, deploy and maintain in developing the proposed maturity model. This
study concentrates on the first five phases.
Findings – The proposed warehouse maturity model can be used as descriptive, benchmarking and a
prescriptive with a road map for improvement.
Practical implications –The warehouse maturity model was proposed to let warehouse managers evaluate
their practices and assess them by maturity level. Then, the proposed warehouse maturity model can be
utilized to develop a set of plans for conducting projects to improve the warehouse practices, techniques
and tools.
Originality/value – The proposed warehouse maturity model contributes to fill the shortages of maturity
model addressing the warehouse environment. In particular, it provides a useful tool to establish the overall
maturity level of a warehouse system. The proposed maturity model supports strategic decisions oriented
toward improvement capabilities of the warehouse and to compete based on service level provided.
Keywords Warehouse performance, Maturity model, Jordan, Warehouse practices
Paper type Research paper
1. Introduction
A business function audit can assess how current arrangements contribute to the purpose,
aims of the organization and how these arrangements support stability, and manage the
obligations and expectations (Cannings and Hills, 2012). It is essentially a comparison of
“what is” and “what should be.”This presents two fundamental challenges–measuring “what
is,” and defining “what should be.” In line with assessing current state and desired state,
researchers have developed a variety of maturity models in the business context, such as
strategic analysis, production management, process management, software engineering
(Paulk et al., 1993), innovation domain, quality management, marketing management, and so
on. Because of the broad range of potential applications, maturity models have gained
popularity in both management and science (Asdeckar and Felch, 2018; Wendler, 2012).
These models provided an understanding for the issue and promoted development actions.
By knowing the company’s present state compared to the ideal maturity state, it is possible to
know how far the goal is. Furthermore, Schiele (2007) puts it: “Maturity model describes
auditable stages which an organization is expected to go through in its quest for greater
sophistication.” In addition, the supply chain and logistics domain also developed their own
maturity models to assess current state and ideal state as listed in Table 1.
Despite the existence of maturity models for the supply chain (McCormack et al., 2008;
Reyes and Giachetti, 2010) and performance measurement systems (Wettstein and Kueng,
2002), there is no specific maturity model for the warehousing function to improve
performance. However, there are preliminary efforts to develop lean warehouse maturity
models, assessment tools, best practices and warehouse audit (Shan, 2008; Sobanski, 2009;
Mahfouz, 2011; Dehdari, 2013; De Visser, 2014; Dotoli et al., 2015; Andjelkovi�c et al., 2016;
Bogale, 2016; Teka, 2017; Razik et al., 2017). Razik et al. (2017) proposed awarehousematurity
model questionnaire aimed at identifying major problems handicapping warehousing
performance improvement in the Moroccan companies. However, the proposed model only
Warehouse
performance
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1741-0401.htm
Received 15 February 2021
Revised 30 April 2021
Accepted 1 May 2021
International Journal of
Productivity and Performance
Management
© Emerald Publishing Limited
1741-0401
DOI 10.1108/IJPPM-01-2021-0043
https://doi.org/10.1108/IJPPM-01-2021-0043
addresses obstacles to improve warehouse performance and not a comprehensive model in
nature. Furthermore, all initiatives were too specific to achieve a comprehensive model to
assess all aspects of warehouse related issues and too limited in their scope. In addition, the
preliminary efforts to address a warehouse performance did not pinpoint the impacts of
warehouse practices and the causes.
Warehousing plays a crucial and important role in modern supply chains and have an
essential role in global logistics systems to ensure high levels of customer service and overall
performance of the supply chain (€Ozt€urko�glu et al., 2014). Therefore, the motivation and aim
of this study is to proposeSoftware Technology, Vol. 75, pp. 122-134.
Teka, A. (2017), “Assessment of Warehousing Practices: A Case of Finfine Furniture Factory S. Co”,
PhD Thesis, Addis Ababa University, avaialble at: http://localhost:80/xmlui/handle/
123456789/12866.
Van Aken, J.E. (2005), “Management research as a design science: articulating the research products of
mode 2 knowledge production in management”, British Journal of Management, Wiley Online
Library, Vol. 16 No. 1, pp. 19-36.
IJPPM
https://doi.org/10.1108/TQM-06-2018-0077
https://doi.org/10.1108/TQM-06-2018-0077
https://doi.org/10.1108/ET-02-2014-0010
https://doi.org/10.1108/JMTM-07-2015-0053
http://localhost:80/xmlui/handle/123456789/12866
http://localhost:80/xmlui/handle/123456789/12866
Wendler, R. (2012), “The maturity of maturity model research: a systematic mapping study”,
Information and Software Technology, Vol. 54 No. 12, pp. 1317-1339.
Wettstein, T. and Kueng, P. (2002), “A maturity model for performance measurement systems”, WIT
Transactions on Information and Communication Technologies, Vol. 26, pp. 113-122.
Yang, L. and Chen, J. (2012), “Information systems utilization to improve distribution center
performance: from the perspective of task characteristics and customers”, Advances in
Information Sciences and Service Sciences, Vol. 4 No. 1, pp. 230-238.
About the authors
Dr. Loay Salhieh, Professor of Industrial Engineering at German Jordanian University, joined the faculty
in 2010. Dr. Loay has a PhD in Industrial andManufacturing Engineering fromWayne State University,
Detroit, Michigan, USA, May 2002. Loay’s research interests include warehousing, transportation,
inventory, operations and supply chain management. Loay Salhieh is the corresponding author and can
be contacted at: loay.salhieh@gju.edu.jo
Eng.WaedAlswaer has a bachelor’s degree in biomedical engineering. Sheworks as Projects’Officer
at the Deanship of Scientific Research at the German Jordanian University.
For instructions on how to order reprints of this article, please visit our website:
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Warehouse
performance
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mailto:loay.salhieh@gju.edu.jo
https://www.researchgate.net/publication/352003537
	A proposed maturity model to improve warehouse performance
	Introduction
	Theoretical background and related literature review
	Initiatives to improve warehouse operations
	Performance perspective
	Activity profiling perspective
	Best practices perspective
	Model development
	Model development methodology
	Maturity model scope
	Design
	Maturity stages of warehouse
	Model dimensions of warehouse maturity
	Maturity model population
	Maturity model testing
	Deploy
	A diagnostic model for improvement
	Results and discussion
	Maturity assessment in two third-party logistics providers
	Conclusion and future research
	Referencesa maturity-audit model as a common ground among researchers
and practitioners to advance theory and practice related to improving warehouse
performance regardless of the type of industry they serve. This study provides three key
contributions. First, it reviews available efforts to improve warehouse operations and
consequently performance and discuss these efforts in developing the proposed model.
Second, the proposed maturity-audit model is comprehensive as it includes aggregate and
profile performance measures (Søgaard et al., 2019). Third, the nature of the proposed model
can be used as descriptive, benchmarking and a prescriptive as there is a call for more
prescriptive maturity models (Tarhan et al., 2016).
The remainder of this article structured as follows. Section two provides the theoretical
background and related literature. Section three provides a proposed roadmap forwarehouse
improvement, and section four discusses results. Finally, conclusion and suggestions for
future research discussed.
2. Theoretical background and related literature review
The objective of this section is twofold. First, present a brief description of scholars’
initiatives to improve warehouse operations and consequently performance. The second
objective presents literature related to methodology to develop maturity models.
2.1 Initiatives to improve warehouse operations
There are a variety of initiatives to evaluate current state of a warehouse from different
perspectives such as performance, profiling and best practices utilized by a warehouse.
2.1.1 Performance perspective. The purpose of auditing warehouse performance is to get
an early warning of trouble, to facilitate the search for best practices, and create an audit trail
that records progress in improving the utilization of space and time (Neely et al., 1995). There
are several reasons for measuring performance: for improving performance, for avoiding
inconveniences before it is too late, for monitoring customer relations, for process and cost
control and for maintaining quality (Ackerman, 2004; Faber et al., 2013; Jothimani and
Sarmah, 2014). The main instruments for assessing performance are performance indicators,
also named key performance indicators (Liviu et al., 2009). They are specific characteristics of
the process, which are measured in order to describe if the process is realized according to
Maturity model Author
Maturity models in supply chain sustainability Correia et al. (2017)
The supply chain process management maturity model Oliveira et al. (2011)
Maturity model for the delivery process in supply chains Asdecker and Felch (2018)
Supply chain capability maturity model Reyes and Giachetti (2010)
The logistic maturity model Battista and Schiraldi (2013)
Purchasing maturity models Søgaard et al. (2019)
Table 1.
The supply chain and
logistics domain
maturity models
IJPPM
pre-established standards (goals). Accordingly, scholars have proposed a variety of
performance initiatives to evaluate current state of a warehouse that addressed
performance on the aggregate and specific activities in the warehouse environment
(Goomas et al., 2011; Lao et al., 2011; Ramaa et al., 2012; Saetta et al., 2012; Yang and Chen,
2012; Cao and Jiang, 2013; Faber et al., 2013). However, the aggregation of indicators can
considerably simplify the analysis of a system, summarizing the information of a given set of
subindicators (Franceschini et al., 2006). On the aggregate level, performance measures must
consider the existing indicators of the warehouse activities and knowing that there are limits
in the decision-maker’s ability to process large sets of performance expressions (Clivill�e et al.,
2007). Furthermore, the aggregate operational performance measure must answer the
question “Overall, how well are we doing?” (Melnyk et al., 2004; Clivill�e et al., 2007; Rodriguez
et al., 2009; Staudt et al., 2015; Laosirihongthong et al., 2018). Accordingly, this study will
consider aggregated (integrated) performance measures as recommended by Staudt et al.
(2015) and Laosirihongthong et al. (2018). However, the aim of maturity-audit model is to
investigate the existence of such measure, and if exist how far are they from best-in-class
operations. Therefore, this study will adopt performance measures that are used by most
professionals as reported by the Warehousing Education and Research Council (2015) as
listed in Table 2. Furthermore, Axelsson and Frankel (2014) developed a performance
measurement system for warehouse activities as presented in Table 3. In other words, if these
data are captured at the activity level, then warehouse performance indicators listed in
Table 2 are calculated.
2.1.2 Activity profiling perspective. Activity profiling serves as a baseline to identify
problems connected to information and material handling in the warehouse operation. In the
end, the activity profiling will justify investments and enable required improvements
(Bartholdi and Hackman, 2011). Lewczuk and _Zak (2013) explain that activity profiling
involves the analysis of historical data, product characteristics and locations, packing
patterns and warehouse layout. All these factors are important when identifying potential
Performance indicator Description Best-in-Class
Perfect order (from
supplier) index
This index entails orders received on time, in correct quantities,
correct articles, damage free with correct documentation.
Therefore, this index is calculated by multiplying performance
indicators from Table 3 (R1*R2*R3*R4)
≥96.8%
Perfect put-away index This index entails time to put away goods from dock into storage
areas (racks and shelves), items in correct locations, damage free
because of put away activity. Therefore, this index is calculated
by multiplying performance indicators from Table 3
(PT1*PT2*PT3)
≥99.95%
Perfect picking index This index entails number of orders picked per hour, accuracy of
picked orders, damage free because of picking activities, and no
back orders. Therefore, this index is calculated by multiplying
performance indicators from Table 3 (P1*P2*P3*P4)
≥99.95%
Perfect order (customer)
index
This index entails orders shipped on time, complete orders,
damage free, with accurate documentations. Therefore, this
index is calculated by multiplying performance indicators from
Table 3 (D1*D2*D3*D4)
≥99%
Perfect warehouse index This overall index entails multiplication of (Perfect order (from
supplier) index * perfect put-away index * perfect picking index *
perfect order (customer) index)
≥95.74%
Safety (accidents per
year)
Number of accidents per a year 1 accident per
year
Table 2.
Integrated warehouse
performance measures
Warehouse
performance
improvements. Therefore, armed with activity profiling knowledge, you can start to make
decisions about the changes in processes, capacity, throughput and inventory that will yield
greater efficiency and the flexibility to adapt to both your internal customers’ and external
customers’ needs. This study categorizes activity profiling into three main categories:
purchase order profile, customer order profiles and item activity profiles. The purpose of the
purchase order profiling is to use it in receiving and put-away process design. With help
of purchase order profile it is possible to plan receiving mode disposition, put-away batch
sizing and put-away tour construction and is an inbound activity. However, a customer order
profile deals with behavior of customer orders and is an outbound activity, i.e. ordering
patterns of the customer order (Bartholdi and Hackman, 2011). Frazelle (2002) emphasized
that how the customer orders products has an impact on the expected work in a warehouse,
as each order is a shopping list containing a single or a number of pick lines. These pick lines
Warehouse
activity Performance indicator Description Best-in-Class
Receiving
PR1 % of on time orders
(supplier)
Orders received on time ≥99%
PR2 % of complete orders Orders received in correct quantitiesand
correct articles
≥99.5%
PR3 % of supplier orders
received damage free
The number of orders that are processed
damage free as a percentage of total orders
≥99%
PR4 % of accurate received
documents
Received orders have correct documentation ≥99.26%
Put-away
PT1 %Dock-to-Stock cycle less
than 2 h
Equals the time, typically measured in hours,
required to put away goods
≥95%
PT2 Put away accuracy How many items are in the correct storage
locations?
100%
PT3 % SKU damage free How many items are damaged as a result of
put away activity?
≥99.9%
Picking
PP1 % # orders picked/hr
greater than or equal 24
orders
Picks per person and time unit ≥24 orders
PP2 % of picking accuracy This measure the accuracy of the order
picking process against errors caught prior to
shipment, such as during packaging
≥99.84%
PP3 % of SKU damage free Howmany items damaged because of Picking
activity?
≥99.9%
PP4 % of orders shipped with
no back orders
The portion of total orders that shipped on
time with no back orders
≥99.5%
Despatch/shipping
PD1 % of orders shipped on
time
The percentage of orders shipped at the
planned time, meaning off the dock and in
transit to final destination
≥99.87%
PD2 % of orders complete Measure Complete orders ≥99.5%
PD3 % of orders delivered
damage free
How many items damaged because of
delivery?
≥99.9%
PD4 % of orders accurately
documented
Shipping documentation accuracy ≥99.9%
Table 3.
Warehouse activity
measures
IJPPM
generates travel to the item location and subsequently packing, checking and shipping to the
customer (Lewczuk and _Zak, 2013). According to Frazelle (2002), there are different
components of customer order profiles as shown in Table 4. On the other hand, the main
assignment of the item activity profiling is to designate for each item where it should be
located in the warehouse, howmuch space it requires for storage and to what storage mode it
should be located (Frazelle, 2002). It will also profile popularity and volume for an individual
item (Park, 2012). Another factor identified by the item activity profiling is which items that
tend to be ordered together, and this information is important for the decision of storage
location in the warehouse (De Koster et al., 2007). The different components of item activity
profiling are presented in Table 4 (Bartholdi and Hackman, 2011; Frazelle, 2002).
2.1.3 Best practices perspective. Borrowing from supply chain management practices, we
can define warehouse practices as a set of activities undertaken in an organization to promote
effective management of its warehouse operations. The understanding and practicing of
warehouse operation best practices has become an essential prerequisite for staying
competitive in the global race and for enhancing profitably (Richards, 2017). However, despite
the increased attention paid to warehousing, the literature has not been able to offer much by
way of guidance to help the practice of warehouse except the book published by Richards
(2017). Furthermore, it is impossible to agree on a common set of practices and standard
operating procedures that fits all, giving the type of the warehouse, goods handled and the
industry it serves (Pires et al., 2017). In addition, there are a variety of organizations reporting
their own warehouse practices, standard operating procedures and warehouse audit
checklists based on claimed best practices in the warehouse environment. However, there are
warehouse practices related to quality, lean, waste reductions and sort, set in order, shine,
standardize, and sustain (5S) practices that could be implemented in any warehouse
environment (Mustafa, 2015; Harun et al., 2017; Asdeckar and Felch, 2018). Accordingly,
Salhieh et al. (2019) and Abushaikha et al. (2018) have showed that all warehouse
environments can adopt a proposed set of waste reduction practices. In addition, they showed
the impact these waste reduction practices have on warehouse operational performance.
Allied with waste reduction, 5S plus safety lean tool is able to eliminate many of the forms of
waste, create and enhance visual management and can reduce potential for errors in
warehousing management (Mustafa, 2015). Furthermore, previous studies claimed that there
Planning and
design issues Key questions
Required
profile Profile components
Impact on
warehouse
activity
measures
Receiving and
put-away
process design
Receiving mode
disposition, put-away
batch sizing, Put-away
tour construction
Purchase
order profile
(POP)
Order mix distributions,
lines per receipt
distribution, lines and cube
per receipt distribution
PT1; PD1
Order picking
and shipping
process design
Order batch size, pick
wave planning, picking
tour construction,
shipping mode
disposition
Customer
order profile
(COP)
Order mix distributions,
lines per order distribution,
lines and cube per order
distribution
PP1; PD1
Slotting Zone definition, storage
mode selection and
sizing, pick face sizing,
item location assignment
Item activity
profile (IAP)
Popularity profile, cube
movement/volume profile,
order completion profile,
demand correlation profile,
demand variable profile
PT2; PP1; PD1 Table 4.
Activity profiling and
its impact on
warehouse
performance indicators
Warehouse
performance
is a relationship between 5S plus safety lean tool and warehouse operational performance
(Gergova, 2010;Mustafa, 2015; Srinivasan et al., 2016). Therefore, since the aim of this study is
to propose a comprehensive maturity-audit model that fits all warehouses regardless of type,
goods handled and industry served, this study will adopt waste reduction practices and 5S
plus safety lean tool under the practice domain of the proposed maturity-audit model. Tables
5 and 6 presents a description of waste reduction practices and lean 6S (5Sþ safety) (Gamage
et al., 2012; Skeldon et al., 2014; Srinivasan et al., 2016; Salhieh et al., 2019) adopted in this
study respectively. However, this study will not represent the “Sustain” step in the proposed
maturity model, since this step is the final phase of 5S plus safety lean, which assures that the
changes sustained.
2.2 Model development
Maturity models date back to the late 1980s, attributed to the work of the Carnegie
Mellon University (Paulk, 2009). Maturity models can be seen as an audit tool for
evaluating the current stage of a measured issue (Aho, 2012), and representing theories
of stage-based evolution, their basic purpose consists in describing stages and
maturation paths. Rummler and Brache (1995) metaphorically refer to such audit tools
as engines for continuously improving systems, roadmaps for guiding organizations and
blueprints for designing new entities. The concept of maturity has enjoyed widespread
attention from academics in numerous fields. Although it is most widely used in the
software industry and engineering, it is also been used in many other arenas (R€oglinger
et al., 2012). Some applications have been developed in areas such as industrial
maintenance (Macchi and Fumagalli, 2013), product development (Farrukh et al., 2003),
logistics (Battista and Schiraldi, 2013), collaboration (Campos et al., 2013), quality
management systems (Morsal et al., 2009), and environmental concerns (Ormazabal and
Sarriegi, 2014), purchasing (Søgaard et al., 2019), supply chain (Reyes and Giachetti,
2010) among others. However, there are quite a few maturity models in existence these
days, particularly to assess warehouse maturity, which seems to be a knowledge gap
that necessitates further investigation. For example, Asdecker and Felch (2018)
discussed digitization maturity level which included the warehouse environment.
Furthermore, scholars have introduced models to assess lean maturity in the warehouse
environment (Sobanski, 2009; Overboom et al., 2010; Dehdari, 2013). In addition, Razik
et al. (2017) proposed a maturity model for the warehousing function in Moroccan
companies based on the concept of critical success factors.
Typically, maturity models classifiedaccording to three application-specific purposes
(Becker et al., 2009; De Bruin et al., 2005; Iversen et al., 1999; Maier et al., 2009; P€oppelbuß and
R€oglinger, 2011) descriptive, prescriptive and comparative. A maturity model serves a
descriptive purpose of use if applied for assessments. While a maturity model serves as a
comparative purpose of use if it allows for internal or external benchmarking (P€oppelbuß and
R€oglinger, 2011). However, a maturity model serves a prescriptive purpose of use if it
indicates how to identify desirable maturity levels and provides guidelines on improvement
measures (Becker et al., 2009). This study claims that the developed maturity model
encompasses the three characteristics as it can be used as descriptive, benchmarking and
prescriptive as will be demonstrated in results section in this study. Furthermore, this study
will follow Asdeckar and Felch’s (2018) methodology in developing the study proposed
warehouse maturity model. Accordingly, this study believes that the proposed warehouse
maturity model is a pertinent field problem; therefore, we consider design science research
(DSR) to represent an appropriate foundation (Van Aken, 2005), which focuses on the
development and application of artifacts (Hevner et al., 2004). However, this study focuses on
the design and evaluation phases of DSR by thoroughly describing the model’s development
IJPPM
Warehouse
activity items Item description
Impact on warehouse
activity measures
Receiving (R)
R1 As a warehouse manager, you are involved with purchasing
in specifying and agreeing the packaging, items per carton,
carton per pallet, and labeling requirement
PR2; PR3
R2 You ask your suppliers to send deliveries with the most
suitable packaging for you
PR3
R3 You specify a time schedule for the suppliers to make the
delivery
PR1
R4 You receive a notification from the suppliers/shipper before a
delivery arrives at your warehouse. (ASN 5 advanced
shipping notification)
PT1
R5 You are able to plan the correct equipment (forklift trollies,
powered trucks and pallets jacks) to use in unloading before
the delivery arrives
PR3; PT1
R6 You are able to plan enough labor to unload the delivery
before it arrives
PT1
R7 You are able to plan sufficient space to unload the delivery
before it arrives. You have always stock-keeping units (SKU)
master data available, e.g. for new products, that you are able
to store and handle these products appropriately?
PT1
R8 You perform cross-docking operations when possible or
needed
D1; D2
R9 It is easy to identify deliveries from suppliers (product,
description, pack quantity)
PR2; PR4; PT1
R10 You do carry out inspections and quality checks on most of
the goods received. In other words, you do count and identify
100% of the received products
PR2; PR3
R11 You usually breakdown deliveries into smaller or lager
increments (pallets to cartons or vice versa) for storage based
on data collected from customer orders. In other words, you
do not require deliveries from your supplier in the normal
selling quantity in order to increase the speed of throughput
and simplify picking. (You do not order in logistics units)
PP1
Put-away (PA)
PA1 We have a system (computerized or warehouse manager)
which allocate product locations prior to offloading and
instruct the operator as to where to place the goods
PT1; PT2
PA2 You notice any delays in put-away because of labor or
equipment occupied or missing
PT1
PA3 The rack configuration is flexible enough to accommodate
size of pallet received from suppliers
PT1
PA4 You create a time schedule to separate the operations of the
put-away and picking team
PD1, PT3
Picking (P)
P1 You slot the heaviest SKUs in weight at the locations nearest
to the start points of the pick
PP1
P2 You slot items that usually sold together next to each other PP1
P3 You use technologies in picking operations such as pick-to-
light, voice picking etc.
PP1; PP2
P4 You use double (volume and frequency) ABC categorization
in order to slot SKUs
PP1
(continued )
Table 5.
Waste reduction
practices and its
impact on warehouse
performance indicators
Warehouse
performance
and evaluation (€Osterle et al., 2011). Therefore, we build on the often cited generic six phases
of maturity development presented by De Bruin et al. (2005).
2.2.1Model development methodology.DeBruin et al. (2005) suggested six relevant phases:
scope, design, populate, test, deploy and maintain. This study concentrates on the first five
phases, because the sixth phase would require a longitudinal study, which suggested as
future research.
2.2.1.1 Maturity model scope. The first phase defines the scope and boundaries of the
model, which can be either general or domain-specific. Moreover, stakeholders that can assist
in model development or benefit from the application of the model must be identified.
Therefore, the proposed model’s scope designed for third-part logistics firms, and thus
domain-specific. Moreover, two types of stakeholders’ considered relevant for model
development: academia and industry. Therefore, the development process builds on available
publications and original empirical work to adequately represent the practitioners.
2.2.1.2 Design. The second phase determines the architecture of the model based on five
subcriteria: audience, method of application, driver of application, respondents and
application. It is important to define the target audience to meet their needs. Accordingly,
the targeted audience is warehouse managers (because they are responsible for developing
and maintaining the developed maturity model), as well as external auditors and consultants
(because they are often engaged in guiding organizational change). The major drivers of the
model’s application are market dynamics that force organizations to rethink their business
models. The respondents are warehouse managers and mid-level staff, as they possess the
expertise to assess their warehouse maturity capabilities. The model can be applied to
multiple entities (third-part logistics, private warehouses and public warehouses) in multiple
regions. After clarifying why and how the model is applied, the stages and dimensions must
be defined as discussed in the following two subsections.
2.2.1.2.1. Maturity stages of warehouse. According to De Bruin et al. (2005), the number of
stages may vary from model to model, but what is important is that the final stages are
distinct and well-defined, and that there is a logical progression through stages. They also
stress the need to provide a summary of the major requirements and measures of the stages.
Warehouse
activity items Item description
Impact on warehouse
activity measures
P5 Fastest-moving SKUs placed in the middle row so that the
order picker does not have to spend time bending and
stretching
PP1
P6 The picker pick the exact quantity required PP2; PD2
P7 You use a warehouse management system to create an
efficient route within the warehouse in the picking process
PP1
Despatch/shipping (D)
D1 There is sufficient space at the loading bay to stage the loads PD1
D2 Truck arrivals are subject to a system in the shipping area PD1
D3 We have grids marked out on the warehouse floor at the
despatch area to replicate the floor area of the largest vehicle
PD1
D4 Vehicles at the despatch bay do not wait a long time until the
despatch team is ready
PD1
D5 At our warehouse, the checking of vehicle papers at the
despatch bay ensures the match of the SKUs to the right
vehicles
PD4
D6 Despatch operator checks and inspects that picked SKUs and
quantities are correct
PD2
Table 5.
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Therefore, maturity stages represent a certain level of maturity and enable the improvement
of the selected domain in a targeted way (Fraser et al., 2002). Each stage requires an
appropriate denotation and a general description. Since warehouse maturity is relatively a
new research domain, this study proposes four stages as shown in Table 7.
Category Activity descriptionImpact on warehouse
activity measures
Sort
S1.1 Only the required stock and packaging present in the work area PT1; Safety
S1.2 Only the required tools and equipment are present in the work area PT1; PD1; Safety
S1.3 Only the required paperwork is present in the work area (signage) PR4; PD4
Straighten
S2.1 Location for all stock are clearly defined and labeled PT1; PT2; PP1; PP2; Safety
S2.2 Equipment and tools properly labeled and have a clearly defined
storage location
PT1; PP1; Safety
S2.3 Paper work/scanners/voice equipment properly labeled and has
clearly defined location
PT1; PP1
S2.4 Walkways, access to equipment and work area boundaries clearly
defined and marked
PT1; PP1; PD1; Safety
Shine
S3.1 Storage containers, shelving/racking and storage areas are clean
and damage free
PT3; Safety
S3.2 Tools and equipment are fully maintained, clean and damage free PT1; PT3; PP1; PP3; PD1;
PD3; Safety
S3.3 Work surface are clean and damage free PP1; safety
S3.4 Cleaning equipment available and neatly stored PP1
Standardize
S4.1 Tools, equipment, paperwork stored neatly and returned
immediately after use
PT1; PP1; PD1
S4.2 Maintenance records for tools and materials handling equipment
are easily accessible and up to date
PT3; PD3
S4.3 Waste products (waste oil, rubbish) consistently cleaned up and
removed from work area
Safety
Safety
S6.1 Are employees wearing suitable PPE (personal protective
equipment) required for their current work?
Safety
S6.2 Walkways and access to safety equipment are clearly identified and
unobstructed (no hazards or obstacles in the way of fire
extinguishers, emergency access doors)
Safety
S6.3 Is the working environment suitable for the work in hand (lighting,
air quality, temperature)?
Safety
S6.4 Are the equipment and tools provided correctly for the currentwork
activity?
Safety
Maturity stage Description
Negligible Less than 25% of domain activities practiced or implemented
Low More than or equal to 25% and less than 50% of domain activities practiced or implemented
Moderate More than or Equal to 50%andLess than 75%of domain activities practiced or implemented
High More than or equal to 75% of domain activities practiced or implemented
Table 6.
6S audit tool and its
impact on warehouse
performance indicators
Table 7.
Warehouse maturity-
auditable stages
Warehouse
performance
2.2.1.2.2. Model dimensions of warehouse maturity. The aforementioned proposed stages are
applied to the model’s dimensions. Furthermore, the inclusion of several dimensions allows
for the modeling of complex domains. Each dimension may represent a different maturity
stage, which facilitates detailed analyses and the identification of specific opportunities to
make improvements. Furthermore, the model’s dimensions add specific content to the
previously defined maturity stages (Fraser et al., 2002). Each dimension consists of elements
or activities that allow a detailed understanding of the described phenomenon (Fraser et al.,
2002). Methodologically, an appropriate denotation and a general definition of each element
are required (De Bruin et al., 2005). The four stages are applied to five dimensions (integrated
warehouse performance measures, warehouse activity measures, activity profiling, waste
reduction practices and 6S audit tool). Each dimension has several elements as shown in
Tables 2–6 respectively. Furthermore, this study will use a yes/no scale since a specific and
accurate answer is measured. In other words, is the warehouse practicing the measured
element and is it implemented through some type of standard operating procedures.
2.2.1.3 Maturity model population. The third phase broaches the issue of the specific
content of the model by defining model components and subcomponents. A component
denotes what needs to be measured. According to De Bruin et al. (2005), components can be
defined by a review of the literature or the use of empirical approaches such as stakeholder
interviews, surveys, focus groups and case studies. Accordingly, this study has used a
literature review to denote components in every dimension, and then used a structured
interview to confirm relevancy of the items in every dimension. Therefore, participants in the
structured interviewwere asked to rate the relevance of the identified itemswithin each of the
proposed dimensions on a four-tiered Likert scale (not relevant, marginally relevant, relevant
and highly relevant). The participants in the structured interview chosen from several
sources are shown in Table 8. Overall, all of the cited elements seem relevant for the
warehouse with averages varying from 2.83 to 3.81.
2.2.1.4 Maturity model testing. The fourth phase tests the validity and reliability of the
model to strengthen the populated model’s relevance and rigor. The model’s validity
guarantees that the model measures what it intends to measure, whereas reliability refers to
whether the results are exact and repeatable. De Bruin et al. (2005) suggested several methods
to ensure the model’s validity and reliability, such as case studies, surveys and literature
reviews. They conclude that “[. . .] the manner in which testing is undertaken can vary
between models clarifying why and how the model is applied, the stages and dimensions
must be defined. Accordingly, since all proposed dimensions and itemswithin borrowed from
previous research as discussed in the literature review section, therefore, the validity and
reliability of the dimensions confirmed.
2.2.1.5 Deploy. The fifth phase combines the model’s distribution within business practice
for determining its generalizability. De Bruin et al. (2005) proposed a two-step procedure to
ensure the general acceptance of the model: applying the model to one of the involved
stakeholders and applying the model to organizations that did not participate in the model’s
Source Criteria Number
German flying profess program (Logistics)
practiced at German Jordanian University
Have practical and academic expertise in assessing
warehouse environment
3
Logistics faculty members at German
Jordanian University
Have published and teach warehouse course 2
Students enrolled in the Master Logistics
program at German Jordanian University
Graduated or preparing their thesis, and have a
practical warehouse expertisemore than five years as
general managers or managers
3Table 8.
Participants in the
structured interview
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development and testing. Since the development of the model was the authors’ responsibility,
then the proposed model applied to two third-part logistics companies operated in Jordan as
discussed in the results section. Figure 1 provides an overview of the finalmodel. The detailed
description of the items related to dimensions is presented in Tables 2–6.
3. A diagnostic model for improvement
Maturity models following a prescriptive purpose of use need to include improvement
measures for eachmaturity level and available level of granularity in the sense of good or best
practices (P€oppelbuß and R€oglinger, 2011). Furthermore, Ahlemann et al. (2005) require
prescriptive maturity models to disclose potential for improvement. Therefore, maturity
models expected to disclose current and desirable maturity levels and to include respective
improvement measures (Becker et al., 2009). In addition, Rummler and Brache (1995)
metaphorically refer to such tools as engines for continuously improving systems, roadmaps
for guiding organizations and blueprints for designing new entities. Therefore, this study
must first establish a correlation between items proposed in the activity profile, waste
reduction practices, 6S andwarehouse activity measures. Although there are few studies that
established a correlation between these items and warehouse performance measures
(Frazelle, 2002; De Koster et al., 2007; Gergova, 2010; Bartholdi and Hackman, 2011; Park,
2012; Lewczuk and _Zak, 2013; Mustafa, 2015; Abushaikha and Salhieh, 2016; Srinivasan et al.,
2016; Salhieh et al., 2019), but therewas no correlation specifically with the proposed
warehouse activity measures. Therefore, in order to establish a correlation between profiling
activities, waste reduction practices and 6S with warehouse activities measures, a Delphi
technique is used. The Delphi technique is especially appropriate when expert opinions are
often the only source of information (Rowe and Wright, 1999). It is not surprising that a
number of supply chain researchers have used the Delphi technique, as research in supply
chain management still needs more grounding in order to be developed as a discipline
(Melnyk et al., 2009; Markmann et al., 2013; Piecyk and McKinnon, 2013; Abushaikha and
Salhieh, 2016; Salhieh et al., 2019). In addition to literature scarcity addressing maturity
models in the warehouse, another major reason that makes the Delphi technique the most
appropriate to develop such a correlation is the complexity of the subject which requires the
Figure 1.
Overview of the
warehouse maturity-
audit model
Warehouse
performance
knowledge of experts who understand the different practices that would produce
improvements in the warehouse environment.
The Delphi technique is a method for consensus building by using a series of
questionnaires to collect data from a panel of selected subjects (Dalkey and Helmer, 1963;
Linstone et al., 1975). Theoretically, the Delphi process continuously iterated until consensus
is determined. However, researchers pointed out three iterations are often sufficient to collect
the needed information and to reach a consensus in most cases (Custer et al., 1999; Ludwig,
1997). Furthermore, Delphi studies pointed out that the first iteration is a “brainstorming”
stage, where panelists respond to open-ended questions, which serve as the cornerstone of
soliciting specific information in later iterations (Custer et al., 1999; Piecyk and McKinnon,
2013). Accordingly, this study has sent a matrix structure of the different items proposed in
activity profiling, waste reduction, 6S and warehouse activity measures. The respondents
asked to rate the correlation score based on the following scores:
0 5 No correlation
1 5 The item only remotely affects the warehouse activity measure
3 5 The process input has a moderate effect on warehouse activity measure
9 5 The process input has a direct and strong effect on warehouse activity measure
For determining consensus in later iterations, decision rules were established. For example, at
least 70%ofDelphi subjects need to rate three or nine. For the size of experts’ panel, Okoli and
Pawlowski (2004) recommended 10–18, other studies indicated that a minimum panel size of
10–15 is needed (Sitlington and Coetzer, 2015).
In the context of this study, we needed to devise criteria for the selection of experts,
practitioners and academics, as well as addressing the issue of the number and size of panel to
use (Brill et al., 2006; Okoli and Pawlowski, 2004). For practitioners, it was decided that they
must have a minimum of five-year experience as warehouse operations manager. With
respect to academics, the study employed three criteria: academic qualifications, experience
in teaching warehousing and scholarly publication history in warehousing subjects. As for
the experts’ panel size, 12 panelists were selected (50 percent practitioners and 50 percent
academics) from our network of German and Jordanian lecturers and consultants. The results
of the first iteration, with justification of each score analyzed and accordingly correlations
with scores of three or nine retained if at least 70% of Delphi subjects rate three or nine. The
results used for the second round of data collection, and participants were required agree or
disagree. In the second round, areas of disagreement and agreement identified and consensus
began forming. Correlations that scored less than 70% of agreement removed from the next
round. In the third and final round, each participant received again the matrix that included
the items and ratings summarized from the second round and asked to revise their judgments.
Based on the response of this round, Kendall’s W, also known as Kendall’s coefficient of
concordance, used to estimate the level of consensus between the panelists. According to
Schmidt (1997), a value of Kendall’sW of 0.7 or higher can be interpreted as strong agreement,
and the results of Kendall’s W were higher than 0.7. Based on the results, the items that
correlate with warehouse activity measures are finalized as shown in Tables 4–6.
Consequently, the proposed roadmap as shown in Figure 2 would accompany the proposed
maturity model for improvement purposes based on Delphi results.
4. Results and discussion
4.1 Maturity assessment in two third-party logistics providers
Two third-party logistics providers (A and B) participated in the application (their names not
provided due to confidentiality agreements) of the proposed maturity model. Company A
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handles 1,000 SKUs, while company B handles 500 SKUs. A panel consists of general
manager, warehouse manager and supervisors selected in each investigated company, to
obtain the relative implementation for each dimension proposed in the maturity model.
However, the authors have physically audited the implementation of the 6S lean tool in the
investigated companies and rated this dimension based on observation. As a first step,
Table 9 shows the results of the investigated dimensions’ proposed in the maturity model,
Integrated Warehouse 
Performance Measures
Warehouse Ac�vity measures PR1 PR2 PR3 PR4 PT1 PT2 PT3 PP1 PP2 PP3 PP4 PD1 PD2 PD3 PD4
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
PA1
PA2
PA3
PA4
P1
P2
P3
P4
P5
P6
P7
D1
D2
D3
D4
D5
D6
Purchase order profile POP
Customer order profile COP
Item ac�vity profile. IAP
S1.1
S1.2
S1.3
S2.1
S2.2
S2.3
S2.4
S3.1
S3.2
S3.3
S3.4
S4.1
S4.2
S4.3
S6.1
S6.2
S6.3
S6.4
Best Practices
Activity Profiling
Safety
6S Audit Tool
Sort
Straighten
Shine
Standardize
Safety
Receiving 
Put-away
Picking
shipping
Perfect warehouse Index = (Perfect order (from supplier) Index * Perfect put-
away Index * Perfect picking Index * Perfect order (customer) Index)
Perfect order (from 
supplier) index
Perfect put-
away index
Perfect picking 
index
Perfect order 
(customer) index
Figure 2.
Roadmap for
improvement
Warehouse
performance
Warehouse activity measures
Company A Company B
Item (Y/N) Value (measures) (Y/N) Value (measures)
Perfect order (from supplier) index PR1 N N
PR2 Y 99.81 N
PR3 Y 99.1 N
PR4 Y 100 N
Perfect put-away index PT1 N N
PT2 N N
PT3 Y 99.9 N
Perfect picking index PP1 N
PP2 Y 99.96 N
PP3 Y 100 N
PP4 Y 97 N
Perfect order (customer) index PD1 Y 100 N
PD2 Y 99.9 N
PD3 Y 99.9 N
PD4 Y 100 N
Safety Safety Y 3 per year 4 per year
Activity profiling POP N N
COP N N
IAP N N
Best practices
Receiving R1 N N
R2 N N
R3 Y Y
R4 N Y
R5 Y Y
R6 Y Y
R7 Y N
R8 Y N
R9 Y Y
R10 N Y
R11 Y N
Put-away PA1 Y N
PA2 Y Y
PA3 N Y
PA4 Y Y
Picking P1 N N
P2 N Y
P3 N N
P4 N N
P5 N N
P6 Y Y
P7 N Y
Shipping D1 Y Y
D2 Y Y
D3 N N
D4 N Y
D5 Y Y
D6 Y Y
6S audit tool
Sort S1.1 N Y
S1.2 N Y
S1.3 Y N
(continued )
Table 9.
Maturity dimensions
measures (current
status)
IJPPM
and Table 10 presents maturity level results for each investigated dimension regarding their
usage. The previous two tables are used as is current situation of the two companies.
Therefore, Table 10 shows that company “A” utilized 75% of proposed warehouse activity
measures and did not utilize any profiling techniques. Furthermore, company “A”
implements 50% of proposed waste reduction practices, and practiced 56% of 6S audit
tools. In contrast, company “B” did not utilize any of the proposed warehouse activity
measures nor any activity profiling techniques. However, company “B” utilized 61% of waste
reduction practices and practiced 78% of 6S audit tool.
As a second step to use the proposedwarehousematuritymodel as a benchmark, a request
to measure the missing warehouse performance indicators send to the companyto report
such an indicator over a week period. Accordingly, Table 11 presents warehouse
performance indicators for both companies with best in-class measures. Table 11 shows
that company “A” is performing much better than company “B”, but below best in-class
measures except on perfect order (customer) index measures. However, company “B” is
performing much lower than best in-class measures on all warehouse activity measures.
Furthermore, the results from Table 11 show the width of the gap between company’s
warehouse performance and best in-class, and reflect the magnitude of improvement
initiatives to lessen the gap.
Consequently, as a third step, the company may use the proposed road map to improve
warehouse activity performance. Therefore, by comparing the current situation and the
proposed road map, the company can determine areas of improvement as shown in Figure 3.
For example, suppose company “A” decided to improve perfect put-away index from current
situation (91.7%) to best in-class (99.95%), then the company should start implementing the
followings: R4, PA3, POP, IAP, S1.1, S1.2, S2.4, S4.1 and S4.2.
Warehouse activity measures
Company A Company B
Item (Y/N) Value (measures) (Y/N) Value (measures)
Straighten S2.1 Y Y
S2.2 Y Y
S2.3 Y Y
S2.4 N N
Shine S3.1 Y Y
S3.2 Y Y
S3.3 Y Y
S3.4 Y N
Standardize S4.1 N Y
S4.2 N Y
S4.3 Y Y
Safety S6.1 N Y
S6.2 N Y
S6.3 Y Y
S6.4 Y N Table 9.
Company A Company B
Value Maturity level Value Maturity level
Warehouse activity measures 75.00% Stage 4: high 0.00% Stage 1: negligible
Activity profiling 0.00% Stage 1: negligible 0.00% Stage 1: negligible
Waste-reduction practices 50.00% Stage 3: moderate 61.00% Stage 3: moderate
6S audit tool 56.00% Stage 3: moderate 78.00% Stage 4: high
Table 10.
Results of maturity
levels for company “A
and B”
Warehouse
performance
5. Conclusion and future research
Enterprises seek to have tools, models or methodologies to help them improve their business
processes. This paper described the development and application of a warehouse maturity
model. This is a meta-model aggregate and organizes initiatives to improve warehouse
management as identified from previous research and practical experiences. In addition, the
proposed maturity model is comprehensive in the sense that can be used as evaluating as is
status, benchmark and prescriptive with a road map to improvements. This study suggests
that measurement and performance system, in the form of maturity models developed from
best-in class perspective, can significantly contribute to the warehouse improvement
initiatives. In addition, the study has presented a roadmap on the manner in which
warehouses can adoptmore sophisticated warehouse practices to improve their performance.
Furthermore, as for warehouse activities, use of self-administered maturity model along with
the implementation of the changes that lead to the higher levels of maturity prescribed by the
model and supported by the use of diagnostic model to improvement can be a valuable input
into warehouse management.
From the academic perspective, the proposedwarehousematuritymodel contributes to fill
the shortages of maturity model addressing the warehouse environment. In particular, it
provides a useful tool to establish the overall maturity level of a warehouse system. The
proposed maturity model supports strategic decisions oriented toward improvement
capabilities of the warehouse and to compete based on service level provided. From the
point of view of practical, maturity models have broad application, in that they emphasize
performance measurement and continuous process improvement in whatever process or
activities involved. The warehouse maturity model proposed to let warehouse managers
evaluate their practices and assess them by maturity level. Then, the proposed warehouse
maturitymodel can be utilized to develop a set of plans for conducting projects to improve the
warehouse practices, techniques and tools.
Our findings are beset with limitations, some of which stimulate further research. First,
this study focuses on the design and evaluation phases of DSR by thoroughly describing the
Company A (%) Company B (%) Best-in-Class
Perfect order (from supplier) index 97.0 83.9 ≥ 99.95%
PR1 98.1 95.3 ≥99%
PR2 99.81 97.2 ≥99.5%
PR3 99.1 96.2 ≥99%
PR4 100 94.2 ≥99.26%
Perfect put-away index 91.7 70.4 ≥ 99.95%
PT1 93.4 89.5 ≥95%
PT2 98.3 90.2 100%
PT3 99.9 87.2 ≥99.9%
Perfect picking index 89.2 57.7 ≥ 99%
PP1 22 (92%) 19 (79%) ≥ 24 orders (100%)
PP2 99.96 90 ≥99.84%
PP3 100 91.2 ≥99.9%
PP4 97 89 ≥99.5%
Perfect order (customer) index 99.8 77.0 ≥ 95.74%
PD1 100 95.2 ≥99.87%
PD2 99.9 94 ≥99.5%
PD3 99.9 93.2 ≥99.9%
PD4 100 92.3 ≥99.9%
Safety 3 per year 4 per year 1 accident per year
Table 11.
Warehouse
performance indicators
with best in-class
measures
IJPPM
Integrated Warehouse 
Performance Measures
Warehouse Ac�vity measures PT1 PT2 PT3
R1 N
R2 N
R3 Y
R4 N
R5 Y
R6 Y
R7 Y
R8 Y
R9 Y
R10 N
R11 Y
PA1 Y
PA2 Y
PA3 N
PA4 Y
P1 N
P2 N
P3 N
P4 N
P5 N
P6 Y
P7 N
D1 Y
D2 Y
D3 N
D4 N
D5 Y
D6 Y
Purchase order profile POP N
Customer order profile COP N
Item ac�vity profile. IAP N
S1.1 N
S1.2 N
S1.3 Y
S2.1 Y
S2.2 Y
S2.3 Y
S2.4 N
S3.1 Y
S3.2 Y
S3.3 Y
S3.4 N
S4.1 N
S4.2 N
S4.3 Y
S6.1 N
S6.2 N
S6.3 Y
S6.4 Y
Company "A" Improvement Ini�a�ves
Best Practices
Activity Profiling
Safety
6S Audit Tool
Sort
Straighten
Shine
Standardize
Receiving 
Put-away
Picking
shipping
Perfect put-
away index
Figure 3.
Company “A”
improvement
initiatives for perfect
put-away index
Warehouse
performance
model’s development and evaluation (€Osterle et al., 2011). However, the sixth phase
“maintain” requires a longitudinal study. Second, this study only applied to two third-party
logistics service providers. Third, this study have considered performance criteria identified
by best in-class from the perspective of improving services and did not consider others as cost
and productivity measures. Following are questions for future research:
(1) What are the main barriers that must be overcome by warehouse managers when
they decide to apply the proposed warehouse maturity model oriented to the control
and improvement of warehouse processes?
(2) Is there a precedence of improvement activities in the proposed warehouse maturity
model beyond the basic and advance categories indicated in this research?
(3) Does the proposed warehouse maturity model have enough generalizability to be
implemented in different warehouse environments?
(4) What are the costs involved in the planning and implementing of the proposed
warehouse maturity models?
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