What Can be Learned from More Than 100 Case Studies of Lean in
Services?
DIANA ISABEL PINTO PEREIRA, PAULO S. A. SOUSA, MARIA R. A. MOREIRA
Faculty of Economics,
University of Porto,
R. Dr. Roberto Frias, s/n, 4200-464 - Porto,
PORTUGAL
Abstract:- This research investigates the implementation of lean practices in services in order to identify those
that have a greater influence on company performance. Regression analysis with data from a systematic
literature review was the basis to study the relationship between lean and performance. For this purpose, a total
of 104 case studies were considered. A main finding was that some lean practices, such as “voice of the
customer” and “cross-functional teams” have a significant positive influence on performance. Also, the results
suggest that the more engaged managers are and the more they invest in training, the better company
performance will be. Finally, one may also conclude that knowledge about the determinants of lean
management will allow managers to be aware of what is decisive to improve company performance.
Key-Words: - Lean management, Systematic literature review, Regression analysis, Service industry, Case
studies
Received: April 8, 2023. Revised: July 13, 2023. Accepted: July 21, 2023. Published: August 4, 2023.
1 Introduction
In today's global economy, companies face
increasing pressure to reduce costs and respond
rapidly to worldwide competition, [1]. Customers
are more demanding [1], and customized products
are becoming the big trend of the XXI century,
turning mass production into a huge challenge, [2].
From this need to adapt to ever-changing customer
demands, lean management has arisen. Originating
in Japan and the Toyota Production System, lean is
a management philosophy rooted in producing at a
minimal cost and the pace of customers’ demand,
therefore reducing any kind of waste, [2].
Lean management can be successfully
implemented in any industry, [3]. However, it
cannot be equally applied by all companies, given
the differences among industries or even among
regions, [4].
Despite being first introduced in manufacturing,
lean management is becoming increasingly popular
in services, [5]. So, although the use of this
philosophy is by now well settled in the
manufacturing sector, [6], it is relatively new for
service companies, [7].
Nevertheless, the application of this philosophy
can be seen in many services such as healthcare,
banks and financial institutions, education, call
centers, and IT, among others, [7].
As the number of studies analyzing the impact
of lean on services is increasing, it becomes relevant
to further investigate this topic. Thus, the main
purpose of this research is to identify the factors that
have a greater influence on the lean performance of
service companies.
To achieve this, a systematic literature review of
case studies on lean management in the service
industry was carried out. The data collected was
then used as a basis to study the relationship
between lean and performance, by means of a
regression model.
We could find several literature reviews and
bibliometrics of lean management in specific
services, mainly by healthcare companies, [8], [9],
[10], [11]. We could also find a meta-review paper
analyzing the state of the art on lean management in
services, [5], essentially focusing on the
applicability of lean principles. So, to our best
knowledge, ours is the first study that assesses lean
practices by different types of services and does so
by converting the identified case studies into
observations to go on doing regression analysis.
Therefore, it fills the gap in the literature concerning
the identification of the elements that explain a
higher or lower influence of lean on company
performance. Furthermore, we believe this study can
be of extreme help to managers operating in the
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service industry that want to be aware of what is
important or decisive to implement this philosophy.
This paper is organized as follows: next section
presents a literature review concerning lean thinking
(fundamental concepts, practices, tools and
techniques, benefits, implementation issues, and
critical success factors), and the research
framework; then the methodology used is described
and, afterward, the main results, conclusions, and
practical and theoretical impacts are discussed.
2 Literature Review
Mass production established at the beginning of the
20th century, allowed consumers to get low prices
(costs), but, on the other hand, restricted access to
product variety, [12]. However, after the II World
War, the context of the Japanese market for
automobiles was characterized by scarcity of
resources and intense domestic competition, [13].
To survive in this context, Japanese car
manufacturers concluded that mass production was
no longer a viable option. Hence, in the 1950s, the
engineers Taiichi Ohno and Shigeo Shingo
developed the Toyota Production System, [2], which
later became known as “lean production”, a term
coined by John Krafcki in 1988, [12]. This
denomination was then popularized through the
book “The machine that changed the world”, where
it was introduced as a dynamic process that
emphasizes the elimination of waste and continuous
improvement combined with employees’
empowerment, [12].
Nevertheless, for many, the concept of lean
production is not clear, [13], and several authors
have tried to better define it (e.g., [14], [15], [16],
[17]). Such definitions have different emphases:
elimination of waste, value, employee engagement,
customers, continuous improvement, increasing
quality and efficiency, and lower cost. Even so, they
can be understood as complementary, in the sense
that, the elimination of waste and continuous
improvement can be achieved by identifying value,
reducing non-value-adding activities, creating better
working conditions, easing flows within supply
chains, and engaging all employees, all of which
will lead to increased quality and efficiency and to
lower costs that, subsequently, will increase both
company’s and end customer’s value.
Hence, the main goals of lean are to eliminate
waste, [12], and to increase value for customers,
[13]. Moreover, in accordance with [12], lean
thinking is guided by five principles: (1) value, (2)
value stream, (3) flow, (4) pull, and finally, (5)
perfection. Once created and analyzed the value
stream, a lean company must identify and eliminate
non-value-adding activities, improve flows, produce
based on demand-pull systems, and continuously
strive for improvements without disregarding the
importance of a strong involvement of employees,
[18]. From this, one may highlight two main
concepts, basic to lean production: waste and value.
[12], claimed that what does not create value is
a waste (“muda”) and must be eliminated,
minimized, or converted into value. Taiichi Ohno
(1988) has identified seven categories of waste: i)
transportation; ii) inventory; iii) motion; iv) waiting;
v) overproduction; vi) processing, and vii) defects,
[19].
Although lean was first introduced in
manufacturing, it is becoming increasingly popular
in services, [5]. Thus, [20], adapted these seven
types of waste to services, identifying the following
categories: delay, duplication, unnecessary
movement, unclear communication, incorrect
inventory, opportunity loss, and errors. Furthermore,
the authors also supported that it should be added a
further type of waste for both manufacturing and
services: “not using the mind of employees”.
Regarding value, [13], claimed that the
perception of value was usually and wrongly seen as
a reduction of costs. Instead, the value should be
seen from a customer perspective, and, if so, it can
be increased either by removing wasteful activities
or adding product/service features that customer
value. Indeed, customer value can be increased by
reducing costs, but also by improving customer
satisfaction from, for example, the reduction of
waiting times and defects, [21]. Several practices,
tools, and techniques are mostly used to optimize
processes by eliminating waste, [21].
2.1 Lean Management: Tools and
Implementation Issues
The lean strategy’s umbrella encompasses plenty of
methods that intend to improve the performance of
organizations, [22]. Having a set of reliable tools
and techniques is crucial to decrease waste and
provide value to customers, [23]. However, the
implementation of lean is not straightforward for all
organizations, as it requires adaptation to the
different processes, markets, and supply chain
characteristics, which means that, depending on the
environment, some practices could be suitable to an
organization and some could not, [18].
[24], categorize inter-related practices into four
bundles: Just-in-time (JIT), Total Quality
Management (TQM), Total Preventive Management
(TPM), and Human Resource Management (HRM).
JIT is a program whose main purpose is to
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continuously reduce all forms of waste, [25].
Therefore, JIT production is based on producing or
ordering exactly the quantity that is needed at the
moment that is needed, [26]. TPM is designed to
maximize equipment effectiveness while the goal of
TQM is the continuous improvement as well as the
sustainability of products and processes quality,
[25]. HRM is viewed as a program that supports all
the other three, since, for these programs to succeed,
it is crucial to have, for instance, cross-functional
training, and employee involvement, [25].
This research follows the following structure to
analyze some of the lean tools for each bundle. For
JIT we considered the tools: Piece flow, Small-lot
production, Standardization of work, Pull system,
Cellular production, Line balancing; Heijunka,
Kanban, Visual control, and Jidoka; for TQM we
included: Value stream mapping (VSM), Kaizen, 5
Whys, Cause and effect diagrams, Pareto analysis,
PDCA (Plan-Do-Check-Act), and Supply quality
management; for TPM we considered: OEE
(Overall Equipment Effectiveness), SMED (Single-
Minute Exchange of Die), 5S (Sort, Set in order,
Shine, Standardize, Sustain), Preventive
maintenance and Breakdown maintenance; finally
for HRM we included: Flexible teams, Cross-
functional Teams, and Self-directed work teams.
[25], stated that JIT, TQM, and TPM form a
comprehensive and consistent set of practices that
aim to improve performance through waste
reduction and continuous improvement.
Additionally, lean is frequently combined with
another approach used to process improvement –
Six-Sigma. Six-Sigma is a program centered on the
customer that uses problem-solving methodologies
and highlights data-based decision-making, [27]. A
commonly used problem-solving methodology is
DMAIC, which stands for Define, Measure,
Analyze, Improve, and Control (De Koning et al.,
2008). In the define phase, the SIPOC diagram
(Suppliers-Inputs-Processes-Outputs-Customers)
and VOC analysis (Voice of Customer) are
frequently used to identify all the important
elements for process improvement and to make sure
that they are in line with customer requirements,
[28].
Several benefits of lean implementation, both
qualitative and quantitative, have been pointed out
by various authors. Quantitative benefits include
improvement in production lead time, processing
time, cycle time, set-up time, inventory, defects, and
equipment effectiveness, while qualitative gains
comprehend, among others, improved employee
morale, effective communication, standardized
housekeeping, and team decision-making, [2].
Reviewing several studies, [29], found that lean
benefits can also be found in different types of
services. In healthcare, lean helps to reduce waiting
time, improved the quality of care, improved
productivity and efficiency, capacity expansion
without additional facilities, and increased the
utilization of operating theatres. In software service
companies, lean leads to lower variability in
performance, fewer defects, and rework, improved
operational performance, and improved quality. In
education, lean allows improved quality, the
relevance of course materials, reduction in delivery
time of knowledge, and delivery of higher value.
And finally, in the public sector, delivering a high-
quality service that meets customer requirements
with efficient resource utilization is one of the
benefits of lean, [30].
Notwithstanding providing plenty of benefits,
lean implementation is not always effective and
sustainable, [2]. Thus, [2], identified some critical
issues and categorized them into pre-
implementation issues, implementation issues, and
post-implementation issues. The first category
includes issues such as misconceptions about the
objectives of lean management and lack of
communication, top management commitment,
training, and education programs. One possible
implementation issue is the non-effective supplier
relationship. And finally, post-implementation
issues include, for instance, a lack of proper post-
implementation planning: an organization should
review the entire process and create opportunities
for continuous improvement.
Some factors that are fundamental to a
successful implementation of lean were pointed out
by, [31]. They constitute the critical success factors
of lean: i) Leadership and management
commitment: strong leadership would allow a
flexible organization structure, as well as knowledge
enrichment of the workforce, [31], and will also
promote the removal of barriers, [2], ii) Financial
capability: lean implementation requires some
financial capabilities like, for instance, hiring
consultants and training of people; iii) Skills and
expertise: it is important that employees are open to
the idea of skill enhancement and, in this era of
fierce competition, the capability of innovation and
differentiation of the employees can also be critical;
and iv) Organizational culture: the culture of the
organization must be supportive to lean
implementation, and communication and employee
involvement to achieve improvements are key.
Despite all the above, we should keep in mind
that lean is not the best choice for all companies:
lean must be compatible with the company’s
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products, processes, and customers and lean
practices should be adapted for each business
environment, [1].
2.2 Services: Characteristics and
Categorization
According to [32], the service sector contributes to
more than 50% of the GPD (Gross Domestic
Product) of top economies, becoming thus globally
vital [29].
Service is an activity that usually includes
interaction with the customer with the purpose of
providing a solution to its problem, [29], [33].
Therefore, the service industry is very different from
manufacturing given its characteristics:
intangibility, heterogeneity, and inseparability, [34].
Other characteristics such as perishability are also
associated with services, [37], [29].
Regarding the type of service, [35], developed a
service process matrix that highlights two key
elements: first, the labor intensity of the service, and
second, customer interaction and service
customization, [36]. This two-by-two matrix
presents four types of services: Service factory,
Service shop, mass Services, and Professional
Services.
A service factory requires low labor intensity
and a low degree of interaction with customers and
customization. According to [36], this type of
service offers limited variety but has advantages in
terms of price, speed, and personal touch. It includes
services such as airlines, trucking, hotels, and
resorts, [35]. The service shop takes place when the
degree of interaction with customers and
customization is increased. Unlike service factories,
these organizations offer a high variety of services
which supports their competitive advantage but
makes them somewhat difficult to control, [36].
Examples of service shops are hospitals, auto repair
shops, and other repair services, [36]. Mass service
businesses are characterized by high labor intensity
and low degrees of interaction with customers and
customization. Having a limited-service mix, these
organizations have a chance to compete in price,
[36]. In this category, one can find services such as
retail, wholesaling, education, laundry, cleaning,
and many routine computer software and data-
processing functions, [35]. When the degrees of
interaction with customers and customization
increase, we are talking about professional services.
This kind of service includes doctors, lawyers,
accountants, architects, investment bankers, and
other organizations which depend on the
professional skills of, usually, few individuals, [35],
[36].
Nonetheless, there are other proposals to
categorize services. For instance, taking into
consideration the service process perspective, [37],
as well as, [38], have proposed a taxonomy of
services: people-processing services – the presence
of the customer is essential –, possession-processing
services – customers’ presence is not necessary
since the service is performed on a product from the
customer and therefore, its presence is not necessary
– and information-processing services – it does not
require the presence of the customer at all, [5].
Regarding performance in services, it is
important to have in mind three perspectives: the
service provider – does the company accomplish its
objectives? –, interest groups – does the network
meet the shared objectives? – and the customers –
does the service meet the customers’ expectations?
Yet, the principal focus of service must be to
provide value to the customer, [39].
According to the study of [39], the first
perspective includes measures such as efficiency
(e.g.: costs, value-added, equipment utilization rate),
quality (e.g.: customer satisfaction), personnel (e.g.:
well-being at work), and profitability (e.g.: gross
margin). With regard to the network, some
examples of measures are the efficiency of
cooperation and the success of shared planning.
Finally, a service company must always consider the
customer’s perceived value to measure its
performance.
As the purpose of this research is to study the
impact of lean on performance, it also makes sense
to identify measures that indicate the success of
lean. Performance measures include costs (e.g. costs
with unnecessary resources, saving for doing it right
at the first time), quality (e.g., customer satisfaction,
percentage of complaints), flexibility (e.g., number
of customized solutions), and productivity (e.g.,
number of customers served per hour). Also, time-
related measures (lead time, processing time, etc.)
were found to be quite significant to the evaluation
of the usefulness of lean on performance. Finally, as
explained in previous sections, the primary goals of
lean are to eliminate waste, [12], and, as in services,
to increase value for customers, [13]. Therefore, the
elimination of waste and customer satisfaction
should also be measured to evaluate the
performance of the lean implementation.
2.3 Research Framework and ‘Theoretical’
Model
The main aim of this research is to investigate lean
implementation within services. We want to study
which lean factors mostly affect performance in
service companies. Also, we want to deeper analyze
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which practices are more used and which of them
have a greater impact.
In this way, in our model, the independent
variables are the practices included in each bundle,
as from the literature review (JIT, TQM, TPM, and
HRM) and the practices related to Six-Sigma; the
number of practices used; the type of service; the
company size and the degree of management
commitment, employee involvement, and training.
To study performance, we chose as a dependent
variable the number of performance measures that
show improvement, but also: the performance
measures quality/defects, customer satisfaction,
productivity/efficiency, cost savings, elimination of
waste, and time. Figure 1, below, depicts our
research framework.
Fig. 1: A research framework
Our ‘theoretical’ model was designed to identify
the factors (their importance and expected signal)
that have a greater influence on the lean
performance of companies.
From the different theoretical approaches
discussed in the previous sections, seven elements
may have a greater or lesser impact on companies’
performance. Accordingly, those are (1) the Number
of practices adopted, (2) Usage of JIT, TQM, TPM,
HRM, and Six-Sigma practices, (3) Management
commitment, (4) Employee involvement, (5)
Training, (6) Type of service and (7) Size of the
company.
The number of practices adopted is an
exploratory variable; however, as lean is guided by
five principles (Womack & Jones, 1996), we expect
that all are addressed in order to successfully
implement it, and, for that reason, a higher number
of practices adopted should drive to a higher impact
of lean on performance.
Regarding the use of JIT, TQM, TPM, HRM,
and Six-Sigma practices, [25], [26], [27], these
bundles have different goals: JIT intends to reduce
all forms of waste, TQM is focused on continuous
improvement and sustainability, TPM relies on
equipment effectiveness, and HRM works as a
support for all these three, [25]. Furthermore, Six
Sigma is highly related to problem-solving
methodologies, [27]. Therefore, it is expected that
all the practices included in these bundles contribute
to better performance: for instance, the use of value
stream mapping through its focus on eliminating
waste and efficiency, and kaizen by being cantered
in continuous improvement, [26]. The JIT bundle
includes several practices: cellular production;
kanban, heijunka, visual control, one-piece flow,
standardization, line balancing, and pull system. As
a fundamental principle in JIT, the elimination of
waste will also be considered as its practice. The
TQM bundle contains the following practices: value
stream mapping, Kaizen, PDCA, Cause and effect
diagrams, Pareto analysis, five whys, and some
supportive charts such as run chart and control
chart. TPM also includes 5S, as the other TPM
practices are more related to manufacturing. The
HRM bundle includes self-directed work teams and
flexible cross-functional teams, and Six Sigma
comprises DMAIC, SIPOC, and VOC. The use of
Six Sigma will also be considered as a practice.
The third element, management commitment is
one of the critical success factors of lean, it is
anticipated that the greater the management
commitment, the greater the impact on lean
performance. Concerning employee involvement, it
is expected to have a higher impact on performance
when there is stronger employee involvement.
Regarding the fifth element, training, we can say
that more investment in training should lead to a
higher impact on performance. The existing
heterogeneity between the different kinds of
services makes it difficult to treat them as if they
were the same. Finally, the element size of the
company: if, on one hand, large companies have
financial capabilities that allow them to invest in
training programs and innovation, which can be
crucial to lean performance, on the other hand, they
usually have a more complex structure that does not
support flexibility. Thus, the expected signal can be
either positive or negative.
This relation depicts the theoretical model:
Being:
Performance: the several performance measures:
Number of performance measures that show
=
Number of practices adopted;
Use of the practices in i;
Management commitment;
Training
Employee involvement;
Type of service;
Size of company
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improvement, Quality/defects, Customer
satisfaction, Productivity/efficiency, Cost savings,
Elimination of waste, Time;
i: the bundles' JIT, TQM, TPM, HRM, and Six-Sig.
Figure 2 synthetizes the determinants considered in
the theoretical model and their expected impact on
lean performance.
*Exploratory variable
Fig. 2: Determinants of the theoretical model
3 Materials and Methods
For the systematic literature review, we followed the
structure proposed by, [40]: planning, conducting,
and reporting. The first phase consisted in
identifying the objectives for this review and
developing a protocol to decide the inclusion
criteria. To be as accurate as possible in addressing
the research questions, the inclusion criteria should
be decided carefully. Firstly, only case studies must
be used as a source of data collection. Therefore,
literature reviews and surveys were excluded.
Secondly, the articles selected must only analyze the
implementation of lean in services as this is the
purpose of this study. Finally, the articles to include
must analyze the relationship between lean
implementation and the performance of the
companies. For that reason, studies that do not
report performance results after lean implementation
were excluded.
The process of selecting articles began with a
literature search on the bibliographic database
Scopus, B-On, and Web of Knowledge. The search
was made with the aim of finding papers that have
performed literature review studies about the topic
under study: the relationship between lean practices
and lean performance, in the case of services. The
keywords searched in different fields (abstract,
article title, and subject) were “review”, “lean”,
“service”, and “literature analysis”. This process
returned four articles, [5], [29], [7], [41]. The
articles that were analyzed by these authors were
included in this research as much as possible.
In order to complement the case studies
(articles) found in these four literature review
papers, another search in Scopus, B-On, and Web of
Knowledge was made, using the terms “lean
service” and “case study” in a different field
(abstract, article title, and subject).
This process is illustrated in Figure 3.
In the final, 80 articles were listed in [5], 122 in
[29], more than 70 in [7], and 172 in [41]. Searching
the online databases, 426 articles were found. As it
was expected, some of these articles were common.
After excluding the articles that were not
adequate according to the above-mentioned
inclusion criteria, 72 articles were considered
suitable for our analysis (Appendix 1). As some of
these articles had multiple and statistically
independent case studies, a total of 104 case studies
were considered for this analysis.
The database containing all the information
collected from the 72 studies is available upon
request to the authors.
Then, all data regarding the type of service, size
of companies, country, number of (and which) lean
practices that were adopted, and the results obtained
in terms of performance were registered. Moreover,
the existence of critical success factors studied in
the literature review was also evaluated.
The practices were coded “1” if used and “0” if
not used.
The type of service was classified into the
following groups: healthcare, hotel industry,
housing services, telecommunications, call centers,
banking, financial and insurance services, software
and IT industries, distribution, logistics and retail
industries, education, public sector, and engineering,
in line with the division done by Hadid and
Mansouri (2014). In the case of companies from the
public sector, an effort was made to classify them as
thinly as possible, as there are plenty of services
provided by the public sector. For instance, local
authorities or governments were classified as public
administration.
Company size, if the information was available,
was divided into large, medium, or small, on the
basis of information provided, and/or the number of
employees and turnover.
In each case, performance measures and critical
success factors (management commitment/
Group
Variables
Expected signal
Number of practices
adopted
Nr Lean Practices Adopted
*
Use of JIT practices
Cellular Production; Kanban; Heijunka; Visual
Control; One Piece Flow; Elimination Waste;
Standardization; Line Balancing; Pull System
+
Use of TQM
practices
VSM; Kaizen; PDCA; Cause Effect Diagrams;
Pareto Analysis; Five Whys; Supportive
Charts
+
Use of TPM practices
Five S
+
Use of HRM
practices
Self-Directed Work Teams; Flexible Cross
Functional Teams
+
Use of Six sigma
practices
Six Sigma; DMAIC; SIPOC; VOC
+
Management
commitment
Management Commitment_Leadership
+
Training
Training
+
Employee
involvement
Culture/Employee Involvement
+
Type of service
Type Of Service
*
Size of company
Company Size
+/-
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leadership, training/education programs, and
organizational culture/employee involvement) were
classified on a 1 to 5 Likert Scale. 1 – The
performance measure had noticeably worsened/ The
critical success factor was quite insufficient; 5 – The
performance measure had noticeably improved/ The
critical success factor was excellent. Scales 2, 3, and
4 are in the middle: 2 are insufficient; 3 –
Indifferent/ Non-significant; 4 – The performance
measure had improved/ The critical success factor
was good.
Fig. 3: Process of articles selection
Regarding the final step, the description and
analysis of the created database are presented in the
next Section. This database was also used to
perform a regression analysis, as the case studies
were converted into observations.
4 Main Results
4.1 Descriptive Analysis
As highlighted before, it was possible to obtain 104
valid case studies of as many companies, to analyze
the implementation of lean management.
The timeframe of the study was divided into the
following periods: 2002-2005, 2006-2009, 2010-
2013, and 2014-2018. The period 2006-2009 is that
comprising more case studies (41), followed by
2010-2013 (34). Regarding company size, big
companies stood out: 50 companies out of the 104
studied. It was not possible to determine the size of
22 companies due to a lack of information. The
sample includes companies from several countries
such as Australia, Canada, China, Denmark, India,
Italy, Mexico, Netherlands, Portugal, Scotland,
Korea, Sweden, Spain, Thailand, and Taiwan. The
two most represented countries are the UK (24% of
the total number of firms) and the USA (10%), all of
which led us to conclude that lean has been and still
is implemented worldwide.
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Regarding the type of service, the selected
companies are from very different sectors:
engineering; education; banking, financial, and
insurance; healthcare; hotels, distribution, retail, and
logistics; public administration; IT and software;
telecommunications, etc. It should be noted that
healthcare is the most frequent service we came
across, representing approximately 32% of the total
number of companies.
Several lean practices were found to be used
and/or in use by the selected companies: SMED,
Kanban, one-piece flow, cause, and effect diagrams,
Pareto analysis, 5 Why’s, and pull system. Value
stream mapping and kaizen were undoubtedly the
most utilized – these two practices were used by
72% of the companies. Nevertheless,
standardization, elimination of waste, 5S, cellular
production, visual control, line balancing, self-
directed work teams, and flexible, cross-functional
teams can also be highlighted. Moreover, a
significant number of these companies combined
lean and six-sigma, using methodologies such as
DMAIC, SIPOC, and VOC. Practices such as
preventive maintenance and breakdown
maintenance were not found to be used as they are
more usual in manufacturing.
Disregarding value stream mapping and kaizen,
the most used practices were slightly different
depending on the type of service. For instance, for
healthcare, the most common was the elimination of
waste, self-directed work teams, and visual control,
while for banking, financial and insurance
companies, the most used were standardization and
line balancing. Furthermore, call centers invested
more in practices such as cellular production, the
voice of customers and flexible, cross-functional
teams, and, for example, companies related to
construction/ engineering focused on the elimination
of waste.
In the analyzed case studies, one can find some
performance measures: time, productivity
/efficiency, quality/defects, revenues/cost savings,
and customer satisfaction, for example. In general,
lean proved to be helpful in improving performance
in the before mentioned aspects, which is consistent
with the benefits presented by [29].
Finally, the existence of critical success factors
was also analyzed, and the ones mentioned are
management commitment, training/educational
programs, and organizational culture/employee
involvement. Companies lacking these factors
showed worse performance than the others. This is
in line with [2], which defends that the lack of
critical factors puts at risk the effectiveness of lean
implementation.
4.2 Correlations between Variables
The correlation matrix showing the most significant
correlations between variables is presented in Figure
4. The dependent variable is the number of
performance measures classified with 4 or 5
(Nperf>4). We also analyzed, as dependent
variables, the following performance measures:
Time, Elimination of waste, Productivity/efficiency,
Quality/defects, Costs savings, and Customer
satisfaction.
It should be highlighted that Pareto analysis, the
voice of the customer, cross-functional teams, and
training, have a significant and positive correlation
with at least 4 of the lean performance measures.
Moreover, the voice of the customer was
revealed to be the practice with more positive,
significant, and strong correlations (rho>0,3) at a
level of significance of 1%: the number of lean
performance measures that were improved
(performance measures classified with 4 or 5),
quality and customer satisfaction. Increasing value
for the customers is one of the main goals of lean
management (Hines et al., 2004); therefore the use
of the voice of the customer becomes essential to
ensure that all elements are in line with its
requirements (Antony et al., 2012).
On the other hand, 5 Whys, 5S, kaizen,
heijunka, visual control, standardization, and
employee involvement do not have a significant
correlation with any lean performance measure.
With regard to performance measures, cost
savings is the one that is significantly and strongly
correlated with more lean practices.
Going deeper into the analysis, one can confirm
a significant correlation between the first dependent
variable – the number of improved performance
measures – and 10 variables. Four of them with a
level of significance of 1% - number of practices
adopted, the voice of the customer, Pareto analysis
and flexible, cross-functional teams, and six with a
level of significance of 5% - VSM, PDCA,
supportive charts, cellular production, DMAIC, and
Training. From there, the variables with a stronger
correlation with performance (rho>0,3) are the
number of lean practices adopted, the voice of
customers, and cross-functional teams. According to
this bivariate analysis perspective, companies that
use these practices tend to have better performance.
The same happens in companies that invest in
training and use as many lean practices as possible.
The number of adopted practices turned out to be
important since lean is guided by five principles,
[12] and it can be inferred that it requires different
practices to address each of these principles.
However, the use of PDCA (with a negative sign),
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does not grant the same benefits. The variable time
has a significant correlation with 1 variable at a
level of significance of 1% - cellular production,
and 2 variables at a level of significance of 5% –
line balancing and flexible, cross-functional teams –
which means that the use of these practices is
associated to a better time performance.
The variable elimination of waste has a positive
and significant correlation with the elimination of
waste (practice) at a level of significance of 1% and
with cellular production and cross-functional teams
at a level of significance of 5%. Therefore, the use
of these practices and the investment in training tend
to lead to higher performance concerning the
elimination of waste.
Regarding productivity and efficiency, there are
2 variables with a significant correlation at a level of
significance of 5% - PDCA and line balancing –,
and 2 at a level of 1% - pull system and flexible,
cross-functional teams. With a positive sign, line
balancing, pull system, and flexibility, cross-
functional teams tend to contribute to better
productivity and efficiency, while PDCA does not
seem to have the same benefit.
Concerning quality, the voice of the customer,
training, and the number of lean practices adopted
have a significant correlation with it, at a level of
significance of 1%, and VSM, one-piece flow,
flexible, cross-functional teams, and management
commitment at a level of 5%. Hence, the use of
these practices, the existence of management
commitment, and investment in training lead to
higher quality; the very same happens if companies
use as many practices as possible.
Fig. 4: Spearman correlation matrix
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Concerning quality, the voice of the customer,
training, and the number of lean practices adopted
have a significant correlation with it, at a level of
significance of 1%, and VSM, one-piece flow,
flexible, cross-functional teams, and management
commitment at a level of 5%. Hence, the use of
these practices, the existence of management
commitment, and investment in training lead to
higher quality; the very same happens if companies
use as many practices as possible.
With respect to cost savings, six-sigma,
DMAIC, SIPOC, Pareto analysis, and cause and
effect diagrams were found to have a significant
correlation at a level of significance of 1%. This can
be explained by the problem-solving character of
these tools, [42], [27]. The same happens with the
number of lean practices adopted, VSM, and
supportive charts. Additionally, it was found a
significant correlation between the voice of
customers and self-directed work teams at a level of
5%. With a negative sign, self-directed work teams
do not tend to have the same positive influence as
the remaining mentioned practices regarding cost
savings.
Finally, customer satisfaction has a significant
correlation with the voice of customers at a level of
significance of 1% and with line balancing at a level
of significance of 5%. Therefore, given the positive
sign, the use of the voice of the customer tends to
improve customer satisfaction.
4.3 Results of Multivariate Analysis and
Discussion of Results
Two linear regression models were estimated to
explain lean performance: one considering as
dependent variable the number of performance
measures that improved due to lean implementation
(classified with 4 or 5), including cost, quality, time,
productivity/efficiency, customer satisfaction, and
elimination of waste – model 1 –, and another one
considering only one performance measure as
dependent variable: quality – model 2 –, as this is
one of the most relevant measures linked to the
emergence of lean management.
Model 1:
Being:
Nperf ≥ 4: the number of performance measures
classified with 4 or 5;
i: the practices VSM, Kaizen, PDCA, Cause and
Effect Diagrams, Pareto Analysis, 5 Whys,
Supportive charts, 5S, Cellular production, Kanban,
Heijunka, Visual control, One-piece flow,
Elimination of waste, Standardization, Line
Balancing, Pull system, Six-sigma, DMAIC,
SIPOC, VOC, Self-Directed Work teams, Flexible
cross-functional teams.
Model 2:
Being:
Quality: the quality performance measure;
i: the practices VSM, Kaizen, 5S, Cellular
production, Visual Control, Elimination of waste,
Standardization, Line Balancing, Six-Sigma, Voice
of the customer, and Self-directed work.
4.3.1 Results of Model 1
We ensured that the model met all linear model
assumptions using the ‘gvlma’ package, [43], in R
version 3.5.0. This package implements the testing
procedure developed in [43]. The Shapiro test was
also used to test the normality of residuals.
Initially, this model also included the type of
service, company size, and the number of practices
used, but these variables had to be discarded
because, despite a good adjustment, they did not
fulfill the normality condition resulting from the
application of the Shapiro-Wilk test to the residuals.
The results (detailed in Figure 5) obtained show
that eleven factors explain the impact of lean on
performance. Of these eleven factors, nine have a
positive sign: Pareto analysis, 5 Whys, supportive
charts, heijunka, pull system, the voice of customer,
flexible, cross-functional teams, management
commitment, and training, which means that the use
of these practices, the commitment of the
management team and the training of employees
tend to contribute to higher performance.
As expected, the use of tools from JIT (pull
system and heijunka), TQM (Pareto analysis, 5
Whys, supportive charts), HRM (Flexible cross-
functional teams), and Six-Sigma (Voice of
Customer) leads to higher performance, [25], [26],
[27]. Still, the use of TPM did not prove to
significantly help to improve performance.
Furthermore, the positive and statistically
significant sign of management commitment and
training points out that the higher degree of
management commitment and training, the better
performance. Indeed, these two are considered
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critical success factors for the implementation of
lean, [31], [2], and, for that reason, this result was
expected. On the other hand, it was expected that
organizational culture/employee involvement was
another critical success factor, [31], which was also
highlighted by this sample under analysis.
Conversely, the other two factors – Kaizen and
Visual Control – that explain the dependent variable
have a negative sign. It was expected to be a
positive sign, given the possibility of highlighting
mistakes and defects provided by visual control, [1],
and the continuous improvement character of
Kaizen, [26]. In the case of kaizen, it should be
taken into consideration that it works based on
gradual and incremental changes, and its effects
may not be readily perceived in a short period of
time, [22]. Another possible explanation for these
results may be some implementation problems with
these lean practices and thus they were not fully and
effectively implemented, [22]. Or, if these practices
were implemented at the first stages of the lean, they
may have had a significant improvement at that time
and not at the time the case was analyzed.
Fig. 5: Results of the 1st. model
4.3.2 Results of Model 2
Regarding the second model, we ensured it fulfilled
all linear model assumptions via the ‘gvlma’
package. The Shapiro test was also used to test the
normality of residuals.
In this model, it was possible to include the type
of service, the company size, and the number of lean
practices used.
The results of this model (listed in Figure 6)
show that nine factors explain performance in terms
of quality, five of which with a positive sign: service
Software and IT, number of practices used, line
balancing, management commitment, and training.
The type of service was an exploratory variable
and the statistically significant and positive sign of
Software and IT means that being a company in this
sector is a determinant of better quality
performance. Thus, software and IT appears to be
predisposed to be a service to adopt lean
management.
Fig. 6: Results of the 2nd. model
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Another exploratory variable analyzed in this
model is the number of practices used. Again, the
positive and statistically significant sign for this
variable means that the more practices are used, the
higher the quality is achieved. This is in accordance
with what was expected since lean is guided by five
principles, [12], and all should be addressed to
successfully implement it.
In this model, the use of line balancing proved
to be a factor that contributes to higher performance.
This was also expected given the presented
motivations of JIT. Moreover, according to [22], JIT
has the highest impact on performance which
concerns quality.
In conformity with the first model, management
commitment and training are also factors that
determine and influence positively the quality of the
service. The more committed managers are and the
more they invest in employee training, the greater
the quality that the company achieves.
Again, in accordance to model 1, kaizen and
visual control present a negative and statistically
significant sign. In this second model, the same
happened in the case of the variable self-directed
work teams.
Finally, the sign for the company size, in terms
of impact on performance, was expected to be either
positive or negative since the larger the company the
more financial resources it has, but it also has
concomitantly less flexibility, [44]. Our results
show it is possible to conclude that the
implementation of lean in medium-sized companies
is not likely to be linked to higher quality.
4.4 Implications
This study has theoretical and practical implications,
enriching the literature and providing some valuable
managerial insights.
Firstly, the analyzed case studies were
converted into observations in order to allow
regression analysis, and, to our knowledge, this is
the first time such an approach is followed to study
lean-in services.
Practically, this study can be extremely helpful
for managers that want to be aware of lean
implementation in services and its value, to know
which are the most used practices, and which are the
factors that have a greater influence on performance.
In this way, engineering managers should direct
their efforts when implementing lean management,
by showing all their commitment and investing in
educational programs to prepare the most important
assets of any company – the employees.
Individually, this analysis can provide some
insights for engineering managers that are
considering the implementation of lean or how to
achieve better results with it.
As lean practices “cross-functional teams”,
“management commitment” and “training” proved
to have a positive influence on performance, the
more engaged engineering managers are and the
more they invest in the training of employees, the
better performance companies will achieve.
5 Conclusion
For many years now, companies have been facing
plenty of challenges with more and more demanding
customers and high pressure to reduce costs. In this
context, lean management emerges as an attractive
option to develop improvement actions and to be
ahead of the competition. Given the importance of
the service sector in the economy and the growing
use of this philosophy in these areas, this study had
the main goal to identify the chief factors that
influence lean performance in service companies.
The results showed that value stream mapping
and kaizen are undoubtedly the more adopted
practices. Nevertheless, six-sigma practices,
standardization, elimination of waste, 5S, cellular
production, visual control, line balancing, self-
directed work teams, and flexible, cross-functional
teams can also be highlighted. Furthermore, lean
proved to be useful in improving different
performance measures such as time, productivity,
quality, costs, and customer satisfaction.
It should not be anticipated that all lean
practices contribute to improving all performance
measures. The voice of the customer, Pareto
analysis, and cross-functional teams should be
highlighted as the practices that positively influence
more performance measures, with a level of
significance at 1%.
Given the results obtained with two developed
and tested models, several factors have a positive
influence on lean performance in a global way: the
use of Pareto analysis, 5 Whys, supportive charts,
heijunka, pull system, the voice of the customer, and
flexible, cross-functional teams. Specifically,
regarding quality, line balancing can also be
relevant. Thus, diverse lean practices proved to have
a positive influence on performance.
On the other hand, and contrary to expectations,
there are very well-known practices (e.g. kaizen and
visual control) that have been shown to have a
negative impact on lean-on performance.
Finally, it would be insightful to further
investigate this topic, as this research suggests a
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need for further empirical evidence regarding lean
practices and their relationship with performance.
Future research should focus on how to implement
lean management in services; for instance, it would
be important to find out which practices should be
implemented simultaneously or if they should be
implemented sequentially.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflict of interest to declare
that is relevant to the content of this article.
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Appendix 1
List of studies included in the cases analysis (by year)
Author(s)
Journal
Allway and Corbett (2002)
Journal of Organizational Excellence
Cuatrecasas-Arbós (2002)
International journal of production economics
Swank (2003)
Harvard business review
Brown et al. (2004)
Interfaces
Farrar (2004)
Lean Construction Journal
Emiliani (2004)
Quality Assurance in Education
Cuatrecasas-Arbós (2004)
International Journal of Services Technology and Management
Furterer and Elshennawy (2005)
Total Quality Management & Business Excelence
Emiliani (2005)
Quality Assurance in Education
Lummus et al. (2006)
Total Quality Management & Business Excelence
Agbulos et al. (2006)
Journal of construction engineering and management
Su et al. (2006)
International Journal of Six Sigma and Competitive Advantage
Al-Aomar (2006)
International Journal of Product Development
Al-Sudairi (2007)
Construction Innovation
Fillingham (2007)
Leadership in Health Services
Ben-Tovim et al. (2007)
Australian Health Review
Lee et al. (2007)
Service Industries Journal
Change and Su (2007)
International Journal of Six Sigma and Competitive Advantage
Lodge and Bamford (2008)
Public Money & Management
Papadopoulos and Merali (2008)
Public Money & Management
Kress (2008)
Journal of Access Services
Mcquade (2008)
Public Money & Management
De Koning et al. (2008)
International Journal of Six Sigma and Competitive Advantage
Radnor and Walley (2008)
Public money and management
Hines et al. (2008)
Public money and management
Waterbury and Bonilla (2008)
International Journal of Six Sigma and Competitive Advantage
Jin et al. (2008)
International Journal of Six Sigma and Competitive Advantage
Kung et al. (2008)
Canadian Journal of Civil Engineering
Julien and Tjahjono (2009)
Business Process Management Journal
Barraza et al. (2009)
The TQM Journal
Song et al. (2009)
Int. J. Services and Standards
Piercy and Rich (2009)
International journal of operations & production management
Castle and Harvey (2009)
International Journal of Productivity and Performance
Management
Fischman (2010)
Quality Management in Health Care
Wang and Chen (2010)
Total Quality Management & Business Excelence
Delgado et al. (2010)
Journal of Manufacturing Technology Management
Laureani et al. (2010)
International Journal of Productivity and Performance
Management
Van Leeuwen and Does (2010)
Quality Engineering
Radnor (2010)
Journal of Manufacturing Technology Management
Laureani and Antony (2010)
International Journal of Productivity and Performance
Management
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.149
Diana Isabel Pinto Pereira,
Paulo S. A. Sousa, Maria R. A. Moreira
E-ISSN: 2224-2899
1697
Volume 20, 2023
Suárez-Barraza and Ramis-Pujol (2010)
Journal of Manufacturing Technology Management
Grove et al. (2010)
Leadership in Health Services
LaGanga (2011)
Journal of Operations Management
Larsson et al. (2011)
Production, Planning & Control
Karstoft and Tarp (2011)
Insights into imaging
Bonaccorsi (2011)
Journal of Service Science and management
Doman (2011)
Quality Assurance in Education
De Souza and Pidd (2011)
Public Money & Management
Staats et al. (2011)
Journal of Operations Management
Malladi et al. (2011)
International Journal of Business Information Systems
Nepal et al. (2011)
Engineering Management Journal
Mazzocato et al. (2012)
BMC health services research
Cheng and Chang (2012)
Total Quality Management & Business Excelence
Jaca et al. (2012)
Total Quality Management & Business Excelence
Bortolotti and Romano (2012)
Production Planning & Control
Psychogios et al. (2012)
International Journal of Quality & Reliability Management
Chadha et al. (2012)
Clinical Governance: An International Journal
Mazur et al. (2012)
Engineering Management Journal
Kumar et al. (2013)
International Journal of Productivity and Performance
Management
Di Pietro et al. (2013)
Total Quality Management & Business Excelence
Chiarini (2013)
Leadership in Health Services
Balazin and Stefanic (2013)
International Journal of Services and Operations Management
Radnor and Johnston (2013)
Production Planning & Control
Bhat et al. (2014)
International Journal of Productivity and Performance
Management
Drotz and Poksinska (2014)
Journal of Health, Organisation and Management
Mazzocato et al. (2014)
Journal of Health, Organisation and Management
Gutierrez-Gutierrez et al. (2016)
International Journal of Lean Six Sigma
Haddad et al. (2016)
Engineering Management Journal
Salam and Khan (2016)
International Journal of Services and Operations Management
Ratnayake and Chaudry (2017)
International Journal of Lean Six Sigma
Antony et al. (2017)
Total Quality Management and Business Excellence
Antony et al. (2018)
International Journal of Productivity and Performance
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.149
Diana Isabel Pinto Pereira,
Paulo S. A. Sousa, Maria R. A. Moreira
E-ISSN: 2224-2899
1698
Volume 20, 2023
