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OPERATIONS, INFORMATION & TECHNOLOGY | RESEARCH ARTICLE
The influence of performance objectives on the
implementation of lean manufacturing practices:
An analysis based on strategic groups
Hamilton Pozo, Orlando Roque da Silva and Takeshy Tachizawa
Cogent Business & Management (2017), 4: 1405718
Pozo et al., Cogent Business & Management (2017),
4: 1405718
https://doi.org/10.1080/23311975.2017.1405718
OPERATIONS, INFORMATION & TECHNOLOGY | RESEARCH ARTICLE
The influence of performance objectives on the
implementation of lean manufacturing practices:
An analysis based on strategic groups
Hamilton Pozo
1
*, Orlando Roque da Silva
2
and Takeshy Tachizawa
3
Abstract:This paper explores how the choices about, and implementation of, lean
production practices are influenced by performance goals prioritized by firms in the
context of operations strategy. We analyzed a set of 56 companies (divided into four
strategic groups) in the auto parts industry in the cities of Campinas and Jundiaí
areas (Brazil). These groups of firms that adopt similar strategic orientations were
used to investigate the relationship between the implementation of lean manufac-
turing practices and the choice of performance objectives. The results suggest that
taking into consideration strategic groups can improve the understanding of how
performance objectives define lean manufacturing practices adopted by manufac-
turing companies.
Subjects: Manufacturing & Processing; Operations Management; Operations Research;
Business, Management and Accounting
Keywords: operations strategy; strategic group; lean manufacturing; performance
objective; competitive strategies
*Corresponding author: Hamilton Pozo,
CEETEPS, Faculty of Technology Santos,
Fatec Rubens Lara, CEETEPS, Rua Mario
Carpenter, 3 - Cj. 62, Santos 11055260,
SP, Brazil
E-mail: hprbrazil@hotmail.com
Reviewing editor:
Shaofeng Liu, University of Plymouth,
UK
Additional information is available at
the end of the article
ABOUT THE AUTHOR
Hamilton Pozo, holds a PhD of Business
Administration from California C. University and
degree of Mechanical Engineer. He has more than
25 years working as a professor and research in
Brazil. He was Director of Operations and Strategic
Plan in several companies. He helps students to
embark the glorious career path in the field. This
paper is a part of his project on performance
improvement in productivity and quality
manufacturing auto parts companies in Brazil.
Orlando Roque da Silva has a degree in
Business Administration and a PhD in Production
Engineering. The author has experience in the
area of Operations Strategy and Innovation
Management, working mainly on the following
topics: advanced manufacturing, innovation
systems and systems dynamics.
Takeshy Tachizawa has a degree and PhD
in Business Administration. The author has
experience in the area of Operations Strategy and
Sustainable Management, working mainly on the
following topics: Operation and management
system, Supply Chain Sustainable.
PUBLIC INTEREST STATEMENT
Lean production, competitive forces are not static
but dynamic, and therefore manufacturers of
auto parts and vehicles should not only rely on
excellence in production, but mainly because
increasing dierences in operational performance
between competitors become and brings together
new forms of organization. The main novelty in
this paper are extensive eciency improvements
in production strategies when applying lean
manufacturing system combining with strategic
group. The utilization of presented process
could have the advantages of competitive and
acceptability in industrial production process. The
results suggest that taking into consideration
strategic groups can improve the understanding
of how performance objectives define lean
production practices adopted by manufacturing
companies. Understanding these eects can
improve the understanding of how performance
objectives define this practice adopted by
manufacturing companies to lead it to success.
Received: 07 September 2017
Accepted: 12 November 2017
First Published: 03 January 2018
© 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution
(CC-BY) 4.0 license.
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Hamilton Pozo
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1. Introduction
Accordingly, Slack and Lewis (2009) state that operations strategy can have a heavy impact on busi-
ness competitiveness, not only in the short term but also in the long run. The dilemma is that, when it
comes to speeding up operations using distributed resources throughout companies, the impacts are
dicult to identify in their entirety. This is the paradox of operations strategy, which is at the heart of
the management of companies’ strategic intentions and practices and plays a vital role in the success
of organizations, but it is so comprehensive that it becomes easy to underestimate its importance.
The structure and competitive strategies of the auto parts industry have undergone profound
changes in recent years, mainly due to the diusion of the complex automotive production model of
lean manufacturing. However, we understand that competitive forces are not static but dynamic,
and therefore manufacturers of auto parts and vehicles should not only rely on excellence in produc-
tion, but mainly because increasing dierences in operational performance between competitors
become and brings together new forms of organization, new management practices, and an inten-
sive use of automated equipment (Holweg & Pil, 2004). The central pillars determining these chang-
es include restructuring practices of automakers and the relationships between these and their
suppliers that it is crucial to define involvement among them, and considering involvement as “an
act of sharing the activities of a group” (Webster’s, 2009, p. 711). Accelerating the process of product
innovation and the creation of trade blocs.
The adoption of this model of production through the deployment of lean production practices has
contributed to improvements in the operating performance of many companies but has also created
some frustration (Womack, Jones, & Roos, 2004). The following questions arise:
• Should lean production be considered an operations strategy in the auto parts industry if it does
not always reach the performance levels expected when deploying these practices?
• What is the relationship between the implementation of lean production practices and perfor-
mance improvements?
This paper examines how the implementation of lean production practices can influence the operat-
ing performance of companies in the auto parts industry. As the deployment of these practices is
rarely quantified using a cross-section data type (Cua, McKone, & Schroeder, 2001), we used a quan-
titative approach supported by non-parametric statistics linked to the concept of strategic groups.
According Bozarth, Warsing, Flynn, and Flynn (2009), strategic groups have received more attention
in research on operations strategy since Porter (2008) focused on these in his book Competitive
Strategy. The utility of strategic groups manifests itself where there are many competitors, in that it
facilitates conclusions in analyses of industrial sectors. However, in these analyses, precision is lost
since the focus is on what companies have to be liking to put them in strategic groups. We lose the
level of detail in what makes each company dierent. Nonetheless, the benefit is that we can better
understand what happens in industries by focusing only on strategic groups.
2. Literature review
2.1. Operations strategies
A vast literature already exists on strategies and operations. For this paper, we consider both the
most recent publications and some older classics on the analysis of operating strategies. Initially
developed by Skinner (1969) and most recently refined by Hayes and Wheelwright (1984), Platts and
Gregory (1990), Slack and Lewis (2009), this literature seeks to show that there is no single optimal
path for companies operating within their resources, as Henry Ford once believed (Morgan, 2007).
The two central elements of this framework are the competitive priorities and decision categories
within which the array of decisions that make up production strategies have to be made (Hayes &
Wheelwright, 1984). This basic structure for operations strategy detailed in 1984 is still used in
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research by Boyer and Lewis (2002), Hayes, Pisano, Upton, and Wheelwright (2005). There is a high
degree of agreement that operations strategies focus on competitiveness in cost, quality, delivery,
and flexibility (Dangayach & Deshmukh, 2001).
Operations strategy, however, is changing from a “market-based” to a “resource-based” vision.
The first view sees operations as a perfectly adjustable system and focused on successfully following
the rules dictated by markets, while the second view suggests that it is more profitable to focus on
developing, protecting, and leveraging operational resources of companies when seeking competi-
tive advantage. The study also suggests to the researcher’ s world that researchers should propose
various frameworks along with implementation steps to adapt the particular framework in
organizations.
Most of the models are concentrating only on the manufacturing area rather than on the whole
company. It is evident that most of the models belong to the category of the lean production pro-
cess and, therefore, it becomes necessary to develop models in relation to the lean process. However,
Anand and Kodali (2010) proposed a model that allows implementing the lean system specific to
any type of organization. Several methods and approaches exist such as computer simulation, sta-
tistical analysis, lean tools for improving the eciency and productivity by determining the best
combination of resources in production lines, construction process, energy, services, and supply
chains (Zahraee, Hashemi, Abdi, Shahpanah, & Rohani, 2014).
Also, when studying and investigating the production of specific phenomenon of the company-
specific production system, it has been a strong and recent trend across many manufacturing indus-
tries to develop and deploy such a corporate improvement programmer (Netland, 2015).
This paradigm shift began with evidence that high performance can be mainly explained by the
strength of the resources of a company and not by the strength of its market position (Rumelt, 2011;
Wernerfelt, 1984). The resource-based view has gained more importance since Prahalad and Hamel
(1995) emphasized the link between core competencies and competitiveness. Unfortunately, the
application of these concepts in real business strategies may be insucient (Hayes et al., 2005).
Even today, it is dicult to find companies that use operations functions as a competitive weapon.
One reason is the diculty of “operationalizing” the content of operations strategy.
Although the theory of a resource-based perspective has a clear call, there have been studies on
advantages based on resources within a more general, network context, extending the theory of
resource-based viewpoint further. This view assumes that extended strategic resources that are
outside companies emphasize inter-firm relationships. An example of this is the development of
Toyota’s highly ecient supply network (Slack & Lewis, 2009).
Decisions in operations strategy, according to Slack and Lewis (2009), consider a set of areas—
such as capacity, supply chains (including procurement and logistics), process technology, organiza-
tion development—familiar to managers in a wide variety of operations. Researchers involved in
manufacturing futures surveys have suggested that actions rather than decisions should be includ-
ed within operations strategy. Also, it is possible to a simple tool for identifying strategic objectives
as part of the design of strategy maps, based on the balanced scorecard, and meant to be used in
organizations to establish performance indicators (Bellisario, Appolloni, & Ranalli, 2015).
The use of lean production practices within operations strategy represents both decisions and ac-
tions and, therefore, can be an important part of company standards, although lean production
practices may not necessarily cover all aspects that make up the decision areas suggested by Slack
and Lewis (2009). For example, questions about location are not extensively described in the
literature on lean production, and these are not a part of practices suggested in later research.
Still, the strategic model of operations is a means by which companies should be able to
improve their internal and external processes, which should lead to improved performance
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(Bozarth & McDermott, 1998). Slack and Lewis’s (2009) model of decision areas and performance
objectives is an appropriate benchmark with which to analyze the implementation of lean
production.
2.2. Lean production practices and performance objectives
Lean production system consists of a set of powerful tools that assisted in the identification and
steady elimination of waste such as value stream mapping, warehouse keeping, single minute ex-
change of die and standardized work (Chopra, Kalra, & Jawalkar, 2014). Several publications, books,
and periodicals cover the concepts of the lean production system helping the popularization of its
philosophy and since then, the lean concept has become an area of academic interest due to its dis-
semination in a wide range of industries and countries, particularly in operations management ac-
cording to Marodin and Saurin (2013). Some recent studies show that the literature review on lean
production becomes important and according to Stentoft Arlbjørn and Vagn Freytag (2013) who
carried out a deeper research of the literature on lean production presented the impact of this pro-
cess in various sectors of the industry as well as its impact on the productivity of the industry. The
research of Moyano-Fuentes and Sacristán-Díaz (2012) showed the impact that lean production has
on the processes with the application of LP principles in all industrial sectors around the world.
The conceptual framework of lean production used in this research is represented by Figure 1. This
is a simplified version of lean operation (manufacture), where companies are grouped into strategic
groups according to their competitive priorities in the market.
Many papers have been published since the 1990s on the relationship between lean production
practices and performance (Dangayach & Deshmukh, 2001). Generally, it is believed that just-in-time
(JIT) practices lead to shorter lead times and lower inventories, and practices of total quality manage-
ment (TQM) lead to improved quality. Empirical studies show that this relationship is not always in fact
true. In recent years we have seen the development of more research on lean production as a concept,
in order to validate or refute claims about lean production practices and performance objectives.
Cua et al. (2001) mention some studies that consider the main pillars of lean production to be JIT,
TQM, and total productive maintenance (TPM) working together. In the last years, several research
eorts summarized in the literature review indicate how lean production show significant improve-
ments by being energy ecient and using best practices this production system reduce wastes
(Bergmiller, 2006). In the last 10 years, many researches have been published on lean production
Figure 1. Conceptual lean
production.
Source: Adaptation of Hines,
Holweg, and Rich (2004).
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showing its importance and the results obtained. Marodin and Saurin (2013) in their research on the
subject observed similar results (improvement of productivity, motivation of employees, and com-
petitiveness) inhis research study.
While researchers recognize the value of investigating these interrelated practices simultaneously
(JIT, TQM, and TPM), there are few studies that provide an empirical examination of the joint imple-
mentation of TQM, JIT, and TPM practices (Wakchaure, Nandurkar, & Kallurkar, 2011). Based on a
literature review of practices considering all three pillars of lean manufacturing (TQM, JIT, and TPM),
notably, TQM is quite broadly defined, encompassing relationships between product design, suppli-
ers, and customers, while JIT and TPM have more specific features. Performance objectives, there-
fore, reflect traditional competitive priorities, such as quality, cost, delivery on time, flexibility to
allow changes in volume.
Wakchaure et al. (2011) analyzed practices that best explain performance dierences in firms.
This was done on two levels: on sets of practices (TQM, JIT, TPM, and common practices) and indi-
vidual practices. The results showed that JIT, TPM, and TQM were significant in explaining the rela-
tionship between lean production and performance objectives. On the practice level alone, not all
practices contributed to explaining this relationship. Hence, in a conclusion relevant to this work, the
researchers found that it is more appropriate to consider the three pillars of lean production (JIT,
TPM, and TQM) together to understand better how they are influenced by performance goals, when
these are given priority.
2.3. Strategic groups
A strategic group is formed by grouping together companies operating with similar strategies.
Industry analysis done with the concept of strategic groups assumes that a given company is not in
competition with every other company with the same intensity. Generally, an industry consists of
several strategic groups, which include companies that have similarities across multiple strategic
dimensions, such as degree of specialization, which refers to the extension of product lines; brand
image—usually based on advertising and a sales force; and the choice of distribution channels,
whether companies’ own or other generalist or specialist distributors. Other dimensions include
product quality, in terms of raw materials, specifications, and so on; the technological domain,
whether companies mimic or lead in adopting new technologies; degree of vertical integration; posi-
tion in terms of costs; extent of additional services oered, such as technical assistance; pricing poli-
cies; and relationships with public authorities, which may be reflected in obtaining grants or
submitting the firm to regulations.
The formation of strategic groups is related to companies’ ownership of dierent resources and
capabilities, which enable some of them to make specific investments in mobility barriers. Metternich,
Bechtlo, and Seifermann (2013) suggested that the eective and ecient clustering of machine or
cell is improved by moving employees, workstations, or both into a U-shaped line which improve the
employees’ interaction that include training problem, process problem solving, training on continu-
ous improvement tools and technique, development of idea management and development of re-
ward and recognition system. Continuous improvement depends on employee perception,
adaptation, team work, leader engagement, motivation, initiative, and training.
Companies are likely to adopt dierent strategies even when they have the same features and
capabilities, if they have dierent preferences as to how to make investments and position them-
selves in relation to risk. Another factor that explains the dierences between business strategies is
the historical evolution of industries, since the cost of adopting a strategy tends to be lower for the
first companies in an industry. As industries develop, barriers to mobility are strengthened by exog-
enous causes or as a result of investments made by already established companies.
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3. Material and methods
The conceptual framework used in this research is represented by Figure 2. This is a simplified ver-
sion of operations strategy, where companies are grouped into strategic groups according to their
competitive priorities.
Two relationships were investigated, one being the relationship between strategic groups and
operational performance and the other, relationships between strategic groups and degrees of im-
plementation of lean production pillars (as summed scales). Due to time limitations in this paper, the
connections between practices of lean manufacturing and operational performance are not exam-
ined directly but instead dealt with by constructing relationships through strategic groups. The call
is then addressed indirectly.
3.1. Data analysis and sample
In this research target companies were categorized based on a group of 56 companies located in
two cities, Campinas and Jundiai (Brazil), from March to October 2012. These companies are auto
parts manufacturers divided into five industries: metallurgical, mechanical processing, plastics, ma-
chinery and equipment, and electrical and electronic. Data analysis in quantitative research is char-
acterized as numerical descriptive inferential analysis. In this way, the descriptive analysis was
initially developed, proceeding with the modeling of structural equations. The software used for data
analysis was BioStat version 5.3.
This research is a cross-sectional study that was conducted in person with direct interviews. It
means that various companies have been asked about the same things and measures. Moreover,
data collection only conducted through in person. To motivate the companies contributed in this
survey, we’ll share the results of this research as promised to all respondents.
The questionnaire consisted of four categories of questions: contextual issues, questions about
competing priorities, practicalities and issues related to performance goals, and both current perfor-
mance objectives and performance objectives of the past five years. The performance objectives
were considered to be cost, quality, reliability, speed of delivery to the customer, time to market
entry of new products, value added per employee, design/innovation, product features, product va-
riety, and customization of products.
The questionnaire focus is to identify the pillars lean production practice and the performance
objectives. The first step was designed to evaluate and map the current state of companies in
Figure 2. Conceptual
framework.
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relation to the lean production particle in relation to performance objectives and their relations. To
evaluate this relationship, Cronbach’s alpha was used to estimate the reliability of the questionnaire
used in this research. These indicators can be seen in Table 1. Accordingly, some questions were
prepared based on lean principles and their corresponding indicators.
According with research by Wanderley, Meira, Souza, and Miranda (2003), the companies were
divided into four strategic groups among a number of competing priorities assigned to each of them
(Groups GE-A, GE-B GE-C, and GE-D). These priorities are: cost, quality, reliability, speed of delivery,
time placing product on the market, design and innovation, product features, variety of products
and customized products.
To test the survey and find out whether it implies the intended purpose of it, final version of survey
was tested by one private consultant in lean manufacturing in Brazil as well as the project supervi-
sor. Consequently, some small changes were made to improve the survey quality.
Table 1. Pillar of lean production and their indicators in this research
Source: Research data.
Pillar of lean production Indicators lean manufacturing practices
JIT 1. Production processes
2. Reduction in cycle time
3. Agile manufacturing
4. Rapid techniques and tools
5. Production systems focused on the factory
6. JIT production flow/continuous
7. Pull system/Kanban
8. Bottleneck/removal restriction
TPM 1. Autonomous maintenance
2. Planning and scheduling of maintenance
3. Preventive or predictive maintenance
4. Program improvements in safety
TQM 1. Formal programs of continuous improvement
2. Software quality management
3. Total quality management
4. Measures of process capability
5. Benchmarking
TECH 1. Planning systems and advanced programming
2. Enterprise resource planning systems
3. Finite capacity scheduling
4. Demand management/forecast
RCLI 1. Continuous program replenishment
2. Customers participate in product development
3. Evaluation of industrial plant by clients
4. Survey of customer satisfaction
RFOR 1. Major suppliers based on JIT deliveries
2. Stocks managed by supplier
3. Suppliers commit themselves to reducing costs
4. Suppliers involved in development of new products
CFOR 1. Program for certification of suppliers
2. Suppliers evaluated based on total cost
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4. Analysis and results
In this study, we used Cronbach’s alpha to estimate the reliability of the questionnaire used in this
research. We measured correlations between questionnaire responses by analyzing answers given
by respondents, looking at average correlations between questions. The software used was BioStat
version 5.3 for data analysis.
The general rule used was that the existing scales should exceed a Cronbach’s alpha level of 0.70.
This proved to be the case for the three pillars considered: JIT, TPM, and TQM. Compared to Cua et al.
(2001), the JIT and TPM pillars have the same content, while the third pillar was divided into TQM itself,
client relations (RCLI), supplier relations (RFOR), and supplier certification (CFOR) for this study—al-
though RCLI and RFOR presented values below the minimum Cronbach’s alpha of 0.70. The pillar “tech-
nology” (TECH) is also a pillar of lean production, per se, and it was included to check the influence of
technology on lean production practices. These interrelated pillars and practices are shown in Table 2.
Table 2. Pillars of analysis and their lean production practice
Source: Research data.
Pillar of lean production Lean manufacturing practices Cronbach’s α (aC)
JIT (a
C
= 0.826) 1. Production processes 0.610
2. Reduction in cycle time 0.571
3. Agile manufacturing 0.742
4. Rapid techniques and tools 0.733
5. Production systems focused on the factory 0.708
6. JIT production flow/continuous 0.658
7. Pull system/Kanban 0.754
8. Bottleneck/removal restriction 0.523
TPM (a
C
= 0.717) 1. Autonomous maintenance 0.679
2. Planning and scheduling of maintenance 0.601
3. Preventive or predictive maintenance 0.904
4. Program improvements in safety 0.748
TQM (a
C
= 0.720) 1. Formal programs of continuous improvement 0.570
2. Software quality management 0.794
3. Total quality management 0.885
4. Measures of process capability 0.667
5. Benchmarking 0.617
TECH (a
C
= 0.681) 1. Planning systems and advanced programming 0.630
2. Enterprise resource planning systems 0.741
3. Finite capacity scheduling 0.832
4. Demand management/forecast 0.678
RCLI (a
C
= 0.641) 1. Continuous program replenishment 0.771
2. Customers participate in product development 0.712
3. Evaluation of industrial plant by clients 0.606
4. Survey of customer satisfaction 0.701
RFOR (a
C
= 0.742) 1. Major suppliers based on JIT deliveries 0.730
2. Stocks managed by supplier 0.595
3. Suppliers commit themselves to reducing costs 0.773
4. Suppliers involved in development of new
products
0.720
CFOR (a
C
= 0.619) 1. Program for certification of suppliers 0.680
2. Suppliers evaluated based on total cost 0.572
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Before describing the data analysis is necessary to present the results of another analysis that led
to the formation of strategic groups. Four strategic groups were identified, all significantly dierent
in their most important competitive priorities. The companies received 100 points to distribute
among a number of competing priorities, and this was the basis for the identification of groups. This
process has been suggested a somewhat dierent way by Hill (2000) and used by Berry, Hill, and
Klompmaker (1999) with success. The strategic groups are named based on its competitive priorities
considered most important, as shown by Table 3 and Graphic 1.
This result is similar to research by Wanderley et al. (2003), entitled priorities competitive in the
strategic management of manufacture: a study in Pernambuco State (BR) processing industries. This
study presents the competitive priorities in Pernambuco processing industry, where they were sur-
veyed 101 industrial processing companies with over 200 employees and listed in the Industrial
Registry of Pernambuco (IRP).
Thus, the strategic group A (GE-A) has a very high focus almost exclusively on cost and quality to
a lesser extent, accordingly, the delivery rate and variety of products. Competitive priority time to
introduce new products on the market has little influence in this strategic group. These results point
to low value-added products that dispute the market almost exclusively based on the selling price.
Table 3. Classification of performance objectives in strategic groups
Source: Research data.
Competitive priority Strategic groups
GE-A GE-B GE-C GE-D
Cost
Average 46.3 18.0 18.2 3.8
Classification 1 3 2 4
Quality
Average 14.1 36.2 20.3 18.4
Classification 4 1 3 2
Reliability
Average 10.9 26.2 30.1 9.8
Classification 3 2 1 4
Speed of delivery
Average 11.4 7.3 20.1 3.2
Classification 2 3 1 4
Time placing product on the market
Average 4.8 24.4 5.9 6.3
Classification 4 1 3 2
Design and innovation
Average 8.8 12.8 5.1 41.9
Classification 3 2 4 1
Product features
Average 8.9 15.2 5.9 17.5
Classification 3 2 4 1
Variety of products
Average 10.9 15.1 6.2 20.1
Classification 3 2 4 1
Customized products
Average 8.9 14.1 8.7 15.7
Classification 4 2 3 1
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The strategic group B (GE-B) and strategic group C (GE-C) has an emphasis on quality and delivery
reliability, but dier on the time to introduce new products on the market (GE-B is dominant) and
speed in delivery (GE-C is dominant). Design and innovation has little influence on the GE-C, while in
Group B, the speed of delivery is the least of priorities. These results point to regular supply of items
for automakers, hence the need to prioritize the quality, reliability, speed in delivery and the intro-
duction of new products on the market.
The strategic group D (GE-D) has a competitive priority to meet the feature (esthetic) in their prod-
ucts. This is a new perspective, where customer and subjectivity of style and fashion changes can
heavily influence the performance of the company. This fact is consistent with the cost being the
lowest priority. Firms in this group emphasize the esthetic appearance of the cost. The variety of
products, quality and product features also influence but to a lesser degree than the design and the
innovation. Thus, the group gives low priority to the cost and the lead time to introduce new prod-
ucts on the market.
After analysis, the results point to an apparent inconsistency within the GE-D when to prioritize
design and innovation the lead-time factor for market entry appears, also as important. Is with
Schumpeter (1978) calls the temporary monopoly, or is, the advantage gained by being the first to
put the innovative product to other companies to copy the idea and launch similar products. As the
GE-D does not prioritize this time he loses the temporary monopoly advantage.
All tests for dierences between the strategic groups were non-parametric. Parametric tests as-
sume, among other things, normal populations groups and homogeneity of variance. In practice,
these conditions are based on the central limit theorem, which normally requires the use of many
cases (Virgillito, 2006). Since this study used a small amount of cases, the assumptions for paramet-
ric tests are not necessarily applicable, which is why we used non-parametric tests.
One of the tests used was the Kruskal-Willis test, non-parametric, used to compare three or more
samples. It was used to test the null hypothesis—that all populations have identical distribution of
functions—against the alternative hypothesis that at least two of the populations have dierent
distribution functions. This test was performed and revealed that cost, quality, delivery reliability,
speed of delivery, design and innovation, and product characteristics dier significantly between
groups. The time to put the product to the market, the product variety and customization of products
were not significantly dierent between groups.
Thus, the Kruskal-Willis test showed that the strategic groups dier significantly from each other.
The Mann–Whitney test, conducted later, also showed that the groups dier in their main competi-
tive priorities, i.e. price for the GE-A, line quality and delivery reliability for the GE-B, delivery reliability
and speed delivery to GE-C and finally design and innovation for the GE-D.
Graphic 1. Classification of
performance objectives in
strategic groups.
Source: Research data.
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4.1. Strategic groups and performance
When we analyzed the role of strategic groups in choices of priority performance goals, a further
question appeared: Do the strategic groups alone explain choices of performance goals?
To answer this question, it was necessary to show statistically significant dierences between the
groups and then turn our attention to a more qualitative assessment. Table 4 shows the statistical
results. Strategic group A presents the lowest value added per employee of the four groups. Strategic
group D has the highest value added per employee. However, as revealed in Table 4, strategic group
D is in a vulnerable position, because, for this group, the cost of guarantees, rate of rejection by cus-
tomers, and production costs increase significantly more than for the other groups. Indeed, the
other groups showed decreasing values for these three measures.
One possible explanation for strategic group D being quite dierent from the others may be the
fact that growing numbers of customers are becoming more demanding with respect to the design
of products. The fact of this strategic group showing an overly low yearly added value per employee
indicates that it has a low contribution margin, probably due to price competition. Therefore, this
group has to focus on lowering costs, so cost is a priority. Group A has a well-defined strategic choice
of performance goals when compared with other strategic groups, as shown in Table 5. The group
Table 4. Significant dierences in performance between groups with strategic application of Mann–Whitneya
Source: Research data.
aThe letters in parentheses indicate the strategic group that has the greatest value, where there is no other significant dierence in value.
Items GE-A×GE-B GE-A×GE-C GE-A×GE-D GE-B×GE-C GE-B×GE-D GE-C×GE-D
Value added per employee in the
year
0.056 (B) a 0.054 (C) 0.164 (D) – – –
Warranty cost – – 0.011 (D) – – 0.018 (D)
Rate of rejection customer – – 0.068 (D) – – 0.153 (D)
Cost of production (without
materials)
– – 0.020 (D) – 0.043 (D) –
Cost of production (with materials) – – 0.091 (D) – 0.241 (D) 0.072 (D)
Table 5. Scoring for classification of competitive priorities related to performance objectives
Source: Research data.
aPoints are based on the rating that each group receives for the strategic performance measure in question.
Items GP-A GP-B GP-C GP-D
Cost 1. Scrap and rework 1
a
3 2 4
2. Cost of warranty 1 3 2 4
3. Cost of quality 1 3 2 4
4. Inventory turnover of raw materials 1 3 2 4
5. Inventory turnover of goods in process 2 3 4 1
6. Inventories of finished goods 2 4 1 3
Average 1.63 3.13 2.00 3.25
Quality 7. Finished product with no rework 2 3 1 4
8. Defect rate in plants 3 1 4 2
9. Rejection ratio per customer 2 1 3 4
Average 2.33 1.67 2.67 3.33
Reliability 10. Delivery on time 4 2 1 3
Average 4 2 1 3
Speed of delivery 11. Lead time of purchase 3 4 2 1
12. Lead production team 4 3 2 1
13. Lead sales team 4 3 2 1
Average 3.67 3.33 2.00 1.00
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also gives a high degree of importance to quality, but its importance is not as evident as other per-
formance goals.
The prioritization of cost and quality performance objectives by strategic group A osets its deliv-
ery speed and reliability. Strategic group B places a higher degree of priority on quality, but, overall,
this group has the worst score. This group emphasizes quality and reliability, which is also reflected
in the choice of lean production practices. Strategic group C has its prioritization better distributed in
its total score, but it puts an emphasis on speed of delivery and reliability. This strategic group shows
a high degree of external fit, so much so that it is able to deliver what the market wants quickly and
reliably.
Finally, strategic group D’s prioritization shows evidence of good speed of delivery, but, overall,
this group does not emphasize any of the performance objectives that have a direct relationship to
practices of lean production, as shown in Table 5. Quality alone seems to have a high degree of im-
portance, but, in reality, this is a consequence of prioritization of design and innovation. In the Table
4, shows that strategic group D has a significantly worse outcome than other groups regarding rejec-
tion rates per customer. The data analysis shows that its rate of rejection, unlike other groups that
focus on action, is due to poor acceptance of its products’ design.
This can lead to the conclusion that an adjustment is needed in choices of lean production prac-
tices in this group. However, this strategic group is new in the context of determining research op-
erations. It also has a strong emphasis on multifunctional performance goals. Therefore, it should
not be judged solely on the basis of the degree of priority given to performance goals. Nonetheless,
an analysis of the performance objectives of this group indicates that it needs to improve its choices
in the future if these companies want to be able to sustain a high value added per employee.
4.2. Strategic groups and implementation of pillars of lean production
To analyze the implementation of pillars of lean production by strategic groups, we based this on the
results shown in Table 1 and the degree of implementation of these pillars in dierent groups.
Various tests to measure the dierences between groups were performed. First, we ran a
Kruskal-Willis test for dierences between the groups and then performed a Mann–Whitney test for
dierences between the pillars, seen individually and group to group. Finally, using the Wilcoxon
test, we looked at whether the deployment of the pillars of lean production is dierent in each group.
The results are shown in Table 6 and Graphic 2.
Table 6. Degree of implementation of lean production practices in strategic groups
Source: Research data.
aNumbers in brackets refer to the lean production practices adopted, as shown in Table 1.
Group strategic JIT TPM TQM TECN RCLI RFOR CFOR
GE-A – (1, 2, 3, 4)
a
−1, 2 −2, 4 −4 −1, 3 −2
Average 2.915 3.362 2.942 2.310 2.694 2.433 2.914
Classification 1 1 1 4 3 1 2
GE-B (3, 5, 6, 7) −1 (3, 4, 5) −1, 3 −3, 4 −1
Average 2.440 2.898 2.711 3.280 2.822 2.087 2.953
Classification 4 2 2 1 2 3 1
GE-C (1, 2, 3, 4) – – – – −1, 2 –
Average 2.875 2.803 2.693 2.769 2.884 2.066 2.564
Classification 2 3 3 3 1 4 3
GE-D −1, 4 −4 −2 −4 −2, 3 −4 –
Average 2.813 2.810 2.197 2.805 2.486 2.258 2.205
Classification 3 4 4 2 4 2 4
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The Kruskal-Willis test revealed no significance, which indicates that all strategic groups should be
considered as coming from the same population in terms of lean production practices. This finding
is quite dierent when compared with the Kruskal-Willis test’s results on competitive priorities in the
strategic groups. The Mann–Whitney test for dierent degrees of implementation of pillars of lean
production, when applied to the four strategic groups, showed that the other strategic groups dier
from strategic group C in their degree of implementation of TPM and dier from group D in the de-
gree of strategic deployment of TQM.
The Wilcoxon test for dierences in the groups confirmed that the groups emphasize dierent
pillars. For example, strategic group A has a significantly greater degree of implementation of TPM
than most of the other pillars, while strategic group B has a significantly lower degree of deployment
of RFOR. The Wilcoxon test showed that the groups dier in who chooses to apply what, but this dif-
ference is not significant between groups.
Table 6 indicates which groups emphasize the strategic pillars JIT, TPM, TQM, and RFOR. Table 1
shows that lean production practices, including these pillars, generally are favorable for lower and
short-term costs, so these strategic groups reveal a high degree of internal adjustment.
An emphasis on CFOR was expected for group B because it has a strategic focus on quality rather
than cost. Moreover, the implementation of new technologies by this strategic group can be ex-
plained by its emphasis on delivery reliability.
It can also be noted that the most customer-oriented group is strategic group C, which has the
highest score for RCLI. This pillar is mainly the aspect of time in relation to customers and is thus
consistent with the strategic focus of group C. Strategic group B is number two in TQM, while strate-
gic group C is number two in JIT, which is also in line with their goals. Hence, these groups show a
high degree of internal consistency.
The strategic choices of group D are dicult to explain, in part because other practices beyond
pillars of lean production can be extremely important for these companies and we do not have
enough information about these practices. However, based on the data at hand, we can see that this
group emphasizes TECH and RFOR. The first has to do with the use of technologies to develop new
products and the second has to do with relationships with suppliers with respect to lower costs and
shorter delivery times. This seems to be valid when considering delivery problems, but, as Table 4
shows, that group does not prioritize cost or quality. Based on Table 2, it can be seen that this group
does not emphasize good performance in delivery, so this group does not have a high degree of in-
ternal adjustment.
The numbers in parentheses in Table 6 refer to the practices of lean production including TECH,
which is not really a pillar of lean production but is an aid to understanding applications. These data
suggest that strategic group A has more extensively applied lean production practices, followed by
strategic group B, which for some yet unidentified reason is particularly interested in deploying tech-
nological, finite capacity scheduling. The group has deployed fewer lean production practices than
strategic group D.
Graphic 2. Average of degree
of lean production practices in
strategic groups.
Source: Research data.
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The analysis leads us to conclude that strategic groups A, B, and C have implemented lean produc-
tion practices in terms of performance objectives and that prioritized groups are selective about the
pillars that receive greater emphasis. This is most clearly demonstrated by strategic group A.
Strategic group C shows overall good performance, which can be achieved without the implementa-
tion of a series of lean production practices. An analysis of the combination of the operating perfor-
mance of group D with its strategic deployments of lean practices can lead to two possible
conclusions: that these companies are not good at running their operations strategy or some of the
companies included in the analysis do not attribute an important role to strategies for operations.
Given these possibilities, we cannot reach more definite conclusions about that group.
5. Discussion of results
Data analysis showed that the strategic groups dier, both with regard to dierent sets of perfor-
mance goals and to the sets of pillars of lean production that they choose to deploy. The analyses
also indicated that there are links between the deployment of pillars of lean production and the
performance objectives prioritized. For example, the groups showed that the strategic deployment
of the pillars TPM, TQM, and RFOR apparently goes hand in hand with good performance at low cost.
However, a high degree of deployment is not necessary to achieve a satisfactory performance in key
areas, as strategic group C demonstrated. This strategic group has good performance in speed of
delivery but only uses pillars of lean production (i.e. JIT and RFOR) at a moderate level.
According to the present study, which shows the relations between strategic groups with perfor-
mace objectives and the pillars of lean production. The magnificence of strategic group success is
growing day by day because of its positive impact on industrial performance. It is evident that the
result obtained with the results helps similar results presented in papers published in recent years.
The study showed that empirical approaches in recent years on the topic, according to Stentoft
Arlbjørn and Vagn Freytag (2013) is important for competitive organizations. The fundamental bases
of group strategy were implemented at the operational level to improve productivity and reduce
non-value-activities. This is one of the reasons for the research (performance goals and pillars of
lean production) within the group strategy at the operational level that helped companies to im-
prove their performance.
This study provides evidence that more complex relationships between pillars of lean production
and performance goals can be found. Not all pillars are equally important for all performance objec-
tives. Moreover, there is reason to believe that there are relationships between how the members of
each group strategically deploy pillars of lean production and performance objectives that have not
been discovered or understood in depth.
This study shows that the use of strategic groups could help explain how choices of pillars of lean
production and their practices are influenced by the performance goals prioritized by companies to
achieve higher levels of competitiveness. Studies that address performance objectives can gain
more insight by considering strategies for business operations. Given resource constraints, compa-
nies may not want to improve everything all the time: they have to focus their eorts. Strategic
groups can be a valuable tool for understanding the choices that companies must make to achieve
high levels of excellence, assisting these companies in their choice of the necessary lean production
practices.
Manufacturing concepts are being adopted by modern manufacturing organizations to achieve
competitive advantage. Lean manufacturing ensures waste elimination, streamlined processes and
value addition and the research show that and modern manufacturing engineers are required to
understand the importance of strategic groups. The result in Table 3 classification of performance
objectives in strategic groups shows the high focus:
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• Group GB-A on cost and (low value-added products that dispute the market almost exclusively
based on the selling price);
• Group GE-B on quality and place time product on market (has their emphasis);
• Group GE-C on reliability and speed of delivery (speed of delivery and reliability are the
priorities);
• Group GE-D on design and innovation, product features, variety of products and customized
products (firms in this group emphasize the esthetic appearance of the cost. The variety of prod-
ucts, quality and product features also influence but to a lesser degree than the design and the
innovation).
In Table 5, the competitive priorities related to performance objectives, shows the high
performance:
• Group GP-A on cost;
• Group GP-B on quality;
• Group GP-C on reliability;
• Group GP-D on speed of delivery.
The Wilcoxon test looked at whether the deployment of the pillars of lean production is dierent in
each group and the groups emphasize the strategic pillars in Table 6 and shows:
• Group GE-A emphasis on JIT, TPM, TQM, and RFOR;
• Group GE-B emphasis on TECN and CFOR;
• Group GE-C emphasis on RCLI;
• Group GE-D emphasis secondary on TECN and RCLI.
6. Conclusion
This study established connections between the defined groups’ strategic pillars of lean production
and performance goals using cross-sectional data. Dierent strategic groups have dierent perfor-
mance goals and emphasize the application of dierent pillars and lean production practices. In
particular, strategic groups A and D show that they are parting ways. The results are indicative only,
and the sample size is too small to obtain highly significant statistical results. However, the results
summarized in Tables 2–6 indicate that ratings of lean production practices using strategic groups
can produce important results in operations strategy and that there is reason to investigate lean
production practices further in this context.
This study identified a new strategic group, where esthetics and industrial design are given prior-
ity. Several articles have recently been published demonstrating the importance of image, design,
and esthetics in manufacturing companies, as well as how design can influence operations strate-
gies. Within the limitations of this study and the sample, the linkage between pillars of lean produc-
tion and performance goals has been thoroughly explored, as has the role played by lean production
practices.
In general, as Frohlich and Dixon (2001) argue, there is a need to replicate studies. Therefore,
more studies should be conducted in dierent sectors of the economy in dierent geographical re-
gions so that a general picture can be formed of how operations strategies are handled by dierent
strategic groups in their lean production practices, as well as what results are obtained and how
they are aected by prioritized performance objectives.
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Funding
The authors received no direct funding for this research.
Author details
Hamilton Pozo
1
E-mail: hprbrazil@hotmail.com
ORCID ID: http://orcid.org/0000-0001-7990-5010
Orlando Roque da Silva
2
E-mail: orlando.roque@faccamp.br
Takeshy Tachizawa
3
E-mail: usptakes@uol.com.br
1
Faculty of Technology Santos, Fatec Rubens Lara, CEETEPS,
Rua Mario Carpenter, 3 - Cj. 62, Santos 11055260, SP, Brazil.
2
Department of Technological Innovation, Faculdades
Metropolitan Unidas, Av. Santo Amaro, São Paulo 1239,
Brazil.
3
Department of Micro and Small Business, Faculdade Campo
Limpo Paulista, Rua Guatemala, 167, Campo Limpo Paulista
13231-230, SP, Brazil.
Citation information
Cite this article as: The influence of performance objectives
on the implementation of lean manufacturing practices:
An analysis based on strategic groups, Hamilton Pozo,
Orlando Roque da Silva & Takeshy Tachizawa, Cogent
Business & Management(2017), 4: 1405718.
Cover image
Source: Adaptation of Hines, Holweg, and Rich (2004).
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