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Exploring the role of human factors in Lean Management
Abstract
Purpose - Although lean popularity is rapidly growing, its implementation is far from
problem-free and companies may experience difficulties in sustaining long-term success.
In this paper, it is argued that human factors, affected by the implementation of both hard
(defined as technical and analytical tools) and soft (concerning people and relations) lean
practices, play a key role in achieving long-term superior performance.
Design/methodology/approach - The analysis of the relationships between lean
practices implementation, human factors and operational performance is carried out in a
single exploratory case study. A retrospective approach is adopted to trace the changes of
human and operational performance before, during and after the introduction of lean
management implementation. In particular, a mixed method concurrent design merging
results of a qualitative analysis with data collected from a survey is selected to provide a
more realistic benefit in the exploration of the proposed research questions.
Findings - The results show a short-term direct relationship between the implementation
of lean practices (hard and soft) and physical work environment and job characteristics,
directly connected to operational outcome. In the long term, operational performance is
influenced by employee behaviour outcome, conditioned by physical work environment
and job characteristics with the mediating effect of individual characteristics.
Practical implications - The proposed model supports the building of a roadmap for lean
management implementation, taking into account the role of the human factors to achieve
superior operational performance. Moreover, it helps managers implement a monitoring
system to check how social and technical components of a lean management system
evolve over time. Finally, it supports the definition of specific training programs, tailored
for specific workers’ types.
Originality/value - This paper extends theory in lean management, highlighting how
hard and soft lean practices have to interact to enable a successful lean management
implementation.
Keywords: Lean Management; Human factors; Lean practices; Physical work
environment characteristics; Job characteristics; Employee behaviour outcome.
1 Introduction
The philosophy and practices associated with lean management (LM) have been around
for many years, and have been applied in a number of settings (Netland and Powell, 2016)
for the systematic elimination of waste (muda), i.e., everything that does not contribute
to the added value of a product, under the perspective of customer’s needs and
requirements (Womack et al., 1990). The philosophy underlying a LM approach, named
lean thinking, relies on five main principles (Womack and Jones, 1996): i) define what
values for the customer; ii) identify the value stream; iii) make the value flow; iv)
customer “pulls” the value stream; and v) strive for perfection. LM is considered by
managers as a means to improve multiple dimensions of operational performance,
including production cost reduction and speed, quality, dependability and flexibility
improvement (Belekoukias et al., 2014).
While there are many lean success stories, a number of real cases failed to achieve
superior performance by applying a lean approach (Ballé, 2005). Several scholars
discussed the causes of this lack of success, many of which are indicative of problems
that arise in the human element during a changeover to lean (Sawhney and Chason, 2005;
Veech, 2004). The application of lean tools without a simultaneous strive for a lean mind
is not enough for a conventional organisation to become a lean firm (Liker, 2004): a
balanced and holistic system view that emphasises the role of persons delivering value to
customers in improving corporate performance is needed (Bicheno, 2004). Therefore, a
focus on human factors involvement and motivation is imperative when implementing a
lean approach: “Focus on the people and the results will follow. Focus on the results, and
you’ll have the same troubles as everyone else - poor follow-up, lack of interest, no
ownership of improvements, diminishing productivity” (Mann, 2014).
Extant literature concerning the role of human factors in LM applications is
fragmented, with partial and conflicting claims emerging from operations management
(OM) researchers, ergonomics scholars, sociologists, and psychologists (de Treville and
Antonakis, 2006; Hasle et al., 2012). Previous studies recognised the importance of the
human aspect of a LM system (Agarwal et al., 2013; Arezes et al., 2015), considering
both the management and the workforce (Sawhney and Chason, 2005): people are, at the
same time, at the centre, the source and the main drivers of lean applications. However,
while LM proponents argue that lean workers show an intrinsically motivated behaviour
and appear more productive, leading to improved operational performance, LM
opponents suggest that workers operate in limiting and alienating conditions that create a
dependent and deskilled workforce (de Treville and Antonakis, 2006). Only a few studies
empirically support such hypotheses (Longoni et al., 2013; Farris et al., 2009; Saurin and
Ferreira, 2009; Parker, 2003; Jun et al., 2006), with a mixture of both positive and
negative results. Therefore, the debate about relationships between LM implantation and
human factor elements is still open and require further analyses.
Moreover, most of the studies focusing on the impact of LM implementation on
operational performance are constrained to facets of lean, often limited to specific
technical tools (Cua et al, 2001), while human aspects of lean implementation are often
neglected or partially considered (Yang et al., 2012).
Finally, as indicated in literature the introduction of lean approaches often does
not provide companies those long-term operational benefits promised in the literature
(Bhasin, 2012; McLean et al., 2015). As LM is a philosophy that requires long-term
commitment to achieve better results (Bhasin and Burcher, 2006), considering the
temporal progression of a lean implementation emerges as essential to sustain operational
performances in the long term. However, researchers are restricting their studies to short-
term data instead of longitudinal data collection (Jasti and Kodali, 2014). Therefore, in
order to fully understand the LM system, researchers have to give equal priority to the
longitudinal data collection method.
In summary, due to the lack of appropriate models encompassing all the
dimensions related to the role of human factors in LM applications considering a
longitudinal viewpoint, two decades of discussion have yielded little progress. Thus, a
comprehensive theoretical model is necessary to analyse the complex network of
variables both “acting upon” and “experienced by” the employees (Genaidy and
Karwowski, 2003). Such variables should include physical, cognitive, organisational,
economic, technological, and social parameters, and must be analysed and examined with
respect to work outcomes to uncover the best human performance practices.
In such a context, this paper aims at contributing to filling this gap by exploring
the effects of lean practice implementation on human-related factors (physical and
psychological human aspects) as well as the effects of human-related factors on
operational performance in the short and long term, adopting a retrospective perspective,
considering both worker and management standpoints.
The paper is organised as follows. Section 2 presents the theoretical background
of the study and the research conceptual framework. In Section 3, the research
methodology will be described and the empirical study carried out in a manufacturing
company will be presented. In Section 4, research findings and implications are presented.
Finally, the last section closes the paper with the most relevant conclusions and future
research directions.
2 Theoretical background and research framework
As argued by Shah and Ward (2007), LM is “an integrated socio-technical system whose
main objective is to eliminate waste by concurrently reducing or minimizing supplier,
customer, and internal variability”. It is a multi-dimensional approach that encompasses
a wide variety of management practices, working synergistically and mutually
reinforcing. In particular, LM practices have been defined by the same authors (Shah and
Ward, 2003) as “tools for creating a streamlined, high quality system that produces
finished products at the pace of customer demand with little or no waste”. Despite the
wide range of LM practices available to companies for LM implementation, the selection
of appropriate lean tools, together with their applicability, incorporation and acceptance
within operations remains the major problem for many companies (Herron and Braiden,
2006). Moreover, as reviewed by Larteb et al. (2015), lean conceptualisation varies
considerably among studies in terms of practices considered. Nevertheless, a recognised
approach in the study of LM implementation distinguished practices between hard and
soft (Fotopoulos and Psomas, 2009). As defined by Bortolotti et al. (2015), hard practices
refer to “technical and analytical tools introduced to improve production systems” (i.e.,
statistical process control or Kanban), while soft practices are related to “principles,
managerial concepts, people, and relations” (i.e., continuous improvement, top
management leadership, customer and supplier involvement). In Table 1, the main hard
and soft LM practices are reported.
Despite the implementation of hard practices has been demonstrated being a
strong predictor of lean contribution to operational performance improvements, soft
practices are essential for obtaining greater outcomes (Bortolotti et al., 2015): the efficacy
of hard practices is magnified when soft practices are simultaneously and systematically
applied (Rodriguez et al., 2016).
<Insert here Table 1>
According to the socio-technical system theory (Hasle et al., 2012; Hallgren and
Olhager, 2009), lean will produce the greatest increases in performance when both social
and technical components are addressed. To guide the investigation reported in this paper,
ergonomics literature has been reviewed and used to build the research conceptual model.
In particular, the broad definition of ergonomics proposed by the International
Ergonomics Association (IEA) has been employed: “Ergonomics (or human factors) is
… concerned with the understanding of interactions among humans and other elements
of a system, …in order to optimize human well-being and overall system performance.”
(IEA, 2000). This definition has several implications: i) ergonomics has both social and
economic objectives; ii) ergonomics takes into consideration both physical and
psychological human aspects; and iii) ergonomics looks for improvements in both
technical and organisational domains (Dul and Neumann, 2009). Therefore, the
implication of lean on physical and psychosocial work environment as well as on
performance improvement are hereafter described and modelled into a research
conceptual model that will guide the empirical investigation.
2.1 Physical work environment characteristics
Physical features of a workplace environment, defined as the characteristics of the
physical space in which work is done (Vischer, 2007) and describing all material objects
and stimuli that employees interact with in their working lives (Elsbach and Pratt, 2007),
have been proved to influence health and safety, comfort, and job satisfaction of the
people within it (Dawal and Taha, 2006; Kahya, 2007; Nazari et al., 2012; Dianat et al.,
2016) due to the continuous and dynamic interaction between the employees and their
surrounding environment (Parsons, 2000). Salient factors within the physical work
environment that may impact on employees can be divided into several broad areas
(Elnaga, 2013; McCoy, 2002; Vischer, 2007):
Workplace safety – Lean tools are argued to positively impact workplace safety,
in particular by implementing 5S methodology, visual management tools, poka-
yoke solutions, standard work approaches (Kamishibai) and problem-solving
methods (Hafey, 2009). A safe workplace can reduce workers’ stress and fatigue,
and increase their productivity. Reducing injuries means reducing operating costs,
improving operator’s commitment, and enhancing corporate’s image (Aqlan et
al., 2013).
Workplace spatial arrangements (Layout) – Lean employees work in a U-shaped
cell layout, with all machines necessary to produce a product or a family of
products (Aase et al., 2004). The U-shaped layout is able to decrease work
monotony (Miltenburg, 2001) as team members could adopt different and flexible
work patterns or cell operating nodes (Arezes et al., 2015).
Workplace cleanliness – Workplace cleanliness is created by removing waste
from the work area (Hutchins, 2007). In particular, Seiso (2nd s of the 5S
methodology) requires cleanliness in the workplace (Gapp et al., 2008), which is
everyone’s responsibility. A clean environment has a positive psychological
effect on human beings. People will be happy to work in a clean environment and
their motivation may be raised. If this happens, productivity will increase as a
result (Makhbul, 2013). Moreover, some products may require clean
environments in order to uphold product quality. If contamination happens due to
uncleanliness in the workplace, products will be rejected or reworked with a
negative effect on productivity.
Ambient properties (i.e., noise, temperature, air quality, and vibration). – In work
areas in which thermal comfort, appropriate illumination, pleasant sound and
comfortable air quality are enhanced, work performance may be encouraged
(McCoy, 2002). On the contrary, several studies have demonstrated that
inappropriate ambient properties are environmental stressors, with a negative
effect on worker morale, productivity, and health (Vischer, 2007). In literature,
no studies on the effect of LM on ambient properties have been found.
Functional comfort – Functional comfort addresses how effective the workplace
is in helping users perform their tasks, being a tool to help get work done. In a
lean context, the workplace is explicitly designed to minimise unnecessary and
risky workers’ motions (Womack and Jones, 1996), in accordance with a human-
centred system design approach (Gill, 2012). When the workstation is designed
from an operator’s perspective all the material and tools used during a work shift
should be placed inside the comfort zone to aid the operator to work as efficiently
as possible, aiming at “working smarter, not harder” (Eswaramoorthi et al., 2010).
To reduce unnecessary wear on the human body, ensuring good ergonomics, and
generate increased productivity and safety, materials and tools most frequently
used should be placed as close as possible to the operator, as prescribed by Seiri
and Seiton practices included in the 5S methodology (Finnsgård et al., 2011; Gapp
et al., 2008).
2.2 Job characteristics
According to the job characteristics model (JCM) introduced in 1976 by Hackman and
Oldham, and refined by Parker and Wall (1998), a job is characterised by five core
dimensions:
Skill variety is defined as “the degree to which a job requires a variety of different
activities in carrying out the work, which involves the use of a number of different
skills and talents of the person” (Hackman and Oldham, 1976). In a lean context,
lean practices implementation results in an increase in skill variety if workers are
involved in problem-solving activities, training programs, and job rotating
policies (de Treville and Antonakis, 2006).
Task identity is defined as “the degree to which the job requires completion of a
“whole” and identifiable piece of work; that is, doing a job from beginning to end
with a visible outcome” (Hackman and Oldham, 1976). As argued by de Treville
and Antonakis (2006), LM implementation is positively related to task identity if
the worker is aware of his/her tasks’ contribution to the whole product
manufacturing process. On the contrary, a flexible allocation of tasks to workers
and the removal of boundaries between functions, as indicated by a lean approach,
cause a decrease in task identity, preventing the workers from producing a whole
identifiable piece of work (Mehta and Shah, 2005).
Task significance, considered “the degree to which the job has a substantial impact
on the lives or work of other people, whether in the immediate organisation or in
the internal environment” (Hackman and Oldham, 1976), is reduced in
organisational environment characterised by a low degree of technical uncertainty
(i.e., of the amount of information processing and decision making required when
executing a task) (Mehta and Shah, 2005).
Autonomy is “the degree to which the job provides substantial freedom,
independence, and discretion to the individual in scheduling the work and in
determining the procedures to be used in carrying it out” (Hackman and Oldham,
1976). While several scholars (de Treville and Antonakis, 2006; Genaidy and
Karwowski, 2003) state that autonomy is positively affected by LM
implementation in situations where workers actively participate in the decision-
making process, other studies (Parker, 2003; Hasle et al., 2012; Mehta and Shah,
2005) found strong evidence of a negative effect of lean LM on job autonomy, as
most of the workers follow imposed rigid work patterns, which tend to limit their
freedom to decide on their work.
Feedback, defined as “the degree to which carrying out the work activities
required by the job results in the individual obtaining direct and clear information
about the effectiveness of his/her performance” (Hackman and Oldham, 1976), is
improved by LM applications (de Treville and Antonakis, 2006). Feedback in LM
allows prompt response to deviation from the target, production improvement as
well as increase in accountability (Mehta and Shah, 2005).
2.3 Employee behaviour outcome
Employee behaviour was formalised in ‘80s by introducing the Individual Behaviour (IB)
concept, defined as the attitude and actions or deeds of an individual working in an
organization (Arnett, 1988). IB is affected by three factors, namely job satisfaction,
commitment, and job stress, that represent the main components of the Employees’
Affective Attitude (EAA) construct (Rodwell et al., 1998). Usually, employees develop
different attitudes towards their work depending on their working environment and job
characteristics (Monge and Poole, 2008). As reviewed by Parker (2003), a mixture of
both positive and negative consequences of LM implementation on employee behaviour
outcomes is reported in the literature:
Job satisfaction is defined as the “extent of satisfaction an employee extracts
while performing the assigned task” (Muchinsky, 2006). Job satisfaction relates
to job characteristics such as monotony, type of work, control over the work, as
well as the working environment. Both negative and positive consequences have
been described in several studies (Lipińska-Grobelny and Papieska, 2012), or at
least a mixture of both negative and positive results (Jackson and Mullarkey,
2000). According to Rodriguez et al. (2016), in a lean environment, employee job
satisfaction can be improved when human resource practices (e.g., teamwork,
praise for higher performance, regular feedback, training, focus on customer
value, empowerment, and better understanding of individual tasks and the
customers’ needs) are implemented.
Job commitment is the “extent to which an employee feels attachment or
trustworthiness towards an organization” (Muchinsky, 2006). In the literature
review of lean production from 1999 through 2006 conducted by Hasle et al.
(2012), studies have reported both negative and positive effects of lean production
on organisational commitment.
Job stress is a feeling of discomfort related to factors such as time deadlines and
anxiety, which may lead to degraded performance (Parker and DeCotiis, 1983).
Job stress is one of the most cited negative effects of LM on workers’ outcome,
especially when considering JIT methods (Koukoulaki, 2014 Conti et al., 2006;
Hasle et al., 2012).
2.4 Operational performance
As extensively reviewed by Belekoukias et al. (2014), extant literature attributes a wide
range of operational benefits to the implementation of LM philosophy and practices,
including production cost reduction and speed, quality, dependability and flexibility
improvement. In particular, when measuring the direct impact of lean practices on
operational performance, different authors have tried to connect and reflect the combined
effect of these practices into one indicator (Bayou and de Korvin, 2008), now popularly
known as “leanness”. It includes (Belekoukias et al., 2014): i) quality; ii) speed; iii)
dependability; iv) flexibility; and v) cost.
Moreover, several disagreeing studies have recently been published about the
existence and strength of the relationship between employee behaviour outcome and
operational performance in a LM context. Bowling (2007) suggested that this relationship
is spurious. In contrast, a number of scholars (Riketta, 2008; Mehta and Shah, 2005; de
Treville and Antonakis, 2006) proposed that a significant relationship between these two
variables does in fact exist. On the basis of these mixed results, additional examination
of the relationship between employee behaviour outcome and performance is necessary
(Rodriguez et al., 2016).
2.5 Building a complete perspective
As previously argued, the inclusion of both technical and human elements in a LM
approach produces the greatest improvements in performance. However, the theoretical
background earlier presented demonstrates that existing literature provides limited
evidence on how the two elements interact and how are they are interconnected. This
research addresses this gap by exploring the impact of hard and soft lean practices on job
and physical work environment characteristics and how these characteristics influence
the employee behaviour outcome, intended as the direct element acting upon the
operational performance of the system. Specifically, the paper aims to answer the
following main research question: How do lean technical and human elements influence
operational performance?
The initial conceptual model that underpins this research is depicted in Figure 1.
<Insert here Figure 1>
Table 2 provides definitions for the six constructs included in the conceptual
model, as well as information on their operationalisation.
<Insert here Table 2>
3 Research methodology
The paper is based on an in-depth single exploratory case study conducted in company
Alpha, a medium manufacturer of draft dispensing equipment for beer, wine, water and
other soft drinks. The company has recently expanded its production plants and has
introduced a multi-year lean manufacturing project to continuously advance its
production efficiency, drive out waste and increase the productivity of the whole supply
chain.
Although multiple case studies are considered to build more robust theory (Yin,
2009) due to their potential for triangulation, single case studies may be useful for
longitudinal research (Voss et al., 2002), in order to determine patterns efficiently.
Indeed, by using longitudinal studies, it is possible for researchers to learn more about
cause and effect relationships and make connections in a clearer manner.
The present study relies on a mixed methods concurrent design (Driscoll et al.,
2007). Mixed methods research is formally defined as “the class of research where the
researcher mixes or combines quantitative and qualitative research techniques, methods,
approaches, concepts or language into a single study” (Johnson and Onwuegbuzie, 2004).
Qualitative methods included open-ended interviewing, observation, participant
observation, and analysis of responses to open-ended items on a survey questionnaire
(Kaplan and Duchon, 1988). Quantitative methods were employed to collect and analyse
data from survey questionnaires. Compared to mono-method research, mixed methods
research frequently results in superior research due to its methodological pluralism or
eclecticism (Johnson and Onwuegbuzie, 2004) and provide pragmatic advantages when
exploring complex research questions (Driscoll et al., 2007). The qualitative data provide
a deep understanding of survey responses, and statistical analysis can provide a detailed
assessment of patterns of responses. Moreover, a retrospective approach was adopted to
trace the changes in the LM socio-technical system and in its operational performance. In
particular, three time periods were identified: 1) before the introduction of LM; 2) first
phase of LM implementation (2011-2013); 3) second phase of LM implementation (2014-
2016). Only employees who experience all the three phases were included in the sample.
In particular, based on the research conceptual model previously presented, two
research protocols were developed including both open-ended and scaled-response
questions. Scaled-response questions were answered on a 5-point Likert-scale. The first
research protocol, covering the Hard Lean Management Practices and Soft Lean
Management Practices as well as the Operational Performance constructs, was addressed
to top managers to analyse the implementation level of LM practices and how it varied
throughout the years, after the implementation of the first LM project. The second
research protocol was addressed to workers to assess the changes introduced by LM
activities in job and physical work environment characteristics and employee behaviour
outcome. In the past, a limited number of studies considered bot the managerial and
worker perspective (Genaidy and Karwowski, 2003). In both protocols, questions about
respondent’s general information (years of experience in Alpha, age, gender, education,
main activity, type of contract, participation in lean practice implementation) were
introduced.
Based on responses from Alpha’s employees at a single lean site, the authors
explored the relationships between the technical and human elements of a LM system and
their impacts on operational performance.
4 Data analysis and results
A logic model approach was used to analyse the data, consisting of “matching empirically
observed events to theoretically predicted events” (Yin, 2009). The research conceptual
model presented in Figure 1 represents a preliminary theoretical expectation of how the
implementation of lean practices impacts on job and physical work environment
characteristics, how these characteristics influence employee behaviour outcome and how
the employee behaviour outcome affects operational performance. During the analysis,
data was compared to the research conceptual model, through pattern matching
investigation, also exploring rival or alternative models (Yin, 2009). The final model
represents new theory that builds on both the initial research conceptual model and
alternative explanations suggested by data analysis.
4.1 Demographic details
Eighty-one operational workers and five managers (representing the totality of Alpha’s
employees) were interviewed. Table 3 presents demographic and job characteristics of
the study participants. All study participants had a normal 8 h work shift. The age of
participants ranged between 20 and 58 years, of which about half of them (n = 42; 49%)
aged 35–47 years. The job experience of the respondents ranged between 6 and 30 years.
34% of the participant had primary school education, while 48% had secondary education
and 18% had university degrees.
<Insert here Table 3>
4.2 Statistical data analysis
First, for each item, responses were analysed using frequency distribution analysis, mean
and median values. Since all the items were evaluated using an ordinal scale (1 to 5 Likert
scale) and the analysis referred to the comparison of repeated measurements on a single
sample overtime, a Wilcoxon signed rank-test (with a p-value of 0,05) was identified as
the right test to verify statistically differences of the achieved values between different
periods (Table 4). Afterwards, achieved results combined with findings from interviews
were used to support the general discussion, as presented in the following sections.
<Insert Table 4 >
4.3 Lean Manufacturing implementation in Alpha
Alpha is an owned family business company specialized in supplying complete
dispensing equipment such as towers, cooling units, accessories and spare parts for beer,
wine, soft drink and water. Thanks to its management dedication in investing in
innovation and flexibility, the company is present worldwide, and serves the major iconic
brands in the beverage field in more than 110 countries.
The ability to promptly face the challenges of the market and satisfy new
emerging customers’ needs with high-performance and state-of-the-art technological
solutions is one of the intrinsic characteristics of this company success.
In 2009, also due to the effect of the world economic crisis, Alpha felt the
necessity to rethink its strategy and strengthened its identity. In 2010 the company
decided to change the way of managing its production processes adopting lean
management principles to reduce customers’ dissatisfaction due to delays in products
delivery, its inability to properly control and manage the costs related to the process and
high quantity of work-in-progress materials. In particular, Alpha’s top management spent
its first LM year to attend conferences, workshops and training courses to better
understand the main challenges and opportunities coming from the adoption of a lean
thinking philosophy.
The first application in production was launched in January 2011 (first phase:
2011-2013). Supported by an external consultant, it concerned of a pilot project focussed
on a physical re-organisation of an existing assembly line. The project aimed at helping
Alpha management and staff get a deep understanding of how lean strategy has to be
developed day-by-day as well as to acquire the basic and fundamental knowledge
necessary to expand the application of lean principles to the other production lines.
Indeed, one year later the company decided to extend its lean management project,
replicating the methods previously implemented in the pilot case to a second line and
introducing in the first line material handling optimisation techniques. The
implementation of the lean management hard practices was completed on the first line
during the third year of the project, thanks to the introduction of approaches to improve
equipment availability, flexibility and process quality. At the same time, some material
handling optimisation techniques were applied on the second line while a third lean
project was launched in a third assembly line.
The fourth year marked an important turning point for the Lean Management
implementation in Alpha (second phase: 2014-2016). Indeed, it is from this year that
Alpha started to emphasise the strategic role of lean culture through the implementation
of specific techniques and methods devoted to enhancing continuous improvement and
involvement within the organisation at every level (from management to operators) across
the value chain from suppliers to customers. Moreover, the company started to launch the
implementation of lean paradigm in other departments including research and
development, sales and marketing and administration.
4.4 LM practice implementation
As summarised in Tables 5.a and 5.b, Alpha started the implementation of lean hard
practices mainly focussing on the development of specific techniques and methodologies
to support process efficiency, with an explicit interest in equipment layout (mean +2,73
and median +3,00) and material handling (mean +2,67 and median +3,00). In particular,
the main actions were carried out to enhance the efficiency of the assembly lines, through
the adoption of 5S standard, the introduction of innovative layouts enabling a single-piece
production organization and the implementation of a kanban scheduling approach
supported by a “milk-run” material handling system (Mizosumashi), an Hejunka planning
system and the definition of job standards (Kamishibai). The second phase of the lean
manufacturing implementation was characterised by an acceleration of the physical re-
organisation (mean +0,80 from 2,73 to 3,53 and median +1,00 from 3,00 to 4,00) and
material handling logic (mean +1,40 from 2,67 to 4,07 and median +1,00 from 3,00 to
4,00). In particular, the re-layout was extended to the whole organisation, while the
introduction of a material handler speeded up the transformation towards a flow
production system. During this phase Alpha also focused on pursuing efficiency and
effectiveness by improving equipment availability (autonomous maintenance: mean
+1,20 from 2,30 to 3,50 and median +1,00 from 2,50 to 3,50) and process quality
(statistical process control: mean +1,00 from 2,32 to 3,32 and median +2,00 from 2,00 to
4,00), through the introduction of Total Productive Maintenance, and Total Quality
management tools and Andon boards. Moreover, JIT delivery by suppliers and set-up
time reduction techniques (SMED) were further introduced to extend the Kanban system
upstream and to gain more flexibility.
Several soft practices were also applied to ease the success of lean
implementation. As shown in Table 5.a, the first of LM implementation was characterised
by a strong focalisation on improving top management leadership (mean +2,30 and
median +2,00): project goals and values were set, adequate resources and funding were
provided to establish a system to properly plan, manage and measure process performance
(Hoshin Kanri). Moreover, necessary mechanisms to enable a cultural change across the
whole organisation were implemented through the development of training programs
involving the whole organisation (mean +2,57 and median +3,00). During the second
phase, Alpha extended its focus towards the establishment of a lean culture at the
operational level, translating strategic goals into day-by-day actions. This goal was
achieved by applying techniques and methods to implement structured practice routines
(Kata pattern) involving employees, customers and suppliers in a continuous
improvement loop (Plan-Do-Check-Act cycle).
4.5 Physical work environment characteristics
In average, physical work environment characteristics improved with the implementation
of LM practices (Table 5.b), with a greater advance during the second phase, in particular
for ambient properties (mean +0,55 from 3,28 to 3,83 and median +1,00 from 3,00 to
4,00) and workplace layout (mean +0,42 from 3,07 to 3,49 and median +1,00 from 3,00
to 4,00). During the first phase, workplace safety received the highest progress (mean
+0,36 from 3,49 to 3,85) while ambient properties downgraded (mean -0,16 from 3,44 to
3,28 and median -1,00 from 4,00 to 3,00).
4.6 Job characteristics
All the job characteristics improved during the first and the second phase (Table 5.b),
with a higher degree during the second phase. Specifically, during the first phase
modifications on job characteristics mostly affected the degree of skill variety required to
carry out the work (mean +0,38 from 2,89 to 3,27) and the establishment of feedback
loops towards both the operators and the managers (mean +0,29 from 2,64 to 2,93). On
the contrary, job autonomy did not change. During the second phase, a clearer
identification of the “piece of work” to be carried out by the workers was obtained (task
identity: mean +0,33 from 3,09 to 3,42), as well as more freedom, independence, and
discretion to the individual in scheduling the work and in determining the procedures to
be used in carrying it out (mean +0,31 from 3,46 to 3,77). Also in this second phase, skill
variety range was enlarged (mean +0,30 from 3,27 to 3,57 and median +1,00 from 3,00
to 4,00).
4.7 Employee behaviour outcome
As reported in Table 5.b, during the first change employee behaviour outcome worsened:
job stress increased (mean +0,66 from 2,77 to 3,43 and median +1,00 from 3,00 to 4,00)
and job satisfaction decreased (mean +0,41 from 3,56 to 3,15 and median -1,00 from 4,00
to 3,00). However, during the second phase, conditions improved: a higher job
satisfaction was revealed (mean +0,47 from 3,15 to 3,62 and median +1,00 from 3,00 to
4,00) and stress level was reduced (mean -0,23 from 3,43 to 3,20 and median -1,00 from
4,00 to 3,00). Job commitment increased in both phases, with a greater degree during the
second change. Nevertheless, commitment raise characterising the first phase of the lean
project is not statistically validated.
<Insert Table 5.a>
<Insert Table 5.b>
4.8 Operational outcome
Table 6 discusses the effects of changes introduced by LM on operational performance.
Positive values indicate an improvement in the performance indicator, negative values
represent a decline in performance. All the values reported in Table 6 are positive,
demonstrating that LM contributes to achieving superior performance. However, the size
of improvement varies among the operational indicators and between phase 1 and 2.
<Insert here Table 6>
In particular, the introduction of LM practices during the first phase brought the
largest improvements in all the indicators (+2,6 in average). During the second phase,
only speed shown a small advance (+ 1,0).
5 Discussion of results
Quantitative data, combined with semi-structured interviews with both operators and
managers, support the discussion of results and the definition of relationships between
the selected variables.
The effect of LM practices on physical work environment characteristics and job
characteristics. As shown by data analysis, during the first and the second phase of the
lean project development the level of both soft and hard practices adoption increased. At
the same time, an improvement occurred in all the physical work environment and job
characteristics items, except for the ambient properties, which level reduced even though
such variation cannot be considered statistically significant. In conclusion, data suggest
that the implementation of hard and soft LM practices may lead to positive effects of
physical and organisational working conditions. However, quantitative data analysis does
not provide any support to understand how hard and soft practices can act on work
environment and job characteristics. Differently, interesting considerations in this sense
can be extrapolated from qualitative analysis. Indeed, interviews suggest that physical
work environment characteristics are mostly affected by the implementation of hard
practices: for example, the introduction of techniques aiming at arranging the equipment
layout for a production continuous flow (i.e., 5s), combined with a kanban system, had a
positive effect on workplace safety and cleanliness.
“First of all, we decided to focus our attention to making our collaborators more
confident on lean management initiatives, by providing cleaner and safer working
stations.” (Lean Manager)
“Actually, sometimes I wish I do not have to clean my working station as
requested (especially at the end of a long working day), but I know that such activity is
fundamental for maintaining a good feeling and they also pay me to do it!” (Operator).
However, in the second phase, the positive effect of hard practices was amplified
by a higher degree of worker’s involvement in continuous improvement activities. The
greatest effects were observed in the workplace layout, designed by the operator itself,
and in the ambient properties, defined on the basis of operator’s perceived comfort. Such
consideration further explains why the variation of the two aforementioned parameters
was not supported by statistical validity in the first part of the project.
“The self-organisation of the working station, in accordance with the lean
management standards, is the natural consequence of our idea about employees’
engagement. We do believe that each operator should have the opportunity to take part
of the continuous improvement process playing an active role in all those initiatives that
Alpha has launched to improve the production plant configuration and organisation.”
(Lean Manager)
“Thanks to the opportunity to organise my working station as I desire, I feel as I
was at ‘home’. I can work better and with less stress.” (Operator)
From a qualitative point of view it can be also argued that job characteristics are to a large
extent related to the implementation of lean soft practices. More specifically, the
interviews found evidence of a relationship between training activities and the degree of
skill variety: through cross-training programmes, supported by a job rotation policy,
workers are trained on all the different tasks, duties, and responsibilities related to a
specific work cell or work area. Cross-training provides workers with a clear
understanding of the entire team function and the multiple tasks within the cell.
“People improve their competences and thus their contribution to our operations
performances only when they have the opportunity to apply their ideas. I do prefer a
failed project due to a wrong application than zero results because of no proposals.”
(General Manager)
“I’m so happy for being part of this lean manufacturing training program. Since
I have been involved, I have felt more confident in what my work consisted in and on
which are the implications of my day-by-day decisions on my colleagues’ work.”
(Operator)
A positive relationship between management leadership and the implementation
of feedback loops can be also identified: for example, with daily gemba walks (“go to the
place where people create value”) managers support operators keeping track of how work
is progressing, rapidly identifying problems where and when they occur, and facilitating
conversations about where it could improve.
“Before this project, I used to solve problems from my office’s desk. Now I spend
the majority of my working day together with the operators. It takes time, but the
performances have increased a lot, indeed!” (Production Manager)
“This project gave me the opportunity to collaborate directly with my boss and to
make my opinion worth.” (Operator)
The intensity of such relationships grows if hard methods for job organisation
(i.e., practices for continuous flow equipment layout and Kanban system - including 5s,
Mizosumashi and Kamishibai) are implemented, with a positive effect on task identity
too. Indeed, job autonomy and significance grew (with a statistical significance) during
the second phase when worker’s involvement in continuous improvement activities was
implemented, demonstrating that theoretical training programmes create the bases for the
improvement but are not sufficient to make the change real.
In conclusion, we can state that the implementation of hard LM practices leads to
improved physical work environment characteristics (to a larger extent than soft
practices) and to improved job characteristics (in particular when considering equipment
layout for continuous and Kanban practices). Moreover, the implementation of soft LM
practices leads to improved job characteristics (to a larger extent than hard practices) and
improved physical work environment characteristics (in particular when considering
involvement in continuous improvement practices).
The effects of physical work environment and job characteristics on employee behaviour
outcome. As underlined by data analysis, physical work environment and job
characteristics’ trends may differ from the employees’ behaviour outcomes tendency.
Indeed, during the first phase of LM project, a reduction of employees’ satisfaction and
an increase of their stress happened. At the same time, a negligible (and not statistically
significant) increase of commitment occurred. On the contrary, during the second phase
of the lean project, the increase in physical work environment and job characteristics
corresponded to a significant improvement of job satisfaction, commitment and to a stress
reduction. This result suggests that employee behaviour outcome may be influenced by
other factors, such as the type of practices adopted and their level of implementation, as
also underlined by the interviews. Indeed, during the first phase of the changeover to lean,
the employee behaviour outcome worsened mostly due to changes introduced in job
characteristics. Improvements in the physical work environment did not seem able to
compensate job characteristics modifications, demonstrating that workers give greater
importance to the way their tasks are organised than the environment they work within
(excluding safety aspects, addressed as primary important dimension). In particular,
having a larger range of skills and feedback information about how work is progressing
could cause, in the first place, a lower job satisfaction and a higher stress.
“After implementing lean methodologies my working conditions have improved a
lot! My work is still heavy, anyway! Indeed, my activities have become easier but I have
much more responsibilities and different task to perform.” (Operator)
“Before lean, I was involved in a specific production phase. It was so boring but
much easier. Now I have a wider view of the whole assembly process, making additional
tasks in other assembly lines sometimes. Next month I will start a new training program
to learn how to conduct set-up and simple maintenance activities. I like it but it is so
stressful.” (Operator)
A higher involvement in continuous improvement activities, placing at the centre
of the change the worker as an active agent of the change and not as a passive element,
improves job autonomy and job significance, with positive consequences on workers in
terms of higher commitment, reduced stress and greater satisfaction.
“Finally, I feel part of this company and a key contributor to its results.”
(Operator)
“I’m so happy because the process of change that this company is experiencing
is also due to my contribution.” (Operator)
In addition, it was found that the human responses to the job characteristic factors
depend on individual differences. However, this consideration is only qualitative as not
statistically demonstrated, due to the sample size.
The higher the age and the years of experience in the company of the respondents
the lower their satisfaction and commitment towards LM, whilst a direct
relationship with stress emerges. Results underline the existence of a direct
relationship between lean and cultural change and that lean approach encounters
major difficulties if implemented in organisations where the capabilities that have
been acquired over time may limit management engagement in adaptive change.
Different types of commitments emerge considering the educational level of
respondents, their age, their work experience and the type of contracts. In
particular, people with a primary school educational level subject to short-term
contracts present the highest level of commitment in doing activities. Young
people with a very short experience, are characterised by a high level of
commitment to taking operational decisions, while people with a high educational
level declare having a very strong commitment in creating new solutions. Such
results underline how lean manufacturing facilitates cooperation at any level of
the organisation, eliminate cultural barriers, and stimulate vertical and horizontal
information sharing.
Satisfaction and commitment are higher in those departments where specific lean
projects were developed. Moreover, people involved in training activities and lean
projects declare a major satisfaction and commitment. Conversely, workers who
did not participate in any lean project appear dissatisfied and not happy about the
company. This result underlines how an effective communication system could
avoid internal dissatisfaction especially for workers who were not involved in lean
initiatives.
In conclusion, we can state that improved physical work environment and job
characteristics lead to employee behaviour outcomes. In particular, improved job
characteristics lead to employee behaviour outcome (to a larger extent than physical work
environment characteristics), with the moderating role of individual characteristics.
The overall effect on operational outcome. Data analysis indicate that the relation
between operational outcomes and work environmental characteristics, job characteristics
and employee behaviour can change overtime. Indeed, while operational outcomes
increased during the first phase, they remained the same during the second phase of the
project, characterised by an increase of work environmental and job characteristics as
well as employee behavioural outcome. Discussion with managers and employees
showed that in the short term, higher operational performances were achieved through
changes in physical work environment and job characteristics. For example, investments
in methods for creating a continuous flow and in the establishment of a Kanban system
improved workplace characteristics that, in turn, contributed to achieving higher speed
and volume flexibility. Considering job characteristics, a higher level of skill variety
directly influenced flexibility results. In addition, qualitative analysis suggests that work
environmental and job characteristics influence operational outcome in the short-term.
Moreover, changes in operational outcome were observed also in the long term as indirect
influence of physical work environment and job characteristics changes, acting through
employee behaviour outcome modifications, demonstrating that better employee
behaviour outcome will lead to improved operational outcome in the long term.
From the discussion of results, an updated model, that reflects results, could be drawn
(Figure 2).
<Insert here Figure 2>
6 Conclusion
Relationships between the social and technical components of a LM system are complex
and determine the overall system performance in both the short and the long term. In this
paper, a comprehensive model that includes and connects technical, physical, cognitive,
organisational, social and performance variables related to the implementation of a LM
approach is proposed. The research model variables were identified through an extensive
multi-disciplinary literature review, while critical insights about how these variables are
connected were empirically gained through an in-depth single exploratory case study.
Both qualitative and quantitative methods were used to build empirical evidence. Results
show a direct relationship between hard and soft practices implementation and physical
work environment and job characteristics, that, in turn, directly impact on operational
outcome in the short term. Hard practices have a higher impact on physical work
environment characteristics, while soft practices mostly affect job characteristics. In the
long term, operational performance is influenced by employee behaviour outcome,
conditioned by physical work environment and, mainly, by job characteristics with the
moderating effect of individual characteristics.
This model can help practitioners build a roadmap for a LM implementation,
considering the role of human factors to achieve superior operational performance. Based
on the roadmap, an implementation monitoring system could be developed to check how
the social and technical components of the LM system evolve over time. Moreover, the
identification of the moderating effect of individual characteristics on the relationship
between job characteristics and operational outcome could support the definition of
specific training programs, tailored for specific workers’ typologies.
As a single case study, a generalisation appears clearly limited. However, the
purpose here is the theoretical generalisation in a model that in the future should be tested
to verify statistical significance of propositions and to assess the relative impacts of each
measurement variable and construct, also considering a longer timeline. Several
extensions of the model could be proposed:
Evaluation of the effect of rewarding systems on employee behaviour outcome
and employee operational outcome;
Inclusion of middle and top manager behaviour outcome and evaluation of its
effect on operational outcome;
Expanding the performance construct to other types of performance, in line with
a triple bottom line approach (economic, environmental and social).
Evaluation on how the evolution of technology and in particular smart
technologies can lead to the amplification of benefits for company operations as
well to influence workers’ environmental (physical and psychological
conditions), thus influencing their behavioural outcome.
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