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Measuring and building lean thinking for value creation in supply chains Rania A.M. Shamah

Measuring and building lean thinking 17

Business Administration Department, Arab Open University, Cairo, Egypt Abstract Purpose – This study aims to develop a standardised instrument to measure the impact of lean thinking on supply chain value. This tool can be used to examine supply chain readiness and thus enhance overall value. It can also observe the potential role of customers, competitors and suppliers in increasing supply chain performance. Design/methodology/approach – A survey of previous studies is undertaken in the Egyptian industrial sector. The study also uses a questionnaire provided across all managerial levels of Egyptian firms. This questionnaire is divided into two main sections: the first section is considered to be about lean thinking stages for waste elimination, namely muri, mura and muda, while the second section relates to the value creation dimensions. Findings – The developed instrument accesses and analyses different types of lean thinking for identifying lean degree in supply chains. Consequently, it could lead to enhancing value creation in supply chains. This explorative study also indicates that the Egyptian industrial sector is willing to go lean. Research limitations/implications – Some limitations exist in this study. First, the survey was conducted on the Egyptian industrial sector. The applicability of the proposed scale should thus be further tested in different countries and service mixtures. Practical implications – Internal resistance is more of a barrier than external (customers, suppliers or competitors) resistance to lean thinking. Thus, organisations should focus first on internal (functional) integration and then move on to interorganisational integration. Further, people are more critical than technology in implementing lean thinking. Originality/value – There is little empirical research on the implementation of lean thinking. Practitioners and researchers should find value in this unique instrument. Keywords Lean thinking, Value creation, Supply chain management, Lean production Paper type Research paper

Introduction Enterprises create value when they implement strategies that respond to market opportunities by exploiting their internal resources and capabilities (Penrose, 1959; Andrews, 1971; Marr and Neely, 2004) and by integrating strategic relationships within key suppliers. Therefore, the relationships between an enterprise and its suppliers have changed radically during the past few years. Firms are increasingly concentrating on their core competencies and externalising conventionally important activities such as manufacturing, design and logistics. This externalisation of value activities relies on the creation of strong supplier partnerships in areas that have high strategic relevance for the enterprise’s customer and thus leads to hierarchical supply chain networks comprising several layers of suppliers. The management of hierarchical supply systems covering industrial components and parts has been studied within both logistics, or supply chain management, and

International Journal of Lean Six Sigma Vol. 4 No. 1, 2013 pp. 17-35 q Emerald Group Publishing Limited 2040-4166 DOI 10.1108/20401461311310490

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business marketing. However, the more complex collaborations necessary when generating innovative products, services or system solutions through a joint value creation process are not clearly understood (Christopher, 1998; Ford et al., 1998; Cooper et al., 1997; Sheth and Sharma, 1997; Moller and Torronen, 2003) between all supply chain parties. Consequently, both product provider and supplier often have to make substantial adaptations and commit vast resources in the development of collaborating supplier relationships (Brennan and Turnbull, 1999; Ford and McDowell, 1999; Brassard & Ritter, 2001; Spekman et al., 2000) such as productivity, TQM and BPM. These efforts reflect the investment character of partnership establishment. Moller and Torronen (2003) address the impact of strategic supplier value creation. Thus, the strategic nature of key supplier relationships makes it essential for the buyer to be able to assess the value creation potential of available suppliers. Hence, lean thinking is a core competence that can reform the supply chain structure, firm positions and organisational functions within the industry. Lean thinking is accepted as a key part of an enterprise’s strategy for long-term manufacturing survival based on removing the waste in current systems, concentrating on adding value that customers pay for and improving product flow in order to increase productivity and reduce lead times (Lee-Mortimer, 2006). Moreover, lean practices focus on a reduction in variability, especially that stemming from improper operating conditions or policies (Gliatis et al., 2012). Nevertheless, lean is seen as representing a clear way to face the increasing challenge posed by low-cost economies (Lee-Mortimer, 2006). In this study, I see lean as a strategic philosophy that leads to added perceived value for all supply chain parties (i.e. stakeholders), while, at the same time, eliminating waste based on the 1950s Toyota Production System (TPS). This system classified three types of waste: (1) Muri. Focuses on what work can be avoided proactively by design. (2) Mura. Focuses on implementation and the elimination of fluctuations in scheduling or preparation level. (3) Muda. Discovered after the process is in place and which deals with reactivity variation in output (Shamah, 2008a, b). Therefore, lean can be conceptualised as a driver for developing supply chain core competence through allocating whole resources that would help in creating value to stakeholders; in other words, increasing the level of performance and enhancing competitiveness. Moreover, I reproduce the co-operation relationship between supply chain parties by seeing lean as a firm’s belief in receiving value creation within its partner’s reliability and integrity, which leads to positive outcomes. Literature review This section sheds light on lean thinking and value creation and highlights the impact of lean thinking on value creation. First, lean thinking; leanness originated from the Japanese manufacturer Toyota in the 1950s (Monden, 1983; Ohon, 1988a, b; Shingo, 1988). Lean got its name from Womack et al. (1990) who chronicle the movement of automobile manufacturing from craft production to mass production to lean production. Womack and Jones (1996a, b) developed the lean tool to cover the following “lean principles”:

. . . .

.

identifying customer value; managing the value stream; developing the capacity to flow production; applying a pull system to support the flow of materials to constrained operations; and detecting excellence by eliminating all forms of waste in the production system.

In the 1990s, lean production was treated as a fashion (Bjo¨rkman, 1997; Sturdy, 2004) alongside BPM and TQM. Nowadays, lean is not only popular in manufacturing. However, it has progressed from the operational level to the strategic level (Hines et al., 2004) as well as to the empirical area beyond manufacturing to areas such as shoe manufacturers (Gati-Wechsler and Torres, 2008), the supply chain for personal computers (Ben et al., 1999), the food and farming supply chain (Cox and Chicksand, 2005) and healthcare (Waring and Bishop, 2010; Wang and Huzzard, 2011). According to Corbett (2007), “the lean approach percolates into ever wider circles of operations; it ceases to be about best practice and starts to become a part of the fabric of doing business”. The lean concept has many spots for practitioners, for example, it aggregates related principles of improvement via TQM; synchronicity and coordination via JIT; and integration via computer-aided processes to the areas of design, factory management, supply and distribution (Forrester et al., 1996). Lewis (2000) illustrates lean as: [. . .] a reduced level of input resources in the system for a given level of output. This is achieved through removing waste (Muda) from the system. This is primarily waste in the form of resources (raw materials, WIP etc) that are transformed in manufacturing however also includes transforming resources such as people, process technology, facilities, etc.

Consequently, Womack and Jones (2003) define waste as “any human activity which absorbs resources without creating value” and classify seven waste activities: 1) Overproduction; 2) Waiting time (for the next process step); 3) Transportation (unnecessary movement of materials); 4) Over Processing (rework and reprocessing); 5) Inventory (excess inventory not directly required for current orders); 6) Movements (unnecessary movements by employees during course of their work); and finally; 7) Defects.

Ohno (1988a, b) and Emiliani (1998) address another waste behaviour. Nevertheless, lean is considered to be about controlling resources in accordance with customers’ needs and reducing unnecessary waste “including the waste of time” (Andersson et al., 2006). Second, value creation; the literature distinguishes value creation as a core objective for enterprises. Numerous authors state that an enterprise’s obligation is to create value for shareholders, while others insist that value must be created not just for shareholders, but for stakeholders since it is morally the right thing to do. Others insist that a corporation’s only moral obligation is to make a profit. It is obvious that there is a lack of attention in the management literature to the main concept of value itself. Some researchers pay more attention to the meaning and discussion of “value”. Moreover, presented models fail to describe how an organisation creates value (Haksever et al., 2004). Value creation cuts across two dimensions. The first is the magnitude of returns in excess of the cost of capital that a company can, or will, generate. The second

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dimension is how long a business can earn returns in excess of the cost of capital (Mauboussin and Bartholdson, 2002). Porter (1985) defines value as “what buyers are willing to pay” and adds that superior value results when a firm offers lower prices than competitors for equivalent benefits or when it provides unique benefits that more than offset a higher price. Most economists, however, make a clear distinction between the value and price of a good or service. Cengiz et al. (2004) define value as “the capacity of a good, service, or an activity, or activities of an organisation to satisfy a need, or provide a benefit to a person or legal entity”. In this study, I rely on this definition. Thus, value creation relies on three parties: customers, employees and investors (O’Malley, 1998). Therefore, matching customer and provider practices requires not only the recognition of what value means but also the process of value creation (Alderson, 1957; Ramirez, 1999; Normann, 2001; Sheth and Uslay, 2007; Gronroos, 2008; Lusch et al., 2008; Shamah, 2012). Therefore, to be able to co-create a unique customer experience enterprises must co-create an empowered employee experience “inside” the supply chain (Ramaswamy, 2009) and match productivity and quality between all parties in the supply chain. Finally, we should note the impact of lean thinking on value creation. Lean thinking can be used as a framework for improvement for in recurring manufacturing activities and upstream in non-recurring processes such as product development (Mascitelli, 2000). Womack et al. (1990) argue that lean manufacturing has greatly improved production efficiency. One of the main objectives of strategy formulation and implementation is the creation of sustainable advantages for firms (Forrester et al., 2010). Therefore, firms need to recognise why some companies perform better than others even though they operate in similar markets and competitive situations (De Oliveira and Fensterseifer, 2003). These differences in performance may be attributed to a differentiation in internal factors such as knowledge and other strategic assets that influence firm performance. This philosophy is personified in the resource-based view approach, which supposes firms are different mixtures of productive and strategic resources and capabilities that lead them to different performance potentials. The resource-based view gained prominence in the strategy literature by emphasising the firm’s internal resources as the main determinants for improved performance (Forrester et al., 2010). At the beginning of lean implementation, it was limited to tool-based manufacturing approaches that aimed to provide qualitative products within lower costs in discrete manufacturing processes (Wang and Huzzard, 2011). In the 1990s, the lean concept was extended from the “shop-floor” operational level to the strategic level (Hines et al., 2004). Lean is now applied across a broad range of industries and it has moved from a purely “shop-floor” focus on waste and cost reduction to an approach that enhances customer perceived value by adding product or service features and/or removing wasteful activities (Hines et al., 2004). In other words, lean means adding value to stakeholders. Hence, supply chains focus on achieving efficient value creation. According to Womack and Jones (1996a, b) and Mascitelli (2000), there are five principles that firms should follow to attain overall value creation: (1) Define the value of their products as perceived by their customers; the goal is to deliver products that precisely match the customer’s needs without waste.

(2) Understand the value stream within the company, a value stream is the sequence of activities and process steps that is essential for creating and delivering a product. Although it may seem as though everything that goes on in a company is part of the value stream, in reality much effort is wasted on non-value-adding tasks. Mapping the organisation’s value stream enables it to categorise activities into value-added (those tasks that transform the product in some measurable way) and non-value-adding tasks (wasted effort that could be eliminated without any impact on the customer). (3) Eliminate barriers to the flow of value. These barriers can take the form of large batches of inventory or capacity bottlenecks in the factory, or it could be in the form of excessive meetings, approvals, documentation, etc. during the product development process. (4) Illustrated by the pull, a firm is free to allow its customers to “pull” value, meaning that all production activities are triggered by real demand from the marketplace. (5) Continuous repentance of the previous steps to ensure that methods and systems are constantly being purged of waste. Purpose and theoretical approach This study develops a standardised instrument to measure the degree of leanness in firms and determines the relationship between lean thinking and enterprise value creation, which can be used to scrutinise supply chain willingness to go lean. Further, observing the potential role of customers, competitors and suppliers can increase supply chain performance. Scales and measurement tools in this study This instrument is divided into two main sections. The first section is considered to be about lean thinking. It is based on Karlsson and Ahlstro¨m’s (1996) framework and Soriano-Meier and Forrester’s (2002) leanness scale, which are modified to meet the research purpose, as I argue in this study that the core leanness dimensions are lean thinking concepts; improvement program; waste elimination; and lean culture. The second section concerns the value creation scale. This is generated based on the previous literature review, and I argue in this study that the core value creation dimensions are reducing operational cost & achieving customer satisfaction; product mix; knowledge accumulation; joint productivity; and perceived quality (PQ). In addition, this study is based on data collected from an in-depth survey and interviews in the Egyptian industrial sector that can be deployed in future studies for testing the leanness of manufacturing firms. For both instrument sections, five-point Likert scales were used to instruct respondents. The aim of the survey was to identify the degree of leanness in the Egyptian industrial sector and the managerial commitment towards lean production and then to examine the relationship between leanness and value creation. Because the relevant data were not available in secondary form, primary data collection was necessary. This instrument thus offers a generic tool for measuring the degree of leanness and degree of managerial commitment and subsequent business performance. The data generated also enabled the testing of a number of research hypotheses.

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Research questions This research asks the following questions: RQ1. Which dimensions affect the progression of lean thinking in supply chains? RQ2. What is the degree of leanness in the Egyptian industrial sector?

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RQ3. What is the impact of lean thinking on value creation? Research methodology This research generates an instrument to access and analyse the degree of leanness for enhancing supply chain value creation. Consequently, this could lead to elevated customer satisfaction, increasing internal-customer performance and leading to continues improvement. This explorative study also tests the ability of the Egyptian industrial sector to go lean. A total of 400 questionnaires were distributed across all managerial levels of the Egyptian industrial sector – on technology companies, such as electronics firms, air conditioner and refrigerator manufacturers and food, medicine, and automobile companies, across all managerial levels, 350 valid and complete questionnaires were returned. The questionnaires were distributed by email and through field visits to companies over a one-year period. For the purpose of this study, the study hypothesis is: H 1.

An interdependent relationship exists between lean thinking and value creation.

H 2.

There is significance relation between value creation and lean thinking dimensions.

Generating the lean thinking instrument used in this study Based on the previous literature review, lean thinking is all about adding value, where value is defined by the customer (Julien and Tjahjono, 2009). Throughout value creation, various participants contribute at different stages of the co-production process (Eichentopf et al., 2011). Therefore, this study argues that lean thinking is the most effective at influencing enterprise value creation. For evaluating changes towards leanness, it is important to distinguish between the determinants and the performance of leanness. The ultimate goal of implementing leanness in an operation is to increase productivity, enhance quality, shorten lead times and reduce costs (Karlsson and Ahlstro¨m, 1996). At the strategic level, it is about enhancing perceived value for stakeholders. Hence, supply chains are value creation networks composed of people, technology and organisations (Maglio et al., 2006). These networks rely on three main parties, namely the provider, suppliers and consumers, by focusing on how to match and merge the received value for each party. Moreover, supply chains need to develop creative ways to address fiscal restraints while fulfilling customer demand for efficient product delivery. Providers see the value creation process as an integrated process of matching different perspectives, namely those of the main organisation, suppliers and customers. Therefore, applying lean thinking leads to efficient value creation, as the core idea is when the product provider focuses on value creation, it affects all supply chain parties. Thus, the appliance of leanness by a product provider is a core constituent.

Applying lean thinking within provider value creation Its applicability and diffusion in industry has become so pervasive that some have even suggested that lean may soon become a “qualifier” (Boyle et al., 2011; Hill, 1991) rather than a source of competitive advantage (Boyle et al., 2011; Crute et al., 2003). Hence, provider value creation core activities are: . Increasing the benefits and use of products through improved quality, function or imaging. . Lowering costs through production, efficiency and other means in order to change attitude and thinking (Sumarna, 2010; Myelin, 2002, 2010). Nevertheless, lean makes optimal use of the skills of the workforce by giving workers more than one task, by integrating direct and indirect work and by encouraging continuous improvement activities. As a result, lean production is able to produce a larger variety of products and services, at lower costs and higher quality, with less of every input, compared with traditional mass production: less human effort, less space, less investment and less development time (Dankbaar, 1997). Once again, Womack and Jones (1996a, b) describe five principles to create a lean manufacturing system: 1) Specify value (what makes the customer happy?; 2) Identify value stream (what is the sequence of processes from supplier to customer?; 3) Create flow (make the value flow and never delay a value-adding activity; do not batch production but make “one-at-a-time”; 4) Pull products through the system (only make what is required by the customer, when it is required; and finally; 5) Perfect the system (continuously improve the system by reducing waste).

Therefore, value creation requires unique activity configuration frameworks according to Normann (2001). These need to be: . Long-linked. Transforming inputs into saleable outputs. For example, an automobile manufacturer uses long-linked technology to create value. . Mediating. Linking customers within a network. The key role of the customer and the network leads us to label these firms “network service firms”. . Intensive. Solving clients’ problems using experts and the application of expertise. The key role of experts and expertise leads us to label these firms “knowledge-intensive firms”. Moreover, lean is used to accelerate the velocity and reduce the cost of any process by removing waste. Therefore, this study argues the following lean fundamentals for achieving continues improvement: . plan for change; . design suitable strategies and procedures to support lean; . implement the suggested plans; . measure employee, department and/or organisational performance; . analyse defects; and . learn from leaders and/or from feedback to create a knowledge base to achieve optimum performance by eliminating waste, exceeding customer expectations and adding value to stakeholders.

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Furthermore, the philosophy of lean considers the interrelationships of these practices in order to improve overall levels of quality, productivity, integration and waste reduction in manufacturing, over functional areas (e.g. R&D, accounting) and along the supply chain (Boyle et al., 2011). Indeed, applying lean in organisations increases competitive advantage by achieving the following benefits: reduced work-in-process; increased inventory turns; increased capacity; cycle-time reduction; and improved customer satisfaction (Andersson et al., 2006; Deming, 1994). Liker (2004) states that: [. . .] to be a lean manufacturer requires a way of thinking that focuses on making the product flow through value adding processes without interruption (one piece flow), a “pull” system that cascades back from customer demand by replenishing only what the next operation takes away at short intervals, and a culture to improve.

Consequently, firms should benchmark their activities against those of competitors to ensure efficiency after applying leanness. Kaplan and Norton (1992) suggest an effective measurement for benchmarking that should be an integral part of the management process. Using the balanced scorecard approach measures organisational performance. A balanced scorecard consists of: (1) Customer perspective. How do customers view the enterprise? (2) Internal perspective. What does the enterprise excel at? How can it maintain competitive advantage? (3) People. The focus is on innovation and learning. Can our people continue to improve and create value? (4) Financial perspective. How does the enterprise look to its stakeholders (gifts, grants, endowments)? Discussions and findings To emphasise, the proposed instrument offers a generic tool for measuring the degree of leanness and the degree of managerial commitment and linking these to business performance. I thus measured the validity and reliability of the suggested measurement dimensions using a confirmatory factor analysis (CFA) before examining the relative importance of each factor. Finally, a correlation analysis was used to determine the initial dimensions. Validity and reliability Identifying the validity of the used dimensions in this study was a major consideration. Therefore, a pilot study was applied using 30 participants randomly picked from the Multinational Automobiles Assembly Companies. This pilot study showed flaws in some of the questionnaire questions. These questions were amended to improve the reliability of the questionnaire and to make sure that the questions were well understood. Further, interviews were conducted with some participants before distributing the final questionnaire. Reliability refers to the property of a measurement instrument (in this study, the survey) that provides similar results for similar inputs. Reliability examines whether the results are consistent, i.e. repeatable. Would similar observations be made or similar results reached by a different researcher?

In social sciences, it is difficult or even impossible to establish absolute standards for human responses to a survey. However, the scales I use should be reasonably consistent. Therefore, reliability analysis was used to determine the accuracy of constructing questions that measure a person’s opinion (Cronbach, 2004). A reliability of 0.6 or higher is sufficient for this study. The Cronbach’s a from the analysis show that the output of the survey is reliable and consistent as presented in Table I.

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CFA In CFA, it is assumed that certain variables correctly measure a certain factor. Based on a hypothesis test, CFA may then be used to find out to which degree the different assumed variables truly measure that certain factor. Therefore, after having conducted an exploratory factor analysis, CFA is conducted by specifying the variables (dimensions) that define each construct or factor. Separate CFAs were carried out because of the large number of variables involved in the study. Moreover, it is accepted practice to carry out separate CFAs for exogenous and endogenous constructs, because in CFA each construct is allowed to be correlated to other constructs included in the CFA (Hair et al., 2010). In addition, this study used CFA for the first order using the maximum likelihood estimation method in the AMOS21 program. Thus, the next section discusses the CFAs for each construct. The dimensions used for measuring this model are represented in Table II. All models’ fitness were evaluated using several criteria, including the x 2 goodness-of-fit test statistic, degree of freedom, x 2/df, goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), root mean square error of approximation (RMSEA) and Tucker-Lewis Index (TLI). The dimensions of all constructs were initially incorporated into the model testing. Several criteria were used to evaluate the items and their dimensions, including unidimensionality, which means that a set of variables only has one underlying dimension in common, such as the reliability of the item and the reliability of the whole construct. According to Janssens et al. (2008) and Hair et al. (2010), to evaluate unidimensionality, the variable measures must all have a high loading (. 0.50) on the factor and must be significant

Dimensions Part 1: lean thinking (LE) 1.1 Lean thinking concepts (LPC) 1.2 Improvement program (IP) 1.3 Waste elimination (WE) 1.3.1 Identify the Muda “nonvalue added work”: 1.3.2 Identify the Muri “overburden” 1.3.3 Identify the Mura “unevenness” 1.4 Lean culture Part 2: value creation (VC) 2.1 Reducing operational cost & achieving customer satisfaction (ROC&ACS) 2.2 Product mix (PMix) 2.3 Knowledge accumulation (KA) 2.4 Joint productivity ( JP) 2.5 Perceived quality (PQ) All items

Cronbach’s a

No. of items

0.895 0.890 0.850 0.890 0.840 0.850 0.845 0.900 0.940 0.900

54 9 13 11 4 3 4 21 44 11

0.936 0.850 0.890 0.744 0.978

5 9 12 7 98

Table I. Total reliability statistics

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Table II. Study dimensions

1. Observed, exogenous variables (standardised) LP Lean thinking concepts IP Improvement program Muda Identify the Muda “non-value added work” Muri Identify the Muri “overburden” Mura Identify the Mura “unevenness” LC Lean culture ROC& ACS Reducing operational cost & achieving customer satisfaction PMix Product mix KA Knowledge accumulation JP Joint productivity PQ Perceived quality 2. Unobserved, endogenous variables LP Lean thinking requirements WE Waste elimination VC Value creation 3. Unobserved, exogenous variables (errors) E1 to E11

(critical ratio ¼ C.R. ¼ t-value . 1.96). Table III and Figure 1 show the initial outputs for the first order CFA model of technical quality using the AMOS21 program. Table III shows that the results clearly reproduce the initial model of the technical quality, as the CFA fits well across all three fit measures, except the probability of the x 2, GFI, AGFI and CFI. Moreover, most factors have a loading (, 0.70) on the factor and are significant. Reliability analysis was performed to test internal consistency. This must always be verified after convergent validity, because a model may be reliable without being convergent (Janssens et al., 2008). Cronbach’s as were chosen, as suggested by Hair et al. (2010) as the most commonly accepted approach for assessing the reliability of a multi-item scale. Hair et al. (2010) recommend a level of 0.70 as the minimum acceptance standard of internal consistency reliability, while Kline (2005) states that 0.60 is generally viewed as the minimum acceptance level. In general, the acceptance Path

Table III. Summary of AMOS output for measuring instrument

LP ˆ LPC LP ˆ IP LP ˆ LC WE ˆ Muda WE ˆ Mura WE ˆ Muri VC ˆ ROC&ACS VC ˆ PMix VC ˆ KA VC ˆ JP VC ˆ PQ

Estimatea

Standardised

1.000 2.123 2.000 1.328 1.000 1.177 1.000 0.916 0.720 1.916 1.720

0.501 0.992 0.890 0.753 0.963 0.948 0.500 0.899 0.706 0.923 0.967

SE

CR

p-value

0.356 0.344 0.154

5.965 5.867 8.622

0.000 0.000 0.000

0.050

23.578

0.000

0.058 0.061 0.070 0.265

15.904 11.714 14.850 13.755

0.000 0.000 0.000 0.000

Notes: aInitial value to start the solution; over fit measures: absolute fit measures: CMIN ¼ 47.856, df ¼ 12, p ¼ 0.000, CMIN/df ¼ 1.439, GFI ¼ 0.842, RMR ¼ 0.016, RMSEA ¼ 0.057; incremental fit measures: IFI ¼ 0.932, TLI ¼ 0.838, CFI ¼ 0.931

Measuring and building lean thinking 27

Figure 1. Measuring instrument

of reliability coefficients as equal to or greater than 0.60 has been used as the reference point for most research. Table IV shows that all components are over 0.70 and that the overall alpha value is 0.726. Therefore, the reliability of LP, WE and VC were accepted. Thus, we can depend on the measurement model. Relative importance for each factor From the descriptive statistics and relative importance of dimensions test it was declared that: the mean, standard deviation and importance of each dimension within the conceptual framework. It is clear that the respondents gave 80.2, 73.9 and 76.3 per cent for LP, WE and VC, respectively. Paths LE ˆ LEC LE ˆ IP LE ˆ LC WE ˆ Muda WE ˆ Muri WE ˆ Mura VC ˆ ROC&ACS VC ˆ PMix VC ˆ KA VC ˆ JP VC ˆ PQ Sum Ave

Loading (l)

l2

1 2 l2

0.501 0.992 0.890 0.753 0.963 0.948 0.934 0.899 0.706 0.923 0.967

0.251 0.984 0.792 0.567 0.927 0.898 0.872 0.808 0.498 0.851 0.935 7.678

0.749 0.016 0.207 0.433 0.073 0.102 0.128 0.192 0.511 0.149 0.065 2.625 0.726

Table IV. Discriminant validity using AVE

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Correlation analysis This is used to describe the strength and direction of the linear relationship between the dimensions in the conceptual model. It varies from 0 (random variable) to 1 (perfect linear relationship) or 21 (perfect negative relationship). In addition, it varies in terms of strength as represented in Table V, shows Pearson correlation. It is obvious from Table V that all correlations have positive relationships at a significance level of 99 per cent between all dimensions. The correlation between any two dimensions ranges from moderate to very high. The strongest correlation between any two dimensions means there is a relationship between LP, WE and VC. Therefore, we can accept the hypothesis of this study. Therefore, H1 is accepted. Regression test is used for the significance of the overall relation between lean thinking and value creation dimensions. As Table VI represents. Since the p-value is less than 0.05, there is a statistically significant relationship between value creation and lean thinking dimensions. The R 2 statistic indicates that the model as fitted explains 26.3 per cent of the variability in value creation by nine independent variables. From those above table it is obvious that value creation could be explained by using the following model: Value Creation ¼ 4:225 2 0:213 Lean Con: þ 0:135 Improve Program þ 0:082 Muda 2 0:160 Muri þ 0:074 Mura þ 0:070 Lean Culture þ 0:110 Perceived Quality 2 0:188 Service environment Quality þ 0:067 Outcome Quality Which means the there is a liner relation between lean thinking and value creation are good relationship as Figures 2 and 3 show. It is obvious from the previous figures that this model does not have problems of: normality; linearity; colloranity; hirosttisty; or outliers. Therefore, H2 is accepted. Research limitations This study has some limitations. First, it was conducted on the Egyptian industrial sector. The applicability of the proposed scale should thus be further tested in different countries and service mixtures. Moreover, internal resistance is more of a barrier than external (customers, suppliers or competitors) resistance to lean thinking. Thus, organisations should focus first on internal (functional) integration and then move onto interorganisational integration. Further, people are more critical than technology in implementing lean thinking. Recommendations Almost every company can implement lean thinking, but to a limited extent (it is a matter of the degree of leanness). For supply chains in the process of applying or considering leanness, I would thus recommend the following: (1) Supply chains can improve provider performance through the practice of supporting behaviours that increase collaboration between supply chain parties. (2) Integrate external sources through the acceptance of external ideas and top-down targeting in order to integrate a certain number of ideas and technologies from external sources.

1 0.493 * 0.808 * 0.547 * 0.593 * 0.577 * 0.613 * 0.606 * 0.593 * 0.547 * 0.613 * 0.857 * 0.585 * 0.654 *

LEC IP LC Muda Muri Mura ROC&ACS PMix KA JP PQ LE WE VC 1 0.769 * 0.904 * 0.932 * 0.743 * 0.848 * 0.471 * 0.932 * 0.904 * 0.848 * 0.855 * 0.941 * 0.748 *

IP

1 0.686 * 0.837 * 0.574 * 0.692 * 0.436 * 0.837 * 0.686 * 0.692 * 0.960 * 0.786 * 0.617 *

LC

1 0.905 * 0.875 * 0.870 * 0.653 * 0.905 * 0.848 * 0.870 * 0.808 * 0.972 * 0.872 *

Muda

1 0.789 * 0.816 * 0.561 * 0.837 * 0.692 * 0.816 * 0.888 * 0.980 * 0.787 *

Muri

Note: *Correlation is significant at: 0.01 level (two-tailed)

LEC

Dimensions

1 0.836 * 0.809 * 0.905 * 0.870 * 0.836 * 0.717 * 0.849 * 0.965 *

Mura

1 0.619 * 0.670 * 0.778 0.837 * 0.813 * 0.861 * 0.890 *

ROC&ACS

1 0.607 * 0.789 * 0.905 * 0.607 * 0.789 * 0.905 *

PMix

1 0.769 * 0.670 * 0.570 * 0.618 * 0.889 *

KA

1 0.607 * 0.769 * 0.670 * 0.808 *

JP

1 0.870 * 0.653 * 0.905 *

PQ

WE

1 0.846 *

LE

1 0.872 * 0.763 *

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Table V. Pearson correlations between dimensions

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Unstandardised coefficients B SE

Independent variables

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Table VI. Regression test results

(Constant) Lean thinking concepts Improvement program Waste elimination identify the Muda Identify the Muri Identify the Mura Lean culture Perceived quality Service environment quality Outcome quality

4.212 20.213 0.135 0.082 20.160 0.074 20.070 0.110 20.188 0.067

Standardised coefficients b

t

Sig.

2 0.257 0.302 0.190 2 0.207 0.191 2 0.150 0.251 2 0.237 0.115

15.587 27.130 5.203 5.050 25.556 4.913 24.082 4.450 23.208 3.042

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.002

0.270 0.030 0.026 0.016 0.029 0.015 0.017 0.025 0.059 0.022

Notes: p-value ¼ 0.000; R 2 ¼ 0.263; F ¼ 16.120

Histogram Dependent Variable: A

Mean = 5.84E-15 Std. Dev. = 0.989 N = 600

80

Frequency

60

40

20

0 –3

Figure 2. Normality of the model

–2 –1 0 1 2 Regression Standardized Residual

3

Cases weighted by W

Conclusion The development of leanness and value creation offers application possibilities for practitioners and academic researchers. First, the use of this scale enables practitioners to track the degree of leanness for all supply chain parties. However, the cautious

Measuring and building lean thinking

Normal P-P Plot of Regression Standardized Residual Dependent Variable: A 1.0

31

Expected Cum Prob

0.8

0.6

0.4

0.2

0.0 0.0

0.2

0.6 0.4 Observed Cum Prob Cases weighted by W

0.8

1.0

inspection of the different factors may show key weaknesses that prevent the delivery of high employee performance. Consequently, managers must carry out suitable actions to improve or to maintain specific aspects of leanness. Second, for academics, this scale may be a potential starting point for comparing different research on lean thinking and value creation in the workplace. References Alderson, W. (1957), Marketing Behavior and Executive Action: A Functionalist Approach to Marketing Theory, Richard D. Irwin, Homewood, IL. Andersson, R., Eriksson, H. and Torstensson, H. (2006), “Similarities and difference between TQM, Six Sigma and lean”, The TQM Magazine, Vol. 18 No. 3, pp. 282-96. Andrews, K.R. (1971), The Concept of Corporate Strategy, Dow Jones-Irwin, Homewood, IL. Ben, N.J., Naim, M.M. and Berry, D. (1999), “Leagility: integrating the lean and agile manufacturing paradigms in the total supply chain – strategies for enriching”, International Journal of Production Economics, Vol. 62 Nos 1/2, pp. 107-18. ˚ . (Ed.), Ledning fo¨r alla? ¨ Bjorkman, T. (1997), “‘Management’ – en modeindustri”, in Sandberg, A Om perspektivbrytningar i fo¨retagsledning, 3rd ed., SNS, Stockholm.

Figure 3. Linearity of the model

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Further reading Deming, E. (1993), The New Economics for Industry Government and Education, MIT Press, Cambridge, MA. Hill, A. and Hill, T. (2009), Manufacturing Operations Strategy, 3rd ed., Palgrave-Macmillan, Basingstoke. Moyano-Fuentes, J. and Sacrista´n-Dı´az, M. (2012), “Learning on lean: a review of thinking and research”, International Journal of Operations & Production Management, Vol. 32 No. 5, pp. 551-82. Ritter, J.R. (1984), “Signaling and the valuation of unseasoned new issues: a comment”, Journal of Finance, Vol. 39, pp. 1231-7. Sanjay, B. (2012), “An appropriate change strategy for lean success”, Management Decision, Vol. 50 No. 3, pp. 439-58. About the author Rania A.M. Shamah is an Associate Professor of Business Administration at Helwan University (part-time) and at the Arab Open University, Faculty of Business Administration (part-time). Shamah is also a consultant/lecturer in business administration at several training centers in the Middle East, especially in the Gulf area, and is a member of the Crisis Research Unit. Shamah is a Referee Editor for the International Journal of Knowledge Management & Culture Change, an Associate Editor for the Disaster Report of Egypt, Crisis Research Unit Press, and is on the organizing committee of the Annual Crisis Conference, run by the Crisis Research Unit. Rania A.M. Shamah can be contacted at: [email protected]

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