A New Holistic Conceptual Framework for Leanness Assessment

Lean principles, aiming at eliminating waste and increasing efficiency at a company, take their roots from the initiatives of Taiichi Ohno. After the implementation of the principles at the Toyota Motor Company for the first time, businesses started to discover the benefits of lean implementation in terms of efficiency increase. As the adaptation of lean into the manufacturing sector is continuing, the necessity of assessing the level of leanness at the firm-level maintains its importance. Taking systems approach as a basis, the lean performance of an organization should be assessed as a whole. Therefore, we propose a holistic leanness assessment framework, which encapsulates various dimensions of the leanness assessment and we identify the importance and causal relationships between the sub-criteria. In order to identify the importance and causal relationships between the sub-criteria, we used fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL). Our findings show that the most influencing factor in the cause group is ‘technology and product design’ which indicates the companies’ necessity to focus on Industry 4.0 during their operations. The results also illustrate that the most influenced factor in the effect group is ‘productivity’, in which companies can investigate strategic competitive advantages. The design of a holistic framework and the implementation of fuzzy DEMATEL offers a way to identify the importance and the causal relationships between the sub-criteria. With the help of a case study conducted in the plastics industry of Turkey, we offer managerial implications that could help managers to implement the proposed structural leanness assessment framework. KeywordsLeanness, Lean manufacturing, Leanness assessment, Fuzzy logic, Fuzzy DEMATEL, Performance assessment.


Introduction
Lean principles, first implemented at the Toyota Motor Company, were mainly developed by Ohno (1988). The principles used at Toyota and then gained a worldwide reputation due to their success. These principles aim at eliminating waste, thus, increasing efficiency. Lean has many definitions in the literature. According to Schonberger (1987), lean is "the most important productivityenhancing management innovation since the turn of the century." The lean concept works synergistically and aims to create systematic and high-quality processes. Shah and Ward (2003) specified that lean also fulfills customer demand within the required time. Lean is a management philosophy with the goal of supplying the customers the right product at the right place, at the right time, at the right quality and quantity. The implementation phase of lean includes the integration the lean principles into all functions of the organization, including accounting, sales and marketing and human resources (Pakdil and Leonard, 2014). As a result, the assessment of leanness in an organization requires an integrated approach that considers all functional developments regarding the implementation of lean. Some qualitative assessments in the literature focus on employee perception (Feld, 2000;Conner, 2001;Goodson, 2002;Soriano-Meier and Forrester, 2002;Doolen and Hacker, 2005;Shah and Ward, 2007;Fullerton and Wempe, 2009;Bhasin, 2011). Other studies have created quantitative assessments using various performance metrics (Bayou and de Korvin, 2008;Behrouzi and Wong, 2011;Wan and Frank Chen, 2008). The managers are very keen on learning how to use the measures for having more control over the lean implementation process. However, all of the models presented for leanness measurement either focus on quantitative measures or qualitative indicators, and none of those studies have concentrated on creating a perspective which combines both the qualitative and quantitative indicators, despite Azadeh et al. (2015) call for a measurement tool for evaluating the efficiency and effectiveness of the implementation of lean throughout an entire organization. Table 1 exhibits a summary of the studies which presented a lean assessment model. These studies usually focus on different aspects of lean management rather than evaluating it as a whole system.  Almomani et al. (2014) The cost of implementation, Time of completion, Benefit, Administrative constraints, Technological capabilities, Risk The Delphi Technique Bayou and de Korvin (2008) Inventory Management, Cost Management Fuzzy Approach Behrouzi and Wong (2011) Waste elimination, JIT Fuzzy Logic Approach Bhasin (2012) Finance, Customer, Process, People, Future Balance Scorecard Doolen and Hacker (2005) Manufacturing In this study, we investigated a comprehensive list of measures consisting of both qualitative and quantitative leanness measures, due to the integration of qualitative and quantitative indicators' potential to create more complete and synergistic utilization of data and the lack of this combined

Supplier Issues
Implementing lean principles in all processes between a buyer and supplier is crucial because when suppliers practice lean processes, they reduce their inventory level and lower the stock out costs. Therefore, the suppliers which adopt lean processes in their internal processes will be more coherent with the buyer's logistics requirements (Wu, 2003). Supplier issues dimension has two sub-dimensions, supplier relationship management, and procurement management. Procurement management is not mentioned in literature as a dimension, sub-dimension or measure; however, some measures in the literature did not fit under the supplier relationship management sub-dimension, so they were collected under a new sub-dimension, called procurement management due to their relevance on the procurement management subject. Table 3 represents the sub-criteria and the measures for the supplier issues main criterion.

Manufacturing Activities
According to Bayou and de Korvin (2008), implementation of leanness strategy into the manufacturing activities is a way to obtain a better output with less input, regarding organizational goals. In their statement, output refers to the quality and quantity of the products for sale, and the ideal customer service level and the input refer to the quantity and the cost of the physical resources used. Narasimhan et al. (2006) also highlighted that waste minimization for the efficient use of an organization's resources is a vital aspect of leanness due to the main aim of lean manufacturing is reducing waste and non-value added activities.
This dimension includes manufacturing related issues as product design, layout design, production planning, inventory management, production process, productivity, and technology. Table 4 represents the sub-criteria and measures for manufacturing activities.  Womack and Jones (1996) emphasized that true lean system applications involve the application of all the principles along the value stream, not just in certain defined parts. However, few studies in the literature explore the integration of lean principles in the marketing function. As Piercy and Morgan (1997) stated, great improvements could result from lean thinking in every business function, and in particular, for marketing. The lean thinking concept should be understood by marketing scholars and executives, who should be proactive in using lean thinking to improve the performance of the marketing function.

Marketing
As mentioned earlier, the lean implementation should be conducted through all functions of an organization according to the systems approach. Therefore, assessment of the lean implementation should include the marketing function as well. Adding the marketing dimension in our conceptual framework we present three sub-dimensions: customer relationship management, customer satisfaction, and sales management. Table 5 represents the sub-criteria and the measures for the marketing main criterion.

Just-in-Time
Just-in-Time is a management practice that supports the idea of having the necessary amount of material available where it is needed when it is needed. The main aim is reducing work-in-process inventory and unnecessary delays on flow time (Demeter and Matyusz, 2011;Furlan et al., 2011). Huson and Nanda (1995) argue that in an integration system, lean production should be considered as a multi-dimensional method, including various management practices, and Just-in-Time is described as one of the key principles (Gurumurthy and Kodali, 2009;Demeter and Matyusz, 2011).
In our framework, we divided the Just-in-time dimension into two sub-dimensions. Adaptation of JIT philosophy, the former, concerns adopting the management practices of JIT throughout an organization; JIT implementation, the latter, is the implementation process of JIT after the internalizing phase. Table 6 represents the sub-criteria and the measures for the just-in-time main criterion.

Cost and Financial Management
Comm and Mathaisel (2000) described leanness as a management philosophy aimed at reducing cost and cycle time throughout the entire value chain while continuing to develop product performance. Hopp and Spearman (2004) stated that the core of lean production is waste reduction, which will lead to cost-reducing. Emiliani (2000) pointed out, that the customer and stockholder pressure on senior management of a firm for the improvement in the financial position creates awareness about the leanness level of the firm.
For reaching a comprehensive assessment of the leanness level of the firm, we combined cost management and financial management in order to establish a new dimension. Table 7 represents the sub-criteria and the measures for the cost management and financial management main criterion.

Workforce
Worker involvement and expansion of their responsibilities and giving them autonomy is vital for continuous quality improvement programs. In implementing lean, beneficial processes include employee recruitment and selection, educating and training, evaluating and rewarding their contributions to the process and increasing their empowerment and responsibility.
The workforce dimension consists of the three sub-dimensions which related to the employeerelated processes. They are employee involvement, employee cross-functioning, and employee benefits. Table 8 represents the sub-criteria and the measures for the workforce main criterion.

Management Responsibility
A radical rethinking over how the management of a firm uses the lean principles and methods is essential for reaching optimal performance level throughout an enterprise. While an organization is at the adaption stage, they focus more on the 'process-centered approach', such as the elimination of waste and reduction of cost. As the stage on to the adaptation phase, the focus should be more on the human-centered approach through empowerment and management of the human resources in the work design (Wong et al., 2014).
Management responsibility dimension consists of two sub-dimensions: Organizational Culture and Management, and Applying lean practices in management. Table 9 represents the sub-criteria and the measures for the management responsibility main criterion.  Brown et al. (2001) indicate that lean manufacturing enables manufacturing with less input, at a lower cost with less development time, and higher quality levels. Producing with a higher quality level brings the usage of the Total Quality Management process, which, according to Demeter and Matyusz (2011) aims at continuous improvement and sustaining the quality of the product and processes. The actions of TQM include the usage of Six Sigma, quality circles, statistical process control, equipment problem solving and poka-yoke. Wan and Frank Chen (2008) also point that there are various tools and techniques developed to solve specific problems in order to eliminate non-value-added activities, and that process will help becoming lean.

Quality Management
The last dimension is comprised of the sub-dimensions of total quality management and value management. Table 10 represents the sub-criteria and the measures for the quality management main criterion.

Methodology
We used the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to assess the cause-effect relationships between the relevant criteria, and allow an analysis of a structured model. DEMATEL method initiated first at the Battelle Memorial Institute (Gabus and Fontela, 1972;Gabus and Fontela, 1973). The method consists of matrices and digraphs in order to categorize the relevant factors as cause factor, or effect factor, and identify the dependencies between the factors. The pairwise comparisons between the relevant criteria are used to represent the mathematical relationships (Wu and Lee, 2007).
There is a set of factors = { 1 , 2 , … , }, in the DEMATEL method. The pairwise comparisons between the relevant criteria are used to represent the mathematical relationships. Due to the subjectivity and vagueness, in pairwise comparisons, the linguistic terms are used to show the degree of effect of each criterion over others. Table 11 shows these linguistic terms described in positive triangular fuzzy numbers ( , , ). The linguistic terms are transferred into fuzzy numbers. Then, the average of pairwise comparisons are defuzzified into crisp values by Converting Fuzzy Data into Crisp Scores (CFCS), which was proposed by Opricovic and Tzeng (2003).
After the defuzzification process, we followed the following step-by-step application: Step 1: The average of pairwise comparisons constitute direct relation matrix, Z. represents the degree of the influence of i th factor to j th factor, i.e. = [ ] × .
Step 3: Using formula (3), we calculated total relation matrix, T. "I" symbolizes here the identity matrix.
Step 4: Using formulas (4)-(6), we found the sum of values in rows and columns of the total relation matrix, T, and symbolized by D, and R, respectively.
= , , = 1,2, … , Step 5: We drew the cause-effect diagram by graphing the dataset. (D+R), and (D-R) values show the values in the horizontal axis, and in the vertical axis, respectively. (D+R) is called "Prominence", which refers to the level of importance, and (D-R) is called "Relation", which categorizes the factors as cause factor, or effect factor, respectively. If the value of (D-R) is positive, the factor is named as cause factor, and if negative, as effect factor (Wu and Lee, 2007).

Case Study and Managerial Implications
After the development of our framework, we conducted an application in 18 companies in the plastics industry in Izmir, Turkey. We selected the plastic industry due to its importance for the Turkish economy. According to the data of Turkish Statistical Institute (TUIK), the plastic industry in Turkey generates 4.8% of the Turkish manufacturing industry in economic terms and mobilizes a labor force rate of 4.2% within the manufacturing industry labor force. The plastic industry is the 11 th biggest industry in Turkey representing nearly 5 billion euros of turnover. Also, the share of plastic industry among the whole manufacturing industry is increasing every year. The export rate of the plastic industry is 4.6% of Turkish manufacturing industry, and it is also developing (Karaca, 2011).
34 experts carried out pairwise comparisons from these 18 companies, including the general managers, the plant managers, and the production managers. We sent the survey questions, in other words, the matrix for getting the judgments of the experts, by e-mail to the 126 representatives of the plastic industry, and 34 of them replied. We limited the scope of the study to the plastics sector in order to prevent the potential ambiguity that may arise when the analysis is conducted in multiple sectors. Hervani et al. (2005) pointed out the fact that since the application and the scales are specific to the organizations, there is no generally applicable tool or approach for generalizing the results. Consistent with this, the proposed framework including 8 criteria, 23 sub-criteria, and 209 measurements may be generalized and used in different applications. However, each application is specific to the company, which means, the results may be different when applied in another company. Table 12 shows the direct relation matrix; Z. We found direct relation matrix by using the formulas (7)-(14) (See Table 13 for the normalized direct relation matrix, X). Following, we calculated normalized direct relation matrix by the formulas (1) and (2). (See Table 14 for the total relation matrix, T). Lastly, we found the total relation matrix by the formula (3) and we calculated D and R values by using the formulas (4)-(6). According to the results, a cause-effect diagram occurs as seen in Figure 3.
Using formulas (4)-(6), row totals (D), and column totals (R) of Total Relation Matrix were found, respectively. The dataset is graphed in order to generate a cause-effect diagram. Horizontal axis shows (D+R) values, which refer to the importance level. The vertical axis represents (D-R) values, which classifies each criterion as either cause or effect group. If the value of (D-R) is positive, then the factor is referred to as cause factor, and if negative, as effect factor (Wu and Lee, 2007).  International Journal of Mathematical, Engineering andManagement Sciences Vol. 5, No. 4, 567-590, 2020 https://doi.org/10.33889/IJMEMS.2020.5.4.047 According to the results of the fuzzy DEMATEL causal diagram (see Figure 3), we state the following: 1 Cause factors refer to the influencing factors. It is critical to monitor cause factors in order to attain high performance from effect factors, which can be referred to as influenced factors (Fontela and Gabus, 1976). Within this context, an adaptation of JIT Philosophy (C13) is the most important factor, because it has the most significant relationship among all factors (it has the highest D+R value). The second and third were JIT Implementation (C14), and Production Process (C7) respectively. Technology (C9) is the most influencing factor, located at the top of the Cause Group, and Productivity (C8) is the most influenced factor, located at the bottom of the Effect Group.
Technology and product design are at the top of the cause group; therefore, the company should focus on the R & D activities, concentrating on the Industry 4.0 from the technology aspect, and concurrent engineering on the product design aspect. As a result, the opportunities in Industry 4.0 may contribute to productivity, which is at the top of the effect group.
The third and fifth influencing factors of Cause Group are organizational structure and employee involvement. Hence, we suggest for management to choose a matrix organization structure. This may increase employee involvement with its interdisciplinary structure and may even contribute to the JIT implementation which was the second most important factor in the results.
The Lean practices in management and the JIT philosophy were the fourth and sixth influencing factors in the results of the study in which the importance of senior management arises. Therefore, the companies may benefit from the establishment of a lean council to allocate the required resources and take decisions in this strategic issue. This will eventually make a contribution to the first and second most important factors of the study, adaptation in JIT philosophy, and JIT implementation, respectively.
The first influenced factor in the Effect Group is productivity; therefore, the company should consider it as a strategic competitive advantage, and focus on the production process which was the third most important factor in our study. Therefore, the first implication is that the design phase should be based on the modularity of the products whereas the second implication is to implement cellular manufacturing to increase the productivity of the production process and the third implication is aforementioned focusing on Industry 4.0.
institutionalize the internal and external customer concepts. In order to do this, related training and educational programs can be organized and conducted to contribute lean practices in management, employee involvement; and therefore, adaptation in JIT philosophy, and JIT implementation.
The technology factor may affect productivity. As technology supports the efficient use of resources it is crucial to improve productivity. Another aspect is the third most important factor which is the production process. The technology shows itself especially through automation in the production process which reveals that the plastics industry should transform Industry 4.0 as soon as possible. Therefore, investing in technology in the production process based on industry 4.0 will bring productivity.
The technology factor may affect customer satisfaction. The technology factor may support customer satisfaction in two ways, firstly, the improved product features and secondly, increased product and quantity variety may contribute to customer satisfaction. In order to pursue the positive effect of technology on customer satisfaction, additional features should be added through product design, which is the next cause-effect. As the next step flexibility should be improved. Especially with JIT implementation which is the second important factor, pull system may contribute to faster order fulfillment and flexibility with satisfying relatively small lot sizes.
Another outcome of the model may be that the effect of product design on productivity. As the design of the product is made simpler and suitable for modularization the productivity will be positively affected. In this way, possible problems and obstacles that may arise against productivity can be observed and even prevented. Especially concurrent engineering can be applied to facilitate the interdisciplinary teamwork within the systems approach.
There may be another effect caused by design for customer satisfaction. Generally, the design is an important issue to increase customer satisfaction with improved and enriched product features and functions. However, in the plastics industry, value-added products play an important role in the competition. Therefore, innovation is an important aspect of this case. In design and innovation activities 3D printers should be hired for faster prototyping and analyzing the product. In addition, Quality Function Deployment (QFD) can be applied, the voice of the customer can be heard and reflected the product within the capabilities of the company. Meanwhile, the second most important factor of the model, JIT implementation, is going to increase customer satisfaction by providing flexibility and shorter lead times as mentioned above.
Organizational structure and management may be revealed as a factor affecting employee benefits. The organizational structure is important for an employee for job satisfaction. One way to increase job satisfaction is related to job enrichment. Therefore, a matrix organization is suggested for these companies to facilitate interdisciplinary teamwork and job enrichment, as just we can see in concurrent engineering and in interdisciplinary design teams. The most important factor appears to be Adaptation to JIT where the matrix organization structure is strongly suggested in the transformation phase.

Conclusion
The concept of lean is founded on the principle that customer needs are to be provided at the right time, at the right place and at the right quantity. When a company adopts the lean management philosophy, it means that the aim of the company is to eliminate waste throughout the company in the process of meeting customers' demands. Currently, manufacturing processes are facing a shift towards lean manufacturing practices due to the lower costs, shorter processing time and more efficient processes. With this kind of transition, there is a need for companies to assess their level of leanness throughout the company. The usage of the assessment will provide the management with information to reveal both their strong and weak aspects.
This study employs a holistic approach, by integrating different dimensions of lean to create a framework which contains 8 criteria: supplier issues, manufacturing activities, marketing, just-intime, cost and financial management, workforce, management responsibility, and quality management. We also present 23 sub-criteria and 209 measurements for the use in the evaluation of the leanness of a firm.
The main three contributions of this study are, 1) to reveal the different dimensions of lean assessment, such as supplier issues, manufacturing activities, marketing, just-in-time, cost & financial management, workforce, management responsibility, and quality management; 2) to present a new holistic leanness assessment framework within a three-level structural format as criteria, subcriteria and measures; and 3) to use fuzzy DEMATEL method in order to determine the importance level and causal relationships between the sub-criteria and consequently, to propose managerial implications which may guide managers to implement the proposed structural leanness assessment framework. Figure 1 shows the flow diagram identifying the structure of the paper. Finally, we conclude with an application of the framework in the plastics industry.
The limitation of this research is that, as with all Multi-Criteria Decision-Making (MCDM) applications, the research includes subjective judgments. The proposed leanness assessment framework may be generalized, however; the result of the implementation of the framework is industry-specific.
Further research could focus on finding the criteria weights, respective measurement weights, and an overall performance score of the company. In addition, different methods may be employed to assess the level of leanness.

Conflict of Interest
The authors confirm that there is no conflict of interest to declare for this publication.