Lean and Green Decision Model for Lean Tools Selection

Abstract

Today’s businesses face a critical need to balance sustainability, environmental responsibility, and operational excellence. Integrating lean and green methodologies holds promise for achieving this balance but presents challenges in real-sector implementation. Despite extensive research in the scientific community, a universally accepted model for integrated lean and green implementation is lacking. Limited research also explores the impact of lean tools on environmental performance indicators. This study aims to identify key economic and environmental performance indicators and investigate how they are influenced by lean tools. The primary objective is to develop a decision-making model facilitating lean tool implementation based on a company’s priorities for economic and environmental performance. The methodology involves a literature review, complemented by questionnaires and interviews with experts from academia and the real sector. The findings show that commonly used lean tools positively influence economic and environmental indicators. The decision-making model, tailored to chosen criteria priorities, was successfully validated using simulated and real company data, which makes it a potentially valuable resource for companies navigating integrated lean and green methodologies. While the obtained results show promise, it is important to highlight the limitations arising from the regional focus on Croatia and the inclusion of a subset of the manufacturing industry.

1. Introduction

It is generally known that industry is one of the most important activities in the economy of any country and that its growth and development is necessary for the further progress of society. In addition to the great influence on the growth and development of society, it is obvious that industry also significantly affects the environment. Consequently, especially nowadays, when the signs of climate change are more and more present, there is an imperative to enhance its efficiency and effectiveness [1]. According to the European Environment Agency 2017 report [2], only the costs of air pollution from major European industrial companies is ranged between 277 and 433 billion EUR. The great potential for reducing the impact of industry on the environment is also confirmed by the UN’s 2030 Agenda for Sustainable Development [3], where achieving sustainable consumption and production is represented as one of the main goals. The risks associated with climate change, along with current global market conditions, including escalating prices and limited availability of raw materials, rising transportation costs, and intensifying competition, have made sustainability an indispensable concept that must be embraced and cultivated across all industries [4]. Accordingly, to maintain a competitive advantage and to adapt to more and more rigorous laws and regulations, companies, in addition to achieving financial sustainability, must direct their focus and investments to harmonizing business with environmental aspects [5].

Achieving economic and environmental sustainability is a difficult task; for the solution of which, many different methods and approaches are proposed. One of the solutions that shows potential for balancing economic and environmental sustainability is the integration of lean and green manufacturing [6]. On the one hand, Lean manufacturing, a widely recognized production methodology, has proven its effectiveness over the course of 40 years in enhancing process efficiency and economic sustainability through the utilization of tools like Just in Time (JIT), Kanban, Kaizen, TPS, and more [7,8]. Accordingly, numerous studies related to the implementation of lean in different scopes are available. Two such studies were conducted by Ulewicz et al., where, in [9], they determined that 5S, continuous flow, standardization, pull-system, and TPM are the most influential lean tools for the ceramics industry, while, in [10], they found out that 5S, process mapping, standard work, TPM, Kaizen, and error-proofing methods play important roles in minimizing the occurrence of potentially dangerous events in metallurgical production processes. On the other hand, green manufacturing is a production approach that should affect the entire product life cycle to increase the energy efficiency of the process and reduce the environmental impact of the product [11]. While lean manufacturing positively impacts the economy by eliminating various types of waste from the value creation process, the green approach focuses on environmental and social factors by promoting energy-saving practices, recycling, and the reuse of products [12]. It is evident that the collaborative integration of lean and green manufacturing holds the potential to generate a positive impact across all three dimensions of sustainability: economic, environmental, and social.

As is often the case, concepts with great potential are challenging to implement, and this challenge persists within lean and green manufacturing, where numerous companies face difficulties in implementation [13]. With the aim of facilitating the implementation process, the scientific community has been actively researching the area of lean and green for more than ten years [14]. A good research direction that can greatly facilitate the implementation of such concept is the development of a dedicated implementation model. Presently, there is no universally accepted model for integrated implementation. However, an increasing number of authors are proposing models that incorporate elements of both lean and green manufacturing, encompassing their principles or specific tools. In their study, Bergmiller and McCright [15] proposed a theoretical model of lean and green production that incorporates a lean and green management system, along with techniques aimed at minimizing losses and enhancing business outcomes, thereby highlighting the parallelism between these approaches. Pampanelli et al. [16] proposed a model focused on the production cell level, which, by integrating lean and green concepts through the Kaizen approach, reduces the process impact on the environment. Sawhney et al. [17] introduced the Environmentally Lean (En-Lean) methodology, which aims to establish the connection between indicators of business impact on the environment and lean principles. However, the methodology overlooks some crucial environmental indicators. Zokei et al. [18] defined The Lean and Green Business System model that consists of four components: (1) Strategy Implementation, (2) Process Management, (3) Leadership and People Engagement, and (4) Supply Chain Management. The model is general and at a higher level of thinking, while it lacks the possibility of simple practical application. Cherrafi et al. [13] proposed a Gemba–Kaizen model, which integrates lean and green concepts, leading to a reduction in resource consumption. The model’s effectiveness heavily relies on strong employee support. Leme Junior et al. [19] presented a lean green model designed specifically for companies with limited production capacities due to machine availability constraints. The model combines the Single Minute Exchange of Die (SMED) technique with Carbon Footprint (CF) analysis to assess eco-efficiency and facilitate cleaner production practices.

Among all the lean tools, Value Stream Mapping (VSM), usually enhanced with an environmental component, is the most frequently mentioned tool when it comes to implementing lean and green practices. Verrier et al. [20] developed a comprehensive framework for lean and green management, integrating indicators that encompass both lean and green approaches to effective management. Mihaljević [21] defined the LEGREM-DHE model, where he integrated the monitoring of green indicators in district heating systems into the VSM tool. Muñoz-Villamizar et al. [22] pointed out the lack of practical integration between productivity and impact on the environment and develop the overall greenness performance for value stream mapping (OGP-VSM) methodology. The OGP-VSM methodology serves only to evaluate the impact of lean tools on the environment, and no information is provided on the way of integrating lean and green. Belhadi et al. [23] improved the existing framework for lean implementation with Green VSM, thus enable improvements in operational and environmental factors.

In addition to connecting lean and green tools, one of the better approaches, which is extremely important in the development of sustainability, is to look at the entire product life cycle (PLC). Linking lean and green tools with PLC can be achieved using Life Cycle Assessment (LCA) methodology that enables the assessment of the environmental impact of the product throughout the entire PLC. Paju et al. [24] combined LCA with VSM and the Discrete Event Simulation (DES) method and thus created the Sustainable Manufacturing Mapping (SMM) concept that facilitates vertical communication. Vinodh et al. [25] also combined LCA and VSM and presented a LCA-integrated sustainable manufacturing mapping tool that considers the environmental, economic, and social aspects. In the paper, they did not explicitly show the connection between lean tools and environmental indicators. The advantages of connecting LCA and VSM were also shown by Salvador et al. [26]. They developed a LCA-VSM model that prioritizes action measures, enabling continuous environmental–economic performance improvement.

Additionally, some authors extend lean and green with approaches such as Six Sigma or Agile and thus try to create an optimal relationship between economic, environmental, and social indicators. When connecting lean, green, and six sigma, authors mainly propose models that imply the application of certain lean or green tools through the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology. Gholami et al. [27] proposed a systematic implementation of Environmental Value Stream Mapping (EVSM) using the DMAIC method, while Yadav et al. [5] recommended a DMAIC-based framework for integrating lean, green, and six sigma. This framework incorporates EVSM, as well as other lean tools like 5S and Kaizen, along with environmentally focused methods such as LCA and traditional statistical tools such as Pareto diagrams and Principal Component Analysis (PCA). To increase sustainability, in addition to lean and green, Udokporo et al. [28] also implemented agile practices. In their research, they proposed a framework based on AHP, statistical inference, and regression analysis that considers PLC and thus facilitates the choice of appropriate lean, green, and agile practices. The framework is primarily intended for the fast-moving consumer goods industry.

From all of the above, it is evident that environmental sustainability is one of the main topics of today and that companies have realized that their survival is contingent upon considering environmental indicators alongside economic ones. “Green thinking is thinking lean” is the statement of Prof. Sobek from Montana State University, USA, which he gave during his talk on “Sustainable production: A global challenge” at an international seminar in Gothenburg, Sweden [29]. As Johansson and Sudin [29] concluded, in his talk, Professor Sobek gave potential synergies between the lean and green concepts. Johansson and Sudin also explained that Dües et al. [30] stated that “lean serves as a catalyst for green, meaning it facilitates a company’s transformation towards green”. All the reviewed studies affirmed the well-established positive impact of lean practices on economic indicators while also acknowledging its positive influence on environmental indicators. The observed positive effect of lean on various aspects of sustainability emphasizes the need for the integration of lean and green management, but despite a large amount of research, their connection in a practical sense is still not fully clarified [31]. All the authors, specifically Zhu et al. [32], emphasized the need for further research that will facilitate their integrated implementation in practice through the creation of a generally accepted methodology. Additionally, there is not much research that has explored how lean tools specifically influence environmental performance indicators of a company. The main reason might be in the general perception on the lean production method, in which the main goal of implementing lean in a company is to improve production; thus, the goals are usually operational excellence, giving better value for the customers, and, ultimately, better profits. To fill this gap, the aim of this research was to analyze what the most important key performance indicators for the companies are and how specific individual lean tools influence these indicators, but besides the economic indicators, the idea was to investigate what the most important environmental indicators are and how they are affected by lean tools. The final goal was to develop a decision-making model for selecting a lean tool for implementation, taking into account the company’s priorities concerning economic and environmental performance indicators.

Thus, intention was to answer the research questions as follows:

Q1. Does the application of lean tools also affect environmental indicators, besides the economic indicators?

Q2. Is it possible to have a targeted influence on a specific environmental indicator by properly choosing a lean tool?

Q3. Is it possible to influence economic (operational) performance indicators and environmental indicators at the same time by the decision of which lean tool to implement in the company?

This research is distinctive in that it not only incorporates insights from existing literature but also integrates industry and academia expert opinions, gathered through interviews and questionnaires. Another innovative aspect is the decision model’s foundation on distinct economic and environmental indicators prioritized by the company, guiding the determination of the most suitable lean tool for the company’s strategic focus. We started from the assumption that such a model would help companies to focus on relevant lean practices in line with their goals and thus avoid the dispersion of over-the-top ideas for improving production, which often leads to failure in their application.

The subsequent sections of this paper are structured as follows: Section 2 elaborates on the methodology employed to conduct the research. Following the methodology, Section 3 presents and discusses the results obtained during and after the model’s development. Finally, in Section 4, a conclusion is drawn, along with outlining plans for future research.

2. Materials and Methods

The methodology of the research, illustrated in Figure 1, is composed of five stages: Literature Review, Semi-Structured Interviews, Expert Group Survey, Statistical Analysis of Survey, and Model Development and Model Verification. Each stage is designed to build upon the previous, creating a comprehensive and robust approach to our research.

2.1. Stage 1: Literature Review

The first stage of our methodology involved a literature review. The objective of this stage was to identify key economic and environmental performance indicators and lean management tools and to explore what has been done regarding the integration of lean and green management. This stage involved a thorough search of various scientific databases, including Science Direct, Scopus, and Web of Science.

The search was conducted using a set of carefully selected keywords, including “lean management”, “green management”, “integration of lean and green management”, “manufacturing management”, “environmental management”, and “key performance indicators”. The preliminary literature review and the corresponding findings encompassed articles published up to 2017. Subsequently, to capture the latest developments and trends, an additional literature review was conducted during the article’s composition. This additional review identified several new articles that reaffirmed the previously observed scenario, enhancing the depth of the research.

2.2. Stage 2: Semi-Structured Interviews

The second stage of our methodology consisted of conducting semi-structured interviews. The goal of this stage was to further clarify the information obtained from the literature about the connection between lean and green production within real production systems. Interviews were conducted with individuals responsible for implementing lean and green management programs in Croatian manufacturing companies. These individuals were selected based on their experience and the duration of their company’s lean and green management implementation initiatives. The main criterion was that the company was practicing lean for at least one year. For a more detailed overview of the participants involved in the interview and details about their companies, please refer to Appendix D.

The questions for the interview were defined based on the information obtained from the literature review, considering that some authors recommend the use of previously defined sets of questions in order to enable a later comparison of the results. Thus, the questionnaire that Biggs [33] used in his research was used to define the questions. The interview protocol was supplemented with questions about the use of information systems in the planning, implementation, and monitoring of lean and green production. The conducted interviews were recorded, transcribed using Express Scribe Transcription Software version 5.78, and thoroughly analyzed to obtain results on the most frequent economic and environmental performance indicators and on the most used lean tools. Information about the most used lean tools was obtained through a direct question, which enabled respondents to name the most important tools for them, while the results for economic and environmental indicators were obtained through questions about the implementation of lean and green production.

After conducting a literature review and semi-structured interviews, most of the utilized lean tools and key economic and environmental indicators that were included in further research were selected. Their final selection was based on a comparison of the frequency of appearances in the literature and interviews and on the experience of the authors.

2.3. Stage 3: Expert Group Survey

The third stage of our methodology involved the survey of an expert group. The aim of this stage was to utilize expert knowledge to assess the impact of individual lean tools on economic and environmental performance indicators. This information was vital for the development of our model. To formulate the survey questions, we utilized data derived from a literature review and thorough analysis of the interviews, focusing on the frequency of lean tools, as well as economic and environmental indicators. The expert group survey consisted of three parts. The first part of the survey was related to the assessment of the impact of selected lean tools on the chosen economic performance indicators. The second part was related to the assessment of the impact of selected lean tools on the chosen environmental performance indicators, and the third part of the survey was related to basic information about the expert. The expert group was composed of individuals from both the industry and the academic community to have different perspectives on the complexities of implementing and integrating lean and green practices. The selection criterion for experts from the industry was that they had a minimum of 2 years of experience working on projects related to lean and green production. The selection criterion for experts from the academic community was their research work in the field of lean and green production, assessed through their published papers and engagement in lean or green production implementation projects.

Table 1. Information about the experts involved in the “Expert Group Survey” research phase.

No.	Sector	Years of Experience	No.	Sector	Years of Experience
1.	Academic	3	16.	Industry	5
2.	Industry	3	17.	Industry	10
3.	Industry	20	18.	Industry	3
4.	Industry	8	19.	Industry	15
5.	Industry	6	20.	Industry	5
6.	Industry	17	21.	Industry	*
7.	Industry	4	22.	Industry	9
8.	Industry	8	23.	Academic	7
9.	Industry	13	24.	Industry	5
10.	Industry	5	25.	Industry	41
11.	Industry	18	26.	Industry	8
12.	Industry	4	27.	Academic	10
13.	Industry	*	28.	Academic	11
14.	Academic	10	29.	Academic	10
15.	Academic	9	30.	Industry	5

* Not answered.

shows information about the experts who were selected to participate in the research. The experts’ experiences were evaluated through self-assessment. All participating experts were affiliated with Croatian companies and academic institutions.

This stage also involved a multi-step statistical analysis of the data obtained from the expert group. The goal was to determine the impact of lean tools on economic and environmental performance indicators and to rank the tools based on the established impact.

The first step of the statistical analysis was the descriptive analysis. Through descriptive analysis, the average response values, median, minimum and maximum response values, and standard deviation were calculated for each of the economic and environmental indicators.

The second step was the normality test of the obtained responses. The Shapiro–Wilk test [34] was used for this purpose. This test determined whether parametric or non-parametric statistical analysis would be used in further steps of the analysis.

The third step was the Mann–Whitney U test of differences [35]. This non-parametric test is used to determine if there are differences between two independent groups on a continuous or ordinal dependent variable. It is the non-parametric equivalent to the independent t-test and is used when the assumptions of the independent t-test are not met. The Mann–Whitney U test checks whether there was a difference between responses related to the impact of lean tools on groups of economic and environmental performance indicators. It also examined whether there was a difference in the perception of the impact of lean tools on groups of economic and environmental performance indicators between experts from the academic and real sectors.

The fourth step was the Spearman correlation test [36]. This non-parametric measure of rank correlation assesses how well the relationship between two variables can be described using a monotonic function. It is used when the data do not meet the assumptions of the Pearson correlation (the relationship between the variables is not linear, or the variables are not normally distributed). This test determined whether there was a correlation between individual economic and environmental performance indicators. A correlation test was also conducted to determine if there is a correlation between the years of experience of the experts and the responses related to certain performance indicators.

The fifth step was the Kruskal–Wallis test [37]. This non-parametric method is used to compare more than two groups that are independent or not related. The test determines whether the medians of two or more groups are different. The Kruskal–Wallis test was used instead of the one-way ANOVA, since the normality test showed that the responses of the expert group did not conform to a normal distribution. The Kruskal–Wallis test, unlike the one-way ANOVA, does not assume a normal distribution, so it provides a more robust approach and more accurate results. This test checked whether there was a significant difference in the perception of the impact of lean tools on a specific economic or environmental indicator. Importantly, this step also involved obtaining the ranks of lean tools based on their perceived impact on economic and environmental indicators. The ranks of the tools were obtained based on the analysis of the questionnaire on the impact of lean tools on economic and environmental indicators, which was completed by an expert group. The analysis of the results of the questionnaire was carried out using the Kruskal–Wallis test. These ranks were crucial for the subsequent model development stage.

2.4. Stage 4: Model Development

The fifth stage of our methodology involved model development. The goal of this stage was to develop a model that would consider the priorities of a specific company regarding economic and environmental performance indicators and, based on that, determine the combination of lean management tools that would have the greatest impact.

The steps in model development included factor analysis [38] and the Analytic Hierarchy Process (AHP) method [39]. Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. It is often used when data have been collected on a large number of variables that are believed to be influenced by a smaller number of factors. Factor analysis was used to reduce the number of variables related to economic and environmental performance indicators to facilitate decision-making. These factors represent the criteria in the AHP method.

The approach involved the use of the AHP method, as it was concluded that the problem involves multiple possible alternatives and priorities, thus making it a multi-criteria decision-making problem. The AHP method is a structured technique for organizing and analyzing complex decisions based on mathematics and psychology. It was developed by Thomas L. Saaty in the 1970s and has been extensively studied and refined since then. The AHP method was used to determine the combination of lean management tools that would have the greatest impact on the company, as measured through economic and environmental indicators.

The AHP method was carried out in three consecutive steps. In the first step, the priority vector of criteria (w) was calculated, which represents the priorities of the company’s economic and environmental indicators. The priority vector of the criteria was obtained using the eigenvector method, according to which, the priority vector (w) was actually the eigenvector of the pair comparison matrix obtained by comparing pairs of criteria through a questionnaire filled out by relevant persons from the company. In the second step, the priority matrix of alternatives (S) was calculated. The alternative priority matrix (S) is an n × m matrix, and each s_ij represents the priority rating of the i-th alternative with respect to the j-th criterion, where m is the number of criteria and n is the number of alternatives. The values of the priority matrix of alternatives were obtained using the eigenvector method from the alternatives pair comparison matrix according to each of the criteria. The last step was the ranking of the alternatives according to the criteria. The ranking of alternatives is actually the sorting of the values of the vector of global priorities (v), where the i-th value of the vector (v_i) represents the global priority assigned to the i-th alternative. The vector of global priorities (v) was obtained by multiplying the priority value matrix of alternatives (S) with the vector of criteria priorities (w).

2.5. Stage 5: Model Verification

In the final stage of this research, the model was verified on simulated and real data from companies using the Expert Choice 11 software package. The purpose of the verification and sensitivity analysis is to show how the alternatives change with respect to a change in the priority of the criteria.

In the first step of model verification, 10 combinations of priority vectors are determined. Each of the combinations has a different priority assigned to each criterion. In all matrices of comparisons of pairs of criteria, care was taken that the value of the consistency ratio (CR) was not greater than 0.10. The mentioned combinations of priority vectors were taken in such a way that they represent the extreme values of individual priorities of the criteria, and such an approach provides information about the change in the value of the tool rank vector due to the change in the value of the priority vector. After the priority criteria vectors for all 10 combinations were obtained, by including them in the developed model, the global priority vectors of the alternatives for each combination were calculated. Mutual comparison of the results obtained in this way made it possible to reach a conclusion about the accuracy and sensitivity of the model.

Similar verification of the model was done in other studies. Thus, Opetuk [40] performed the verification of the model of the introduction of green supply chain management, and Büyüközkan [41] used scenario analysis in the verification of the model of the influence of lean management on the business performance of a company. This way of testing the model enables a “what-if” analysis [42]. Model sensitivity is a common way of testing and verifying models that use the AHP method.

In addition to the phase of testing the model on simulated data, the model was tested on real data obtained from a Croatian medium-sized company. The company was chosen considering that it is one of the few companies in Croatia that has made a LCA analysis for its product, and in that way, it is classified as the leading company in the use of eco-innovative methods to reduce the impact of business on the environment. The choice of the company that performs the LCA analysis was made strategically to facilitate future improvements and extensions of the model. The goal of conducting this use case was to see if the model, given real data on the priorities of the criteria set by the company, would provide reliable results of the rankings of lean tool alternatives. To determine the values of the criteria priority vector that needs to be included in the model to calculate the ranks of the alternatives, a questionnaire was used for the comparison of economic and environmental performance indicators. Using the prepared questionnaire, the individual responsible for decision-making regarding the implementation of lean tools evaluated the company priorities on a scale from 1 to 9 through a comparison of pairs of all the established economic and environmental groups of indicators. Option 1 represented equal priority between two groups in a pair, and option 9 gave absolute priority to one group of indicators. The data collected in this way, with the help of the Expert Choice 11, made it possible to obtain a criteria comparison matrix, as well as a vector of criteria priorities. After the ranks of the lean tools alternatives were obtained, a sensitivity analysis of the model was carried out to further determine the reliability of the obtained results. Model sensitivity analysis was performed in Expert Choice 11 using 3 sensitivity approaches: performance, dynamic, and gradient.

3. Results and Discussion

3.1. Key Economic and Environmental Indicators and Lean Tools

As previously noted, the initial stages of this research involved a literature review and the administration of semi-structured interviews. The primary objective during these phases was to identify essential lean tools and key economic and environmental performance indicators that would subsequently inform the development of the model. The following section presents the results obtained for the observed lean tools and economic and environmental indicators. Comprehensive data on the analyzed literature can be found in Appendix A, Appendix B and Appendix C, while additional insights into the implementation and outcomes of the semi-structured interviews were elaborated upon by the authors in [43].

The Identification of lean tools, subject to assessment for their impact on economic and environmental indicators, involved an analysis of their frequency in the literature and through interviews.

Table 2. A list showing the occurrence frequency of lean tools in the literature review and interviews and their further research consideration status.

Num.	Lean Tools	Frequency of Appearing in the Literature	Frequency of Appearing in Interviews	Sum of Frequencies of Appearing	Considered for Further Research
1.	5S	8	10	18	Yes
2.	TPM	6	8	14	Yes
3.	SMED	8	6	14	Yes
4.	VSM	6	6	12	Yes
5.	Kaizen	5	6	11	Yes
6.	Kanban	8	2	10	Yes
7.	Visual management	4	5	9	No
8.	Analysis tools	3	5	8	No
9.	Key performance indicators (KPI)	1	6	7	Yes
10.	Inventory management	3	4	7	Yes
11.	Standardized work	5	2	7	No
12.	Just in time production (JIT)	6	1	7	Yes
13.	Poka-Yoke	5	0	5	No
14.	Cell production	5	0	5	Yes
15.	Value/Waste	0	1	1	No
16.	One-piece flow	1	0	1	No

shows the comparison, as well as the tools that were considered.

From the 16 tools that had the highest frequency in the literature and through interviews, the following 10 were chosen for further research: 5S, TPM, SMED, VSM, Kaizen, Kanban, KPI, Inventory management, JIT, and Cell production.

Using the same logic as in the selection of lean tools, economic and environmental indicators were selected to be used in the questionnaire for the expert group.

Table 3. A list showing the occurrence frequency of economic indicators in the literature review and interviews and their further research consideration status.

Num.	Economic Indicators	Frequency of Appearing in the Literature	Frequency of Appearing in Interviews	Sum of Frequencies of Appearing	Considered for Further Research
1.	Expenses (OI)	10	7	17	Yes
2.	Quality (OI)	8	6	14	Yes
3.	Profit (FI)	3	10	13	Yes
4.	Flexibility (OI)	7	5	12	Yes
5.	Productivity (OI)	4	7	11	Yes
6.	Lead Time (OI)	4	6	10	Yes
7.	Market Share (TI)	6	2	8	Yes
8.	Delivery (OI)	4	2	6	Yes
9.	Inventory (OI)	2	4	6	No
10.	Sales (TI)	3	2	5	No

shows the most frequent and selected economic indicators.

As can be seen from Table 3, eight economic indicators were selected for further research. In addition to six operational indicators (Costs, Quality, Flexibility, Productivity, Lead Time, and Delivery), two additional indicators were taken into account: one from the group of financial indicators (FI) (Profit) and one from the group of market indicators (MI).

The same analysis was done for selecting environmental indicators. Their frequency in the literature, as well as their frequency through interviews, were considered.

Table 4. A list showing the occurrence frequency of environmental indicators in the literature review and interviews and their further research consideration status.

Red. br.	Environmental Indicators	Frequency of Appearing in the Literature	Frequency of Appearing in Interviews	Sum of Frequencies of Appearing	Considered for Further Research
1.	Resource usage	4	27	31	Yes
2.	Environmental management	17	11	28	Yes
3.	Waste	6	21	27	Yes
4.	Energy use	11	9	20	Yes
5.	Air emissions	16	2	18	Yes
6.	Water usage	10	8	18	Yes
7.	Use of materials	9	6	15	Yes
8.	Wastewater	2	1	3	No
9.	Reputation	2	1	3	No
10.	Product impacts	2	1	3	No
11.	Noise	2	0	2	No

shows the most frequent and selected environmental indicators.

As can be seen from Table 4, seven environmental indicators from seven aspects were chosen for further research. These seven indicators were Resource use, Environmental management, Waste, Energy use, Air emissions, Water use, and Use of materials.

The findings regarding the most frequent lean tools are unsurprising, as the selected tools represent the most well-known and effective options within the lean toolbox. While there may be variations attributable to regional and scope differences, the tools identified in this study align with those observed by researchers in analogous lean implementation studies such as [9,10]. Furthermore, the introductory section illustrates the inclusion of certain observed tools such as VSM, Kaizen, and SMED in previous research focused on the integration of lean and green principles. Despite having a high frequency of occurrence, the Visual Management and Analysis tools were not considered due to past experiences, where these tools were frequently integrated into larger frameworks such as Kaizen and 5S. On the other side, although they do not have the highest frequency sum, the tools Just-in-time (JIT) and Cell manufacturing (CELL) were considered, and the reason for this decision lies in the knowledge that these two tools represent the fundamental tools of lean management and are often mentioned in the literature. As for economic indicators, the data from the table show that most of them are from the group of operational indicators (OI), and the reason for this lies in the fact that these are processing companies that have the ability to monitor these indicators exactly. The results obtained for key economic and environmental indicators are in line with expectations, given the industrial context of the research within production companies. Moreover, the alignment of our findings with the economic and environmental key performance indicators documented in the studies [4,6] underscores the consistency in the observed metrics. While all indicators were considered, a noticeable difference in the frequency of certain indicators emerged. This variance can be primarily attributed to the distinct approach of this research, which not only relied on a literature review but also involved the determination of indicators through interviews.

A literature review and semi-structured interviews provided a more detailed picture of the integration of lean and green production in manufacturing companies. Since many companies do not prioritize environmental concerns when implementing lean tools, the findings from our research must highlight which lean tools are the most environmentally friendly, which means that they not only contribute to economic and operational indicators but also positively impact environmental factors. To facilitate a clearer identification of the tool’s influences on economic and environmental indicators, as well as their average impact, we conducted a survey among experts.

3.2. Expert Group Survey

The expert group consisted of 30 experts in the field of lean management who come from the academic and real sectors. After the experts filled out a questionnaire related to the impact of lean and green tools on economic and environmental indicators, an analysis of the answers was carried out. Of the 30 completed questionnaires, 2 were incomplete, but since the questions in the questionnaire were not mutually dependent, the answers received were accepted, with the analysis considering the final number of completed questionnaires for a certain question (N).

The analysis was carried out in several steps:

• A descriptive analysis of the responses was made.
• A test of the normality of the obtained answers was carried out.
• A Mann–Whitney U difference test was performed.
• A Spearman’s correlation test was performed.
• A Kruskal–Wallis test was used to calculate the tool ranks.

3.2.1. Descriptive Analysis

In the first step of the analysis, a descriptive analysis of the received answers was carried out. For each economic and environmental indicator, we computed the average response value, median, minimum and maximum response values, and standard deviation.

Table 5. Results of a descriptive analysis of expert group survey responses for environmental indicator 1 (EN1).

Tools	N	Mean	Median	Min.	Max.	SD
5S	30	7.2000	7.00	4.00	9.00	1.3995
CELL	29	7.0000	7.00	4.00	9.00	1.3093
INV	30	7.9000	8.50	3.00	9.00	1.6049
JIT	29	7.6207	8.00	3.00	9.00	1.7406
KAI	30	7.8333	8.00	4.00	9.00	1.4162
KPI	30	7.6667	8.00	3.00	9.00	1.5388
PULL	30	7.3333	8.00	3.00	9.00	1.4223
SMED	30	7.2333	8.00	2.00	9.00	1.9241
TPM	30	7.7000	8.00	3.00	9.00	1.3933
VSM	30	7.5667	8.00	5.00	9.00	1.3817

shows a descriptive analysis of the responses for the indicator EN1. The same procedure was done for the other included indicators.

From the entire descriptive analysis, it can be concluded that 5S has the least impact on the average value towards indicator E7 (6.1333), while SMED has the greatest impact on indicator E4 (8.5667). When considering the median, 5S has the lowest median value for indicator E7 (5.5), and the highest median value is 9.0 in several combinations, such as SMED on E4, E5, and E6; VSM on E6; and 5S on EN3. By focusing solely on the median values, one can deduce that 5S exhibits the least positive impact on the market share. This aligns with expectations, as establishing a direct correlation between workplace cleanliness, visual aspects, and safety activities with an increase in the company’s market share can be inherently challenging. On the other hand, SMED has the highest median value, particularly influencing flexibility, productivity, and lead time. This result is explained by the fact that reducing the tool changeover time shortens the overall production time per piece, automatically increasing productivity. Additionally, a shorter tool changeover time allows for more frequent changes, enhancing production flexibility and reducing lead time. Notably, VSM has a significantly positive impact on lead time (E6), attributed to its ability to observe the entire value stream of a product. The last combination with a median value of 9 is the impact of the lean tool 5S on the environmental indicator EN3 (waste). This high median value can be explained by the observation that 5S activities contribute to enhanced workplace maintenance, fostering heightened awareness in waste management. Consequently, this often results in a reduction of waste. The observed positive impacts of VSM on lead time and 5S on waste reduction align with findings from previous research [6]. Furthermore, the research [19] also acknowledges the role of SMED in reducing lead time and enhancing productivity.

3.2.2. Normality Test

The next step in the data analysis was to perform a normality test to determine if the data behaved according to a normal distribution. This step was very important, since the result of this test determined whether to use parametric or non-parametric data analysis. To determine the normality of the data, the Shapiro–Wilk test was used.

It can be seen from

Table 6. Four combinations of indicators and lean tools for which the results of the Shapiro–Wilk test show that, at the 5% significance level (p > 0.05), the hypothesis H0 that claims that the data are adjusted to a normal distribution cannot be rejected.

Indicator	Tool	W
EN6	CELL	0.93288
EN4	CELL	0.92909
EN3	TPM	0.93061
EN7	SMED	0.92985

that, for only four combinations of indicators and lean tools, the H0 hypothesis (H0—the data are adjusted to a normal distribution) cannot be rejected at the 5% significance level. There is a total of 150 such combinations (10 lean tools × 15 economic and environmental indicators). Given this result, it can be concluded that the data do not fit a normal distribution; therefore, further analysis of the obtained responses will be carried out using statistical tools for non-parametric analysis.

3.2.3. Mann–Whitney U Difference Test

To give an answer whether there is a difference between the answers related to economic and environmental indicators, the Mann–Whitney U test was performed, with the following hypotheses:

H0—There is no statistically significant difference between the two groups of indicators (economic and environmental).

H1—There is a statistically significant difference between the two groups of indicators (economic and environmental).

A comparison of the responses between all environmental and economic indicators is presented in

Table 7. Mann–Whitney U test results demonstrating statistically significant differences in the responses for all economic and environmental indicators.

Ranks Sum 1 E	Ranks Sum 2 EN	U	Z	p-Level	Z Adjusted	p-Level	N 1 E	N 2 O
4,998,213	3,823,888	1,902,108	7.4759	0.0000	7.6633	0.0000	2240	1960

.

The result of the Mann–Whitney U test provides information that the H0 hypothesis can be rejected at the 5% significance level. This outcome was anticipated, since lean tools are primarily designed to enhance economic indicators. However, the observed difference could be attributed, in part, to the less prevalent practice of monitoring the effects of lean tools on environmental indicators compared to their impact on economic indicators. The question arises whether there is a difference between the answers given by experts from the academic sector and experts from the real sector. Therefore, a comparison of the answers between the group of experts from the academic sector (AC) and the group of experts from the real sector (RS) was carried out. The results revealed a significant difference in responses (ratings) between experts in the real and academic sectors, with the exception of the Cell production (CELL) tool.

The obtained results aligned with the initial assumption that differences would arise, prompting the inclusion of experts from both the real and academic sectors in the expert group. This approach allows for a comprehensive observation of the received answers, enhancing the model’s credibility by incorporating perspectives from both academic and industry contexts. The academic sector contributes a perspective based in cutting-edge research within the area of lean and green management, incorporating insights from the latest trends in the literature. On the other hand, the real sector provides a viewpoint shaped by the practical application of lean and green management within the real economy conditions of Croatia.

3.2.4. Spearman’s Correlation Test

The fourth step of the analysis was the Spearman’s correlation test. The Spearman’s correlation test is performed when we have a small number of respondents and when the data obtained from the questionnaire do not follow a normal distribution. Given that the answers of the expert group obtained through the questionnaire were not adjusted to a normal distribution, the Spearman’s correlation test was used to test the correlation between the variables.

The interpretation of the calculated values of the correlation coefficients can be carried out according to the instructions given in

Table 8. Instructions for the interpretation of the correlation coefficients obtained by performing the Spearman’s correlation test.

rS	Description
0 to ±0.25	No correlation
±0.26 to ±0.50	Weak correlation
±0.51 to ±0.75	Moderate to good correlation
±0.76 to ±1	Very good to excellent correlation
1	Monotonic correlation

.

First, the Spearman’s correlation test was performed between environmental indicators. The test showed that there was a positive correlation between environmental indicators. The strongest correlation was between indicators EN5 and EN6 (0.900931). There were two more strong correlations between indicators EN4 and EN5 and indicators EN4 and EN6. The correlation between EN5 and EN6 could be explained by an increased concern for the environment, where companies that take care of water use more conscientiously take care of their own emissions into the air. Also, the correlation of indicator EN4 (use of energy) with indicator EN5 (emissions to the air) makes sense, given that, when energy consumption is reduced, emissions to the air resulting from the production of electricity also decrease.

Next, a correlation test was conducted among economic indicators, followed by an examination of the correlation between economic and environmental indicators. Additionally, a correlation analysis was undertaken to determine the relationship between experts’ years of experience and their responses referring to specific indicators. The results of the Spearman’s correlation test are shown in

Table 9. Spearman’s correlation test results examining the relationships between economic indicators, economic and environmental indicators, and experts’ years of experience with answers for individual indicators.

Indicators	Years of Experience	E1	E2	E3	E4	E5	E6	E7	E8
Years of Experience	1.000000	−0.052372	−0.018949	−0.148073	−0.124623	−0.217082	−0.180167	−0.039580	−0.201201
E1	−0.052372	1.000000	0.428044	0.664179	0.368401	0.504486	0.413229	0.362437	0.477145
E2	−0.018949	0.428044	1.000000	0.470104	0.279347	0.511427	0.354820	0.407382	0.393280
E3	−0.148073	0.664179	0.470104	1.000000	0.381508	0.508813	0.397624	0.478441	0.530912
E4	−0.124623	0.368401	0.279347	0.381508	1.000000	0.636382	0.580697	0.390371	0.520394
E5	−0.217082	0.504486	0.511427	0.508813	0.636382	1.000000	0.730605	0.393231	0.645093
E6	−0.180167	0.413229	0.354820	0.397624	0.580697	0.730605	1.000000	0.422970	0.630421
E7	−0.039580	0.362437	0.407382	0.478441	0.390371	0.393231	0.422970	1.000000	0.621485
E8	−0.201201	0.477145	0.393280	0.530912	0.520394	0.645093	0.630421	0.621485	1.000000
EN1	−0.232451	0.398451	0.416948	0.404460	0.474563	0.484594	0.367243	0.388602	0.492298
EN2	−0.178823	0.473523	0.462264	0.411140	0.274869	0.395364	0.329741	0.417008	0.458195
EN3	−0.166220	0.430231	0.473612	0.438985	0.365266	0.476697	0.359579	0.370944	0.452077
EN4	−0.234140	0.442257	0.421029	0.467124	0.446153	0.520004	0.425767	0.451772	0.531968
EN5	−0.181986	0.397058	0.391657	0.463283	0.411818	0.463667	0.361834	0.415600	0.488393
EN6	−0.140033	0.422794	0.418349	0.448940	0.394451	0.480835	0.383899	0.429251	0.497602
EN7	−0.168859	0.442863	0.398971	0.444631	0.414293	0.497400	0.411950	0.387357	0.455898

. Table 9 shows that there is a weak-to-excellent correlation between all economic indicators and between economic and environmental indicators. Also, it can be seen from Table 9 that there is no correlation between the variable “years of experience” and the variables representing economic and environmental indicators. The interpretation of this result suggests that the carefully chosen lower limit for the required years of experience for participating experts in the research was appropriate.

3.2.5. Lean Tools Impact on Economic Indicators

The last step of the analysis was the Kruskal–Wallis test. The first goal of the Kruskal–Wallis test was to examine whether there is a statistically significant difference in the perception of the impact of lean tools on a specific economic indicator.

The Kruskal–Wallis test showed that there are statistically significant differences (at a significance level of 5%) between lean tools according to indicators E1, E2, E4, E5, E6, and E7, while there are no statistically significant differences for indicators E3 and E8. Thus, the lean tool Inventory Management (INV) has the highest average rank in the E1 indicator (180.20); Kaizen (KAI) in the E2 indicator (208.87); KPI in the E3 indicator (177.23); SMED at indicator E4 (228.32), indicator E5 (189.22), and indicator E6 (199.97); lean tool Just-in-time (JIT) production at indicator E7 (188.28); and Value Stream Mapping (VSM) at indicator E8 (177.38).

Considering the outcomes of the Kruskal–Wallis test, which indicate statistically significant differences only for indicators E1, E2, E4, E5, E6, and E7, along with the details highlighted in the preceding paragraph, it becomes possible to determine which of the lean tools has the most substantial impact on these specific indicators Thus, Inventory Management (INV) has the largest impact on Expenses (E1), Kaizen (KAI) on Quality (E2), SMED on Flexibility (E4), Productivity (E5) and Lead Time (E6) and JIT on Market Share (E7).

The largest impact of Inventory Management on expenses makes sense, considering that inventory has tied up capital. The greatest impact of Kaizen on Quality can be explained through continuous process improvement, which is the basis of Kaizen. The greatest impact of SMED on Flexibility, Productivity, and Lead Time is not surprising, considering that reducing the tool change time directly affects Lead Time reduction, reducing the size of batches, which leads to an increase in Productivity and, ultimately, the Flexibility of the company. Finally, the greatest impact of JIT on Market Share can be explained by the current situation on the market, in which customers demand a JIT method of delivery from their suppliers, and companies that can adapt to it could win a larger Market Share.

3.2.6. Lean Tools Impact on Environmental Indicators

The second goal of the Kruskal–Wallis test was to examine whether there is a statistically significant difference in the perception of the impact of lean tools on a specific environmental indicator. The Kruskal–Wallis test shows that there are statistically significant differences (at a significance level of 5%) between lean tools according to indicators EN2 and EN3, while there are no statistically significant differences for indicators EN1, EN4, EN5, EN6, and EN7.

Taking into account the result of the Kruskal–Wallis test and the information that statistically significant differences between the tools exist only for indicators EN2 and EN3, as well as the information mentioned in the previous paragraph, it can be determined for the mentioned indicators which of the lean tools has the greatest influence on them. Thus, the lean tool 5S has the greatest impact on Environmental management (EN2) and Waste (EN3); Kaizen (KAI) on Resource usage (EN1), Energy use (EN4), Water usage (EN6), and Use of materials (EN7); and KPI on Air emissions (EN5).

The obtained results are not surprising, since 5S has a direct impact on the reduction of waste but also on the organization of its collection, classification, and later disposal. On the other hand, Kaizen refers to the continuous improvement of all processes, and this continuous change also refers to the improvement of the company’s environmental management system. As the basis of Kaizen is the PDCA cycle, and it is also mentioned in the most famous standard for environmental management systems ISO 14001 [44], a link between Kaizen and the environmental indicators could be obtained here. Similarly, establishing KPIs that encompass not only economic factors but also incorporate environmental indicators will undeniably influence the approach to environmental and waste management and, subsequently, air emissions.

3.2.7. Tools Ranking According to Impact on Indicator Groups

Ultimately, the Kruskal–Wallis test was employed to determine the ranks of lean tools, assessing their average impact on both economic and environmental indicators as reported by experts. Furthermore, a question emerges regarding the collective average impact of each lean tool on both economic and environmental indicators when considered together. The results are given in

Table 10. Average ranks of lean tools as determined by the Kruskal–Wallis test, analyzing the impact of individual tools on economic indicators, environmental indicators, and the combined influence on economic and environmental indicators.

Tools	Economic	Environmental	Economic and Environmental
5S	1118.89	1105.74	2228.67
CELL	1064.89	859.72	1923.48
INV	978.12	951.68	1932.01
JIT	1142.39	937.79	2077.00
KAI	1228.18	1149.46	2373.06
KPI	1106.71	1102.92	2211.85
PULL	1068.91	865.54	1930.96
SMED	1218.43	832.38	2047.95
TPM	1101.28	946.51	2047.73
VSM	1177.22	1053.26	2232.29

.

As can be seen from Table 10, there are statistically significant differences (at the significance level of 5%) between lean tools, if they are compared according to environmental, economic, and collective data. Therefore, the ranking of lean tools can be presented according to economic, environmental, and collective indicators.

If the impact of lean tools is observed only on economic indicators, it can be seen in Figure 2 that Kaizen (KAI) has the greatest impact, followed by SMED and VSM.

If the impact of lean tools on all environmental indicators is observed, Figure 3 shows that Kaizen (KAI) has the greatest impact on environmental indicators, followed by 5S and KPI. The smallest, but still positive, impact on environmental indicators is from the lean tool Single-Minute Exchange of Die (SMED).

Analyzing the insights from Figure 3, it is evident that it presents a ranking list of green lean tools. Consequently, according to expert opinions, Kaizen emerges as the greenest lean tool. This implies a noteworthy conclusion that the implementation of lean tools possesses the potential to mitigate the environmental impact of businesses assessed through the seven environmental indicators.

When evaluating the simultaneous impact of lean tools on economic and environmental indicators, as illustrated in Figure 4, an integrated approach reveals that Kaizen (KAI), VSM, and 5S exhibit the most substantial influences. Conversely, Cell production (CELL) demonstrates the least impact in this context.

When looking at the impact of lean tools on economic indicators, the positive impact of Kaizen, TPM, and JIT is consistent with the research conducted by Belekoukias et al. [45] on the impact of lean tools and methods on economic indicators (quality, speed, reliability, flexibility, and costs) in the UK. Through the mentioned research, the result was also obtained that VSM has a negative impact on economic indicators, which is not in accordance with this research and the research conducted by Chen and others [46]. As mentioned previously, all lean management tools examined in this research demonstrate a positive impact on economic indicators, with the Inventory Management (INV) tool exhibiting the least favorable influence. On the other hand, when looking at the impact of lean tools only on environmental indicators, the positive impact of VSM, 5S, TPM, and Cell production is consistent with the results of the research translated by Chiarini [47], in which he investigated the impact of five lean tools on environmental improvements. Moreover, in the previously mentioned research, SMED did not demonstrate a substantial influence on environmental improvements, a contrast to the findings in this study where SMED, although showing the smallest impact, remains noteworthy.

The information that the lean tool Kaizen has the greatest impact when environmental and economic indicators are considered, separately or together, which can be explained by the fact that Kaizen means continuous improvement of all processes, and this continuous change therefore brings about the expected results measured by economic and environmental indicators. Also interesting is the information that the lean tool SMED has, according to the expert group’s assessment, a very high impact on economic indicators (right after Kaizen), while, on the other hand, it has the smallest impact on environmental indicators. The impact of SMED on economic indicators can be explained by the impact that SMED has on increasing Flexibility and Productivity and reducing Lead Time in production. On the other hand, the savings achieved by SMED are difficult to relate to environmental indicators. Although 5S is often used in combination with SMED, in this case, it is ranked very high in terms of impact on environmental indicators.

3.3. Model Development

Based on the literature analysis, semi-structured interviews, and the work of the expert group, the goal was to create a decision-making model on the choice of lean tools regarding the economic and environmental goals of the company. The developed model employs the AHP method for multi-criteria decision-making, providing companies with insights into which lean management tools yield optimal results. This consideration takes into account the company’s priorities regarding specific economic and environmental performance indicators.

Before creating the decision model, a factor analysis was carried out to determine whether it is possible to reduce the number of variables (indicators) in the model to make the choice easier for decision makers. Using factor analysis, the number of variables in the model was reduced from 15 to 6. The factors obtained by factor analysis represent the criteria in the AHP method.

3.3.1. Factor Analysis

The goal of factor analysis is to determine a smaller number of fundamental variables in a larger number of interrelated variables that explain such an interrelationship. Factor analysis is performed in several steps [38]:

• Assessment of the suitability of the data for the application of factor analysis.
• Determination of the initial results for factor secretion.
• Determination of the matrix of the factor structure and the final results after extracting the factors.
• Performing factor rotation if the initial matrix of the factor structure is not interpretable or if it does not meet the set criterion of a simple structure.
• Determination of the factor matrices and final results after the rotation of factor i.
• Interpretation of the secreted factors after rotation.

Economic indicators are therefore divided into three factors, as shown in

Table 11. The results of the factor analysis showing the distribution of economic indicators between the three obtained economic factors.

Factor 1 (EF1)	Factor 2 (EF2)	Factor 3 (EF3)
E4: Flexibility	E1: Costs	E7: Market share
E5: Productivity	E2: Quality	E8: Delivery
E6: Lead time	E3: Profit

.

The factors listed in Table 11 shed light on critical aspects of the company’s operations. Economic factor 1 (EF1) reveals the interconnected nature of Flexibility, Productivity, and Lead Time, forming the bedrock for operational excellence. In economic factor 2 (EF2), Costs, Quality, and Profit are intricately examined, offering insights into the financial performance and quality of the company. Economic factor 3 (EF3) captures the dynamics of Market Share and Delivery, providing a holistic view of the company’s overall market performance.

After the factors of the economic indicators were obtained, a factor analysis was also carried out for the environmental indicators. The environmental indicators were divided into three factors, as shown in

Table 12. The results of the factor analysis showing the distribution of environmental indicators between the three obtained environmental factors.

Factor 1 (OF1)	Factor 2 (OF2)	Factor 3 (OF3)
EN4: Usage of energy	EN1: Resource usage	EN2: Environmental management
EN5: Air emissions		EN3: Waste
EN6: Water usage
EN7: Material usage

.

Environmental factor 1 (OF1) represents Energy, Water, Material usage, and Air emissions. Environmental factor 2 (OF2) focuses on Resource usage (land and air), while environmental factor 3 (OF3) encompasses Environmental management and Waste.

3.3.2. Developing a Model with the AHP Method

When developing the decision model, the AHP model is visually represented as depicted in Figure 5. To obtain the result of the AHP method in accordance with the set goal, the Expert Choice 11 program was used. This program is a specialized software tool, which serves as a support for multi-criteria decision-making methods.

In the AHP model, the criteria are represented by economic and environmental factors obtained by factor analysis. In order to obtain a ranking list of lean tools taking into account economic and environmental indicators, it is necessary to first establish a ranking list of alternatives (lean tools) according to the given criteria and then make a ranking list of criteria with regard to the priority of a certain indicator in the company. Not all indicators have the same priority within a company; therefore, a comparison of priorities pairs was made for one company to obtain the priority ranking.

The results of the average ranks of economic and environmental factors and the weights calculated from them are shown in

Table 13. Average ranks and weights of lean tools according to economic factors.

	Factor 1 (EF1)		Factor 2 (EF2)		Factor 3 (EF3)
	E4, E5, E6		E1, E2, E3		E7, E8
Tool	Rank	Weight	Rank	Weight	Rank	Weight
5S	127.3324	0.1044	103.7948	0.0952	99.8754	0.0852
CELL	120.2349	0.0986	95.6989	0.0877	112.8716	0.0962
INV	86.0042	0.0705	112.1244	0.1028	107.7214	0.0918
JIT	113.6665	0.0932	107.5760	0.0986	143.7562	0.1226
KAI	126.1638	0.1034	132.0807	0.1211	121.3144	0.1034
KPI	103.4198	0.0848	124.9571	0.1146	123.7957	0.1056
PULL	120.7252	0.0990	94.1395	0.0863	119.5953	0.1020
SMED	168.1572	0.1379	96.4193	0.0884	115.9521	0.0989
TPM	115.9269	0.0950	116.8527	0.1071	97.9618	0.0835
VSM	138.1120	0.1132	107.0415	0.0981	130.0024	0.1108
Sum	1219.7428	1.0000	1090.6848	1.0000	1172.8464	1.0000

and

Table 14. Average ranks and weights of lean tools according to environmental factors.

	Factor 1 (OF1)		Factor 2 (OF2)		Factor 3 (OF3)
	EN4, EN5, EN6, EN7		EN1		EN2, EN3
Tool	Rank	Weight	Rank	Weight	Rank	Weight
5S	112.8883	0.1007	145.8877	0.1119	151.4102	0.1329
CELL	98.8018	0.0881	111.8316	0.0858	100.3270	0.0881
INV	107.5408	0.0959	124.7533	0.0957	113.2494	0.0994
JIT	109.5138	0.0977	130.1872	0.0999	104.2410	0.0915
KAI	131.9411	0.1177	147.4023	0.1131	138.9878	0.1220
KPI	131.0063	0.1168	132.0843	0.1013	130.2391	0.1143
PULL	101.2667	0.0903	120.3734	0.0924	94.9600	0.0834
SMED	97.6434	0.0871	120.9979	0.0928	85.7758	0.0753
TPM	108.5693	0.0968	131.6159	0.1010	102.0948	0.0896
VSM	122.2537	0.1090	138.1584	0.1060	117.8180	0.1034
Sum	1121.4252	1.0000	1303.2921	1.0000	1139.1031	1.0000

.

The impact assessments of lean tools on economic and environmental indicators were acquired via a questionnaire and the normalization of average ranks for alternatives. Consequently, the weights of lean tools were determined based on the specified criteria, eliminating the need for checking the consistency of the responses. The weights of the alternatives obtained according to the criteria were entered into Expert Choice 11, where they represented the value of the alternative’s priority in relation to the specific criterion.

In the second step, the priority ranks of the criteria were calculated. The criteria priority ranks were obtained by comparing pairs of criteria using the previously described questionnaire for the comparison of economic and environmental performance indicators. The questionnaire could be completed by one or more employees from the company who were in a leading position in production and could assess the priorities of economic and environmental indicators. To determine the priority of a certain criterion (indicator), Saaty’s priority scale was used.

The results of the comparison obtained by the questionnaire were entered into the Expert Choice 11 program in order to calculate the priority vector of the alternatives. Ultimately, a decision-making model was formulated to guide the selection of lean tools, with the overarching goal of seamlessly integrating both lean and green management objectives.

$$\left[\begin{array}{c}\mathrm{Alternatives} \mathrm{priority}\\ \mathrm{matrix} \mathrm{according} \mathrm{to}\\ \mathrm{certain} \mathrm{criteria}\end{array}\right]·\left[\begin{array}{c}\mathrm{Company} \mathrm{criteria}\\ \mathrm{priority} \mathrm{vector}\end{array}\right]=\left[\begin{array}{c}\mathrm{Vector} \mathrm{of} \mathrm{global} \mathrm{priorities}\\ \mathrm{of} \mathrm{alternatives} \mathrm{for}\\ \mathrm{individual} \mathrm{company}\end{array}\right]$$

(1)

In Expression (1), “Vector of global priorities of alternatives for an individual company” represents the ranking list of lean tools obtained by the AHP method, which is related to the priorities that the company gives to certain economic and environmental criteria. The production management model by integrating lean and green management helps companies to prioritize the use of certain lean tools, taking into account the priority that the company gives to each of the six economic and environmental factors.

3.4. Model Verification on Simulated Data

After creating the model using the AHP method, the model was verified on simulated data to determine whether it provides the expected results for extreme values of the criteria’s priority. Concerning the assessment of lean tools based on specific economic and environmental factors, there was an anticipation of which lean tools should a particular combination of extreme values within a specific criterion establish as the most suitable. It was to be expected, for example, that, in a combination in which environmental criteria had absolute priority over economic criteria, and environmental criterion OF3 had absolute or leading priority over the other environmental criteria, that alternative 5S would have the highest priority. If the model showed the expected result for each of the extreme combinations, it would be possible to determine that the model works correctly.

As mentioned earlier, the verification process using simulated data began with the determination of 10 combinations of criteria priority vectors, where each combination assigned different values to individual criteria. All 10 combinations of criteria priority vectors and their values for individual criteria are shown in

Table 15. The average ranks of the criteria priority vectors for all 10 simulated combinations.

	Combination
Criterion	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.
EF1	0.300	0.550	0.178	0.178	0.033	0.028	0.028	0.028	0.167	0.444
EF2	0.300	0.178	0.550	0.189	0.033	0.028	0.028	0.028	0.167	0.153
EF3	0.300	0.189	0.189	0.550	0.033	0.028	0.028	0.028	0.167	0.165
OF1	0.033	0.028	0.028	0.028	0.300	0.550	0.178	0.178	0.167	0.093
OF2	0.033	0.028	0.028	0.028	0.300	0.178	0.550	0.189	0.167	0.057
OF3	0.033	0.028	0.028	0.028	0.300	0.189	0.189	0.550	0.167	0.088

.

The criteria priority vectors shown in Table 15 were obtained with the help of the criteria pair comparison matrices for all 10 simulated combinations. The developed model was then employed to derive global priority vectors for alternatives from the calculated criteria priority vectors, as depicted in

Table 16. Output of the model in the form of global priority vectors of alternatives (lean tools) for combinations 1–5.

Combination 1		Combination 2		Combination 3		Combination 4		Combination 5
Alternative	Priority	Alternative	Priority	Alternative	Priority	Alternative	Priority	Alternative	Priority
KAI	0.110	SMED	0.116	KAI	0.115	JIT	0.111	KAI	0.117
VSM	0.107	VSM	0.109	KPI	0.108	KAI	0.108	5S	0.113
SMED	0.105	KAI	0.108	VSM	0.104	VSM	0.108	KPI	0.110
JIT	0.104	5S	0.100	JIT	0.102	KPI	0.104	VSM	0.106
KPI	0.103	JIT	0.100	TPM	0.100	SMED	0.102	JIT	0.097
5S	0.097	KPI	0.097	SMED	0.098	PULL	0.097	INV	0.096
PULL	0.095	PULL	0.096	5S	0.096	CELL	0.094	TPM	0.096
TPM	0.095	CELL	0.095	INV	0.095	5S	0.093	PULL	0.09
CELL	0.093	TPM	0.095	PULL	0.092	INV	0.091	CELL	0.088
INV	0.090	INV	0.083	CELL	0.091	TPM	0.091	SMED	0.088

and

Table 17. Output of the model in the form of global priority vectors of alternatives (lean tools) for combinations 6–10.

Combination 6		Combination 7		Combination 8		Combination 9		Combination 10
Alternative	Priority	Alternative	Priority	Alternative	Priority	Alternative	Priority	Alternative	Priority
KAI	0.117	KAI	0.115	5S	0.119	KAI	0.114	KAI	0.110
KPI	0.112	5S	0.112	KAI	0.118	KPI	0.107	SMED	0.110
5S	0.108	KPI	0.106	KPI	0.111	VSM	0.107	VSM	0.109
VSM	0.107	VSM	0.106	VSM	0.105	5S	0.105	5S	0.102
JIT	0.098	JIT	0.099	INV	0.097	JIT	0.101	JIT	0.100
INV	0.096	TPM	0.098	JIT	0.096	SMED	0.096	KPI	0.100
TPM	0.096	INV	0.096	TPM	0.094	TPM	0.096	PULL	0.095
PULL	0.090	PULL	0.091	CELL	0.088	INV	0.093	TPM	0.095
CELL	0.088	SMED	0.09	PULL	0.088	PULL	0.092	CELL	0.094
SMED	0.088	CELL	0.087	SMED	0.084	CELL	0.091	INV	0.086

. Based on the obtained results, it can be concluded that the model assigns varied ranks to lean tools depending on the specific values within the criteria’s priority vector.

For this research, the most interesting cases of priority vectors were from combinations 1, 5, and 9, given that 1 and 5 represent extreme values of economic compared to environmental success factors and vice versa, and combination 9 represents the case of vectors with equal priority of all the criteria. A comparison of the values of the priority vectors of alternatives for all combinations is provided by Figure 6.

Looking at Figure 6, we can see the interweaving of lines in the graph that indicate the priority values of the alternatives, which means that changing the criteria also changes the rank of the alternative. If one observes the values of the alternative’s priority vector for combinations in which only the priority of an individual economic factor changes (combinations 2, 3, and 4), it can be seen that, regarding the priority vector of the criteria given for a certain combination, there are differences in the values assumed by certain alternatives. The biggest difference is visible with the SMED alternative. Likewise, if combinations 5, 6, 7, 8, and 9 are compared, it can be concluded from Figure 6 that there are not so pronounced differences between the priority values of the alternatives. Although these differences are not so noticeable in the given figure, Table 16 and Table 17 show that, in each of the combinations, there were changes in the priorities of the alternatives, which suggests a good sensitivity of the model.

To determine the correctness of the model, it is interesting to note that alternative 5S in combination 8 has the highest priority, which is desirable given the fact that alternative 5S has the highest weight in the weighting vector of environmental factor 3. By this alone, it can be concluded that the model works well, considering that it puts the alternatives that have the highest weight in the weighting vector of a certain factor in the first place, which is also the case with combination 8, which places the highest priority of criteria on criterion OF3. Such is the case with combination 2 with SMED, combination 3 with Kaizen (KAI), and combination 4 with JIT and also with other combinations in which environmental factors have a higher priority, as is the case with Kaizen (KAI) in the 6th and 7th combinations. Therefore, it can be concluded that the model has been successfully verified, because it prioritizes the alternatives that have the highest weighting value for the criterion that, in the given combination, has absolute priority.

By carrying out the verification, it was concluded that the model gives different ranks of lean tools with regards to different values of the criteria’s priority vector. It was shown that, in each of the combinations, there were changes in the priorities of the alternatives, which suggests a good sensitivity of the model.

3.5. Use Case: Model Verification on Real Data

Through the phase of model verification on simulated data, it was seen that the model works correctly; therefore, in the second phase, the model will be verified on real data obtained from the chosen Croatian company. The method of choosing a company and conducting model verification on real data was explained in more detail earlier in this article. After the company filled out the questionnaire for the comparison of performance indicators, it was noticed that inconsistency appeared in the answers. To obtain an inconsistency value of less than 0.1, the “Best Fit” option available in the Expert Choice 11 program was used. In this way, the criteria comparison matrix depicted in Figure 7 was derived. Subsequently, the average ranks of the criteria vectors, as illustrated in Figure 8, were determined based on the information gathered from the criteria comparison matrix.

It can be seen that criteria EF1, EF2, and EF3 have the highest criteria priority values. According to these values, using the developed model, the values of the global priority vector of alternatives were calculated. The values of the global priority vector of alternatives are sorted and shown in Figure 9.

From Figure 9, it can be seen that, according to the priorities of the criteria determined by the company, the best results can be achieved using the lean tool Kaizen (KAI), followed by SMED and VSM. To further determine the reliability of the obtained results, a sensitivity analysis of the model was carried out. The model sensitivity analysis was performed in Expert Choice 11 using three approaches: performance, dynamic, and gradient.

Figure 10 shows the model sensitivity according to the performance approach. As can be seen in the picture, the lines showing certain alternatives are intertwined, which means that different combinations of priority indicators result in different values of the ranks of the alternatives, which is ultimately what is expected from this type of model. If, in parallel with this sensitivity analysis, the results of the values of alternatives priority ranks, obtained for different combinations of criteria priorities (Table 16 and Table 17), are observed, it can be once again confirmed that the proposed model yields reliable results.

The dynamic approach to the sensitivity of the model returns the shares of influence that a certain criterion has on the rank value of an individual alternative, expressed in percentages. The results showed that criterion EF1 contributes the most to the value of each rank of the alternative, which is to be expected considering that this criterion has the highest value of the priority rank (0.415). This approach is suitable for testing the sensitivity of the model to small percentage changes in the priority values of the criteria and their influence on the ranks of the alternatives. In this case, the sensitivity testing of the model was carried out in such a way that the percentage share of each criterion increased by five percentage points, and it was observed what happens with the ranks of the alternatives. Carrying out this test for each of the criteria, it can be concluded that, for small changes in one input criterion, the model does not significantly change the values of the ranks of the alternatives (there was no change in the order in more than two tools). This property is desirable, considering that the model is expected to be stable for small changes. Finally, a gradient approach to the sensitivity of the model was carried out to gain insight into the priority values of the alternatives in relation to the priority values of a certain criterion.

Based on the results obtained by testing the model on simulated data and data from the company, it can be concluded that the model has been successfully verified. The model provides reliable ranking results of lean tool alternatives for a combination of priorities that a given company chooses.

4. Conclusions

Reducing the impact of human activities on the environment is a current topic that is best illustrated by the fact that the UN has published a plan for sustainable development until 2030, in which the achievement of sustainable production and consumption is one of the goals. Manufacturing companies have a great potential to contribute to the reduction of the mentioned impact; therefore, a great emphasis in the scientific community, as well as in the real sector, is placed precisely on concepts and methods that integrate economic and environmental sustainability. According to the literature review, there is still no universally accepted model for the integrated implementation of lean and green production methodologies, while this combination represents one of the best approaches for achieving economic and environmental sustainability. Additionally, it was discovered that an insufficient number of articles have devoted themselves to the influence between lean and green and especially to the influence of lean tools on environmental performance indicators. To reduce this gap, the goal of this research was to determine the impact of the most used lean tools on key economic and environmental indicators and to develop a model that will propose the most optimal lean tools based on the selected priorities.

The influence of the most common lean tools on key economic and environmental indicators was assessed via an expert group survey, where professionals from both the industrial and academic sectors evaluated their impacts using tailored questionnaires. Insights into the most common lean tools and key economic and environmental indicators were gathered through a combination of the literature review and semi-structured interviews. The analysis of the results from the expert group survey revealed that all predominant lean tools exhibited a positive influence on both economic and environmental indicators. Among them, Kaizen emerged as the most impactful, showcasing the highest positive effects across both sets of indicators. The existing literature aligns with similar findings regarding the predominant impact on economic performance indicators, whereas the outcomes obtained for environmental indicators contribute to addressing a previously noted gap.

The second objective involved synthesizing information gathered from the existing literature, interview outcomes, and expert group efforts to formulate a decision-making model for selecting lean tools. The constructed model utilizes the AHP method for multi-criteria decision-making, yielding a global priorities vector for alternatives by considering the priority matrix of alternatives (lean tools) and the criteria priorities vector. The calculation of the priority matrix of alternatives commences with ranking alternatives based on criteria derived from the expert group’s questionnaire analysis. Simultaneously, the priority vector of criteria is established through the questionnaire responses provided by a relevant individual within the company. Sorting the values within the global priorities vector of alternatives yields insights into the most optimal lean tool based on the chosen criteria. Verification of the model involved testing it with both simulated data and real data from a company. The results of this verification demonstrated that the model produces reliable rankings of lean tool alternatives for various priority combinations selected by a company. In this way, the second goal of the research is achieved. Since such a similar model has not been found in the existing literature, it is expected that the developed model will be useful both to the academic community for further research and to experts in the field.

Considering all of the above, it can be concluded that the research findings affirmatively demonstrate that the application of lean tools not only impacts economic indicators but also has a substantial effect on environmental indicators. Furthermore, the study establishes that a targeted influence on specific environmental indicators is feasible through the selection of appropriate lean tools. Finally, the research underscores the possibility of concurrently influencing economic performance indicators and environmental indicators by strategically deciding which lean tools to implement within a company. These results highlight the interconnectedness and dual impact of lean methodologies on both economic and environmental dimensions, providing valuable insights for organizations seeking to enhance their sustainability and operational efficiency simultaneously. However, it is crucial to emphasize that the findings of this study derive from data gathered exclusively from Croatian companies and experts, posing a challenge in terms of generalizability. Additionally, while the research encompasses companies and experts from various segments of the manufacturing industry, it does not cover all sectors. Therefore, the transferability of the results obtained in this study to the excluded sectors warrants further investigation.

Through future research, it is planned to expand the previously obtained model with the LCA method in order to enable companies to more easily understand the impact they have on the environment through all phases of the life cycle but also to see how the improvement activities within the company affect other phases of the life cycle. Additionally, since this research had limitations such as just considering Croatian companies for interviews and the questionary survey of the experts and academics, in the future, it would be good to broaden the research to other countries.