Sustainability Indexing Model for Saudi Manufacturing Organizations

Abstract

Saudi Arabia has a 2030 Vision in which sustainability is a central pillar. Sustainability in manufacturing organizations has become a topic of discussion as a potential solution to help them survive and prosper in a competitive market. Here, the objective is to establish a sustainability indexing model for Saudi manufacturing organizations that will help organizations to prepare their sustainability reports in line with international sustainability standards. This study will also help Saudi manufacturing organizations evaluate their level of sustainability and identify barriers to improving their sustainability. The model presented has three sustainability dimensions, 12 criteria, and 29 sub-criteria. The model was implemented in a Saudi manufacturing organization as a case study. To improve the organization’s sustainability level, sustainability barriers were identified and analyzed. The model indicates that eight sub-criteria scored below the threshold value, which was found to be 0.19. These sub-criteria were further considered as sustainability barriers in the case organization.

1. Introduction

Many studies on sustainability assessment have focused on companies’ performance, and different sustainability indicators have been suggested. One of the issues with these studies is the difficulty of comparing companies based on a large number of performance measurements. Another issue is how to combine different sub-criteria into a constructed index, enabling the efficient assessment of a company’s sustainability and benchmarking. Another issue is that the methodologies for combining criteria are neither well established nor available for all sustainability aspects [1]. Moreover, there are no studies found in the literature assessing the sustainability of Saudi manufacturing organizations. On the other hand, there are many sustainability reports issued by big organizations in Saudi Arabia [2,3,4]. However, those sustainability reports did not provide any guidance on sustainability assessment and estimation approaches. Thus, to establish a baseline for any organization’s sustainable performance and to conduct competitive benchmarking, sustainability indexing is a critical competitive factor if it is conducted in the correct way. From the published literature [1], it is evident that researchers and practitioners have implemented sustainability evaluations in different sectors. However, the process of sustainability evaluation is still in its growing stage. There are good efforts to implement sustainability in Saudi Arabia [1,2,3,4]. Many Saudi manufacturing firms are focusing on long-term market sustainability. In fact, Saudi industries are exploring and examining sustainability, but researchers have not explored sustainability in much detail [1]. In particular, the literature is lagging research on sustainability assessment in Saudi manufacturing organizations. The present research aims to evaluate sustainability enablers for Saudi manufacturing organizations using the fuzzy logic assessment approach. Thus, the research questions set for the study are the following: How can sustainability be assessed in Saudi manufacturing organizations? How can sustainability enablers for Saudi manufacturing organizations be evaluated? How can the sustainability level in Saudi manufacturing organizations be estimated?

It is also evident that researchers [5,6,7,8,9,10,11,12,13,14] and or practitioners presented a number of studies on sustainability assessment that focused on organizations’ performance. These studies proposed diverse sustainability indicators, which are measured in different units. One of the issues of these studies is the combination of different indicators into a constructed index that enables an efficient assessment of the sustainability of organizations. Another issue is the difficulty of making comparisons among organizations based on a large number of performance measurements and allowing the organization to carry out the benchmarking. Therefore, methods that aggregate indicators are either not yet well established, are under development, or are not available with respect to all the sustainability aspects. This study aims to solve these shortcomings by presenting a decision-making multi-criteria-based model to analyze, estimate, and decide whether the Saudi Arabian manufacturing organization in focus is sustainable or needs improvement to achieve the three dimensions of sustainability—economic, environmental, and social. However, Saudi Arabian manufacturing organizations are consistently targeting sustainable cultures to provide services and goods that are both profitable and environmentally friendly. Their main objective is to have a sustainable model that helps their organization comply with the local Saudi Vision 2030 and the international Global Reporting Initiative.

Thus, here in the study the main objective is to have a sustainable model that helps Saudi manufacturing organizations comply with the local Saudi Vision 2030 and the international Global Reporting Initiative. Similarly, an objective is to estimate a combined sustainable index for manufacturing organizations. The presented approach also supports manufacturing organizations in finding weaknesses and strengths related to sustainability. In addition, it supports recommending improvements or corrective actions to enhance the sustainability of Saudi Arabian manufacturing organizations. Thus, to help Saudi Arabian manufacturing sector decision makers and practitioners, our approach is to estimate a sustainability index as a baseline for the organization’s sustainable performance and to conduct competitive benchmarking.

The paper is organized as follows: Section 2 is the literature review on sustainability assessment that focused on various organizations’ performance matrices. Section 3 describes research methodology and the approach adopted to identify the sustainability dimensions. Section 4 presents application of the fuzzy sustainability evaluation approach to one of the Saudi manufacturing organizations as a case study. Finally, Section 5 and Section 6 presents a discussion on the case study followed by a conclusion.

2. Literature Review

Many published studies focus on the assessment of sustainability for manufacturing and service organizations. A machining process for sustainability in Indian small-scale industries was evaluated by Agrawal, Chaganti, and Nune in 2020 [5]. They used multiple attributes decision-making methods such as technique for order of preference by similarity to an ideal solution and analytical hierarchy process. The obtained results are compared with each other, and areas of concern for sustainability are identified from the perspective of waste minimization. There are many limitations to their research. First, they’re chosen small-scale industries belong to the same area and may reflect the machining trends of that specific area. Second, they considered attributes and assigned weights that were influenced by the decision maker’s personal view and the understanding of the author, whereas Parmar and Desai [6] evaluated sustainable lean six sigma enablers using the fuzzy decision-making trial and evaluation laboratory technique. They used a causal diagram to establish and evaluate relationships between cause-and-effect enablers. Pourvaziry et al. [reference number] evaluated the dimensions and criteria affecting the sustainable world class manufacturing model. The fuzzy decision-making trial and evaluation laboratory technique and analytic network process were chosen by them for the evaluation for Iran’s automotive industry. Thakur and Vikas [7] identified factors under political, economic, social, technological, environmental, and legal dimensions of sustainable healthcare waste management. They classified the factors of sustainable healthcare waste management into four groups: autonomous, dependent, linkage, and independent groups using integrated total interpretive structural modeling and Fuzzy-cross-impact matrix multiplication analysis. The limitation of their study is that only the qualitative inputs were considered to represent the sustainability dimensions [7].

Similarly, Ying Wang and Yun Yang [8] examined green innovation practices in the Chinese manufacturing industry using sustainability performance indicators. They proposed a hybrid multi-criteria decision making model by combining the fuzzy analytical hierarchy process and fuzzy technique for order of preference by similarity to ideal solution to analyze the effects of green innovation aspects on the sustainability performance of the manufacturing industry. Their research identified several important green innovation aspects; and these were analyzed and ranked for the development of sustainable manufacturing processes and productions in the industry. Moreover, the research considered sustainability performance indicators to analyze and transform the industry’s practices. The analysis of the study showed that an industry excels in green innovation and can achieve a higher level on a sustainability performance basis. This research has several limitations as this study focuses on the garment manufacturing industry in China. It could be generalized to other garment manufacturing industries in Asia or developing countries. Moreover, only six green innovation aspects were taken into account to analyze the decision problem [8], whereas Agrawal and Vinodh [9] derived a conceptual model for sustainability evaluation of additive manufacturing processes using a grey-based approach. They assessed the sustainability index of additive manufacturing processes. Indeed, the grey-based approach used for evaluation was a good tool to solve uncertainty problems when there was insufficient information about the problem. They used the Euclidean distance approach to check the level of sustainability of advance manufacturing processes [9].

Krajnc et al. [10] presented a model for designing a sustainable index that represents the performance of companies using the three dimensions of sustainability: economic, environmental, and societal. The authors provided good guidance for decision making regarding the performance of companies along with all three aspects of sustainability through the design and analysis of a composite sustainable development index. However, the limitation in this study was the selection of criteria, considering the availability of reliable data. Since different criteria in the index for different organizations would prevent decision makers from comparing organizations in the same sector, it is required to determine how and who will select the indicators [10]. On the other hand, Ibrahim Garbie [11] presented full analytical and quantitative models and conferred the value of performance metrics ranging from individual indicators to the overall sustainable development index in order to achieve an optimal point for noticing the impact of them on others. Ibrahim Garbie [11] created a sustainable development index for manufacturing enterprises by analyzing the dimensions of sustainability. Singh, Olugu, and Musa [12] proposed a fuzzy rule-based expert system for sustainable manufacturing performance assessment in small and medium enterprises in Malaysia. Their model is to deal with the subjectivity involved in the performance evaluation of manufacturing small and medium enterprises [12].

Trianni, Cagno, Neri, and Howard [13] measured the industrial sustainability performance of manufacturing firms. They conducted multiple case analyses of 26 small and medium manufacturing enterprises across Germany and Italy operating in the chemical and metalworking sectors. They suggested factors influence the firms’ perspective on sustainability and the way it is managed, as well as the certifications held by firms, which influence the considered indicators. The findings provide further confirmation that some aspects of measuring sustainability are still missing in the EU manufacturing sector. In particular, they face difficulties in properly gauging sustainability performance. Ziout, Azab, Altarazi, and ElMaraghy [14] developed a multi-criteria decision approach to assess the benefits of reusing a manufacturing system in a developing country from a sustainability point of view. Because developing countries have low labor and energy costs, reuse of manufacturing systems is more sustainable. They conducted a survey that showed economic sustainability is the main focus of decision makers in these regions, while the environment has the least significance. These findings should be considered a warning signal to promote environmental sustainability. They applied their model to a case study of a single screw extruder pelletizer [14].

Kusi-Sarpong, Gupta, and Sarkis [15] developed a framework and evaluation methodology for sustainable innovation advancement in the manufacturing sector and its supply chains. They investigated sustainable factors for the manufacturing industry and its associated supply chain innovation practices. The model of best–worst multi-criteria decision making was used as an assessment approach in their study. However, their study involved only the automobile, plastic, and electrical and electronics manufacturing sectors; therefore, it is difficult to generalize the results for one particular sector, whereas, Caldera, Desha, and Dawes [16] used meta-conceptualization to assess the sustainability enablers and barriers of SMEs in Australia. Similarly, Cai and Lai [17] presented a sustainability benchmark assessment approach related to mechanical manufacturing systems that helps improve the sustainability of manufacturing sectors and provides technical support for designing sustainable policies. The method is being tested at a small mechanical manufacturing company in China. A sustainability performance measurement model is presented by Rayhan Sharker et al. [18] which integrates the fuzzy multiple-criteria decision-making approach and the balanced scorecard. Saad et al. [19] presented a multi-dimensional sustainability assessment model to evaluate welding processes in the manufacturing sector. They used grey relation analysis and the complex proportional assessment method.

Thus, researchers presented a number of studies on sustainability assessment that focused on individual organizations’ performance. These studies proposed diverse sustainability indicators, which are measured in different units. One of the issues of these studies is the combination of different indicators and the difficulty of making comparisons among organizations based on a large number of performance measurements and difficulty in not allowing the organization to conduct the benchmarking. Thus, to establish a baseline for any organization’s sustainable performance and to conduct competitive benchmarking as a critical competitive factor, a sustainability index works as an important management and governance tool. To address this, the adopted research methodology to identify the sustainability dimensions and barriers, and the application of the fuzzy sustainability evaluation approach to the Saudi manufacturing organizations are presented in the subsequent sections.

3. Methodology

From the published literature, it is evident that researchers have studied sustainability in manufacturing and service sectors in their regions and adopted different assessment approaches to assess the sustainability enablers and barriers. In this study, the main objective is to have a sustainable model that helps Saudi manufacturing organizations comply with the global and local Saudi Vision 2030 sustainability initiative. The presented approach supports manufacturing organizations in finding weaknesses and strengths related to sustainability. In addition, it supports recommending improvements or corrective actions to enhance the sustainability of any manufacturing organizations. Thus, to help Saudi Arabian manufacturing sector decision makers and practitioners, our approach is to estimate the sustainability index as a baseline for the organization’s sustainable performance and to conduct competitive benchmarking. This study also provides guidance for decision makers about the performance of Saudi manufacturing companies by designing and analyzing a Saudi manufacturing sustainability index (SMSI). The SMSI represents the performance of manufacturing companies in all three dimensions of sustainability—economic, environmental, and social.

Thus, to identify a sustainability indexing model in context with the Saudi Arabian manufacturing environment, we firstly assembled the panel of experts who shortlisted sustainability dimensions, criteria, and sub-criteria. They were also asked to assess the performance ratings and importance weights for the sustainability sub-criteria and importance weights for the sustainability criteria. Linguistic fuzzy terms were proposed for this purpose. Subsequently, we framed a mathematical model to calculate the manufacturing sustainability index using a fuzzy sustainability evaluation approach. Finally, a fuzzy performance importance index was calculated, which helped to identify the barriers hindering the improvement of manufacturing sustainability. A step-by-step illustration of the adopted approach and developed model is illustrated as a case study of a Saudi manufacturing organization and details are provided in the following subsections.

3.1. Panel of Experts

A comprehensive literature review was performed to identify criteria and sub-criteria for the respective dimensions of sustainability. These criteria and sub-criteria were finalized through discussion with an expert panel using the Delphi approach. The expert panel consisted of three academic experts and five technical practitioners. In detail, the first, second, and third are a professor, an associate professor, and a Ph.D. student from the industrial engineering department of a Saudi university, respectively. The fourth is a technical director for sustainability and resilience in a Saudi Authority. The fifth and sixth are a senior sustainability and environmental specialist and a sustainability and environmental specialist from a Saudi manufacturing company, respectively. The seventh is a health, safety, and environmental manager in the HSE department of a Saudi manufacturing organization. The last is a senior administration manager in the human resources department of a Saudi manufacturing organization. Each expert has expertise in the application and implementation of sustainability in different fields of manufacturing, engineering, operations management, health, safety and environment, and human resources. In addition, they have had different decision-making positions with experience extending over more than 15 years, as shown in

Table 1. Expert identification.

Sr. No.	Expert Designation	Experience (Years)	Organization
1	Academic Expert 1	25	University
2	Academic Expert 2	15	University
3	Technical Expert 1	27	Saudi Authority Vision 2030
4	Technical Expert 2	22	Manufacturing
5	Technical Expert 3	16	Manufacturing
6	Vision 2030 consultant	19	Saudi Authority Vision 2030
7	Manager, senior level	18	Manufacturing
8	Ph.D. student	16	University

.

3.2. Identification of Sustainability Dimensions, Criteria, and Sub-Criteria

Sustainability dimensions, criteria, and respective sub-criteria were identified from an extensive literature review of databases including Google Scholar, Science Direct, Scopus, and Web of Science. The search terms used to search the literature were “Sustainability enablers”, “Sustainability dimensions”, “Sustainable Development”, “manufacturing enablers”, “evaluation of sustainability”, “Sustainable Manufacturing” with a mixture of Boolean operators–‘OR’ and ‘AND’. Initially, sustainability dimensions were identified as economic, social, and environmental. After reviewing the search results and international sustainability standards such as the Global Reporting Initiative (GRI), 19 criteria and 53 sub-criteria were proposed to the experts for their consideration. A consensus decision was made to shortlist the proposed criteria and sub-criteria to 12 and 29, respectively, for assessing the sustainability of Saudi manufacturing and service organizations, as shown in

Table 2. Sustainability criteria and sub-criteria affecting Saudi manufacturing organizations.

SustainabilityDimensions(i)	SustainabilityCriteria(j)		SustainabilitySub-Criteria(k)		Objective	Unit of Measurement	References
Environmental Dimension (D1)	C1	Material	SC1	Material productivity	Maximize	%	[20,21,22]
			SC2	Material consumption	Minimize	Kg	[22,23]
	C2	Energy	SC3	Renewable energy	Maximize	kWh	[22,24]
			SC4	Energy consumption	Minimize	kWh	[9,25]
			SC5	Energy (intensity) efficiency	Maximize	kWh/SAR	[22,26]
	C3	Waste Disposal	SC6	Gas waste	Minimize	tCO2e	[9,23,25]
			SC7	Liquid waste	Minimize	L	[23,25,27]
			SC8	Solid waste	Minimize	Kg	[23,25,28]
	C4	Environmental Compliance	SC9	Compliance with environmental laws and regulations	Maximize	No.	[6,22]
			SC10	Environmental policy	Maximize	VP-VG	[6,22]
Economic Dimension (D2)	C5	Economic Performance	SC11	Cost	Minimize	SAR	[23,24,25]
			SC12	Profit	Maximize	SAR	[23,25]
	C6	Market Contribution	SC13	Market presence	Maximize	%	[22,23]
			SC14	Market share	Maximize	%	[22,23]
	C7	Procurement Practices	SC15	Spending on Saudi suppliers	Maximize	%	[22,23]
			SC16	Procurement policy	Maximize	VP-VG	[6,22]
	C8	Economic Compliance	SC17	Economic policy	Maximize	VP-VG	[6,22]
			SC18	Legal actions for illegal practices	Maximize	No.	[6,22]
Social Dimension (D3)	C9	Employment	SC19	Employee hiring trend	Maximize	No.	[22,24,29]
			SC20	Employee turnover	Minimize	No.	[24,25,30]
			SC21	Diversity of employees	Maximize	VP-VG	[23,31]
			SC22	Incidents of discrimination	Minimize	No.	[22,25,32]
			SC23	Employee training	Maximize	Hours	[23,24,25]
	C10	Occupational Health and Safety	SC24	Health and safety management system	Maximize	VP-VG	[9,23,25]
			SC25	Health and safety incidents	Minimize	No.	[9,22,23,25]
	C11	Customer Satisfaction	SC26	Customer privacy	Maximize	VP-VG	[22,25]
			SC27	Customer delight	Maximize	No.	[22,25]
	C12	Social compliance	SC28	Social policy	Maximize	VP-VG	[22,25]
			SC29	Local communities	Maximize	VP-VG	[23,25,33]

.

In Table 2, the first criterion in the environmental dimension is material, which has two sub-criteria: material productivity (SC1) and material consumption (SC2). Material productivity is defined as using less raw, semi-finished, or finished material to make a product or service [20]. It is calculated as the ratio of materials used to the actual materials supplied to make a product or service [21]. Material consumption can be calculated as the produced materials added to the imported materials subtracted from the exported materials. The material can be wood, petroleum, minerals (metals and non-metals), fossil fuel, or food. It is based on an organization’s pattern of consuming sustainable, eco-friendly material, which consumes less energy and produces fewer GHG (greenhouse gas) emissions in comparison with previous years. Whether these materials are recyclable and/or biodegradable, and the reusability aspects of packaging materials, raw materials, and chemically hazardous materials must also be evaluated.

All forms of power generation have an environmental impact on global air, water, and land. The second criterion in the sustainable environmental dimension is energy, which contains three sub-criteria: percent use of renewable energy, energy consumption, and energy efficiency. Percent use of renewable energy is defined as the amount of energy an organization consumes from renewable energy sources (e.g., solar, wind, biomass, hydroelectricity) in its total energy mix. Energy consumption can be defined as the total power or energy the organization is consuming or using. It also takes into account the reduction in consumption from previous years. As energy intensity or efficiency is measured by the quantity of energy required per unit output or activity, the objective is to use less energy to produce a product in order to reduce the energy intensity [26].

Sustainable waste management involves disposing of, reducing, reusing, and preventing waste. The third criterion in the sustainable environmental dimension is waste discarded after primary use or that is worthless, defective, and of no use. Discarded waste can be classified according to sub-criteria based on the basic form in which the waste is discarded by the organization. The sub-criteria are gaseous, liquid, or solid wastes. Gas waste includes gases such as carbon dioxide, carbon monoxide, and sulphur dioxide. Liquid wastes discarded by an organization can include wastewater, fats, oils, or grease, used oil, sludges, and hazardous household liquids. These liquids can be dangerous or potentially harmful to human health or the environment [27]. Finally, solid waste includes all discarded materials, including debris, garbage, commercial waste, and sludge water [28]. The evaluator measures this criterion based on how solid waste is treated by the organization. To evaluate an organization’s sustainability related to waste management, the evaluator should estimate how much waste the organization generates. What steps does management take to generate less waste? Can it be reused or recycled more? Would it be possible to buy and/or sell used materials, or utilize materials and equipment more efficiently? Importantly, does the organization follow current legislation or policies while discarding the waste?

To be sustainable, organizations should follow Saudi environmental laws and regulations, and other necessities such as standards. The final criterion in the environmental dimension is compliance to the environment, which includes two sub-criteria: first, environmental policy, and second, compliance with Saudi environmental laws and regulations. An environmental policy should conform to environmental laws, regulations, standards, and other requirements, and environmental coordinators should be in place to ensure compliance and awareness throughout the organization [34]. An organization’s environmental impact can be improved by adopting ISO 14001 and environmental management systems [35]. Moreover, the following should be tracked: Is an environmental policy in place? How good is it, and how well has the organization implemented it? Is there any non-compliance with Saudi environmental laws and regulations? Have any monetary payment penalties or non-payment sanctions been given because of non-compliance with Saudi sustainability laws and regulations in the reporting year? [36] Thus, environmental dimensions can be measured based on how an organization complies with laws and regulations.

In Table 2, the second sustainability dimension is the economic dimension, which has four criteria: economic performance, market contribution, procurement practices, and economic compliance. Economic performance reflects the amount and value of money, wealth, debt, and investment. Economic performance consists of two sub-criteria: cost and profit. Cost includes all costs related to materials, fixed assets (property, plant, and equipment), manpower, and overheads (utilities, rent, taxes, maintenance, environmental fines, etc.), whereas profit is the revenue that remains after deducting all expenses, including all types of costs such as the cost of goods sold, utility cost, material costs, labor costs, and operating costs. Subsidies or tax relief from the Saudi government should also be included [37]. The second criterion is the market contribution, which includes two sub-criteria: market presence and market share. An organization’s market presence covers its contribution to economic development in the local areas or communities where it operates [29], while market share is the portion of a market or product controlled by a particular company. The third criterion is procurement practices, and consists of two sub-criteria: the organization’s spending on Saudi suppliers and procurement policy. Spending on Saudi suppliers can be measured by how much the company spends on local suppliers. On the other hand, procurement policy refers to purchasing processes and sourcing strategies. The last criterion is economic compliance, which includes two sub-criteria: economic policy and legal actions for illegal practices. Economic policy forces the finance sector to follow a set of rules to protect all stakeholders (including investors and customers) via regulations the financial department must follow. The last sub-criterion is legal actions against illegal practices such as anti-competitive behavior, anti-trust, and monopoly practices. This criterion is evaluated based on how the organization deals with illegal practices.

In Table 2, the third sustainability dimension is the social dimension, which has four criteria: employment, occupational health and safety, customer satisfaction, and social compliance. Employment has five sub-criteria: employee hiring trend, employee turnover, workforce diversity, incidents of discrimination, and employee training. The employee hiring trend indicates how many permanent, full-time, or part-time employees have been hired [29]. The number of employees who leave a company during a given time is referred to as “employee turnover” [30]. The diversity of the workforce can be identified by the employees’ age differences, cultural differences, the inclusion of employees with disabilities, differences in religions and schools of thought, differences in races, and inclusion of both male and female genders [31]. Discrimination could be described as any act of injustice or harmful treatment of an employee or customer based on things such as religions, schools of thought, countries, regions, cities, gender, skin color, tribe, ethnicity, or even age [32], while employee training can be measured as the average number of hours of training per year per employee. How the company trains its employees, and how good the training is should also be evaluated. The second criterion of the social dimension is occupational health and safety (OHS), which has two sub-criteria: the health and safety management system and health and safety incidents. A health and safety management system can be gauged by how a company ensures the workplace is a safe environment for working. Health and safety systems include identification of hazard, assessment of risk, investigation of incident, and worker health promotion, while health and safety incidents are measured by how the company handles safety incidents and the number of incidents in comparison to previous years. The third criterion of the social dimension is customer satisfaction, which has two sub-criteria: customer privacy and customer delightfulness. Customer privacy is measured by how the company deals with validated complaints regarding customer privacy breaches and customer data losses, while customer delight can be measured by how satisfied the customer is with the manufacturing organization’s product. The fourth criterion in the social dimension is social compliance, which has two sub-criteria: social policy and local communities. Social policy can be measured by how businesses protect the health, rights, and safety of their employees, suppliers, and distribution chains. Furthermore, how good is the company’s social policy, and is it complying with it? Social compliance refers to local communities, individuals, or groups of individuals living and/or working in and around the organization who are impacted either positively or negatively in economic, social, or environmental terms by the operations of the manufacturing organization. Moreover, an organization is expected to consider the differentiated nature of communities and the distinct and specific vulnerabilities of these groups to the organization’s activities [33]. It can be measured by how the organization is engaging with local communities.

After identifying and measuring the sustainability dimensions, criteria, and sub-criteria, the next step is to assist decision makers dealing with the performance of manufacturing organizations by estimating a fuzzy sustainability index. The fuzzy sustainability index represents the performance of manufacturing organizations in economic, environmental, and social sustainability dimensions. The model for evaluating Saudi manufacturing sustainability is presented in Figure 1. The sustainability indexing model is inclusive and was established by a comprehensive literature review regarding the assessment of sustainability in manufacturing organizations. The model’s objective is to evaluate the sustainability of Saudi manufacturing organizations. The assessment model will enable such companies to fulfill the requirements of international sustainability standards such as the Global Reporting Initiative (GRI). Moreover, Saudi manufacturing companies will be able to publish their sustainability reports. The model is constructed in three levels. The first level contains the sustainability dimensions. Every sustainability dimension is then divided further into the second level, which is the sustainability criteria. Lastly, in the third level, each sustainability criterion is further subdivided into sustainability sub-criteria.

To estimate a fuzzy sustainability index, the adopted fuzzy model incorporates three sustainability dimensions, 12 criteria, and 29 sub-criteria, as presented in Table 2 and Figure 1. The details of the fuzzy sustainability index evaluation model are presented in the following section.

3.3. Fuzzy Sustainability Evaluation Approach

Sustainability assessment is mainly reliant on the opinions of experts. There might be vagueness and impreciseness in human estimations, which are subjective in nature. To deal with this, linguistic terms can be used. A linguistic variable is a variable whose values are sentences or words, not numbers, for example, the linguistic variables of low and high [38]. Linguistic expressions are ambiguous, and changing them into numerical values can be problematic. Artificial intelligence proposes an answer to address those obstacles by presenting a “fuzzy logic” method. The fuzzy logic approach was adopted here to evaluate the sustainability indicators’ performance ratings and importance weights [39]. The first step in the evaluation model involves estimating performance ratings and importance weights for the sustainability sub-criteria. In the current study, eight experts from XYZ manufacturing organization, “one of the top 10 commercial manufacturing companies in Saudi Arabia”, were asked to give performance ratings and importance weights for sustainability sub-criteria. These experts have diverse experience in various departments. They were asked to judge the performance ratings and importance weights for sustainability sub-criteria and importance weights for sustainability criteria. Linguistic terms were proposed for this purpose, so that the linguistic terms would be converted into their equivalent fuzzy numbers. Subsequently, the Saudi manufacturing sustainability index (SMSI) was calculated using a fuzzy sustainability evaluation approach. The SMSI was coordinated with the expression of linguistic terms by means of the Euclidean distance method to find the sustainability level of the manufacturing company. Lastly, a Saudi manufacturing fuzzy performance importance index (SMFPI) was calculated, which helped to find the barriers hindering the improvement of manufacturing sustainability. A step-by-step illustration of the adopted approach [38,39,40,41,42,43] is listed below, and its application for a case of a Saudi manufacturing organization is presented as a case study in the subsequent section.

• Step 1: Creating a linguistic scale and its equivalent fuzzy number for measuring performance ratings and importance weights.
• Step 2: Data gathering for sustainability assessment.
• Step 3: Combining fuzzy ratings and weights for sustainability sub-criteria.
• Step 4: Calculating of the Saudi manufacturing fuzzy sustainability index (SMFSI).
  • Sub-Step 4.1: Calculation of the Saudi manufacturing sustainability index at the criteria level.
  • Sub-Step 4.2: Calculation of Saudi manufacturing sustainability index at dimension level.
  • Sub-Step 4.3: Determination of the Saudi manufacturing fuzzy sustainability index (SMFSI).
• Step 5: Identifying the Euclidean distance required to match the SMFSI with the near-sustainability level.
• Step 6: Identifying barriers to improving sustainability levels.

4. Case Study: An Illustrative Example

The fuzzy sustainability evaluation approach was employed to measure the sustainability index of one of the manufacturing organizations in Saudi Arabia as per Steps 1 to 7. This organization is referred to as “XYZ” since its management did not consent to us revealing its identity.

• Step 1: Creating a linguistic scale and its equivalent fuzzy number for measuring performance ratings and importance weights.

To assign the performance ratings and importance weights of sustainability indicators, subject matter experts must use linguistic terms, which can be found in

Table 3. Linguistic terms and fuzzy numbers for performance rating and importance.

Performance Rating (R)	Importance Weights (W)	Fuzzy Numbers
Very good (VG)	Very high (VH)	(0.6, 0.8, 1)
Good (G)	High (H)	(0.4, 0.6, 0.8)
Fair (F)	Average (A)	(0.2, 0.4, 0.6)
Poor (P)	Low (L)	(0, 0.2, 0.4)
Very poor (VP)	Very low (VL)	(0, 0, 0.2)

[40]. It is unrealistic for evaluators to determine the score of an ambiguous criterion [39]; consequently, in this research, linguistic terms were used to judge the performance ratings and importance weights of sustainability sub-criteria. The performance rating can be defined as a score or measurement of how well or successfully an organization meets particular criteria or sub-criteria [41]. The linguistic terms and their fuzzy numbers were obtained from a previous study [40] as per Table 3.

• Step 2: Data gathering for sustainability assessment

In order to measure the performance ratings and importance weights, a survey was distributed to eight experts from the organization “XYZ”. Experts replied to a survey by linguistic terms which then change to its equivalent fuzzy numbers. Then, fuzzy arithmetic procedures were implemented to turn these fuzzy numbers into its equivalent fuzzy number named the Saudi manufacturing fuzzy sustainability index (SMFSI) [42]. Responses collected from the experts of the organization “XYZ” are presented in

Table 4. Performance ratings and importance weights for sub-criteria k: experts’ responses about XYZ organization.

		e	1		2		3		4		5		6		7		8
i *	j *	k *	Rijke	Wijke	Rijke	Wijke	Rijke	Wijke	Rijke	Wijke	Rijke	Wijke	Rijke	Wijke	Rijke	Wijke	Rijke	Wijke
D1	C1	SC1	VG@	VH	G	VH	G	H	G	VH	VG	A	G	VH	VG	VH	VG	H
		SC2	G	VH	F	A	G	H	G	VH	VG	VH	G	VH	G	H	VG	A
	C2	SC3	VG	VH	VG	A	G	H	G	H	G	H	G	A	VG	VH	VG	H
		SC4	VG	VH	F	H	G	VH	G	VH	VG	H	G	VH	VG	VH	G	H
		SC5	VG	H	F	A	G	VH	G	H	VG	H	G	VH	G	H	VG	H
	C3	SC6	G	VH	VG	VH	G	H	G	VH	VG	H	G	VH	VG	H	F	VH
		SC7	VG	H	F	VH	G	H	G	A	VG	H	G	VH	VG	H	VG	A
		SC8	VG	H	G	H	G	A	G	VH	VG	H	G	A	VG	H	G	VH
	C4	SC9	VG	VH	VG	H	VG	H	VG	A	VG	H	G	VH	F	H	G	A
		SC10	F	VH	VG	A	G	H	G	VH	VG	A	F	L	G	VH	VG	A
D2	C5	SC11	G	VH	VG	VH	G	VH	VG	H	VG	H	F	H	VG	VH	G	H
		SC12	G	VH	F	H	VG	VH	G	H	F	VH	VG	VH	VG	H	G	VH
	C6	SC13	F	H	G	A	G	H	F	VH	VG	H	F	H	VG	VH	G	A
		SC14	F	A	VG	H	F	H	G	VH	VG	VH	F	H	G	A	VG	A
	C7	SC15	G	VH	VG	H	G	VH	F	H	VG	VH	F	H	VG	VH	VG	VH
		SC16	VG	H	G	H	G	VH	G	VH	VG	H	G	VH	VG	H	VG	A
	C8	SC17	VG	H	F	VH	G	A	G	H	VG	H	G	VH	VG	VH	VG	H
		SC18	VG	H	G	VH	G	H	G	H	VG	A	G	VH	G	H	VG	A
D3	C9	SC19	VG	H	G	H	G	A	G	VH	F	H	G	A	F	H	G	VH
		SC20	VG	A	G	VH	G	H	G	VH	VG	H	G	VH	F	H	G	VH
		SC21	VG	H	G	A	G	H	G	A	F	VH	G	H	F	A	VG	VH
		SC22	F	H	G	A	G	VH	G	H	F	H	G	VH	F	H	G	A
		SC23	F	H	VG	A	G	VH	G	H	F	A	G	H	F	VH	G	A
	C10	SC24	VG	VH	VG	H	G	VH	G	VH	G	VH	G	H	VG	VH	G	H
		SC25	G	VH	G	H	G	H	G	A	G	H	G	VH	VG	H	F	H
	C11	SC26	VG	VH	VG	VH	G	H	G	VH	G	H	G	VH	F	VH	VG	H
		SC27	G	VH	VG	VH	G	H	G	VH	VG	H	G	A	F	H	G	VH
	C12	SC28	G	H	VG	VH	G	H	G	VH	G	H	G	VH	F	H	G	A
		SC29	VG	H	VG	VH	G	H	G	VH	VG	H	G	A	F	H	G	VH

Notes: * Refer to Table 2 @ Refer to Table 3; R_ijke = performance rating by expert e for sustainability sub-criteria k of criteria j with respect to sustainability dimension i; W_ijke = importance weight assigned by expert e for sustainability sub-criteria k of criteria j with respect to sustainability dimension i; e is expert number ‘t’ from 1 to 8.

,

Table 5. Importance weights W_ije for criteria j: experts’ responses about XYZ organization.

					Wije
i *	j * e	1	2	3	4	5	6	7	8
D1	C1	H	VH	VH	H	A	H	H	VH
	C2	H	VH	VH	VH	H	H	VH	VH
	C3	H	H	VH	VH	H	A	H	VH
	C4	H	VH	H	VH	L	A	VH	H
	C5	VH	H	H	VH	VH	VH	H	VH
D2	C6	VH	H	VH	H	A	H	H	H
	C7	VH	H	H	A	VH	H	L	VH
	C8	VH	H	A	L	H	VH	H	H
	C9	H	VH	H	A	H	H	A	VH
D3	C10	VH	H	H	VH	VH	VH	H	A
	C11	VH	H	VH	A	H	H	A	H
	C12	H	VH	H	L	H	A	H	VH

Notes: * refer to Table 2; W_ije = importance weight assigned by expert e for sustainability criteria j for given sustainability dimension i.

and

Table 6. Importance weights W_ie for sustainability dimension i: experts’ responses about XYZ organization.

	Wie
i * e	1	2	3	4	5	6	7	8
D1	VH	H	VH	VH	A	VH	VH	H
D2	H	VH	VH	VH	VH	VH	VH	H
D3	VH	A	H	VH	VH	H	VH	H

Notes: * Refer to Table 2; W_ie = importance weight assigned by expert e for sustainability dimension i.

.

Table 4 presents responses from eight experts in the Saudi manufacturing organization about each sub-criteria affecting the XYZ Saudi manufacturing organization. Both performance ratings (R_ijk) and importance weights (W_ijk) are collected from each expert. In Table 4, for example, Expert 1 responded to the survey that sub-criteria material productivity SC1 has a very good (VG) performance rating and assign a very high (VH) importance weight for SC1, whereas Table 5 highlights responses from eight experts in the XYZ organization about each of the criteria affecting the sustainability of the organization. Importance weights (W_ij) were collected from each expert for each criteria. Thus, in Table 5, for example, Expert 1 responded that material criterion C1 has a high (H) importance weight while Expert 2 believed it to be very high (VH). Similarly, the above Table 6 depicts responses from eight experts in the Saudi manufacturing organization about each of the sustainability dimensions that influence the sustainability index of the organization. In Table 6, for example, Expert 1 responded that environmental dimension (D1) has a very high level of importance weight, while Expert 2 believes the environmental dimension (D1) has a high level of importance weight.

• Step 3: Combining of fuzzy ratings and weights of sustainability sub-criteria

The linguistic terms for performance ratings and importance weights were approximated with fuzzy numbers as tabulated in Table 3, which then had to be aggregated. Various methods can be used to combine the evaluations of various inputs, such as calculating arithmetic mean, median, and mode. In this study, the experts’ opinions were combined by using the arithmetic mean method. The average importance weights and performance ratings of sub-criteria were represented by R_ijk and W_ijk, respectively. The following Equations (1) and (2) [42,43] were used to compute the R_ijk and W_ijk values.

$${ \mathrm{R}}_{\mathrm{ijk}}^{}=\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{R}}_{\mathrm{ijke}}}{\mathrm{t}}\equiv \left(\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{a}}_{\mathrm{ijke}}}{\mathrm{t}},\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{b}}_{\mathrm{ijke}}}{\mathrm{t}},\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{c}}_{\mathrm{ijke}}}{\mathrm{t}}\right)\equiv {\left({\mathrm{a}}_{\mathrm{ijk}},{ \mathrm{b}}_{\mathrm{ijk}},{ \mathrm{c}}_{\mathrm{ijk}}\right)}_{}$$

(1)

$${\mathrm{W}}_{\mathrm{ijk}}^{}=\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{W}}_{\mathrm{ijke}}}{\mathrm{t}}\equiv \left(\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{x}}_{\mathrm{ijke}}}{\mathrm{t}},\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{y}}_{\mathrm{ijke}}}{\mathrm{t}},\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{z}}_{\mathrm{ijke}}}{\mathrm{t}}\right)\equiv {\left({\mathrm{x}}_{\mathrm{ijk}},{ \mathrm{y}}_{\mathrm{ijk}},{ \mathrm{z}}_{\mathrm{ijk}}\right)}_{}$$

(2)

In the above Equations (1) and (2),

${\mathrm{R}}_{\mathrm{ijk}}^{}$ is overall performance rating for given sub-criteria k of criteria j for given sustainability dimension i.

${\mathrm{W}}_{\mathrm{ijk}}^{}$ is overall importance weight for given sub-criteria k of criteria j for given sustainability dimension i.

${\mathrm{R}}_{\mathrm{ijke}}^{}$ is performance rating by expert e for sustainability sub-criteria k of criteria j for given to sustainability dimension i.

${\mathrm{W}}_{\mathrm{ijke}}^{}$ is importance weight assigned by expert e for sustainability sub-criteria k of criteria j for given sustainability dimension i.

e is expert number ‘t’ from 1 to 8 in our case study.

For, ${\mathrm{R}}_{\mathrm{ijke}}^{},{ \mathrm{W}}_{\mathrm{ijke}}^{} \mathrm{and} \mathrm{e}$refer to above Table 4.

${\left({\mathrm{a}}_{\mathrm{ijke}},{ \mathrm{b}}_{\mathrm{ijke}},{ \mathrm{c}}_{\mathrm{ijke}}\right)}_{}$ is triangular fuzzy number representing performance rating by expert e for sustainability sub-criteria k of criteria j for given sustainability dimension i.

${\left({\mathrm{x}}_{\mathrm{ijke}},{ \mathrm{y}}_{\mathrm{ijke}},{ \mathrm{z}}_{\mathrm{ijke}}\right)}_{}$ is importance weight assigned by expert e for sustainability sub-criteria k of criteria j for given sustainability dimension i.

${\left({\mathrm{a}}_{\mathrm{ijk}},{ \mathrm{b}}_{\mathrm{ijk}},{ \mathrm{c}}_{\mathrm{ijk}}\right)}_{}$ is triangular fuzzy number representing performance rating of sub-criteria k for criteria j with respect to sustainability dimension i.

${\left({\mathrm{x}}_{\mathrm{ijk}},{ \mathrm{y}}_{\mathrm{ijk}},{ \mathrm{z}}_{\mathrm{ijk}}\right)}_{}$is triangular fuzzy number representing average importance weight of sub-criteria k for criteria j with respect to sustainability dimension i.

Similarly, an importance weight and corresponding triangular fuzzy number for sustainability criteria j for the given sustainability dimension i was calculated by using Equation (3), whereas an importance weight and corresponding triangular fuzzy number to sustainability dimension i was calculated by using Equation (4), respectively.

$${\mathrm{W}}_{\mathrm{ij}}^{}=\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{W}}_{\mathrm{ije}}}{\mathrm{t}}=\left(\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{x}}_{\mathrm{ije}}}{\mathrm{t}},\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{y}}_{\mathrm{ije}}}{\mathrm{t}},\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{z}}_{\mathrm{ije}}}{\mathrm{t}}\right)\equiv {\left({\mathrm{x}}_{\mathrm{ij}},{ \mathrm{y}}_{\mathrm{ij}},{ \mathrm{z}}_{\mathrm{ij}}\right)}_{}$$

(3)

$${\mathrm{W}}_{\mathrm{i}}^{}=\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{W}}_{\mathrm{ie}}}{\mathrm{t}}=\left(\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{x}}_{\mathrm{ie}}}{\mathrm{t}},\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{y}}_{\mathrm{ie}}}{\mathrm{t}},\frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}}{\mathrm{z}}_{\mathrm{ie}}}{\mathrm{t}}\right)\equiv {\left({\mathrm{x}}_{\mathrm{i}},{ \mathrm{y}}_{\mathrm{i}},{ \mathrm{z}}_{\mathrm{i}}\right)}_{}$$

(4)

In the above Equations (3) and (4),

${\mathrm{W}}_{\mathrm{ije}}^{}$ is importance weight assigned by expert e for sustainability criteria j for the given sustainability dimension i, and ${\left({\mathrm{x}}_{\mathrm{ije}},{ \mathrm{y}}_{\mathrm{ije}},{ \mathrm{z}}_{\mathrm{ije}}\right)}_{}$is corresponding triangular fuzzy number

${\mathrm{W}}_{\mathrm{ie}}^{}$ is importance weight assigned by expert e to sustainability dimension i and ${\left({\mathrm{x}}_{\mathrm{ij}},{ \mathrm{y}}_{\mathrm{ij}},{ \mathrm{z}}_{\mathrm{ij}}\right)}_{}$is corresponding triangular fuzzy number

${\mathrm{W}}_{\mathrm{ij}}^{}$ is importance weight of sustainability criteria j for given sustainability dimension i and $\left({\mathrm{x}}_{\mathrm{ij}},{ \mathrm{y}}_{\mathrm{ij}},{ \mathrm{z}}_{\mathrm{ij}}\right)$ is corresponding triangular fuzzy number.

${\mathrm{W}}_{\mathrm{i}}^{}$ is importance weight of sustainability dimension i and $\left({\mathrm{x}}_{\mathrm{i}},{ \mathrm{y}}_{\mathrm{i}},{ \mathrm{z}}_{\mathrm{i}}\right)$ is corresponding triangular fuzzy number.

e is expert number ‘t’ from 1 to 8 in our case study. For sustainability dimension, sustainability criteria and sub-criteria refer to Table 2, and for assigning triangular fuzzy numbers, refer to Table 3.

As an example, for the case organization, the computation of fuzzy performance rating and importance weight for environmental dimension (i = 1), sustainability criteria energy (j = 2), and sub-criteria energy consumption (k = 4), are presented below. The fuzzy performance rating (R₁₂₄) and fuzzy importance weight (W₁₂₄) for all experts’ responses to sub-criteria energy consumption were calculated using information from Table 4 and Equations (1) and (2), and is as presented below:

$${\mathrm{R}}_{124}^{}\equiv \frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}=8}{\mathrm{R}}_{\mathrm{ijke}}}{\mathrm{t}} \equiv \left(\frac{\left(\mathrm{VG}+\mathrm{F}+\mathrm{G}+\mathrm{G}+\mathrm{VG}+\mathrm{G}+\mathrm{VG}+\mathrm{G}\right)}{8}\right)$$

$${\mathrm{R}}_{124}^{}\equiv \left(\frac{\left(0.6,0.8,1.0\right)+\left(0.2,0.4,0.6\right)+\left(0.4,0.6,0.8\right)+\left(0.4,0.6,0.8\right)+\left(0.6,0.8,1.0\right)+\left(0.4,0.6,0.8\right)+\left(0.6,0.8,1.0\right)+\left(0.4,0.6,0.8\right)}{8}\right)$$

$${\mathrm{R}}_{124}^{}\equiv \left(\frac{\left(0.6+0.2+0.4+0.4+0.6+0.4+0.6+0.4\right)}{8},\frac{\left(0.8+0.4+0.6+0.6+0.8+0.6+0.8+0.6\right)}{8},\frac{\left(1+0.6+0.8+0.8+1+0.8+1+0.8\right)}{8}\right)$$

$${\mathrm{R}}_{124}^{}\equiv \left(0.45, 0.65, 0.85\right)\equiv {\left({\mathrm{a}}_{124},{ \mathrm{b}}_{124},{ \mathrm{c}}_{124}\right)}_{}$$

$${\mathrm{W}}_{124}^{}\equiv \frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}=8}{\mathrm{W}}_{\mathrm{ije}}}{\mathrm{t}}\equiv \left(\frac{\left(\mathrm{VH}+\mathrm{H}+\mathrm{VH}+\mathrm{VH}+\mathrm{H}+\mathrm{VH}+\mathrm{VH}+\mathrm{H}\right)}{8}\right)$$

$${\mathrm{W}}_{124}^{}\equiv \left(\frac{\left(0.6,0.8,1.0\right)+\left(0.4,0.8,1.0\right)+\left(0.6,0.6,0.8\right)+\left(0.6,0.8,1.0\right)+\left(0.4,0.4,0.6\right)+\left(0.6,0.8,1.0\right)+\left(0.6,0.8,1.0\right)+\left(0.4,0.6,0.8\right)}{8}\right)$$

$${\mathrm{W}}_{124}^{}\equiv \left(\frac{\left(0.6+0.4+0.6+0.6+0.4+0.6+0.6+0.4\right)}{8},\frac{\left(0.8+0.8+0.6+0.8+0.4+0.8+0.8+0.6\right)}{8},\frac{\left(1+1+0.8+1+0.6+1+1+0.8\right)}{8}\right)$$

$${\mathrm{W}}_{124}^{}\equiv \left(0.525, 0.725, 0.925\right)\equiv {\left({\mathrm{x}}_{124},{ \mathrm{y}}_{124},{ \mathrm{z}}_{124}\right)}_{}$$

Similarly, fuzzy importance weight W₁₂ for environmental dimension (i = 1), sustainability criteria energy (j = 2) is calculated using Equation (3) and information in Table 5 as below:

$${\mathrm{W}}_{12}^{}\equiv \frac{{{\displaystyle \sum }}_{\mathrm{e}=1}^{\mathrm{t}=8}{\mathrm{W}}_{\mathrm{ije}}}{\mathrm{t}}\equiv \left(\frac{\left(\mathrm{H}+\mathrm{VH}+\mathrm{VH}+\mathrm{VH}+\mathrm{H}+\mathrm{H}+\mathrm{VH}+\mathrm{VH}\right)}{8}\right)$$

$${\mathrm{W}}_{12}^{}\equiv \left(\frac{\left(0.4,0.6,0.8\right)+\left(0.6,0.8,1.0\right)+\left(0.6,0.8,1.0\right)+\left(0.4,0.6,0.8\right)+\left(0.2,0.4,0.6\right)+\left(0.4,06,08\right)+\left(0.4,0.6,0.8\right)+\left(0.6,0.8,1.0\right)}{8}\right)$$

$${\mathrm{W}}_{12}^{}\equiv \left(\frac{\left(0.4+0.6+0.6+0.4+0.2+0.4+0.4+0.6\right)}{8},\frac{\left(0.6+0.8+0.8+0.6+0.4+0.6+0.6+0.8\right)}{8},\frac{\left(0.8+1+1+0.8+0.6+0.8+0.8+1\right)}{8}\right)$$

$${\mathrm{W}}_{12}^{}\equiv \left(0.45, 0.65, 0.85\right)\equiv {\left({\mathrm{x}}_{12},{ \mathrm{y}}_{12},{ \mathrm{z}}_{12}\right)}_{}$$

The calculated performance ratings and importance weights for all sub-criteria (k = 3, 4, and 5) with respect to sustainability criteria energy (j = 2), and triangular fuzzy importance weight W₁₂ for environmental dimension (i = 1) and sustainability criteria energy (j = 2) are presented in

Table 7. Performance ratings and importance weights for all sub-criteria with respect to sustainability criteria energy.

Triangular Fuzzy Importance Weight for Sustainability Criteria j with Respect to Sustainability Dimension i $W_{ij}^{}\equiv \left(x_{ij}, y_{ij}, z_{ij}\right)$	Triangular Fuzzy Importance Weight for Sustainability Sub Criteria k for Sustainability Criteria j with Respect to Sustainability Dimension i $W_{ijk}^{}\equiv \left(x_{ijk}, y_{ijk}, z_{ijk}\right)$	Triangular Fuzzy Performance Rating for Sustainability Sub Criteria k for Sustainability Criteria j with Respect to Sustainability Dimension i $R_{ijk}^{}\equiv \left(a_{ijk}, b_{ijk}, c_{ijk}\right)$	Sustainability Sub Criteria(k)	Objective
${\mathrm{W}}_{12}^{}$ $\equiv \left(0.45, 0.65, 0.85\right)$	${\mathrm{W}}_{123}^{}\equiv$ (0.40, 0.60, 0.80)	${\mathrm{R}}_{123}^{}\equiv$ (0.50, 0.70, 0.90)	Renewable energy (k = 3)	Maximize
for environmental dimension (i = 1), sustainability criteria energy (j = 2)	${\mathrm{W}}_{124}^{}\equiv$ (0.525, 0.725, 0.925)	${\mathrm{R}}_{124}^{}\equiv$0.45, 0.65, 0.85)	Energy consumption (k = 4)	Minimize
	${\mathrm{W}}_{125}^{}\equiv$ (0.425, 0.625, 0.825)	${\mathrm{R}}_{125}^{}\equiv$ (0.45,0.65, 0.85)	Energy efficiency (k = 5)	Maximize

.

In Table 7, the importance weight for the sustainability criterion “energy” is (0.45, 0.65, 0.85), which falls into the high importance weight level 4 as per linguistic terms according to the fuzzy numbers (refer to Table 3). The sustainability sub-criterion “renewable energy” had a performance rating of (0.5, 0.7, 0.9), which can be interpreted as meaning that the organization is using a very good percentage of renewable energy in their total energy mix. At the same time, both sub-criteria energy consumption and energy efficiency scored highly in the performance rating, which means XYZ needs to reduce its energy consumption compared to previous years. The company should also look at ways to reduce the quantity of energy needed per unit output to increase their energy efficiency and thus improve the “energy” sustainability criteria.

• Step 4: Calculation of the Saudi manufacturing fuzzy sustainability index (SMFSI)

The SMFSI represents the sustainability level of the Saudi organization. To compute the SMFSI, the Saudi manufacturing sustainability index (SMSI) was calculated first at the criteria level and then calculated at the dimension level. A sustainability index at the criteria level incorporates numerous sustainability sub-criteria, and a sustainability index at the dimension level contains all sustainability criteria. The details are presented here as sub-steps.

• Sub-Step 4.1: Calculation of the Saudi manufacturing sustainability index at the criteria level

Using combined fuzzy ratings and fuzzy weights of sustainability sub-criteria, the Saudi manufacturing sustainability index (SMSI) was calculated at the criteria level. Equation (5) was used to calculate the sustainability index at the criteria level [43].

$${\mathrm{SMSI}}_{\mathrm{ij}}^{}\equiv \frac{{{\displaystyle \sum }}_{\mathrm{k}}^{}({\mathrm{R}}_{\mathrm{ijk}}^{}\times {\mathrm{W}}_{\mathrm{ijk}}^{})}{{{\displaystyle \sum }}_{\mathrm{k}}^{}{\mathrm{W}}_{\mathrm{ijk}}^{}}\equiv \left(\frac{{{\displaystyle \sum }}_{}^{}({\mathrm{a}}_{\mathrm{ijk}}\times {\mathrm{x}}_{\mathrm{ijk}})}{{{\displaystyle \sum }}_{\mathrm{k}}^{}{\mathrm{x}}_{\mathrm{ijk}}},\frac{{{\displaystyle \sum }}_{}^{}({\mathrm{b}}_{\mathrm{ijk}}\times {\mathrm{y}}_{\mathrm{ijk}})}{{{\displaystyle \sum }}_{\mathrm{k}}^{}{\mathrm{y}}_{\mathrm{ijk}}},\frac{{{\displaystyle \sum }}_{}^{}({\mathrm{c}}_{\mathrm{ijk}}\times {\mathrm{z}}_{\mathrm{ijk}})}{{{\displaystyle \sum }}_{\mathrm{k}}^{}{\mathrm{z}}_{\mathrm{ijk}}}\right)\equiv {\left({\mathrm{d}}_{\mathrm{ij}},{ \mathrm{f}}_{\mathrm{ij}},{ \mathrm{g}}_{\mathrm{ij}}\right)}_{}$$

(5)

In Equation (5),

${\mathrm{SMSI}}_{\mathrm{ij}}^{}$ is Saudi manufacturing sustainability index for sustainability criteria j for given sustainability dimension i.

${\mathrm{W}}_{\mathrm{ijk}}^{}$ is importance weight for sustainability sub criteria k for sustainability criteria j for given sustainability dimension i, and $\left({\mathrm{x}}_{\mathrm{ijk}},{ \mathrm{y}}_{\mathrm{ijk}},{ \mathrm{z}}_{\mathrm{ijk}}\right)$ is its corresponding triangular fuzzy number (refer to Table 7)

${\mathrm{R}}_{\mathrm{ijk}}^{}$ is performance rating for sustainability sub criteria k for sustainability criteria j for given sustainability dimension i, and $\left({\mathrm{a}}_{\mathrm{ijk}},{ \mathrm{b}}_{\mathrm{ijk}},{ \mathrm{c}}_{\mathrm{ijk}}\right)$ is corresponding triangular fuzzy number (refer to Table 7)

${\left({\mathrm{d}}_{\mathrm{ij}},{ \mathrm{f}}_{\mathrm{ij}},{ \mathrm{g}}_{\mathrm{ij}}\right)}_{}$is estimated triangular fuzzy number for sustainability criteria j for given sustainability dimension i

As an example, for the case organization, the computation of the Saudi manufacturing sustainability index for the “environmental” dimension (i = 1) and sustainability criteria “energy” (j = 2), ${\mathrm{SMSI}}_{12}^{}$was calculated using Equation (5) with information in Table 7, and is as presented below:

$${\mathrm{SMSI}}_{12}^{}=\left[\begin{array}{c}\frac{0.5\times 0.4+0.45\times 0.525+0.45\times 0.425}{0.4+0.525+0.425},\\ \frac{0.7\times 0.6+0.65\times 0.725+0.65\times 0.625}{0.6+0.725+0.625},\\ \frac{0.9\times 0.8+0.85\times 0.925+0.85\times 0.825}{0.8+0.925+0.825}\end{array}\right]\equiv \left(0.465,0.665,0.865\right)$$

Thus, using Equations (1)–(5) and information in Table 4, the Saudi manufacturing sustainability index for each sustainability criteria j for the given corresponding sustainability dimension i WAS calculated and presented in

Table 8. Saudi manufacturing sustainability index for each sustainability criteria.

i	Sustainability Criteria Dimensions	j	Sustainability Criteria	Triangular Fuzzy Importance Weight for Sustainability Criteria j with Respect to Sustainability Dimension i	Saudi Manufacturing Sustainability Triangular Fuzzy Index Number for Sustainability Criteria j with Respect to Sustainability Dimension i
				${\mathbf{W}}_{\mathbf{i}\mathbf{j}}^{}\equiv \left({\mathbf{x}}_{\mathbf{i}\mathbf{j}}, {\mathbf{y}}_{\mathbf{i}\mathbf{j}}, {\mathbf{z}}_{\mathbf{i}\mathbf{j}}\right)$	$\mathbf{S}\mathbf{M}\mathbf{I}{\mathbf{S}}_{\mathbf{i}\mathbf{j}}^{}\equiv \left({\mathbf{d}}_{\mathbf{i}\mathbf{j}}, {\mathbf{f}}_{\mathbf{i}\mathbf{j}}, {\mathbf{g}}_{\mathbf{i}\mathbf{j}}\right)$
1	Environmental	1	Materials	(0.45, 0.65, 0.85)	(0.488, 0.688, 0.888)
		2	Energy	(0.45, 0.65, 0.85)	(0.465, 0.665, 0.866)
		3	Waste disposal	(0.45, 0.65, 0.85)	(0.465, 0.665, 0.866)
		4	Environmental compliance	(0.4, 0.6, 0.8)	(0.465, 0.664, 0.864)
2	Economic	5	Economic performance	(0.525, 0.725, 0.925)	(0.449, 0.649, 0.849)
		6	Contribution in market	(0.425, 0.625, 0.825)	(0.374, 0.574, 0.775)
		7	Procurement practices	(0.4, 0.6, 0.8)	(0.473, 0.673, 0.873)
		8	Economic compliance	(0.375, 0.575, 0.775)	(0.475, 0.675, 0.875)
3	Social	9	Employment	(0.4, 0.6, 0.8)	(0.377, 0.576, 0.776)
		10	Occupational health and safety	(0.475, 0.675, 0.875)	(0.441, 0.64, 0.839)
		11	Customer satisfaction	(0.4, 0.6, 0.8)	(0.438, 0.638, 0.838)
		12	Social compliance	(0.375, 0.575, 0.775)	(0.425, 0.625, 0.825)

.

As shown in Table 8, the sustainability criteria “economic performance”, “contribution to the market”, “employment”, “occupational health and safety”, “customer satisfaction”, and “social compliance” had the lowest sustainability index values. Therefore, XYZ should concentrate on these criteria to improve its sustainability index and become an extremely sustainable organization.

• Sub-Step 4.2: Calculation of Saudi manufacturing sustainability index at dimension level

Using the sustainability index at the criteria level, a calculation of the sustainability index at dimension level was carried out. The Saudi manufacturing sustainability index at the dimension level was calculated by Equation (6) [42].

$${\mathrm{SMSI}}_{\mathrm{i}}^{}=\frac{{{\displaystyle \sum }}_{\mathrm{j}}^{}({\mathrm{SMSI}}_{\mathrm{ij}}^{}\times {\mathrm{W}}_{\mathrm{ij}}^{})}{{{\displaystyle \sum }}_{\mathrm{j}}^{}{\mathrm{W}}_{\mathrm{ij}}^{}}=\left(\frac{{{\displaystyle \sum }}_{\mathrm{j}}^{}({\mathrm{d}}_{\mathrm{ij}}\times {\mathrm{x}}_{\mathrm{ij}})}{{{\displaystyle \sum }}_{\mathrm{j}}^{}({\mathrm{x}}_{\mathrm{ij}})},\frac{{{\displaystyle \sum }}_{\mathrm{j}}^{}({\mathrm{f}}_{\mathrm{ij}}\times {\mathrm{y}}_{\mathrm{ij}})}{{{\displaystyle \sum }}_{\mathrm{j}}^{}({\mathrm{y}}_{\mathrm{ij}})},\frac{{{\displaystyle \sum }}_{\mathrm{j}}^{}({\mathrm{g}}_{\mathrm{ij}}\times {\mathrm{z}}_{\mathrm{ij}})}{{{\displaystyle \sum }}_{\mathrm{j}}^{}({\mathrm{z}}_{\mathrm{ij}})}\right)\equiv {\left({\mathrm{d}}_{\mathrm{i}},{ \mathrm{f}}_{\mathrm{i}},{ \mathrm{g}}_{\mathrm{i}}\right)}_{}$$

(6)

where

${\mathrm{SMSI}}_{\mathrm{ij}}^{}$ is Saudi manufacturing sustainability index for sustainability criteria j for given sustainability dimension i.

${\mathrm{W}}_{\mathrm{ij}}^{}$ is importance weight for sustainability criteria j for given sustainability dimension i, and ${\left({\mathrm{x}}_{\mathrm{ij}},{ \mathrm{y}}_{\mathrm{ij}},{ \mathrm{z}}_{\mathrm{ij}}\right)}_{}$is corresponding triangular fuzzy number (refer to Table 8)

${\left({\mathrm{d}}_{\mathrm{ij}},{ \mathrm{f}}_{\mathrm{ij}},{ \mathrm{g}}_{\mathrm{ij}}\right)}_{}$is estimated triangular fuzzy number for sustainability criteria j for given sustainability dimension i (refer to Table 8)

${\left({\mathrm{d}}_{\mathrm{i}},{ \mathrm{f}}_{\mathrm{i}},{ \mathrm{g}}_{\mathrm{i}}\right)}_{}$is triangular fuzzy number representing Saudi manufacturing sustainability for given sustainability dimension i

${\mathrm{SMSI}}_{\mathrm{i}}^{}$ is Saudi manufacturing sustainability index for ith sustainability dimension.

As an example, for the case organization, the computation of the Saudi manufacturing sustainability index for environmental dimension (i = 1) ${\mathrm{SMSI}}_1^{}$was calculated using information from Table 8 and Equation (6), and is as presented below:

$${\mathrm{SMSI}}_1^{}\equiv \left[\begin{array}{c}\frac{0.488\times 0.45+0.465\times 0.45+0.465\times 0.45+0.465\times 0.4}{0.45+0.45+0.45+0.4},\\ \frac{0.688\times 0.65+0.665\times 0.65+0.665\times 0.65+0.664\times 0.6}{0.65+0.65+0.65+0.6},\\ \frac{0.888\times 0.85+0.866\times 0.85+0.866\times 0.85+0.864\times 0.8}{0.85+0.85+0.85+0.8}\end{array}\right]\equiv \left(0.47,0.67,0.87\right)$$

Using Equations (4) and (6), triangular fuzzy importance weight ${\mathrm{W}}_{\mathrm{i}}^{}$ and Saudi manufacturing sustainability index ${\mathrm{SMSI}}_{\mathrm{i}}^{}$ for the sustainability dimension i were calculated and presented in

Table 9. Saudi manufacturing sustainability index for each sustainability dimension.

i	Sustainability Dimension	Triangular Fuzzy Importance Weight for Sustainability Dimension i ${\mathbf{W}}_{\mathbf{i}}^{}\equiv \left({\mathbf{x}}_{\mathbf{i}}, {\mathbf{y}}_{\mathbf{i}}, {\mathbf{z}}_{\mathbf{i}}\right)$	Saudi Manufacturing Sustainability Index of Sustainability Dimension i $\mathbf{S}\mathbf{M}\mathbf{S}{\mathbf{I}}_{\mathbf{i}}^{}\equiv \left({\mathbf{d}}_{\mathbf{i}}, {\mathbf{f}}_{\mathbf{i}}, {\mathbf{g}}_{\mathbf{i}}\right)$
1	Environmental	(0.5, 0.7, 0.9)	(0.47, 0.67, 0.87)
2	Economic	(0.55, 0.75, 0.95)	(0.442, 0.642, 0.843)
3	Social	(0.475, 0.675, 0.875)	(0.421, 0.62, 0.82)

.

As shown in Table 9, the Saudi manufacturing sustainability index for economic and social sustainability dimensions has low fuzzy scores compared to the environmental dimension. Therefore, XYZ should pay more attention to the economic and social dimensions to achieve a better balance between the dimensions of sustainability.

• Sub-Step 4.3: Determination of the Saudi manufacturing fuzzy sustainability index (SMFSI)

The Saudi manufacturing fuzzy sustainability index (SMFSI) can be calculated using the following Equation (7) [20]:

$$\mathrm{SMFSI} \equiv \frac{{{\displaystyle \sum }}_{\mathrm{i}}^{}({\mathrm{SMSI}}_{\mathrm{i}}\times {\mathrm{W}}_{\mathrm{i}})}{{{\displaystyle \sum }}_{\mathrm{i}}^{}{\mathrm{W}}_{\mathrm{i}}}\equiv \left(\frac{{{\displaystyle \sum }}_{\mathrm{i}}({\mathrm{d}}_{\mathrm{i}}\times )}{{{\displaystyle \sum }}_{\mathrm{i}}{\mathrm{x}}_{\mathrm{i}}},\frac{{{\displaystyle \sum }}_{\mathrm{j}}({\mathrm{f}}_{\mathrm{i}}\times {\mathrm{y}}_{\mathrm{i}})}{{{\displaystyle \sum }}_{\mathrm{i}}{\mathrm{y}}_{\mathrm{i}}},\frac{{{\displaystyle \sum }}_{\mathrm{i}}({\mathrm{g}}_{\mathrm{i}}\times {\mathrm{z}}_{\mathrm{i}})}{{{\displaystyle \sum }}_{\mathrm{i}}{\mathrm{z}}_{\mathrm{i}}}\right)\equiv \left(\mathrm{h}, \mathrm{o}, \mathrm{p}\right)$$

(7)

In Equation (7),

${\mathrm{W}}_{\mathrm{i}}^{}$ is importance weight for sustainability dimension i, and $\left({\mathrm{x}}_{\mathrm{i}},{ \mathrm{y}}_{\mathrm{i}},{ \mathrm{z}}_{\mathrm{i}}\right)$ is corresponding fuzzy number (refer to above Table 9)

${\mathrm{SMSI}}_{\mathrm{i}}^{}$ is Saudi manufacturing sustainability index for ith sustainability dimension and ${\left({\mathrm{d}}_{\mathrm{i}},{ \mathrm{f}}_{\mathrm{i}},{ \mathrm{g}}_{\mathrm{i}}\right)}_{}$is corresponding fuzzy number (refer to Table 9)

${\mathrm{SMFSI}}_{}^{}$ is Saudi manufacturing fuzzy sustainability index and ${\left(\mathrm{h}, \mathrm{o}, \mathrm{p}\right)}_{}$ is corresponding triangular fuzzy number

Thus, for the case organization, the ${\mathrm{SMFSI}}_{}^{}$ is the Saudi manufacturing fuzzy sustainability index calculated using Equation (7) and Table 9.

$${\mathrm{SMFSI}}^{}\equiv \left[\begin{array}{c}\frac{0.47\times 0.5+0.442\times 0.55+0.421\times 0.475}{0.5+0.55+0.475},\\ \frac{0.67\times 0.7+0.642\times 0.75+0.62\times 0.675}{0.7+0.75+0.675},\\ \frac{0.87\times 0.9+0.843\times 0.95+0.82\times 0.875}{0.9+0.95+0.875}\end{array}\right]\equiv \left(0.51,0.71,0.91\right)$$

• Step 5: Identifying the Euclidean distance required to match the SMFSI with the near sustainability level

Having calculated the Saudi manufacturing fuzzy sustainability index (SMFSI), it was aligned with linguistic terms (refer to

Table 10. Natural language expression set for labeling the sustainability level.

Linguistic Variable	Saudi Manufacturing Sustainability Level (SMSLr)	$\mathbf{Fuzzy} \mathbf{Numbers} \left({\mathbf{q}}_{\mathit{r}}, {\mathbf{f}}_{\mathit{r}}, {\mathbf{v}}_{\mathit{r}}\right)$
		${\mathbf{q}}_{\mathit{r}}$	${\mathbf{f}}_{\mathit{r}}$	${\mathbf{v}}_{\mathit{r}}$
Extremely sustainable	5	0.7	0.85	1
Highly sustainable	4	0.55	0.7	0.85
Sustainable	3	0.35	0.5	0.65
Fairly sustainable	2	0.15	0.3	0.45
Poorly sustainable	1	0	0.15	0.3

). For this case, the Euclidean distance technique was implemented because it is the best logical technique for observing closeness [40].

Table 10 lists the linguistic terms and fuzzy numbers that were used to determine the Saudi Manufacturing Sustainability Level (SMSL) [42]. There are five sustainability levels (r = 1 to 5) with their five linguistic terms, while $\left({\mathrm{q}}_{\mathrm{r}},{ \mathrm{f}}_{\mathrm{r}},{ \mathrm{v}}_{\mathrm{r}}\right)$ are the corresponding sustainability fuzzy numbers for the given level r.

By implementing the Euclidean distance technique, the Euclidean distance D between SMFSI and SMSL (sustainability level) can be found by Equation (8) [43].

$$\mathrm{D}\left(\mathrm{SMFSI},{\mathrm{SMSL}}_{\mathrm{r}}\right)\equiv \mathrm{D}\left(\left(\mathrm{h},\mathrm{o},\mathrm{p}\right), \left({\mathrm{q}}_{\mathrm{r}},{ \mathrm{f}}_{\mathrm{r}},{ \mathrm{v}}_{\mathrm{r}}\right)\right)\equiv {\left\{{\left(\mathrm{h}-{ \mathrm{q}}_{\mathrm{r}}\right)}^2+{\left(\mathrm{h}-{ \mathrm{f}}_{\mathrm{r}}\right)}^2+{\left(\mathrm{h}-{ \mathrm{v}}_{\mathrm{r}}\right)}^2\right\}}^{1/2}$$

(8)

In Equation (8), $\left({\mathrm{q}}_r,{ \mathrm{f}}_r,{ \mathrm{v}}_r\right)$ represents the corresponding fuzzy number for the Saudi manufacturing sustainability level linguistic variable for each level r ranging from one to five.

The shortest Euclidean distance between SMFSI and SMSL was recognized between five calculated distances using Equation (8). For example, by using SMFSI where (h, o, p) $\equiv$ (0.51, 0.71, 0.91) and SMSLr where level r = 5, SMSL₅ $\equiv$ [ Extremely Sustainable, $\left({\mathrm{q}}_5,{ \mathrm{f}}_5,{ \mathrm{v}}_5\right)\equiv$ (0.7,0.85,1)] for XYZ, the Euclidean distance (D) was calculated for r = 5 as follows:

$$\mathrm{D}\left(\mathrm{SMFSI},{\mathrm{SMSL}}_5\right)\equiv \mathrm{D}\left(\left(\mathrm{h},\mathrm{o},\mathrm{p}\right), \left({\mathrm{q}}_5,{ \mathrm{f}}_5,{ \mathrm{v}}_5\right)\right)\equiv {\left\{{\left(\mathrm{h}-{ \mathrm{q}}_5\right)}^2+{\left(\mathrm{h}-{ \mathrm{f}}_5\right)}^2+{\left(\mathrm{h}-{ \mathrm{v}}_5\right)}^2\right\}}^{\frac{1}{2}}$$

$$\mathrm{D}\left(\left(0.51,0.71,0.91\right), \left(0.7, 0.85, 1\right)\right) \equiv {\left\{{\left(0.51-0.7\right)}^2+{\left(0.71-0.85\right)}^2+{\left(0.91-1\right)}^2\right\}}^{\frac{1}{2}}=0.25$$

Similarly, other Euclidean distances for sustainability level (for r = 1 to 5) were calculated and are presented in

Table 11. Euclidean distance to match SMFSI with all sustainable level.

Sustainability Level	r	Euclidean Distance D
Extremely sustainable (ES)	5	0.25
Highly sustainable (HS)	4	0.07
Sustainable (S)	3	0.37
Fairly sustainable (FS)	2	0.71
Poorly sustainable (PS)	1	0.97

.

D (SMFSI, SMSL_r) represents minimum distance of sustainability level r, here for the case minimum distance is 0.07 of sustainability level of 4. It means that the XYZ organization reaches a highly sustainable level. Accordingly, the sustainability index level of the case organization (XYZ) is assessed as “highly sustainable,” by matching a linguistic label with the minimum Euclidean distance as shown in Figure 2.

• Step 6: Identifying barriers to improving sustainability levels

To improve the sustainability level of a manufacturing organization, sustainability barriers need to be identified and analyzed. Such barriers will impact the sustainability level. The target is to reach the “extremely sustainable” level, which is the highest possible level. The Saudi manufacturing fuzzy performance index (SMFPI) can be used to identify such barriers. Equation (9) was used to calculate the SMFPI [42,43].

$${\mathrm{SMFPI}}_{\mathrm{ijk}}^{}\equiv {\mathrm{W}\text{'}}_{\mathrm{ijk}}^{}\times {\mathrm{R}}_{\mathrm{ijk}}^{}\equiv (1-{\mathrm{W}}_{\mathrm{ijk}}^{}){\mathrm{R}}_{\mathrm{ijk}}^{}\equiv \left(\mathrm{l}, \mathrm{m},\mathrm{n}\right)$$

(9)

A sample calculation of SMFPI of sustainability sub-criteria “material productivity” is as shown below:

$$\begin{gathered} {\mathrm{SMFPI}}_{111}^{}=(1-{\mathrm{W}}_{111}^{})\times {\mathrm{R}}_{111}^{}=\left[\right(1,1,1)–(0.5,0.7,0.9\left)\right]\times (0.5,0.7,0.9)=(0.5,0.3,0.1)\times (0.5,0.3,0.1) \\ {\mathrm{SMFPI}}_{111}^{}=(0.25, 0.21, 0.09) \end{gathered}$$

Thus, for all 29 sustainability sub-criteria, the SMFPI was calculated and presented in

Table 12. Saudi manufacturing sustainability sub criteria fuzzy performance index, ranking score, and ranking.

i *	j *	k *	Saudi Manufacturing Sustainability Sub Criteria Fuzzy Performance Index SMFPI	SMFPIRanking Score	SMFPI Ranking
D1	C1	SC1	(0.25, 0.21, 0.09)	0.1967	17
		SC2	(0.24, 0.2, 0.09)	0.1892	22
	C2	SC3	(0.3, 0.28, 0.18)	0.2667	2
		SC4	(0.21, 0.18, 0.06)	0.1654	25
		SC5	(0.26, 0.24, 0.15)	0.2304	10
	C3	SC6	(0.21, 0.18, 0.06)	0.1654	25
		SC7	(0.29, 0.27, 0.18)	0.2567	4
		SC8	(0.29, 0.27, 0.18)	0.2567	5
	C4	SC9	(0.3, 0.28, 0.18)	0.2667	2
		SC10	(0.28, 0.28, 0.21)	0.2679	1
D2	C5	SC11	(0.24, 0.2, 0.09)	0.1892	22
		SC12	(0.2, 0.17, 0.06)	0.1585	29
	C6	SC13	(0.23, 0.23, 0.16)	0.2167	14
		SC14	(0.25, 0.26, 0.18)	0.2417	8
	C7	SC15	(0.21, 0.18, 0.06)	0.1654	25
		SC16	(0.28, 0.25, 0.14)	0.2317	9
	C8	SC17	(0.26, 0.24, 0.13)	0.2229	11
		SC18	(0.29, 0.27, 0.18)	0.2567	5
D3	C9	SC19	(0.23, 0.23, 0.16)	0.2167	13
		SC20	(22, 0.2, 0.1)	0.1898	21
		SC21	(0.25, 0.26, 0.18)	0.2417	7
		SC22	(0.2, 0.21, 0.15)	0.1967	17
		SC23	(0.22, 0.23, 0.17)	0.2204	12
	C10	SC24	(0.23, 0.19, 0.07)	0.1723	24
		SC25	(0.23, 0.23, 0.14)	0.2117	16
	C11	SC26	(0.21, 0.18, 0.06)	0.1654	25
		SC27	(0.22, 0.2, 0.1)	0.1898	20
	C12	SC28	(0.22, 0.21, 0.12)	0.1967	17
		SC29	(0.25, 0.23, 0.13)	0.2142	15

Notes: * Refer to Table 2.

. However, the SMFPIs had to be ranked, because fuzzy numbers do not always produce an ordered set as real numbers do [40]. There are several existing ways in the literature to rank fuzzy numbers. In this study, the centroid technique was implemented to rank the SMFPIs because the centroid technique is simple and easy to implement. Equation (10) was used to calculate the ranking score base on the centroid technique.

$$\mathrm{Ranking} \mathrm{score}=\left(\frac{\left(\mathrm{l}+4\mathrm{m} + \mathrm{n}\right)}{6}\right)$$

(10)

A sample calculation of ranking score of SMFPI for sustainability sub-criteria “material productivity” was calculated using Equation (10) as here below.

Ranking score for material productivity =$\left(\frac{\left(0.25+4\times 0.21+0.09\right)}{6}\right)$ = 0.197

The ranking score of the sub-criterion titled material productivity is 0.197. Furthermore, the same equation was used to calculate other sustainability sub-criteria ranking scores and presented in Table 12 and accordingly ranked.

Thus, a threshold value needs to be calculated to identify sustainability barriers. Equation (11) is used to calculate the threshold value as calculated below.

$$\mathrm{The} \mathrm{threshold} \mathrm{value}=\left(\frac{\left(\mathrm{Median}+4\times \mathrm{Min} + \mathrm{Max}\right)}{6}\right)$$

(11)

Sustainability barriers are those sustainability sub-criteria with a ranking lower than the threshold value assigned and are thus problems to the company’s sustainability. In the case study, the median ranking score was 0.21, the maximum ranking score was 0.27, and the minimum ranking score was 0.16.

The threshold value for XYZ company $=\left(\frac{\left(0.21+4\times 0.16+0.27\right)}{6}\right)$ = 0.19

The threshold value for XYZ is 0.19. Thus, using this threshold value as benchmark and the Saudi manufacturing sustainability sub criteria fuzzy ranking score from Table 12 were compared and eight sustainability sub-criteria were identified whose performance was lower than the threshold value and are listed in the

Table 13. Sustainability sub-criteria considered as barriers to the organization sustainability.

Sustainability Barriers of ‘XYZ’ Company	Ranking Score
Material consumption type	0.189
Energy consumption	0.165
Gas waste	0.165
Cost	0.189
Profit	0.159
Spending on Saudi suppliers	0.165
Health and safety management system	0.172
Customer privacy	0.165

. These eight sustainability sub-criteria can thus be considered barriers to sustainability. Addressing these barriers to improving the weaker areas of the sustainability sub-criteria will improve sustainability levels.

5. Discussion on Case Study

To stay sustainably competitive, effective, and responsive to market changes, organizations must possess new types of strategies to measure their performance. The study proposed here is a case of diverse sustainability indicators, which are measured in different units and provide guidance on sustainability assessment and estimation approaches. The case study addresses the combination of various sustainability criteria and sub-criteria, as well as the estimation of sustainability based on multiple performance measures. Thus, it allows the management of the organization to estimate the sustainability index, which in turn works as an important management and governance tool.

As shown in Table 12, the manufacturing organization needs to improve the following lowest ranked sustainability sub-criteria: “material consumption type”, “energy consumption”, “gas waste”, “cost”, “profit”, “spending on Saudi suppliers”, “health and safety management system”, and “customer privacy”. As per Table 12, material consumption (MC) in the economy is a sustainability barrier to the organization, which scored 0.189, i.e., slightly under the threshold value. Therefore, the organization needs to focus on consuming sustainable, eco-friendly material that consumes less energy and produces fewer GHG (greenhouse gas) emissions than the materials used currently. Moreover, these materials should be more recyclable, biodegradable, and reusable. The second barrier is energy consumption, which is the amount of energy used. This scored 0.165 in the ranking, i.e., just under the 0.19 threshold value, which indicates that the organization needs to reduce its energy consumption compared to previous years in order to overcome this barrier. The third barrier is gas waste, which includes gases such as carbon dioxide, carbon monoxide, and sulphur dioxide. The “gas waste” ranking score of 0.165 indicates that the organization generates an unacceptable amount of gas waste and should investigate ways to reduce such waste. Cost and profit are also barriers to the organization’s sustainability. Cost includes all costs related to materials, fixed assets (property, plant, and equipment), manpower, and overheads (utilities, rent, taxes, maintenance, environmental fines, etc.). Cost scored 0.189 in the ranking; therefore, XYZ needs to look at each cost type and reduce it to improve the ranking score for the cost barrier. Profit is the revenue that remains after deducting all expenses, including all types of costs such as the cost of goods sold, utility cost, material costs, labor costs, and operating costs. The profit sub-criterion scored 0.159. Thus, XYZ should improve profit by, for example, increasing prices, finding new customers, selling more to existing customers, and emerging new product lines [40]. Spending on Saudi suppliers is another sustainability barrier, with a score of 0.165. This indicates that the organization is not spending enough on local suppliers. Therefore, XYZ needs to purchase more items and services from Saudi suppliers and decrease its spending on non-Saudi suppliers. The health and safety management system is also considered a sustainability barrier for the organization, with a score of 0.172. The organization must ensure the workplace is a safe environment for working, identify hazards, assist with risks, and promote worker health. The last sustainability barrier for the organization is customer privacy, which scored only 0.165. So, the organization should deal with validated complaints regarding customer privacy breaches and customer data losses more seriously. The presented approach is capable of treating multi-criteria situations, and offers the ability to incorporate decision making using quantitative and qualitative preferences. Even though the study was conducted at a local company, it is expected that the findings of the study are generalizable to other similar organizations or other manufacturing industries. The presented sustainability indexing model covers the combination of different sustainability criteria and sub-criteria and making a sustainability estimation based on a multiple performance measures. The model emphasizes three sustainability dimensions (i.e., environmental, economic, and social) which were further divided into twelve criteria and twenty-nine sub-criteria. To test model validity, the model was implemented in a Saudi manufacturing organization as a case study. Subsequently, recommendations were suggested for the organization to overcome these barriers and therefore improve its sustainability level. Thus, the presented model helps a manufacturing organization to gauge their sustainability level by calculating a sustainability index score based on essential criteria and sub-criteria to improve sustainability in the manufacturing domain. This study will pave the way for Saudi manufacturing organizations to be sustainability certified in line with local and international standards.

6. Conclusions

Sustainability in manufacturing organizations has become an essential topic in current times. Many manufacturing organizations worldwide are implementing it as a potential solution to survive and prosper in the competitive market environment. Decision makers must know where their organization stands regarding sustainability. For this reason, sustainability evaluations for manufacturing organizations in Saudi Arabia are essential. Unfortunately, there are few validated approaches for measuring sustainability in manufacturing companies.

This study focused on improving the sustainability of Saudi manufacturing organizations. Furthermore, it identified essential criteria and sub-criteria after a comprehensive literature investigation and interviews with subject matter experts. The study should help Saudi manufacturing organizations gauge their sustainability level by calculating a sustainability index based on essential criteria and sub-criteria to improve sustainability in the manufacturing sector. This study will pave the way for Saudi manufacturing organizations to be sustainability certified by publishing their sustainability reports in line with the GRI standard.

The selection of appropriate sustainability criteria and sub-criteria is vital to obtaining better results when assessing the sustainability of manufacturing organizations. Assessments should be carried out periodically to ensure that managers know how far their organization is from being “extremely sustainable.” Furthermore, if there is a big difference between their sustainability level and the standard sustainability level, they should find the barriers in the sustainability indicators to improve the organization’s overall sustainability level.

This article presents a research study in which three sustainability dimensions were selected for measuring the sustainability of Saudi manufacturing organizations. These three sustainability dimensions—social, environmental, and economic—are exclusively responsible for sustainability in Saudi manufacturing companies. This study identified 12 criteria and 29 sub-criteria, along with three sustainability dimensions. Using the fuzzy logic approach, we found that the case organization was “very sustainable.” Nevertheless, it was below “extremely sustainable”. Unfortunately, there are a few barriers to sustainability that impact the level of sustainability. To identify these barriers, the Saudi manufacturing fuzzy performance importance index (SMFPI) was calculated. By identifying barriers, decision makers can improve organizational sustainability. Future studies should be carried out for other manufacturing organizations to enhance the reliability of the method. Furthermore, the effectiveness of the current index could be improved by conducting evaluations using artificial intelligence methods, for example, decision-making trial and evaluation laboratory, the analytic network process, using the characteristic object method, shape’s indifferent area and midpoint, neural networks, analytical hierarchy process, technique for order of preference by similarity to ideal solution, and other similar approaches.

Finally, this research study was constructed based on the opinions of experts, in which results can be oriented base on personal preferences. Indeed, weights and rating determinations may reflect priorities according to the evaluator’s opinion and therefore suffer from a high degree of subjectivity. Moreover, the study considered only one Saudi manufacturing organization, with expert opinions being used to assess sustainability. Therefore, the results may be different for other countries, other cities, other organizations’ sizes, and manufacturing organizations with different domains, criteria, and sub-criteria.