Next Article in Journal
Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland
Previous Article in Journal
The Role of UNESCO Cultural Heritage and Cultural Sector in Tourism Development: The Case of EU Countries
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Indicators and Framework for Measuring Industrial Sustainability in Italian Footwear Small and Medium Enterprises

by
Azemeraw Tadesse Mengistu
* and
Roberto Panizzolo
Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(10), 5472; https://doi.org/10.3390/su13105472
Submission received: 1 February 2021 / Revised: 26 April 2021 / Accepted: 7 May 2021 / Published: 13 May 2021

Abstract

:
As small and medium enterprises (SMEs) have limited resources, they need a manageable number of indicators that are simple and easy to use for measuring sustainability performance. However, the lack of suitable indicators tailored to industry needs, particularly for SMEs, has been a major challenge in measuring and managing industrial sustainability. Our study aims to empirically analyze and select the useful and applicable indicators to measure sustainability performance in Italian footwear SMEs. To achieve this objective, we proposed a methodological approach to identify, analyze and select sustainability indicators. First, we carried out a systematic review to identify potential sustainability indicators from the literature. Then, we developed a questionnaire based on the identified indicators and pre-tested it with selected industrial experts, scholars, and researchers to further refine the indicators before collecting data. We applied the fuzzy Delphi method to analyze and select the final indicators. Based on a sample of 48 Italian footwear SMEs, the results of our study show that product quality, material consumption, and customer satisfaction were the top priorities among the selected indicators for measuring the economic, environmental, and social dimensions of industrial sustainability, respectively. The selected indicators stressed the measuring of industrial sustainability performance associated with financial benefits, costs, market competitiveness, resources, customers, employees, and the community. Our study proposed a framework that helps to apply the selected indicators for measuring sustainability performance in SMEs. Finally, our study contributes to the existing theory and knowledge of industrial sustainability performance measurement by providing indicators supported by empirical evidence and a framework to put the indicators into practice in the context of SMEs.

1. Introduction

Adopting sustainability practices in manufacturing industries requires a holistic approach at different application scopes, which varies from the production line to the plant, firm, and supply chain level [1]. Industrial sustainability refers to the adoption of sustainability practices at the firm level [2]. It has become an essential topic of discussion [3] and has gained substantial attention among industrial decision-makers, policy-makers, and scholars [2,4]. Manufacturing industries are the main driving force of economic growth and social development of a country [5,6]. However, they are believed to be one of the main causes leading to unintended environmental and social consequences [6]. Subsequently, they are duly required to improve sustainability and be transparent about their sustainability practices [7]. Various stakeholders have put pressure on them to adopt sustainability practices [1,8,9] in order to address the growing concerns of environmental and social impacts [10,11,12].The stakeholders of industrial sustainability include governments, investors, political groups, trade associations, suppliers, employees, customers, and communities [13]. Moreover, sustainability is adopted to gain a competitive advantage [11,14,15]. To effectively adopt sustainability in manufacturing industries, it is essential to measure their performance [3,7]. In this context, industrial sustainability refers to the adoption of sustainability practices at the industrial plant (firm) level [2]. It considers actions that are taken at the material, product, process, plant, and production system levels [16].
The term industrial sustainability was coined by the Institute for Manufacturing at the University of Cambridge, which defines it as “conceptualization, design and manufacture of goods and services that meet the needs of the present generation while not diminishing economic, social and environmental opportunity in the long-term” [13]. Moreover, Zeng et al. [6] defined it as “development that meets the needs of economic growth, social development, environmental protection and results in industrial advantage for the short- and long-term future of the region”. In this study, industrial sustainability is defined as a set of activities that includes all of the following: considering economic, environmental, and social aspects simultaneously while producing products and services; ensuring economic growth, conserving resources, and minimizing negative environmental and social impacts; and meeting stakeholder requirements in the short- to long-term. There is a common understanding that sustainability should emphasize economic, environmental, and social aspects [6,13]. Elkington [17] proposed the triple bottom line (TBL) approach, consisting of three interrelated dimensions of sustainability (economic, environmental, and social). The TBL provides a comprehensive approach for measuring sustainability performance considering these three dimensions [18]. To adequately address industrial sustainability, it is necessary to adopt a holistic approach based on the TBL [3]. Manufacturing industries have a significant impact on the three dimensions of sustainability [19,20]. Thus, they should simultaneously consider economic, environmental, and social dimensions while producing their products and services [21,22,23,24].
Small and medium enterprises (SMEs) contribute significantly to the economic growth of a country through innovation, production volume, and employment generation [25,26,27,28]. Although SMEs have significant economic, environmental, and social implications, they still struggle to address environmental and social dimensions to measure and manage their sustainability performance [29,30], and are primarily focused on the economic dimension [7,31]. This is due to limited resources [7,30,32,33,34], lack of awareness of the associated impacts and benefits of sustainability [30,33], and a lack of skills and expertise [7,30,33]. Moreover, the lack of suitable indicators tailored to SMEs’ needs is still a major challenge in measuring industrial sustainability [9,18]. This creates a potential research opportunity, in particular, empirical research on the analysis and selection of appropriate indicators that are simple and easy to use in the context of SMEs (i.e., indicator-based framework tailored to their characteristics).
Regarding the footwear industry, our literature analysis showed a lack of research on sustainability performance measurement based on the TBL approach. There is limited research that primarily addresses the environmental dimension [35,36]. This motivated us to consider Italian footwear SMEs as our research context to conduct an empirical study. The footwear sector is one of the main industrial sectors driving the economic growth and social development of Italy. According to Assocalzaturifici [37], the sector had about 74,890 employees and a yearly turnover of about 14.3 billion euros by 2019, and consumes a variety of input materials such as leather, synthetic, rubber, and textile materials for production. These figures indicate that the economic, environmental, and social (TBL) implications of the sector have a significant potential for promoting sustainability. The lack of clear sustainability goals, lack of suitable indicators and framework, and limited resources are major challenges in measuring the sustainability performance of footwear firms, particularly SMEs. Thus, our study was intended to address two research questions. Research question one (RQ1): what are the indicators suitable for measuring sustainability in Italian footwear SMEs? Additionally, research question two (RQ2): how can the selected indicators be applied to measure sustainability performance in SMEs? We applied the fuzzy Delphi method (FDM) to answer RQ1. In using FDM, all of the experts’ opinions were incorporated into one investigation to comprehensively consider the uncertainty and ambiguity of their responses and achieve a group consensus. Thus, the results obtained become objective and rational. We proposed a framework to answer RQ2. We believe that properly addressing the research questions will help SMEs to effectively measure and manage their sustainability performance.
The rest of the work is divided into four sections. Section 2 briefly describes the research methodology applied in this study. Section 3 presents the results of the analysis. The results and the proposed framework are briefly discussed in Section 4. Finally, the conclusions of our study are presented in Section 5.

2. Methodology

We proposed the following methodological approach (Figure 1) to address the research question by providing indicators suitable for Italian footwear SMEs to measure their sustainability performance. The main steps in our approach include conducting a systematic review to identify the potential sustainability indicators (step 1), designing the questionnaire (step 2), collecting data (step 3), and analyzing the data with the fuzzy Delphi method (step 4).

2.1. Identification of Potential Sustainability Indicators

We conducted a systematic review to explore indicators within articles published in peer-reviewed journals that are relevant to measuring sustainability performance of manufacturing industries. For this purpose, we selected Scopus and Web of Science (WoS) as search databases, since they provide extensive coverage of peer-reviewed journal articles [38]. We used two sets of keywords correlated with the research topic for the search: (“industrial sustainability” or “sustainable manufactur*” or “sustainable firm*” or “sustainable enterpri*” or “sustainable industr*” or “sustainable factory” or “sustainable production*” or “sustainable organi*” or “sustainable compan*”) in the first set, and (“indicator*” or “metric*” or “performance measure*”) in the second set.
As shown in Figure 2, a total of 1456 papers published up to 2020 were initially found using the keyword searches in Scopus and WoS. Considering 919 articles that were thoroughly peer-reviewed, a total of 537 reviews, conference papers, book chapters, and other documents were excluded; additionally, 329 articles were found to be duplicates. It was not possible to access 10 full-text papers through the online search, and 1 paper was not written in the English language. In the abstract reading, 463 papers that did not focus on measuring, evaluating, or assessing the sustainability of manufacturing industries, and/or were not based a comprehensive approach (i.e., TBL) were excluded. Then, 57 papers that did not consider indicator-based assessment, and/or did not propose indicators relevant to the purpose of this study were also excluded. Finally, 59 papers were selected to explore the indicators.
We carried out a content analysis of the selected papers to identify consistent and frequently used indicators. After recording and organizing all the indicators published in the papers, word-by-word and phrase-by-phrase analyses were conducted to determine their consistency and frequency of use. Indicators that were found to be essentially similar were counted together. On the other hand, indicators that were different were considered to be unique [38,39].

2.2. Questionnaire Design

Based on the identified indicators from the literature analysis, we developed a questionnaire. Then, we conducted pre-testing (pilot testing) of the questionnaire with selected industry experts, scholars, and researchers [40,41]. The pre-testing was aimed at checking clarity (language clarity, context clarity, and content clarity), time (to complete the questionnaire within a few minutes if possible), redundancy (possibility of redundant questions), and relevance (connection to the objective of the study and the appropriateness of the initial indicators). We used the feedback from the pre-test to modify, add, or delete indicators so as to improve the questionnaire and increase its convergence [41].

2.3. Data Collection

The survey sample size depends on data analysis method. In the case of FDM, a small survey sample can be enough to get an objective and reasonable result [42]. Research trends also show that it is an accepted practice to use a small sample size for FDM applications. There are no guidelines or standards for an appropriate sample size for the Delphi method, but the general rule-of-thumb is to have a sample size of 15 to 30 for a homogeneous population (i.e., experts from the same profession), and 5 to 10 for a heterogeneous population (i.e., experts from different profession) [43]. In our study, to acquire the required sample size, the questionnaire was randomly distributed via email to a large sample (more than 1000 firms, regardless of size) among of a population of about 4300 footwear firms in Italy. A total of 53 responses were obtained, and 5 responses were excluded for various reasons, such as that they had missing data or were from large firms. Subsequently, we conducted the data analysis based on valid responses from 48 Italian footwear SMEs. To get empirical evidence from the users of the final selected indicators, and to increase the reliability of the results, the data collection focused on industry experts. Table 1 summarizes the position and work experience of the experts.
As shown in Table 2, the largest proportion (44%) of industrial experts were chief executive officers or general managers. Most of the experts (58%) had over 15 years of work experience.
We conducted a reliability analysis to check the consistency or repeatability of the questionnaire items (i.e., the indicators). The internal consistency method was applied for testing reliability [40]. Cronbach’s alpha, which is the most common test for internal consistency, was used to assess the reliability of the data. Cronbach’s alpha (α) was calculated in IBM SPSS software (version 26). The values of α were 0.710, 0.936, and 0.854 for the economic, environmental, and social dimensions, respectively, which are higher than the minimum acceptable value (0.7).

2.4. Data Analysis: Fuzzy Delphi Method (FDM)

After collecting and organizing the experts’ opinions, we applied FDM to analyze and select the most useful and applicable indicators for measuring industrial sustainability in Italian footwear SMEs. FDM integrates the traditional Delphi method and fuzzy theory to address the drawbacks of the former [44]. The use of fuzzy theory combined with the traditional Delphi method can solve the vagueness and ambiguity of expert judgments to improve the efficiency and quality [41,45]. In FDM, the linguistic variables (qualitative) are converted into fuzzy membership functions (quantitative) for analysis [44]. Triangular, trapezoidal, and Gaussian fuzzy numbers are the membership functions that have been used in previous research [46]. In this study, the triangular fuzzy number was applied as a fuzzy membership function [46,47]. FDM, as applied in this study, avoided the drawbacks of the traditional Delphi method, such as the low convergence of experts’ opinions [48] and the high cost and considerable time needed for collecting opinions [41,44,48] due to the several rounds of a survey undertaken using the traditional Delphi method [47]. In this study’s use of FDM, all of the experts’ opinions were incorporated into one investigation [48,49] to comprehensively consider their uncertainty and ambiguity [47] and to achieve a consensus [49]. Thus, this method is considered to be robust [41] and can create a better data analysis effect [48], and the results obtained are objective and rational [47]. We used the following steps for the FDM analysis:
1.
Extract experts’ opinions: collect and organize the assessment scores given by each expert for each sustainability indicator in the questionnaire.
2.
Aggregate the experts’ opinions: first, convert the linguistic variables used to assess the indicators (i.e., the experts’ opinions) into triangular fuzzy numbers [47], as shown in Table 2. The linguistic variables are used to represent the experts’ opinions on the importance (i.e., usefulness and applicability) of the indicator.
Then, calculate the aggregate triangular fuzzy number (i.e., aggregate assessment score of the experts) for each indicator. To aggregate the experts’ opinions, the approach by Tsai et al. [44] was adapted as follows:
Vij = ( lij ,   mij ,   uij )
where Vij represents the aggregate triangular fuzzy number (i.e., aggregate fuzzy opinion) of indicator (i), and n is the total number of experts (j).
lij = ( j = 1 n aij ) 1 / n
This indicates the geometric mean of the fuzzy numbers at the left end (i.e., lower/minimum scores given by the experts (j) for each indicator (i).
mij = ( j = 1 n bij ) 1 / n
This indicates the geometric mean of the fuzzy numbers in the middle (i.e., median/optimum scores given by the experts).
uij = ( j = 1 n cij ) 1 / n
This indicates the geometric mean value of the fuzzy numbers at the right end (i.e., upper/maximum scores given by the experts).
In this step, the geometric mean was taken to determine the aggregate triangular fuzzy numbers to obtain statistically unbiased results and avoid the impact of extreme values [41,46,49].
3.
Apply defuzzification: apply the center of gravity method (CGM) to defuzzify the aggregate triangular fuzzy number of the indicator:
Si = lij + mij + uij 3
where Si is the final defuzzified score that indicates the aggregate importance of each indicator (i).
4.
Select the final indicators: compare the defuzzified value (Si) with a threshold value (d):
  • If Si ≥ d, the indicator is selected.
  • If Si < d, the indicator is not selected.
The threshold value depends on the fuzzy linguistic scale and user preference [41,47]. If the users want more indicators, they can take a small value of the threshold, and vice versa [47]. In this study, we took a threshold value of (d = 5.6) for a 9-fuzzy linguistic scale to select the indicators [41,47].

3. Results

This section summarizes the results of our analysis based on the systematic review and FDM. Section 3.1 presents the potential sustainability indicators that were identified after conducting the literature analysis and pre-testing. The aggregate fuzzy scores of each indicator and the final selected sustainability indicators based on the defuzzified score are described in Section 3.2.

3.1. Potential Sustainability Indicators

After conducting a content analysis, we identified the most consistent and frequently used indicators for measuring industrial sustainability in the literature [38,39]. As shown in Table 3, 1013 indicators (277 for economic, 402 for environmental, and 334 for social dimensions) were initially explored; 44 indicators (14 for economic, 18 for environmental, and 12 for social dimensions) were used at least five times (i.e., by at least five papers).
Table 3 also shows that the majority of indicators (about 85%) were used only once in the literature, and this is due to (1) a lack of consistency and consensus on how sustainability performance should be measured in manufacturing industries [38,39] and (2) industry context differences affecting the use of indicators for measuring industrial sustainability [3,7]. This result implies that measuring industrial sustainability will continue to invite an ongoing research debate and open potential research opportunities.
Table 4 presents the most consistent and frequently used indicators in the literature. Profit, water consumption, and employment/job opportunity were the most consistent and frequently employed indicators for measuring the economic, environmental, and social dimensions of industrial sustainability, respectively. Indicators in the economic dimension placed more emphasis on measuring the progress in obtaining high financial benefits, including profit [3,18,50] and revenue [3,18,51], from business activities; allocating reasonable expenditure to R&D activities [3,10,18]; reducing costs such as material [18,52,53], labor [18,53,54], energy [1,52,54], operating/operational [3,39,55], maintenance [1,3,39], production [3,9,54], packaging [1,18,20], and inventory [3,53,54] costs; improving product quality [3,52,56]; and properly managing lead time [1,3,7] and delivery time [32,53,57].
In the environmental dimension, more weight was given to indicators that measured progress in the efficient use of input resources such as water [3,18,50], energy [51,52,54], and material [3,18,52] consumption; the use of recycled resources such as recycled water [1,3,8] and recycled material [3,8,58]; the use of renewable energy [1,3,10]; the reduction of emissions consisting of GHG emissions [8,10,54] and air emissions [53,55,59]; and the proper management of waste, including wastewater discharge [8,11,55] and hazardous [34,55,58], solid [10,39,60], and recyclable [3,7,60] wastes.
Regarding the social dimension of industrial sustainability, the focus was on indicators that were used to measure progress in creating employment/job opportunities [3,39,52]; improving the well-being of employees by minimizing employee turnover [18,50,60], minimizing work-related injuries [3,39,50], ensuring employee satisfaction [9,39,51] and occupational health and safety [18,53,57], providing training, development [18,61,62], and a fair salary [12,18,63]; improving the well-being of customers in terms of customer satisfaction [3,51,59] and minimizing customer complaints [19,21,58]; properly managing employee working time in terms of working hours [18,22,57] and lost working days [23,39,50]; and reducing corruption [39,57,61].
In our literature analysis, to explore the indicators, we found that automotive [20,64,65,66,67], food [18,63,68], and electronics [58,69,70] were some of the industrial sectors where previous studies had carried out case studies. There is a lack of research on the analysis and selection of indicators for the footwear industry. This motivated us to consider the footwear industry as our research context.
For this purpose, we initially used the indicators in Table 4 to develop the questionnaire. Due to their high consistency and frequency of use, these indicators can be considered to be more understandable and relevant to manufacturing industries [39]. Then, to further refine the indicators (i.e., modify, add, and delete), we pre-tested the questionnaire with selected industry experts from Italian footwear SMEs, scholars, and researchers. Finally, 40 potential sustainability indicators (12 for economic, 14 for environmental, and 14 for social dimensions) were identified. Table 5 presents the indicators that were used to develop the final questionnaire distributed for data collection.

3.2. Selected Indicators

After collecting the experts’ opinions on the potential indicators, we applied FDM to incorporate fuzzy logic with the opinions to select the final representative indicators. By using FDM, it was possible to analyze a group consensus by addressing uncertainty and ambiguity when evaluating each indicator [41]. Table 6 summarizes the results of the analysis based on FDM.
The results show that 24 indicators were selected to measure industrial sustainability in Italian footwear SMEs (Figure 3). This does not mean that the unselected indicators were irrelevant, but, compared to the selected indicators, they had a lower priority. Among the selected indicators, customer satisfaction (7.88) was the top prioritized indicator, followed by product quality (7.69), on-time delivery (7.56), working conditions (7.37), customer complaints (7.34), lead time (7.29), work-related injuries (7.27), employee satisfaction (7.22), and occupational health and safety (7.10). The other selected indicators were fair salary (6.99), customer health and safety (6.94), training and development (6.84), profit (6.72), employment/job opportunity (6.60), material consumption (6.44), revenue (6.43), working hours (6.32), labor cost (6.06), research and development (R&D) expenditure (6.04), recycled material use (5.96), lost working days (5.81), employee turnover (5.77), energy efficiency (5.72), and material cost (5.71).
The results of our study are based on indicators used to measure sustainability performance in manufacturing industries in the literature. We intended to empirically select and prioritize useful and applicable indicators for measuring sustainability in Italian footwear SMEs from the huge set indicators explored in the literature. The originality of our study lies in involving industry experts’ opinions in selecting and prioritizing the final indicators, and addressing how the selected indicators can be applied to measure sustainability performance in SMEs.

4. Discussion

The results of our study show that product quality, material consumption, and customer satisfaction were the top priorities among the selected indicators for measuring the economic, environmental, and social dimensions of industrial sustainability, respectively. As customers seek to play a significant role in the change towards a sustainable lifestyle, SMEs should respond by producing sustainable products (eco-friendly products). The use of renewable, biodegradable materials and non-hazardous materials promotes product quality in terms of a sustainable product.
Indicators related to financial benefits (profit and revenue), costs (labor and material cost), and market competitiveness (R&D expenditure, on-time delivery, lead time, and product quality) were prioritized for measuring the economic dimension of the sustainability of SMEs. On-time delivery, lead time, and product quality are essential to ensure market competitiveness and financial benefits in the short run. Besides, SMEs need to allocate reasonable expenditure to conduct R&D activities for promoting innovation for producing sustainable products and enhancing market competitiveness in the long run.
Water consumption [3,18,50,60] and greenhouse gas (GHG) emissions [3,8,10,54] were frequently used indicators in previous studies to measure the environmental dimension. However, our empirical study revealed that these indicators are less prioritized. This may be because the production process of footwear SMEs is not water-intensive, as in other industrial sectors such as food and beverages, and produces fewer emissions. On the other hand, material consumption, recycled material use, and energy efficiency were prioritized over other environmental indicators. A wide variety of materials are utilized by the footwear industry to produce a range of products [76]. Leather, synthetics, plastic, rubber, and textiles are the most common materials consumed by the footwear production process [77]. The footwear industry have exerted a significant effort to improve material efficiency and eliminate the use of hazardous materials during production [76]. Italian footwear SMEs that paid more attention to material consumption could measure their progress in terms of improved material efficiency, reduced use of hazardous materials, and the use of eco-friendly and biodegradable materials. They can minimize waste generation by improving material efficiency. The safety of their products for customers can be improved by reducing the use of hazardous materials in the production phase. Moreover, reducing the use of hazardous materials, increasing the use of eco-friendly and biodegradable materials, and promoting the use of recycled materials are significant in minimizing growing concerns about the environmental and social impacts of end-of-life (EOL) products in the post-use phase. SMEs should also measure their progress in saving energy and reducing cost with energy efficiency as a prioritized indicator.
Regarding the social dimension of industrial sustainability, indicators that promote sustainability performance measurement associated with employees, customers, and the community were selected. The footwear industry is among the industrial sectors that are the most low-technology and labor-intensive [78]. As it is a labor-intensive industry, improving employee well-being is required of Italian footwear SMEs. To measure progress towards this goal, working conditions, occupational health and safety, work-related injuries, fair salary, training and development, and employee satisfaction are highly prioritized indicators. They also need to measure progress in improving employee well-being. For this purpose, customer satisfaction, customer complaints, and customer health and safety were identified as relevant indicators. High priority was given to employment/job opportunities for measuring progress towards community development. Moreover, working hours and lost working days were key indicators associated with employees’ work time management.
SMEs have limited resources to measure and manage their sustainability performance. Consequently, they require a manageable number of indicators that are simple and easy to use. Our study analyzed and selected the suitable indicators that have significant impacts and benefits for the sustainability performance of Italian footwear SMEs by addressing the three sustainability dimensions (i.e., economic, environmental, and social). Moreover, as long as SMEs are not facing a scarcity of resources or other challenges, we suggest them to use the other potential sustainability indicators. The selected indicators were built upon the currently available knowledge of industrial sustainability performance measurement, allowing SMEs to take advantage of a big body of validated knowledge without spending time and resources on it.
To address research question two (i.e., to put the selected indicators into practice), we proposed the following four-stage framework, shown in Figure 4, by adapting the methodology suggested by Veleva and Ellenbecker [74] for implementing the indicators of sustainable production.
  • Sustainabilty_Plan: this stage includes setting sustainability goals to improve industrial sustainability performance, selecting indicators to measure progress towards achieving the goals and setting sustainability targets. A manufacturing firm can specify targets in consultation with stakeholders [74]. The target could be critical loads, acceptable limits, or standards set by governmental or non-governmental organizations [51].
  • Sustainability_Apply: this involves defining metrics for the indicators, collecting and organizing data, measuring the sustainability performance for a reporting period (e.g., fiscal year, calendar year, six months, quarter, month [74]), and documenting the results.
  • Sustainability_Check: this focuses on comparing the performance results obtained with the targets, interpreting the comparison results to check whether the performance of the firm is sustainable or not, and communicating the results to the stakeholders to have a common understanding and for taking actions.
  • Sustainability_Action: this consists of taking actions regarding sustainability performance that needs improvement, and reviewing the plan for continuous improvement.
The proposed framework provides a comprehensive view of indicators’ application, ranging from setting sustainability goals to selecting indicators; setting sustainability targets, measuring, evaluating, and interpreting sustainability performance; taking actions on the performance results; and reviewing for continuous improvement. Moreover, it promotes stakeholder engagement, especially in setting sustainability goals and targets, interpreting sustainability performance and taking improvement actions, and reviewing the plan, which eventually builds a high level of trust between SMEs and their stakeholders. It can act as a reporting mechanism and as a continuous improvement tool for industrial sustainability performance.
To make the indicators measurable and manageable, defining a quantifiable metric was essential [69]. In our study, as shown in Table 7, we defined both absolute and relative metrics for the selected indicators.
Our study has significant academic and practical implications. From an academic viewpoint, our study will be a good theoretical base for future research in measuring the sustainability performance of manufacturing industries, mainly the footwear industry. Our study simultaneously conducted an extensive analysis of the indicators published in peer-reviewed articles, carried out an empirical analysis to select and prioritize the indicators, and proposed a framework to put the selected indicators into practice in SMEs. These subsequently contribute to the existing theory and knowledge of industrial sustainability performance measurement. From a practical viewpoint, by providing suitable indicators and a framework for their application, our study can serve as a tool for manufacturing industries, particularly for Italian footwear SMEs, to effectively measure and manage their sustainability performance. Moreover, the proposed framework is flexible and can be applied in different industry contexts.
Even though our study provides a comprehensive methodological approach for selecting and prioritizing indicators, and proposed a framework to put the selected indicators into practice, its scope was limited to the firm level. To get a more comprehensive view of sustainability by including the environmental and social impacts of end-of-life (EOL) products, it would be better to look for additional indicators to measure sustainability performance at the supply chain level. Hence, it would be interesting for future research to expand the methodological approach applied in this study to the entire supply chain consisting of supply, production, distribution, use, and post-use. It would also be interesting for future research to conduct a comparative analysis considering the footwear firms of various countries (e.g., European countries) to identify the similarities and differences in the indicators from the perspective of geographical or national diversity.

5. Conclusions

Our study provides a methodological approach to identify, analyze, and select indicators suitable to measure sustainability in the context of SMEs. It applied FDM, which combines a qualitative method (gathering experts’ opinions using a questionnaire) and a quantitative method (fuzzy analysis considering the ambiguity and subjectivity associated with those opinions) to analyze and select useful and applicable indicators for measuring sustainability performance in Italian footwear SMEs. It also proposed a four-stage framework that helped to effectively apply the selected indicators to measure industrial sustainability performance.
The results of our literature analysis revealed that the majority of indicators (85% of 1013 indicators explored in the literature) were used only once, showing the lack of consistency in the use of indicators to measure sustainability performance in different industry contexts. Our study empirically selected and prioritized indicators for measuring the sustainability performance of Italian footwear SMEs from the wide range of indicators available in the literature. Based on a sample of 48 Italian footwear SMEs, the results of our empirical analysis show that the selected indicators (24 indicators) emphasized measuring industrial sustainability performance associated with financial benefits, cost, market competitiveness, resources, employees, customers, and community. We therefore stress that SMEs focus on and allocate their limited resources to apply the selected indicators for measuring progress towards achieving industrial sustainability goals in terms of increasing financial benefits, reducing costs, and improving market competitiveness, thereby improving resource utilization effectiveness (efficiency improvement, recycling, and substitution) and promoting the well-being of employees, customers, and the community.
The proposed framework is goal-driven, target-based, and continuously improving. Following the framework, SMEs start from the setting of sustainability goals and targets; pass through the selection of indicators needed to measure sustainability performance, perform performance measurement, and evaluate and interpret the performance results by comparing them with the sustainability targets; and finally, act on the performance results and review to bring continuous sustainability performance improvements. Since the framework is based on a predefined list of indicators, it does not overload SMEs with information whose utility is uncertain or placed far in the future.

Author Contributions

Conceptualization, A.T.M. and R.P.; methodology, A.T.M. and R.P.; software, A.T.M.; validation, A.T.M. and R.P.; formal analysis, A.T.M.; investigation, A.T.M. and R.P.; resources, A.T.M. and R.P.; data curation, A.T.M.; writing—original draft preparation, A.T.M.; writing—review and editing, A.T.M. and R.P.; visualization, A.T.M. and R.P.; supervision, R.P.; project administration, A.T.M. and R.P; funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors gratefully acknowledge the advice and contributions from Cipriano Forza, a member of the faculty of the PhD program in Management Engineering at the University of Padova. The authors would also like to thank the Cassa di Risparmio di Padova e Rovigo (CARIPARO), Padova, Italy for its financial support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Huang, A.; Badurdeen, F. Metrics-based approach to evaluate sustainable manufacturing performance at the production line and plant levels. J. Clean. Prod. 2018, 192, 462–476. [Google Scholar] [CrossRef]
  2. Trianni, A.; Cagno, E.; Neri, A. Modelling barriers to the adoption of industrial sustainability measures. J. Clean. Prod. 2017, 168, 1482–1504. [Google Scholar] [CrossRef]
  3. Cagno, E.; Neri, A.; Howard, M.; Brenna, G.; Trianni, A. Industrial sustainability performance measurement systems: A novel framework. J. Clean. Prod. 2019, 230, 1354–1375. [Google Scholar] [CrossRef]
  4. Neri, A.; Cagno, E.; Di Sebastiano, G.; Trianni, A. Industrial sustainability: Modelling drivers and mechanisms with barriers. J. Clean. Prod. 2018, 194, 452–472. [Google Scholar] [CrossRef]
  5. Galal, N.M.; Moneim, A.F.A. A Mathematical Programming Approach to the Optimal Sustainable Product Mix for the Process Industry. Sustainability 2015, 7, 13085–13103. [Google Scholar] [CrossRef] [Green Version]
  6. Zeng, S.X.; Liu, H.C.; Tam, C.M.; Shao, Y.K. Cluster analysis for studying industrial sustainability: An empirical study in Shanghai. J. Clean. Prod. 2008, 16, 1090–1097. [Google Scholar] [CrossRef]
  7. Trianni, A.; Cagno, E.; Neri, A.; Howard, M. Measuring industrial sustainability performance: Empirical evidence from Italian and German manufacturing small and medium enterprises. J. Clean. Prod. 2019, 229, 1355–1376. [Google Scholar] [CrossRef]
  8. Zarte, M.; Pechmann, A.; Nunes, I.L. Indicator framework for sustainable production planning and controlling. Int. J. Sustain. Eng. 2019, 12, 149–158. [Google Scholar] [CrossRef]
  9. Ocampo, L.A.; Clark, E.E.; Promentilla, M.A.B. Computing sustainable manufacturing index with fuzzy analytic hierarchy process. Int. J. Sustain. Eng. 2016, 9, 305–314. [Google Scholar] [CrossRef]
  10. Beekaroo, D.; Callychurn, D.S.; Hurreeram, D.K. Developing a sustainability index for Mauritian manufacturing companies. Ecol. Indic. 2019, 96, 250–257. [Google Scholar] [CrossRef]
  11. Wang, C.; Wang, L.; Dai, S. An indicator approach to industrial sustainability assessment: The case of China’s Capital Economic Circle. J. Clean. Prod. 2018, 194, 473–482. [Google Scholar] [CrossRef]
  12. Samuel, V.B.; Agamuthu, P.; Hashim, M.A. Indicators for assessment of sustainable production: A case study of the petrochemical industry in Malaysia. Ecol. Indic. 2013, 24, 392–402. [Google Scholar] [CrossRef]
  13. Paramanathan, S.; Farrukh, C.; Phaal, R.; Probert, D. Implementing Industrial Sustainability: The Research Issues in Technology Management. R D Manag. 2004, 34, 527–537. [Google Scholar] [CrossRef]
  14. Tseng, M.L.; Divinagracia, L.; Divinagracia, R. Evaluating firm’s sustainable production indicators in uncertainty. Comput. Ind. Eng. 2009, 57, 1393–1403. [Google Scholar] [CrossRef]
  15. Veleva, V.; Bailey, J.; Jurczyk, N. Using Sustainable Production Indicators to Measure Progress in ISO 14001, EHS System and EPA Achievement Track. Corp. Environ. Strategy 2001, 8, 326–338. [Google Scholar] [CrossRef]
  16. Tonelli, F.; Evans, S.; Taticchi, P. Industrial Sustainability: Challenges, perspectives, actions. Int. J. Bus. Innov. Res. 2013, 7, 1751-0252. [Google Scholar] [CrossRef]
  17. Elkington, J. Cannibals with Forks: The Triple Bottom Line of 21st Century Business, 1st ed.; Capstone: Oxford, UK, 1997; pp. 69–94. [Google Scholar]
  18. Ahmad, S.; Wong, K.Y. Development of weighted triple-bottom line sustainability indicators for the Malaysian food manufacturing industry using the Delphi method. J. Clean. Prod. 2019, 229, 1167–1182. [Google Scholar] [CrossRef]
  19. Ahmad, S.; Wong, K.Y.; Zaman, B. A Comprehensive and Integrated Stochastic-Fuzzy Method for Sustainability Assessment in the Malaysian Food Manufacturing Industry. Sustainability 2019, 11, 948. [Google Scholar] [CrossRef] [Green Version]
  20. Ghadimi, P.; Azadnia, A.H.; Yusof, N.M.; Saman, M.Z.M. A weighted fuzzy approach for product sustainability assessment: A case study in automotive industry. J. Clean. Prod. 2012, 33, 10–21. [Google Scholar] [CrossRef]
  21. Watanabe, E.H.; da Silva, R.M.; Tsuzuki, M.S.G.; Junqueira, F.; dos Santos Filho, D.J.; Miyagi, P.E. A Framework to Evaluate the Performance of a New Industrial Business Model. IFAC-PapersOnLine 2016, 49, 61–66. [Google Scholar] [CrossRef]
  22. Lacasa, E.; Santolaya, J.L.; Biedermann, A. Obtaining sustainable production from the product design analysis. J. Clean. Prod. 2016, 139, 706–716. [Google Scholar] [CrossRef]
  23. Eastwood, M.D.; Haapala, K.R. A unit process model based methodology to assist product sustainability assessment during design for manufacturing. J. Clean. Prod. 2015, 108, 54–64. [Google Scholar] [CrossRef]
  24. Haapala, K.R.; Zhao, F.; Camelio, J.; Sutherland, J.W.; Skerlos, S.; Dornfeld, D.; Rickli, J.L. A Review of Engineering Research in Sustainable Manufacturing. J. Manuf. Sci. Eng. 2013, 135, 041013. [Google Scholar] [CrossRef] [Green Version]
  25. Sajan, M.P.; Shalij, P.R.; Ramesh, A.; Biju, A.P. Lean manufacturing practices in Indian manufacturing SMEs and their effect on sustainability performance. J. Manuf. Technol. Manag. 2017, 28, 772–793. [Google Scholar]
  26. Kassem, E.; Trenz, O. Automated Sustainability Assessment System for Small and Medium Enterprises Reporting. Sustainability 2020, 12, 5687. [Google Scholar] [CrossRef]
  27. Lopes de Sousa Jabbour, A.B.; Ndubisi, N.O.; Roman Pais Seles, B.M. Sustainable development in Asian manufacturing SMEs: Progress and directions. Int. J. Prod. Econ. 2020, 225, 107567. [Google Scholar] [CrossRef]
  28. Belas, J.; Strnad, Z.; Gavurová, B.; Cepel, M.; Bilan, Y. Business environment quality factors research—Sme management´s platform. Pol. J. Manag. Stud. 2019, 20, 64–75. [Google Scholar]
  29. Mitchell, S.; O’Dowd, P.; Dimache, A. Manufacturing SMEs doing it for themselves: Developing, testing and piloting an online sustainability and eco-innovation toolkit for SMEs. Int. J. Sustain. Eng. 2020, 13, 159–170. [Google Scholar] [CrossRef]
  30. Journeault, M.; Perron, A.; Vallières, L. The collaborative roles of stakeholders in supporting the adoption of sustainability in SMEs. J. Environ. Manag. 2021, 287, 112349. [Google Scholar] [CrossRef]
  31. Choi, S.; Lee, J.Y. Development of a framework for the integration and management of sustainability for small- and medium-sized enterprises. Int. J. Comput. Integr. Manuf. 2017, 30, 1190–1202. [Google Scholar] [CrossRef]
  32. Hsu, C.H.; Chang, A.Y.; Luo, W. Identifying key performance factors for sustainability development of SMEs—Integrating QFD and fuzzy MADM methods. J. Clean. Prod. 2017, 161, 629–645. [Google Scholar] [CrossRef]
  33. Singh, S.; Olugu, E.U.; Fallahpour, A. Fuzzy-based sustainable manufacturing assessment model for SMEs. Clean Technol. Environ. Policy 2014, 16, 847–860. [Google Scholar] [CrossRef]
  34. Winroth, M.; Almstrom, P.; Andersson, C. Sustainable production indicators at factory level. J. Manuf. Technol. Manag. 2016, 27, 842–873. [Google Scholar] [CrossRef]
  35. Deselnicu, V.; Crudu, M.; Zãinescu, G.; Albu, M.G.; Deselnicu, D.C.; Guţã, S.A.; Bostaca, G. Innovative materials and technologies for sustainable production in leather and footwear sector. Rev. Piel. Incaltaminte 2014, 14, 147–158. [Google Scholar] [CrossRef]
  36. Subic, A.; Shabani, B.; Hedayati, M.; Crossin, E. Performance analysis of the capability assessment tool for sustainable manufacturing. Sustainability 2013, 5, 3543–3561. [Google Scholar] [CrossRef] [Green Version]
  37. Assocalzaturifici. The Italian Footwear Industry—2019 Preliminary Results; Confindustria Moda Research Centre: Milan, Italy, 2020. [Google Scholar]
  38. Ahi, P.; Searcy, C. An analysis of metrics used to measure performance in green and sustainable supply chains. J. Clean. Prod. 2015, 86, 360–377. [Google Scholar] [CrossRef]
  39. Ahmad, S.; Wong, K.Y.; Rajoo, S. Sustainability indicators for manufacturing sectors: A literature survey and maturity analysis from the triple-bottom line perspective. J. Manuf. Technol. Manag. 2019, 30, 312–334. [Google Scholar] [CrossRef]
  40. Forza, C. Survey research in operations management: A process-based perspective. Int. J. Oper. Prod. Manag. 2002, 22, 152–194. [Google Scholar] [CrossRef] [Green Version]
  41. Padilla-Rivera, A.; do Carmo, B.B.T.; Arcese, G.; Merveille, N. Social circular economy indicators: Selection through fuzzy delphi method. Sustain. Prod. Consum 2021, 26, 101–110. [Google Scholar] [CrossRef]
  42. Tahriri, F.; Mousavi, M.; Hozhabri Haghighi, S.; Zawiah Md Dawal, S. The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection. J. Ind. Eng. Int. 2014, 10, 66. [Google Scholar] [CrossRef] [Green Version]
  43. Clayton, M.J. Delphi: A technique to harness expert opinion for critical decision-making tasks in education. Educ. Psychol. 1997, 17, 373–386. [Google Scholar] [CrossRef]
  44. Tsai, H.-C.; Lee, A.-S.; Lee, H.-N.; Chen, C.-N.; Liu, Y.-C. An Application of the Fuzzy Delphi Method and Fuzzy AHP on the Discussion of Training Indicators for the Regional Competition, Taiwan National Skills Competition, in the Trade of Joinery. Sustainability 2020, 12, 4290. [Google Scholar] [CrossRef]
  45. Lee, C.-H.; Wu, K.-J.; Tseng, M.-L. Resource management practice through eco-innovation toward sustainable development using qualitative information and quantitative data. J. Clean. Prod. 2018, 202, 120–129. [Google Scholar] [CrossRef]
  46. Hsu, Y.-L.; Lee, C.-H.; Kreng, V.B. The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Syst. Appl. 2010, 37, 419–425. [Google Scholar] [CrossRef]
  47. Zhang, J. Evaluating regional low-carbon tourism strategies using the fuzzy Delphi- analytic network process approach. J. Clean. Prod. 2017, 141, 409–419. [Google Scholar] [CrossRef]
  48. Ma, Z.; Shao, C.; Ma, S.; Ye, Z. Constructing road safety performance indicators using Fuzzy Delphi Method and Grey Delphi Method. Expert Syst. Appl. 2011, 38, 1509–1514. [Google Scholar] [CrossRef]
  49. Kuo, Y.-F.; Chen, P.-C. Constructing performance appraisal indicators for mobility of the service industries using Fuzzy Delphi Method. Expert Syst. Appl. 2008, 35, 1930–1939. [Google Scholar] [CrossRef]
  50. Vitale, G.; Cupertino, S.; Rinaldi, L.; Riccaboni, A. Integrated Management Approach Towards Sustainability: An Egyptian Business Case Study. Sustainability 2019, 11, 1244. [Google Scholar] [CrossRef] [Green Version]
  51. Song, Z.; Moon, Y. Sustainability metrics for assessing manufacturing systems: A distance-to-target methodology. Environ. Dev. Sustain. 2019, 21, 2811–2834. [Google Scholar] [CrossRef]
  52. Agrawal, R.; Vinodh, S. Sustainability evaluation of additive manufacturing processes using grey-based approach. Grey Syst. Theory Appl. 2020, 10, 393–412. [Google Scholar] [CrossRef]
  53. Singh, R.K.; Modgil, S.; Tiwari, A.A. Identification and evaluation of determinants of sustainable manufacturing: A case of Indian cement manufacturing. Meas. Bus. Excell. 2019, 23, 24–40. [Google Scholar] [CrossRef]
  54. Abedini, A.; Li, W.; Badurdeen, F.; Jawahir, I.S. A metric-based framework for sustainable production scheduling. J. Manuf. Syst. 2020, 54, 174–185. [Google Scholar] [CrossRef]
  55. Hasan, M.S.; Ebrahim, Z.; Wan Mahmood, W.H.; Ab Rahman, M.N. Sustainable-ERP system: A preliminary study on sustainability indicators. JAMT 2017, 11, 61–74. [Google Scholar]
  56. Wu, K.J.; Tseng, M.L.; Lim, M.K.; Chiu, A.S.F. Causal sustainable resource management model using a hierarchical structure and linguistic preferences. J. Clean. Prod. 2019, 229, 640–651. [Google Scholar] [CrossRef]
  57. Raj, A.; Srivastava, S.K. Sustainability performance assessment of an aircraft manufacturing firm. Benchmarking 2018, 25, 1500–1527. [Google Scholar] [CrossRef]
  58. Huang, A.; Badurdeen, F. Sustainable manufacturing performance evaluation at the enterprise level: Index- And value-based methods. Smart Sustain. Manuf. Syst. 2017, 1, 178–203. [Google Scholar] [CrossRef]
  59. Moldavska, A.; Welo, T. Testing and verification of a new corporate sustainability assessment method for manufacturing: A multiple case research study. Sustainability 2018, 10, 4121. [Google Scholar] [CrossRef] [Green Version]
  60. Demartini, M.; Pinna, C.; Aliakbarian, B.; Tonelli, F.; Terzi, S. Soft Drink Supply Chain Sustainability: A Case Based Approach to Identify and Explain Best Practices and Key Performance Indicators. Sustainability 2018, 10, 3540. [Google Scholar] [CrossRef] [Green Version]
  61. Elhuni, R.M.; Ahmad, M.M. Key Performance Indicators for Sustainable Production Evaluation in Oil and Gas Sector. Procedia Manuf. 2017, 11, 718–724. [Google Scholar] [CrossRef]
  62. Feil, A.A.; de Quevedo, D.M.; Schreiber, D. Selection and identification of the indicators for quickly measuring sustainability in micro and small furniture industries. Sustain. Prod. Consum. 2015, 3, 34–44. [Google Scholar] [CrossRef]
  63. Harik, R.; El Hachem, W.; Medini, K.; Bernard, A. Towards a holistic sustainability index for measuring sustainability of manufacturing companies. Int. J. Prod. Res. 2015, 53, 4117–4139. [Google Scholar] [CrossRef]
  64. Moldavska, A.; Welo, T. A Holistic approach to corporate sustainability assessment: Incorporating sustainable development goals into sustainable manufacturing performance evaluation. J. Manuf. Syst. 2019, 50, 53–68. [Google Scholar] [CrossRef]
  65. Singh, S.; Olugu, E.U.; Musa, S.N.; Mahat, A.B. Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework. J. Intell. Manuf. 2018, 29, 1–18. [Google Scholar] [CrossRef]
  66. Vinodh, S.; Ben Ruben, R.; Asokan, P. Life cycle assessment integrated value stream mapping framework to ensure sustainable manufacturing: A case study. Clean Technol. Environ. Policy 2016, 18, 279–295. [Google Scholar] [CrossRef]
  67. Lee, J.Y.; Kang, H.S.; Noh, S.D. MAS2: An integrated modeling and simulation-based life cycle evaluation approach for sustainable manufacturing. J. Clean. Prod. 2014, 66, 146–163. [Google Scholar] [CrossRef]
  68. Yakovleva, N.; Flynn, A. Innovation and sustainability in the food system: A case of chicken production and consumption in the UK. J. Environ. Policy Plan. 2004, 6, 227–250. [Google Scholar] [CrossRef]
  69. Shuaib, M.; Seevers, D.; Zhang, X.; Badurdeen, F.; Rouch, K.E.; Jawahir, I.S. Product sustainability index (ProdSI): A metrics-based framework to evaluate the total life cycle sustainability of manufactured products. J. Ind. Ecol. 2014, 18, 491–507. [Google Scholar] [CrossRef]
  70. Li, T.; Zhang, H.; Yuan, C.; Liu, Z.; Fan, C. A PCA-based method for construction of composite sustainability indicators. Int. J. Life Cycle Assess 2012, 17, 593–603. [Google Scholar] [CrossRef]
  71. OECD. OECD Glossary of Statistical Terms; OECD Publishing: Paris, France, 2008. [Google Scholar]
  72. GRI. GRI Standards Glossary; GRI: Amsterdam, The Netherlands, 2020. [Google Scholar]
  73. Tseng, M.L. Modeling sustainable production indicators with linguistic preferences. J. Clean. Prod. 2013, 40, 46–56. [Google Scholar] [CrossRef]
  74. Veleva, V.; Ellenbecker, M. Indicators of sustainable production: Framework and methodology. J. Clean. Prod. 2001, 9, 519–549. [Google Scholar] [CrossRef]
  75. GRI. GRI Sustainability Reporting Standards (GRI Standards); GRI: Amsterdam, The Netherlands, 2016. [Google Scholar]
  76. Staikos, T.; Rahimifard, S. An end-of-life decision support tool for product recovery considerations in the footwear industry. Int. J. Comput. Integr. Manuf. 2007, 20, 602–615. [Google Scholar] [CrossRef]
  77. Sellitto, M.A.; de Almeida, F.A. Strategies for value recovery from industrial waste: Case studies of six industries from Brazil. Benchmark. Int. J. 2019, 27, 867–885. [Google Scholar] [CrossRef]
  78. Scott, A.J. The Changing Global Geography of Low-Technology, Labor-Intensive Industry: Clothing, Footwear, and Furniture. World Dev. 2006, 34, 1517–1536. [Google Scholar] [CrossRef]
  79. Grecu, V.; Ciobotea, R.-I.-G.; Florea, A. Software Application for Organizational Sustainability Performance Assessment. Sustainability 2020, 12, 4435. [Google Scholar] [CrossRef]
Figure 1. Methodological approach used to conduct the study.
Figure 1. Methodological approach used to conduct the study.
Sustainability 13 05472 g001
Figure 2. Approach of the systematic review.
Figure 2. Approach of the systematic review.
Sustainability 13 05472 g002
Figure 3. Final selected indicators.
Figure 3. Final selected indicators.
Sustainability 13 05472 g003
Figure 4. Proposed framework for applying the indicators.
Figure 4. Proposed framework for applying the indicators.
Sustainability 13 05472 g004
Table 1. Profile of experts by frequency.
Table 1. Profile of experts by frequency.
VariablePositionFrequency Percentage (%)
PositionChief Executive Officer/General Manager2144%
Production Manager715%
Operation Manager919%
Expert/Professional Employee of Sustainability613%
Others510%
Work ExperienceOver 20 years2349%
15 to 20 years49%
10 to 15 years1021%
5 to 10 years613%
Less than 5 years49%
Table 2. Linguistic variables with their corresponding scales and triangular fuzzy numbers.
Table 2. Linguistic variables with their corresponding scales and triangular fuzzy numbers.
Fuzzy ScalesLinguistic VariablesTriangular Fuzzy Numbers (a, b, c)
1Not important (NI)(1, 1, 3)
3Slightly important (SI)(1, 3, 5)
5Moderately important (MI)(3, 5, 7)
7Important (I)(5, 7, 9)
9Very important (VI)(7, 9, 9)
Table 3. Identified indicators by frequency of use.
Table 3. Identified indicators by frequency of use.
Frequency of UseIdentified Indicators Frequency of UseIdentified Indicators
186015
25816
335171
416181
51319
61020
7621
8122
9123
10124
11425
121261
131271
142Total1013
Table 4. Frequently used TBL sustainability indicators.
Table 4. Frequently used TBL sustainability indicators.
Indicators for Economic DimensionFrequency of Use Indicators for Environmental DimensionFrequency of UseIndicators for Social DimensionFrequency of Use
Profit14Water consumption27Employment/Job opportunity11
Research and development expenditure14Energy consumption26Employee turnover11
Product quality13Greenhouse gas emissions18Work-related injuries10
Revenue12Material consumption17Customer satisfaction7
Material cost11Renewable energy use9Employee satisfaction6
Labor cost11Recycled water use7Working hours6
Energy cost8Recycled material use7Corruption6
Operating/Operational cost7Wastewater discharge7Occupational health and safety5
Maintenance cost6Hazardous waste7Training and development5
Production cost6Land use6Fair salary5
Packaging cost6Solid waste6Customer complaints5
Lead time6Recyclable waste6Lost working days5
Inventory cost5Packaging material consumption5
On-time delivery 5Electricity consumption5
Air emissions5
Global warming potential5
Energy efficiency5
Energy intensity5
Table 5. Indicators identified after the literature analysis and pre-testing.
Table 5. Indicators identified after the literature analysis and pre-testing.
Sustainability
Dimensions
IndicatorsShort Descriptions
EconomicProfitExcess revenue over the cost of producing the product [71]
RevenueValue of output (product) sold, i.e., the number of products sold times the unit price [71]
Research and development expenditureExpenses allocated to carry out research and development (R&D) activities [71]
Material costCost of input materials used to produce the product [71]
Labor costSalaries and wages of active employees, pensions, various social charges, and related costs [71]
Energy costCost allocated for the quantity of energy consumed [71]
Maintenance costCosts (such as expenses for lubricants, spare parts, tools and equipment, and maintenance crew) incurred to carry out maintenance activities [71]
Packaging costCost allocated for packaging material
Inventory costExpenses associated with holding and storing raw materials and products
Product qualityFeatures incorporated that can meet customer needs
Lead timeTime between order placement and shipment
On-time deliveryDelivery of finished products on time
EnvironmentalWater consumptionUse of water for processing, washing, drinking, and related activities [71]
Recycled water useReuse of wastewater after treatment [8]
Energy consumptionUse of energy (electricity, fuel) for manufacturing process, lighting, heating, and other purposes [71]
Renewable energy useUse of energy comes from renewable sources such as solar, wind, hydro, biomass, and others [72]
Energy efficiencyRatio of energy used in manufacturing process, heating, lighting, and other purposes to input energy [51]
Material consumptionInput materials consumed to produce the output (product) [19]
Recycled material useUse of recycled input materials by replacing virgin materials [72]
Packaging material consumptionUse of materials such as containers or wrapping for handling, protecting, and marketing the product
Land useUse of land for industrial activities [71]
Greenhouse gas emissionsRelease of greenhouse gases (GHGs) such as carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), chlorofluorocarbons (CFCs), and others contributing to the greenhouse effect/global warming [71]
Wastewater dischargeIndustrial sewage (used water) released to surface water, groundwater, seawater, or a third party [72]
Hazardous wasteWaste with toxic, infectious, radioactive, or flammable properties that poses a potential hazard to human health, other living organisms, and the environment [71]
Solid waste disposalDisposal of solid waste (waste with low liquid content) that is not recycled [71]
Recyclable wasteWaste that can be used in the production and consumption processes [71]
SocialEmployment/Job opportunityOpportunities created for employment [71]
Fair salaryRegular fair payments to employees for their service [71]
Employee turnoverEmployees leaving the organization voluntarily or due to dismissal, retirement, or death [72]
Employee satisfactionContentment of employees with their job
Occupational health and safetyPromotion of employee health and safety by preventing work-related injuries and illnesses [72]
Training and developmentOrganizational activities to enhance employees’ knowledge and skills for the better performance of specific tasks
Working conditionsPromoting a safe working environment by preventing work-related injuries and illnesses due to exposure to hazardous substances, dust, high temperature, loud noise, and other risk factors
Work-related injuriesInjuries arising from exposure to hazards and accidents at work [72]
Working hoursHours that employees spend doing paid work [71]
Lost Working daysLost days due to work-related injuries and illnesses [73,74]
Customer health and safetySystematic efforts to address incidents concerning the health and safety impacts of products and services on customers [75]
Customer satisfactionHow well customers’ needs are met by the products and services offered
Customer complaintsCustomers’ feedback on the products and services that did not meet their needs
CorruptionAbuse of power in leadership for personal, financial, or other benefits [71,72]
Table 6. Aggregate assessment scores of the indicators.
Table 6. Aggregate assessment scores of the indicators.
Sustainability
Dimensions
Indicators (i)Aggregate Fuzzy OpinionDefuzzified Score (Si)Selected
Min (lij)Optimum (mij)Max (uij)
EconomicProfit4.826.978.386.72Yes
Revenue4.436.568.296.43Yes
Research and development expenditure4.116.187.846.04Yes
Material cost3.675.767.695.71Yes
Labor cost4.056.217.946.06Yes
Energy cost3.265.547.485.42
Maintenance cost2.924.716.864.83
Packaging cost2.454.536.674.55
Inventory cost2.684.516.724.64
Product quality6.078.168.847.69Yes
Lead time5.557.618.727.29Yes
On-time delivery5.897.948.867.56Yes
EnvironmentalWater consumption2.283.495.723.83
Recycled water use2.363.405.703.82
Energy consumption3.485.367.355.40
Renewable energy use3.235.287.175.23
Energy efficiency3.805.817.555.72Yes
Energy intensity2.974.916.964.95
Material consumption4.516.668.166.44Yes
Recycled material use3.986.107.815.96Yes
Packaging material consumption3.535.267.285.36
Land use2.173.725.903.93
Greenhouse gas emissions2.494.326.324.37
Wastewater discharge2.283.835.974.03
Solid waste disposal3.135.167.135.14
Recyclable waste3.055.127.005.05
SocialEmployment/Job opportunity4.616.788.416.60Yes
Fair salary5.157.228.596.99Yes
Employee turnover3.785.867.665.77Yes
Employee satisfaction5.437.498.727.22Yes
Occupational health and safety5.317.428.577.10Yes
Training and development4.947.008.596.84Yes
Working conditions5.677.738.727.37Yes
Work-related injuries5.517.578.727.27Yes
Working hours4.336.508.156.32Yes
Lost working days3.745.977.735.81Yes
Customer health and safety5.227.178.446.94Yes
Customer satisfaction6.348.388.917.88Yes
Customer complaints5.607.708.717.34Yes
Corruption3.204.536.654.79
Table 7. Metrics defined for the indicators.
Table 7. Metrics defined for the indicators.
IndicatorsMetricsAdapted From
AbsoluteRelative
ProfitNet profit gained during the reporting period (USD, Euro)Net profit to total revenue ratio (%)[61]
RevenueTotal revenue generated during the reporting period (USD, Euro)Revenue generated per unit of product sold (USD, Euro/uop)[19]
Research & development expenditureR&D spending during the reporting period (USD, Euro)R&D spending to total revenue ratio (%)[19,79]
Material costTotal material cost during the reporting period (USD, Euro)Percentage of material cost relative to total revenue (%)[19]
Labor costTotal labor cost during the reporting period (USD, Euro)Percentage of labor cost relative to total revenue (%)[19]
Product quality Number of products that met customer requirements during the reporting period (#)Percentage of products that met customer requirements (%)Proposed metrics
Lead time Total number of products produced within the required lead time (#)Percentage of products produced within the required lead time (%)Proposed metrics
On-time delivery Total number of products delivered on time during the reporting period (#)Percentage of products delivered on time (%)Proposed metrics
Material consumption Total weight or volume of materials consumed during the reporting period (kg, m3, L, m2, pc)Material consumption per unit of product produced (kg, m3, l, m2, pc/uop); material efficiency (%); percentage of biodegradable materials used (%); percentage of renewable materials used (%); percentage of hazardous materials used (%)[1,74]
Recycled material useTotal weight or volume of recycled materials used during the reporting period (kg, m3, L, m2, pc)Percentage of recycled materials used (%)[1]
Energy efficiency----Ratio of final energy used for production to the total input energy (%)[51]
Employment/Job opportunityTotal number of new employees hired during the reporting period (#)Recruitment efficiency (%)[75]
Fair salary----Average salary per employee (USD, Euro/emp)[19]
Employee turnoverTotal number of employee turnover during the reporting period (#)Percentage of employee turnover (%) [1,75]
Employee satisfactionTotal number of employees who reported job satisfaction during the reporting period (#)Percentage of employees who reported job satisfaction (%)[1,74]
Occupational health and safety (OHS)Total number of employees covered by the OHS program (#); total number of fatalities as a result of work-related injuries (#); total number of fatalities as a result of work-related illnesses (#); total number of cases of work-related illnesses during the reporting period (#)Percentage of employees covered by OHS program (%); percentage of fatalities as a result of work-related injuries (%); percentage of fatalities as a result of work-related illnesses (%); percentage of cases of work-related illnesses (%)[75]
Training and developmentTotal number of total employees who received a regular performance and career development review (#); total training hours during the reporting period (h)Percentage of employees who received a regular performance and career development review (%); average training hours per employee (h/emp) [75]
Working conditionsTotal number of employees working in decent conditions (#)Percentage of employees working in decent conditions (%)Proposed metrics
Work-related injuriesTotal number of work-related injuries during the reporting period (#)Work-related injuries per employee (#/emp)[75]
Working hoursTotal working hours during the reporting period (h)Average working hours per employee (h/emp)[8]
Lost Working daysTotal lost working days due to injuries and illnesses during the reporting period (day)Percentage of lost working days due to injuries and illnesses (%)[73,74]
Number of employeesTotal number of active employees during the reporting period (#)Number of active employees per unit of product produced (#/uop)[3,74]
Customer health and safetyTotal number of incidents concerning the health and safety impacts of the products and services provided during the reporting period (#)Number of health and safety incidents per unit of product sold (#/uop)[75]
Customer satisfactionTotal number of customers who reported satisfaction with the products and services offered during the reporting period (#)Percentage of customers who reported satisfaction with the products and services offered (%)[19]
Customer complaintsTotal number of customer complaints during the reporting period (#)Customer complaints per unit of product sold (#/uop)[19,74]
Note: #: number, kg: kilogram, m3: cubic meter, m2: square meter, L: liter, pc: piece, h: hour, uop: unit of product (pair of shoes), emp: employee.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Mengistu, A.T.; Panizzolo, R. Indicators and Framework for Measuring Industrial Sustainability in Italian Footwear Small and Medium Enterprises. Sustainability 2021, 13, 5472. https://doi.org/10.3390/su13105472

AMA Style

Mengistu AT, Panizzolo R. Indicators and Framework for Measuring Industrial Sustainability in Italian Footwear Small and Medium Enterprises. Sustainability. 2021; 13(10):5472. https://doi.org/10.3390/su13105472

Chicago/Turabian Style

Mengistu, Azemeraw Tadesse, and Roberto Panizzolo. 2021. "Indicators and Framework for Measuring Industrial Sustainability in Italian Footwear Small and Medium Enterprises" Sustainability 13, no. 10: 5472. https://doi.org/10.3390/su13105472

APA Style

Mengistu, A. T., & Panizzolo, R. (2021). Indicators and Framework for Measuring Industrial Sustainability in Italian Footwear Small and Medium Enterprises. Sustainability, 13(10), 5472. https://doi.org/10.3390/su13105472

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop