1 Introduction

The concept of sustainability has increasingly been used to deal with growing concerns regarding the environmental and social impacts of development (Huang & Badurdeen, 2018). Specifically, sustainable manufacturing is increasingly being used to properly manage the environmental and social impacts of manufacturing industries (Ahmad & Wong, 2019; Singh et al., 2014), helping them to contribute to achieving the sustainable development goals (SDGs) (Hashim et al., 2021). Sustainable manufacturing has also become a key factor enabling manufacturing firms to stay relevant in today’s competitive business environment (Singh et al., 2019). Eventually, manufacturing industries will need to transform their traditional manufacturing practices that primarily focus on economic benefits into sustainable manufacturing practices that consider environmental and social responsibility in addition to pursuing profitability (Shuaib et al., 2014; Singh et al., 2019).

For the effective adoption of sustainability in manufacturing industries, a comprehensive framework is required to measure their performance at the product, operation process, and production system levels. The scope of sustainability performance measurement varies from the production line to the plant, firm, and supply chain (Huang & Badurdeen, 2018). Industrial sustainability involves adopting sustainability practices at the firm level (Trianni et al., 2017). In this paper, our discussion of industrial sustainability considers the sustainability of manufacturing industries at the firm level. Industrial sustainability has become an essential topic of discussion (Cagno et al., 2019; Smart et al., 2017) and has been given much attention by industrial decision-makers, policymakers, and scholars (Neri et al., 2018; Trianni et al., 2017). It accounts for actions that are taken at the levels of material, product, process, plant, and production system (Tonelli et al., 2013). The term industrial sustainability was coined by the Institute for Manufacturing at the University of Cambridge, and it stated that industrial sustainability should promote the production of goods and services that meet the needs of the present generation while not compromising economic, environmental, and social opportunities in the long-term (Paramanathan et al., 2004). According to Zeng et al. (2008) industrial sustainability should support economic growth, environmental protection, and social development to create industrial advantages in the short- and long-term.

Along with the growing significance of sustainable development, the theories of industrial sustainability have evolved. The main theories linked to industrial sustainability are corporate social responsibility, stakeholder theory, and corporate sustainability (Chang et al., 2017). Corporate social responsibility refers to practices undertaken by corporates to improve their performance so that they can achieve social responsibility and long-term sustainability and establish trust with stakeholders (Köseoglu et al., 2021). Stakeholder theory provides a logical perspective of how firms can manage their relationships with stakeholders to facilitate the development of competitive resources and attain sustainable success. It also provides useful insights into the practices of sustainable and ethical value creation (Parmar et al., 2010). Corporate sustainability focuses not only on corporates’ economic aspects but also takes into account their environmental and social aspects. It helps companies to achieve economic growth while fulfilling their social responsibility and becoming more environmentally friendly (Aktaş & Demirel, 2021).

The manufacturing sector is one of the main driving forces of a country's economic growth and social development (Galal & Moneim, 2015; Zeng et al., 2008). This sector, however, is considered to be one of the main contributors to environmental degradation (Zeng et al., 2008). While the past few years have seen increasing emphasis placed on sustainability, the manufacturing sector has been slow to transform its traditional manufacturing practices, which focus primarily on economic benefits, into sustainable manufacturing practices that also consider environmental and social responsibility. Some of the sustainability challenges faced by the sector are an increase in pollution, greenhouse gases, and global warming, and a decrease in biodiversity (Aktaş & Demirel, 2021). As a result, manufacturing companies need to improve their sustainability performance and be transparent about their sustainability practices (Trianni et al., 2019). Various stakeholders have put pressure on them to adopt sustainability practices (Huang & Badurdeen, 2018; Ocampo et al., 2016; Zarte et al., 2019) to address the growing concerns of the environmental and social impacts of development (Beekaroo et al., 2019; Samuel et al., 2013; Wang et al., 2018). The stakeholders of industrial sustainability include governments, investors, political groups, trade associations, suppliers, employees, customers, and communities (Paramanathan et al., 2004). Moreover, sustainability is adopted so that companies can gain a competitive advantage (Tseng et al., 2009; Veleva et al., 2001; Wang et al., 2018). The effective implementation of sustainability practices will help companies to enhance sustainable manufacturing by ensuring economic growth, conserving natural resources, minimizing negative environmental and social impacts, and meeting the requirements of stakeholders.

To adequately address industrial sustainability, it is useful to adopt a holistic approach based on the triple bottom line (TBL) (Cagno et al., 2019). As was proposed by Elkington (1997), the TBL approach consists of three correlated dimensions of sustainability (i.e., economic, environmental, and social dimensions); and provides a comprehensive approach for measuring the sustainability performance of manufacturing industries (Ahmad & Wong, 2019). As manufacturing industries have a significant impact on the three sustainability dimensions (Ahmad et al., 2019b; Ghadimi et al., 2012), they should simultaneously consider them while producing their products and services (Eastwood & Haapala, 2015; Haapala et al., 2013; Lacasa et al., 2016; Watanabe et al., 2016). For example, the manufacturing industries in the European Union countries (EU-27) contributed a value-added of nearly 2.1 trillion Euros to the gross domestic product (Eurostat, 2019); they generated about 370 million metric tons of CO2 equivalent greenhouse emissions (Statista, 2019) in 2019; and almost 35 million employees were working in the manufacturing industries in the European Union, including the United Kingdom in 2020 (Statista, 2020). These figures imply that manufacturing industries have significant potential to address the issues of sustainability. In order to effectively adopt sustainability practices in a manufacturing industry, measuring its performance is essential (Cagno et al., 2019; Trianni et al., 2019). Industrial sustainability cannot be properly managed if it is not effectively measured using appropriate indicators (Feil et al., 2015; Huang & Badurdeen, 2018; Trianni et al., 2019). For this purpose, the use of multidimensional indicators based on the TBL approach for measuring sustainability performance is crucial (Ahmad & Wong, 2019; Moldavska and Welo, 2019; Trianni et al., 2019; Wang et al., 2018; Winroth et al., 2016). Moreover, as contextual factors such as industry type, firm size, and geographical area affect the use of indicators for measuring industrial sustainability (Cagno et al., 2019; Trianni et al., 2019), adapting suitable indicators tailored to manufacturing industry contexts is essential (Medini et al., 2015).

Normally, indicators provide information about physical, economic, or social issues (Veleva & Ellenbecker, 2001) by translating complex issues into manageable and easily understood information for decision-making (Samuel et al., 2013). In particular, sustainability indicators are used to measure and evaluate progress towards achieving sustainability goals and targets (Ahi & Searcy, 2015). They are used to put economic, environmental, and social sustainability goals into practice (Samuel et al., 2013) and help manufacturing industries report their contribution to achieving the SDGs (Moldavska and Welo, 2019). The economic dimension is the most important goal of manufacturing industries (Wang et al., 2018; Zarte et al., 2019). In the economic dimension, for instance, the indicators used to consist of profit (Ahmad et al., 2019a; Hasan et al., 2017; Joung et al., 2013; Song & Moon, 2019), costs (Ahmad et al., 2019a; Hasan et al., 2017; Joung et al., 2013; Song & Moon, 2019; Wang et al., 2018), and investment (Hasan et al., 2017; Joung et al., 2013; Song & Moon, 2019). The environmental dimension considers the impacts on the environment resulting from manufacturing industries’ processes and products (Wang et al., 2018; Zarte et al., 2019). The indicators in the environmental dimension, for example, include resources (Ahmad et al., 2019a; Hasan et al., 2017; Joung et al., 2013; Song & Moon, 2019), emissions (Ahmad et al., 2019a; Hasan et al., 2017; Joung et al., 2013; Song & Moon, 2019; Wang et al., 2018), wastes (Ahmad et al., 2019a; Hasan et al., 2017; Song & Moon, 2019; Wang et al., 2018), pollution (Hasan et al., 2017; Joung et al., 2013), and natural habitat conservation (Joung et al., 2013). The social dimension considers human needs (Zarte et al., 2019). From the viewpoint of the manufacturing industry, the indicators in the social dimension address issues related to employees, customers, and the community (Ahmad et al., 2019a; Joung et al., 2013). Indicators related to employees, for instance, include income (wage or salary) (Ahmad et al., 2019a; Wang et al., 2018), occupational health and safety (Hasan et al., 2017; Joung et al., 2013; Song & Moon, 2019), employee development (Hasan et al., 2017; Joung et al., 2013; Song & Moon, 2019), and employee satisfaction (Joung et al., 2013; Song & Moon, 2019); those related to customers consist of customer health and safety (Ahmad et al., 2019a; Hasan et al., 2017; Joung et al., 2013; Song & Moon, 2019) and customer satisfaction (Ahmad et al., 2019a; Joung et al., 2013; Song & Moon, 2019); and those related to the community comprise job opportunity (Ahmad et al., 2019a; Hasan et al., 2017; Song & Moon, 2019), corruption (Ahmad et al., 2019a; Joung et al., 2013), and community development (Ahmad et al., 2019a; Joung et al., 2013).

The purpose of this paper is to explore and analyze the indicators that have been described in the literature for industrial sustainability performance measurement. In doing so, we will provide valuable information on the wide range of indicators available in the literature to academicians, practitioners, and policymakers that will be useful for the accounting, auditing, and management of industrial sustainability. To achieve the aim, two research questions were formulated: (1) what indicators are the consistently and frequently used to measure industrial sustainability in the literature? Additionally, (2) into which themes can the indicators be categorized? By addressing research question one, this paper provides useful insights to academicians and practitioners regarding the mainstream industrial sustainability indicators used in the literature. By addressing research question two, on the other hand, this paper helps to link the indicators to organization goals and the respective sustainable development goals. The rest of the paper is organized into three sections. Section 2 briefly describes the methodology applied in this paper. Next, the results are briefly discussed in Sect. 3. Finally, our conclusions as well as avenues for future research are described in Sect. 4.

2 Methodology

2.1 Research design

To address our research questions, a systematic review was conducted (Ahi & Searcy, 2015; Ahmad et al., 2019a; Feil et al., 2019) to explore the indicators described in peer-reviewed articles that are relevant to the sustainability performance measurement of manufacturing industries. For this purpose, Scopus and Web of Science were selected as search databases since they provide a wide coverage of peer-reviewed journal articles on research topics related to sustainability performance measurement. This paper focuses on academic literature since, unlike non-academic literature such as industry reports and sustainability standards and guidelines, it provides frequently updated indicators, making it possible to carry out consistency analysis of indicators over a substantial period of time. In this paper, the following procedures were used to carry out the systematic review (Feil et al., 2019): (a) defining the aim of the review, (b) selecting keywords and databases, (c) defining a paper screening and selection approach, (d) the coding and analysis of data from the selected papers, and (e) the presentation of the results.

2.2 Data collection

To collect data, we searched for papers in the literature using two sets of keywords related to the topic of this paper: "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 Fig. 1, a total of 1666 papers were initially found using the keywords search in Scopus and WoS. Considering the full list of 1068 articles that had been thoroughly peer-reviewed, a total of 598 reviews, conference papers, book chapters, and other documents were excluded; additionally, 387 articles were found to be duplicates. Moreover, it was not possible to access 11 full-text papers through our online search, and 1 paper was not written in the English language. In our reading of the abstracts, 538 papers that did not focus on measuring, evaluating, or assessing the sustainability of manufacturing industries and/or did not use a comprehensive approach (i.e., TBL) were excluded after an analysis of the purpose, methodology and/or scope of the paper. Then, 68 papers that did not consider indicator-based assessment and/or did not propose indicators relevant to the purpose of this paper were also excluded after analyzing the detailed contents of the papers. Finally, 63 papers were selected for exploring and analyzing the indicators.

Fig. 1
figure 1

Approach used for screening and selecting papers

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2.3 Data analysis

To address research question one, a content analysis was carried out to identify the most consistently and frequently used indicators from the selected papers. In the content analysis, all indicators described in the papers were recorded and organized in Microsoft Excel. Next, they were coded into as either economic, environmental, or social indicators based on their context and purpose. Then, a frequency count was conducted to determine how many times (i.e., by how many papers) each indicator was used. In the frequency count, word-by-word and phrase-by-phrase analyses were carried out to determine the indicators’ 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 indicators (Ahi & Searcy, 2015; Ahmad et al., 2019a).

The indicators were logically categorized into themes to address research question two. A cause-and-effect diagram was used to provide a logical causal relationship between the indicators, organizational goals, and industrial sustainability performance. To organize and map the relationship in the cause-and-effect diagram, the potential organizational goals used to improve industrial sustainability performance were defined; then, the consistently and frequently used indicators were logically linked to their respective organizational goals.

3 Results and discussion

3.1 Descriptive analysis of the papers

Figure 2 shows the distribution of the selected papers from academic journals that published two or more papers. It highlights the multidisciplinary nature of the systematic review conducted in this paper by examining journals focused on multidisciplinary subjects such as sustainability, engineering, and business and management (Ahi & Searcy, 2015). Six journals namely, Journal of Cleaner Production, Sustainability (Switzerland), IFAC-PapersOnLine, Journal of Advanced Manufacturing Technology, Ecological Indicators, and Journal of Manufacturing Systems were found to be the leading journals that had published over 50% of the selected papers. Among them, the Journal of Cleaner Production was the top contributor to the papers.

Fig. 2
figure 2

Distribution of papers across the academic journals

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As shown in Fig. 3, an increasing trend is evident, showing the growing research interest in the sustainability measurement of manufacturing industries over the past 20 years (2001 to 2020). Within this time frame, a wide range of indicators has been employed by previous research for measuring industrial sustainability. Of these indicators, very few were consistently and frequently used (i.e., indicators used by older published papers were later applied by recently published papers). For instance, profit, as used by Yakovleva and Flynn (2004), was later employed by Grecu et al. (2020) for measuring the economic dimension of sustainability; water consumption, as used by Veleva and Ellenbecker (2001), was later applied by Jamil et al. (2020) to measure the environmental dimension; and employment/job opportunity, as used by Yakovleva and Flynn (2004), was later employed by Agrawal and Vinodh (2020) for measuring the social dimension.

Fig. 3
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Distribution of papers according to their publication year

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As can be seen in Fig. 4, automotive (Ghadimi et al., 2012; Lee et al., 2014; Moldavska and Welo, 2019; Singh et al., 2018; Vinodh et al., 2016), food (Ahmad & Wong, 2019; Harik et al., 2015; Yakovleva & Flynn, 2004), electronics (Huang & Badurdeen, 2017; Li et al., 2012; Shuaib et al., 2014), and plastic (Ocampo et al., 2016; Song & Moon, 2019) were the industries most often used by previous studies/papers for conducting case studies. In addition, we identified the indicators that were most commonly used by these manufacturing industries for measuring their sustainability performance. Accordingly, the indicators such as water consumption (Ahmad & Wong, 2019; Huang & Badurdeen, 2017; Lee et al., 2014; Ocampo et al., 2016), energy consumption (Li et al., 2012; Song & Moon, 2019; Vinodh et al., 2016; Yakovleva & Flynn, 2004), and material consumption (Ahmad & Wong, 2019; Lee et al., 2014; Ocampo et al., 2016; Shuaib et al., 2014) were commonly used to measure the environmental sustainability dimension of these industries.

Fig. 4
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Distribution of papers by case study

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3.2 Analysis of indicators by frequency of use

By applying content analysis, a total of 1041 indicators (290 for economic, 410 for environmental, and 341 for social dimensions) were explored. Table 1 presents the total number of indicators identified from the literature according to their frequency of use (i.e., by how many papers they were used in) after conducting the content analysis.

Table 1 The indicators identified according to their frequency of use
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As seen in Table 1, the majority of indicators (884 out of 1041) appeared only once in the reviewed literature (i.e., they were not used by more than one paper). The availability of this wide range of indicators could be due to the lack of consensus regarding whether and how sustainability performance should be measured in manufacturing industries (Ahi & Searcy, 2015; Ahmad et al., 2019a) and the differences in the contexts of the manufacturing industries affecting the use of indicators for measuring sustainability performance (Cagno et al., 2019; Trianni et al., 2019). Moreover, a lack of consensus regarding the definition of sustainability in the context of manufacturing industry, research purpose, and approach differences will affect a wide range of indicators. This result implies that measuring sustainability performance in different manufacturing industry contexts will cause an ongoing research debate and open potential research opportunities. On the other hand, few indicators have been consistently and frequently used for measuring industrial sustainability performance in the literature.

As shown in Table 2, the results of the content analysis also show that 49 indicators (14 for economic, 22 for environmental, and 13 for social dimensions) were used at least 5 times (i.e., by at least by 5 papers). Due to their consistency and frequency, these indicators are considered to be the most understandable and relevant to various manufacturing industries (Ahmad et al., 2019a). Profit, water consumption, and employment/job opportunity were found to be the most consistently and frequently used indicators for measuring the economic, environmental, and social dimensions of industrial sustainability, respectively. In the economic dimension, the focus was on indicators that are used to measure the progress in obtaining significant financial benefits. These include profit (de Faria et al., 2021; Grecu et al., 2020; Yakovleva & Flynn, 2004) and revenue (Ahmad & Wong, 2019; Li et al., 2012; Song & Moon, 2019) from business activities; allocating reasonable expenditure to R&D activities (Beekaroo et al., 2019; Grecu et al., 2020; Li et al., 2012); reducing material (Agrawal & Vinodh, 2020; Ahmad & Wong, 2019; Jayal et al., 2010), labor (Abedini et al., 2020; Ahmad & Wong, 2019; Jayal et al., 2010), energy (Abedini et al., 2020; Agrawal & Vinodh, 2020; Jayal et al., 2010), operating/operational (Ahmad et al., 2019a; Cagno et al., 2019; Ghadimi et al., 2012), maintenance (Ahmad et al., 2019a; Cagno et al., 2019; Jayal et al., 2010), production (Abedini et al., 2020; Cagno et al., 2019; Jayal et al., 2010), packaging (Ahmad & Wong, 2019; Huang & Badurdeen, 2018; Jayal et al., 2010), and inventory (Abedini et al., 2020; Cagno et al., 2019; Ghadimi et al., 2012) costs; improving product quality (Agrawal & Vinodh, 2020; Hendiani et al., 2020; Singh et al., 2014); and properly managing lead time (Cagno et al., 2019; Medini et al., 2015; Trianni et al., 2019) and delivery time (Raj & Srivastava, 2018; Singh et al., 2019; Winroth et al., 2016).

Table 2 Sustainability indicators that appeared at least five times in the reviewed literature
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Regarding the environmental dimension of industrial sustainability, more attention was given to indicators that are used to measure progress in the efficient use of input resources such as water (de Faria et al., 2021; Jamil et al., 2020; Veleva & Ellenbecker, 2001), energy (Hendiani et al., 2020; Jamil et al., 2020; Veleva & Ellenbecker, 2001), and material (Agrawal & Vinodh, 2020; Ahmad & Wong, 2019; Veleva & Ellenbecker, 2001) consumption; the use of recycled resources which include recycled water (Cagno et al., 2019; Samuel et al., 2013; Zarte et al., 2019) and recycled material (Cagno et al., 2019; De Araujo & De Oliveira, 2012; Zarte et al., 2019); the use of renewable energy (Beekaroo et al., 2019; Cagno et al., 2019; Jayal et al., 2010); the reduction in emissions consisting of GHG (Abedini et al., 2020; Grecu et al., 2020; Li et al., 2012), air (Joung et al., 2013; Moldavska and Welo, 2018; Singh et al., 2019), and ozone-depleting substances (Grecu et al., 2020; Raj & Srivastava, 2018; Samuel et al., 2013); and the proper management of wastes such as wastewater discharge (De Araujo & De Oliveira, 2012; Wang et al., 2018; Zarte et al., 2019) and hazardous (Grecu et al., 2020; Huang & Badurdeen, 2017; Jayal et al., 2010), solid (Ahmad et al., 2019a; Beekaroo et al., 2019; Shuaib et al., 2014) and recyclable (Cagno et al., 2019; Li et al., 2012; Trianni et al., 2019) wastes.

Indicators in the social dimension placed more emphasis on measuring the progress in creating employment/job opportunities (Agrawal & Vinodh, 2020; Ahmad et al., 2019a; Yakovleva & Flynn, 2004); improving the well-being of employees, which consisted of minimizing employee turnover (Ahmad & Wong, 2019; Veleva & Ellenbecker, 2001; Vitale et al., 2019) and work-related injuries (Ahmad et al., 2019a; Cagno et al., 2019; Li et al., 2012), ensuring employee satisfaction (Ahmad et al., 2019a; Joung et al., 2013; Song & Moon, 2019) and occupational health and safety (Ahmad & Wong, 2019; de Faria et al., 2021; Raj & Srivastava, 2018), and providing training and development (Ahmad & Wong, 2019; Elhuni & Ahmad, 2017; Feil et al., 2015) and a fair salary (Ahmad & Wong, 2019; Harik et al., 2015; Samuel et al., 2013); improving the well-being of customers in terms of customer satisfaction (Cagno et al., 2019; Hendiani et al., 2020; Joung et al., 2013) and minimizing customer complaints (Ahmad et al., 2019b; Huang & Badurdeen, 2017; Veleva & Ellenbecker, 2001); properly managing employees working time such as working hours (Ahmad & Wong, 2019; Medini et al., 2015; Raj & Srivastava, 2018) and lost working days (Ahmad et al., 2019a; Veleva & Ellenbecker, 2001; Vitale et al., 2019); and reducing corruption (Ahmad et al., 2019a; Raj & Srivastava, 2018; Samuel et al., 2013).

With increasing stakeholder pressure on manufacturing industries to provide more transparency about their sustainability practices and improve sustainability performance, there has been a growing interest in measuring and evaluating industrial sustainability performance. However, as manufacturing industries have limited resources, they need to use a manageable number of suitable indicators in order to measure and report their sustainability performance effectively. The indicators analyzed in this paper can be effectively tailored to manufacturing industries’ needs regarding measuring their sustainability performance. Moreover, these indicators can be applied to define, implement, evaluate and monitor policies to enhance sustainable manufacturing by considering economic, environmental, and social aspects while producing products and services, ensuring economic growth, conserving natural resources, minimizing negative environmental and social impacts, and meeting the requirements of stakeholders. Eventually, we aim contribute to the efforts to achieve the SDGs.

3.3 Categorization of the indicators

Figure 5 presents a hierarchal structure showing the categorization of the indicators that are most consistently and frequently used for measuring industrial sustainability in the literature. The indicators identified in the reviewed literature can logically be categorized for measuring industrial sustainability performance related to financial benefits, costs, and market competitiveness in the economic dimension; resources, emissions, and wastes in the environmental dimension; and employees, customers, and community in the social dimension. Most of the proposed categories (themes) are in line with the categories of the NIST’s sustainability manufacturing indicator repository (Joung et al., 2013).

Fig. 5
figure 5

Hierarchal structure of indicator categorization

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The hierarchal structure of indicator categorization demonstrates that manufacturing industries always need to increase their financial benefits to maintain their existence in the market. They should also improve their market competitiveness, employ cost reduction strategies, efficiently use resources, conserve resources, properly manage wastes, and apply emissions reduction strategies while producing their products and services. In addition, they need to promote the well-being of their employees and customers and fulfill other stakeholders’ needs (such as the community). The synergistic effect of these efforts can lead to achieving industrial sustainability (economic, environmental, and social) goals. To effectively measure and manage the progress towards attaining industrial sustainability goals, the use of suitable indicators is critical (Ahmad & Wong, 2019; Hendiani et al., 2020; Wang et al., 2018). We used a cause-and-effect diagram to organize and map the indicators, as shown in Fig. 6, to express their effect on the organizational goals and, in turn, on the industrial sustainability performance (overall goal). By achieving these goals, manufacturing industries can contribute to achieving the SDGs. Promoting the well-being of employees, customers, and the community can contribute to good health and well-being (SDG 3). Increasing financial benefits; reducing costs; improving market competitiveness; and promoting the well-being of employees, customers, and the community can promote decent work and economic growth (SDG 8). By improving the effectiveness of resource utilization, manufacturing industries can also contribute to responsible consumption and production (SDG 12). The reduction of emissions and proper management of wastes can have an impact on climate action (SDG 13).

Fig. 6
figure 6

Cause-and-effect diagram of the indicators. ( +) implies improve, increase; (−) implies reduce, minimize; and ( ~) implies optimum, reasonable

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By linking the indicators to their respective SDGs, as shown in Table 3, this paper provides a valuable insight into how manufacturing industries can use the indicators to track their progress towards achieving the SDGs. Furthermore, the link between the indicators and their respective SDGs provides a broader view of sustainability to manufacturing industries to ensure the well-being of stakeholders, decent work and economic growth, sustainable consumption and production, and combat climate change and its impacts. Therefore, following a bottom-up approach, manufacturing industries need to apply the indicators to measure and manage their organizational goals, improve sustainability performance, and contribute to achieving the SDGs.

Table 3 Link between the indicators and their respective SDGs
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3.4 Metrics defined for the indicators

Defining quantifiable metrics is essential in order to make industrial sustainability measurable and manageable (Shuaib et al., 2014). As shown in Tables 4, 5, and 6, both absolute and relative metrics were defined for the consistent and frequently used economic, environmental and social indicators, respectively (Mengistu & Panizzolo, 2021). The metrics defined for the indicators can enable us to carry out industrial sustainability performance measurement using data science. By providing a predefined list of indicators and their metrics, manufacturing industries will not be overloaded with information whose utility is uncertain. Additionally, this improves the effectiveness of sustainability performance measurement in manufacturing industries. However, conducting an empirical study is necessary in order to refine and normalize the indicators and their metrics (i.e., modifying, adding, and/or deleting indicators and their metrics) and tailor them according to manufacturing industries’ needs and priorities.

Table 4 Metrics defined for the economic dimension indicators
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Table 5 Metrics defined for the environmental dimension indicators
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Table 6 Metrics defined for the social dimension indicators
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As shown in Table 4, the metrics can be used to properly measure and manage the economic dimension sustainability performance associated with financial benefits (profit and revenue), costs (material, labor, energy, operating, maintenance, production, packaging, and inventory), and market competitiveness (R&D expenditure, on-time delivery, lead time, and product quality). Measuring and managing cost reduction, on-time delivery, lead time and product quality is essential in order to maintain market competitiveness and financial benefits in the short run. Moreover, it is crucial to determine reasonable expenditure levels in order to conduct R&D activities to promote the production of sustainable products and enhance market competitiveness in the long run.

From Table 5, it can be seen that the metrics are defined to effectively measure and manage the environmental sustainability dimension performance related to resources (water, energy, material, and land), emissions (GHGs, air, and ozone-depleting substances), and wastes (hazardous, solid, and recyclable). The metrics can be used to measure progress towards improving resource utilization efficiencies such as energy and material efficiency, the use of recycled resources such as recycled water and materials, the use of renewable energy and materials, the use of eco-friendly and biodegradable materials, and the use of non-hazardous materials to improve product safety and quality. They can also be used to address the performance of different waste management strategies.

Table 6 shows the metrics defined to measure and manage performance in the social sustainability dimension, associated with the well-being of employees, customers, and the community. Measuring the social sustainability dimension has been difficult compared to the other sustainability dimensions (Ahmad et al., 2019a). The metrics presented in Table 6 will be helpful for easily measuring the social sustainability dimension. Subsequently, the metrics defined for employee turnover, employee satisfaction, occupational health and safety, training and development, fair salary, and work-related injuries can be used to measure progress in improving employee well-being. It is also necessary to measure progress in promoting customer well-being. For this purpose, the metrics for customer satisfaction and customer complaints are useful. The metrics for employment/job opportunity and corruption can be used for measuring progress towards community development. Moreover, the metrics for working hours and lost working days can be used to measure performance associated with employees’ time management.

4 Conclusions

This paper provides an in-depth analysis of indicators that have been published in the literature related to the sustainability performance measurement of manufacturing industries. Our findings show that the majority of the indicators available in the literature have only been used once, showing a lack of consistency in the use of indicators (i.e., the lack of consensus on a single set of indicators) to measure sustainability performance in different manufacturing industry contexts. However, a few of the indicators were consistently and frequently used; these indicators emphasized measuring industrial sustainability performance correlated with financial benefits, costs, and market competitiveness for the economic dimension; resources, emissions, and wastes for the environmental dimension; and employees, customers, and the community for the social dimension. These results suggest that manufacturing industries need to focus on applying indicators for measuring and managing progress towards achieving industrial sustainability goals in terms of increasing financial benefits, reducing costs, improving market competitiveness, improving the effectiveness of resources utilization, reducing emissions, properly managing wastes, and improving the well-being of stakeholders (employees, customers, and the community). In doing so, manufacturing industries can contribute to achieving the SDGs by promoting health and well-being, promoting sustainable economic growth, providing productive employment and decent work, ensuring responsible consumption and production, and combating climate change and its impacts. To this end, this paper emphasizes the necessity of tailoring these indicators to the contexts of different manufacturing industries in order to effectively measure and manage the progress made towards achieving industrial sustainability goals.

This paper has relevant academic, practical, and policy implications. From an academic perspective, this paper will provide a substantial theoretical basis for future research on measuring the sustainability performance of manufacturing industries. We have carried out an extensive analysis of the wide range of sustainability indicators available in the literature, which can contribute to the existing theory and knowledge in the field of industrial sustainability performance measurement. The quantifiable metrics defined for the indicators can also be used by future research to envision the sustainability of manufacturing industries using data science. From a practical perspective, manufacturing industries can adapt the indicators and metrics provided by this paper to measure sustainability performance and contribute to achieving the SDGs. Moreover, these metrics will help the manufacturing industries to identify the data that need to be collected and organized for measuring their sustainability performance effectively. It also has policy implications, as linking the indicators to their respective SDGs could be used as an input for policymakers in the field of sustainable manufacturing that can influence policies such as socio-economic, environmental, and social responsibility.

The significant number of indicators that are only used once in the reviewed literature should inform future research agendas; by conducting a detailed analysis of the classification of these indicators, further investigation into the possible reasons why the majority of indicators are not commonly used for measuring industrial sustainability, the pattern analysis of indicators, and the identification of indicators for emerging sustainability trends. Although this paper provides a comprehensive methodological approach for identifying and analyzing the indicators described in peer-reviewed journal articles, its scope was limited at the firm level. However, it would be helpful to determine additional indicators that could be used to measure sustainability performance at the supply chain level to obtain a more comprehensive view of sustainable manufacturing. Hence, it would be interesting for future research to expand the methodological approach applied in this paper to the entire supply chain in the stages of supply, production, distribution, use, and post-use. Furthermore, we focused on the indicators that have been used by scientific papers (i.e., academic papers). Thus, as an additional avenue, future research could consider analyzing indicators that have been used by organizations engaged in sustainability performance measurement. The other limitation of this paper is that it does not support its findings with empirical analysis to refine and normalize the indicators and their metrics. Therefore, it would also be interesting for future research to tailor the indicators to different manufacturing industry contexts by carrying out an empirical study. Furthermore, it would also be valuable to conduct an empirical study such as a survey on the use of these indicators (i.e., similarities and differences) from the perspective of national and geographical contexts.