Sustainability is a crucial global concern underlying all aspects of human activities today, broadly dealing with the system's ability in its entity (earth) to support and sustain a healthy life (Mani et al., 2005). Tracing modern civilization's history alongside environmental vitality, industrialization bolstered consumerism (economic growth) have seeded the gruesome manifestations of unsustainability we are witnessing today, e.g., ocean plastics, biodiversity loss, soil infertility, greenhouse gas (GHG) emissions, forever chemicals, and pandemics. While native cultures always emphasized caution for centuries against rapid modernization (Mani et al., 2005), modern researchers (US Government Printing Office, 1980; Carson et al., 2002) had also forewarned of imminent unsustainable consequences based on trends prevalent nearly half a century back. Among the foremost to postulate a sustainable development model was the Gandhian Economist J.C. Kumarappa way back in the 1940s. He discussed an approach to development that does no irreparable damage to the environment (Kumarappa, 1946). Despite early caution on unsustainable consequences, the world finally heeded to the need for sustainable development at the World Commission on Environment and Development in 1984, accepting Brundtland's definition of sustainable development. Brundtland's report “Our Common Future” finds one of the earliest associations of the term “sustainability” with “industry” and thus manufacturing (Brundtland, 1987). The sustainability perspective was originally efficiency-based which led to economic benefits to industries. Later, the perspective changed to product performance-based, which dealt with operational environmental concerns. The increase in population, consumerism, and enhancing lifestyles has resulted in unprecedented resource consumption, driving manufacturing-dominated growth globally. The transdisciplinary domain of sustainability science can be characterized by dynamic interactions between society (including economy) and nature and fundamentally requires a systems perspective (Mani et al., 2005; Sala et al., 2013a; Kumar and Mani, 2019). Sustainability and manufacturing are diverse yet intricately connected areas of research.

Manufacturing is a critical sub-component of a larger product life cycle involving design, material extraction, production, assembly, transport, use (service/maintenance), and post-use phases (recycling and disposal). It is economically defined as the process of transforming materials into usable products using one or more manufacturing processes, finishing, and assembly operations. Manufacturing can be viewed as operating at different levels from a production layout point of view viz. unit manufacturing process/single machine, work cell/manufacturing line/multi-machine, facility/factory, and multi factory/production network (Radnor and Barnes, 2007; Cao and Folan, 2012). The term “manufacturing” is generally used interchangeably but is conceptually different from “production,” which is limited to production planning, material processing, and quality assurance. Manufacturing has a broader scope covering all functions or activities starting from product ideation and its disposal along the value chain (Herausgeber, 2004). It consists of humans, production machines and systems, control and automation systems, storage and material handling systems, supply chain networks, and global business operations with various technical and planning characteristics (Esmaeilian et al., 2016), networked over diverse geographies (Chandran et al., 2016). Figure 1 presents an overview of different levels of manufacturing, associated entities, decision-makers, and factors affecting its operation. Manufacturing generally involves a globally distributed network that utilizes local and global (material and energy) resources to produce products used and disposed of in different geographical locations (Duflou et al., 2012; Chakrabati, 2015). It is one of the most resource-intensive (e.g., material, energy, and water) sectors with economies of scale, lower labor costs, and higher employment potential. Organizations understand that sustainability is critical to modern manufacturing, as it provides a market competitive edge and visibility. It has led to the evolution of various paradigms such as lean manufacturing, cleaner production, sustainable manufacturing (SM), industrial sustainability, sustainable production, industrial ecology, and circular economy.

Developing economies perceive manufacturing as a source of growth as it boosts employment opportunities and economic growth, although it comes with an environmental cost. It causes various regional and global impacts (embodied and active) on the natural environment and human health by emitting various pollutants to air, water, and soil. Globally, the industry sector was the second largest contributor (24%; 14 GtCO₂eq) to the direct GHG emissions in 2018, primarily driven by the manufacturing of pulp and paper, food and tobacco, glass and ceramics, refrigerants, solvents etc. (Lamb et al., 2021). Manufacturing is the third-largest contributor to the CO₂ emissions in the United States after transportation and electricity production (US EPA, 2021). Recent trends (from 2001 to 2010) show a strong correlation between the world's energy consumption, emissions, material requirements, and GDP (Gutowski et al., 2013). A historic shift of manufacturing location toward developing nations like China and India can be observed in the late 20th century (Gutowski et al., 2013), primarily due to cheap labor, conducive (not so stringent) regulations, and vast sales market. In 2018, the Chinese manufacturing sector contributed 28.4% of the global manufacturing output (Ritcher, 2020). In 2015, manufacturing (excluding agriculture, mining, and construction) accounted for 23% of total energy consumption for the International Energy Agency member countries, which is the second highest among all the sectors, followed by transportation (36%) (International Energy Agency, 2017). Indian manufacturing sectors accounted for 44% of total electricity consumption in 2015 (NITI Aayog, 2017). The manufacturing output shows a high correlation with the CO₂ emissions (Gutowski et al., 2013), e.g., increased air and water pollution in China (Vennemo et al., 2009). A study on soil contamination in Europe found that industrial and commercial activities are the second highest contributor (~33.3%) to soil contamination; metal industries are reported to be the most frequent source (Panagos et al., 2013). Sites surrounding Chinese steel industries have been identified as heavy metals contamination hotspots compared to non-industrialized sites (Qing et al., 2015). Industrial activities cause environmental burdens and adverse effects on the health of local communities (WHO, 2009; Mudu et al., 2014). The detrimental impacts of environmental pollution on human health are well documented (Revel et al., 2018; Gupta et al., 2019; Nobile et al., 2020). Global environmental assessment reports have reiterated the requirement for an urgent focus on planetary wellness (UN Environment, 2019; Singh et al., 2021).

Besides, various kinds of impacts on social groups (consumers, workers, and society) have been recorded in history (Sutherland et al., 2016), e.g., cases of work stress leading to several suicides among Chinese workers (Ngai and Chan, 2012). Apart from negative impacts directly associated with the manufacturing activities and the supply chain, numerous indirect impacts are associated with the product life cycle stages on the endless spatial and temporal scales traceable to design and manufacturing decisions. BPA used in the production of various consumer products (Vandenberg et al., 2007) is found in plastic consumer products (use and post-use phase), e.g., in the food storage cans, baby bottles, individuals in developed nations, aquatic animals, and the environment. Further, chronic exposure risks of aluminum leading to Alzheimer's disease are well-documented (Wang et al., 2016). BPA used as a color developer in various paper products has significant exposure to the human population (Liao and Kannan, 2011; Porras et al., 2014). Estimates of 5.25 trillion plastic particles weighing 268,940 tons floating in the sea have been reported (Eriksen et al., 2014). More shreds of evidence of microplastics in the human placenta (Ragusa et al., 2021) and endocrine-disrupting chemicals in a wide variety of products (Wong and Durrani, 2017) are alarming. Such impacts are not directly addressed in the conventional efficiency-based corporate sustainability reporting and life cycle assessment (LCA)-based methods. The sustainability of the manufacturing enterprises is majorly addressed with the help of sustainability standards, such as environmental management and LCA standards (ISO 14001, 14040, and 14044), ISO 50001 energy management standard, global reporting standards (GRI), and sustainability accounting standards board (SASB) standards.

Existing sustainability assessment (SA) literature predominantly focuses on efficiency improvements without considering absolute (local and global) pollution limits and rebound effects (Hauschild, 2015; Gutowski, 2018; Fantke and Illner, 2019). An analysis of 83 case studies revealed the gaps in the implementation of sustainability measures in manufacturing (Despeisse et al., 2012). Environmental impact and economic benefits are more frequently reported than social aspects. SA methods operating at the factory level lack a generic and holistic approach (Chen et al., 2013). Various SA methods for discrete manufacturing process (gate-to-gate) level observed a shift toward life cycle sustainability analysis (LCSA) (Gbededo et al., 2018). Most studies focus on environmental dimensions, whereas social dimensions are overlooked in the SA of the automotive sector (Pallaro et al., 2015). The need for integrating the three sustainability dimensions (planet, people, profit) for a holistic assessment has been consistently reiterated in literature.

Impacts occurring in a product life cycle can be categorized as embodied and active (Kumar and Mani, 2019) (Table 1). Impacts occurring in the production of a product can be defined as embodied impacts and primarily addressed using an efficiency perspective, e.g., energy and resource efficiency. In contrast, active impacts are ongoing impacts that are likely to occur during a product's use and post-use phases, e.g., human toxicity impacts due to product use and toxic materials emissions to the environment due to product use (Kumar and Mani, 2019). Embodied impacts are reasonably straightforward to identify and deal with. They mainly involve efficiency improvements and are primarily associated with design and manufacturing phases. System boundaries for embodied impacts are well-known and easy to define and regulate. Assessing embodied impacts generally leads to economic benefits to the company. Efficiency-based approaches counterintuitively lead to rebound effects, resulting in a higher resource and energy net consumption. Effectiveness-based active impacts involve inherent uncertainty and are difficult to estimate.

On the other hand, active impacts are not straightforward and can be notorious to identify and deal with. Use-phase exposure to organic and inorganic substances and material disintegration is omitted in the existing SA practices in methodological considerations, impact assessment, and systemic consequences. Assessing (effectiveness-based) use phase impacts is considered challenging in contrast to those related to (efficiency-based) production and disposal phases, as product characteristics, user's behavior, and circumstances of use both vary (Schmidt I. et al., 2004). A manufactured product's performance also depends on the use phase in the living environment, e.g., a fuel-efficient car is just as polluting as any other car when driven in a heavy-traffic scenario. This distinction is crucial for the SM community to gain clarity on effective measures that could aid the identification of appropriate indicators and product life cycles that genuinely contribute to sustainability.

The need for the transition from efficiency to effectiveness in industrial sustainability has recently been reviewed in the context of product design, manufacturing process planning, and CE (Haapala et al., 2013). A comprehensive evaluation of SA practices in manufacturing from an efficiency and effectiveness perspective has not been conducted so far. In this direction, we have discussed the distinction between efficiency and effectiveness assessment methods and presented the way forward to develop effectiveness-based approaches. This article presents challenges and opportunities for effectiveness-based SA in manufacturing.

Efficiency and effectiveness have been debated in different contexts in the literature of sustainability and manufacturing. Efficiency focuses on minimizing resources to achieve the desired output level, while effectiveness emphasizes ascertaining and making the right choices for utilizing resources (Duflou et al., 2012). International policy and industry sustainability initiatives focus on energy efficiency, GHG emissions reduction, carbon neutrality, etc. Corporate sustainability reports commonly practice relative/efficiency improvements with a minimal mention (~5% of all corporate reporting) of planetary boundaries and ecological limits (Bjørn et al., 2017a). Efficiency may not assure or yield effective results; effective results may not involve an efficient approach as a necessary condition. A good example could be derived from military operations, where effectively achieving results is the only performance driver and not how efficiently an operation is performed. The same principle operates in the design/manufacturing of vehicles for operation in adverse conditions, where reliability or fail-safe operation is the fundamental consideration, and not so much fuel efficiency.

In the context of manufacturing industries, efficiency-based performance measures address customer satisfaction by the firm/manufacturer (internally), keeping in mind the firm's economic viability; In contrast, effectiveness measures focus on fulfilling customer requirements (Neely et al., 1995). Energy effectiveness is a far more complex and broader concept as compared to energy efficiency and involves value judgment and ethical considerations about the best utilization of resources (Shade and Sutherland, 2018).

Effective sustainability principles include an adequately defined scope of the assessment, transparent and effective communications, and stakeholder participation (Mani et al., 2005; Pintér et al., 2012). Eco-effectiveness implies proactively looking at positive contributions through absolute improvements (“doing good than just doing less bad”) for positive recoupling between economy and ecology (Hauschild, 2015; Herrmann et al., 2015). Figure 2 (adapted from Kumar and Mani, 2021a) illustrates the distinction between efficiency and effectiveness in SA. The ability to foresee likely impacts of human activities is crucial to guide assessment methods toward sustainability (Gibson, 2006), requiring an effectiveness-based systems approach. An effective sustainable design approach simultaneously addresses market success (economy), environmental and social challenges, and undesirable consequences (such as rebound effects) that can overshadow economic, environmental, and social benefits (Hauschild, 2015). The importance of transdisciplinary studies to cope with complex sustainability challenges has been reiterated in literature (Bond and Morrison-Saunders, 2013). Stakeholder engagement and communication throughout the assessment practice are vital for ensuring SA effectiveness (Gutowski et al., 2013; Chandran et al., 2016). The effectiveness in SA requires improved assessment methods and enhanced implementation and decision-making for sustainability.

Various aspects (see Figure 2) of effectiveness assessment are discussed briefly:

SA has its roots in the environment and pollution domain. It has evolved as the extended version of existing environmental assessment methods such as environmental impact assessment (EIA) and strategic environmental assessment (SEA) by integrating social and economic dimensions to address the triple bottom line (TBL) concept (Pope et al., 2004). SA comprises two dimensions: the concept of sustainability (definition) and the decision-making context (purpose of assessment) (Pope et al., 2017). Several SA frameworks, methodologies, tools, and indicators have been reviewed (Ness et al., 2007; Singh et al., 2012). Table 2 presents various SA methodologies developed for product/processes in chronological order.

SA is a predictive decision-making tool for sustainability (Pope et al., 2017) that should follow a systems approach and involve stakeholders throughout the assessment process (NRC, 2013). SA are comprehensive context-dependent assessments to identify emergent properties and involve multiple field-specific methods depending on the application (Sala et al., 2015). The terminology in SA literature is characterized as the framework (e.g., LCSA), methodologies (e.g., LCA, S-LCA, LCC), methods or tools (e.g., LCIA methods such as Eco-Indicator99, CML, and ReCiPe; software (GaBi, SimaPro, and OpenLCA); and indicators (DALY, acidification potential, and ozone depletion potential, etc.) (Sala et al., 2013b). SA methods are categorized into monetary, biophysical, and indicator-based methods following anthropocentric, eco-centric, and mixed valuation perspectives (Gasparatos and Scolobig, 2012). Another broad categorization of SA methods is product-based (which follows a life cycle perspective), indicators-based, and integrated assessment methods (focused on policy implementation) (Ness et al., 2007). Integrated assessment methods are prospective on a temporal scale, whereas indicator-based methods are retrospective (Ness et al., 2007). Most assessment methods are reductionist except indicator-based multi-criteria methods (Gasparatos and Scolobig, 2012). Forecasting methods are more favorable for SA to help decision-makers identify impacts, benefits, risks, and vulnerabilities of action(s) of integrated natural and societal systems in short and long-time scenarios at local and global scales (Ness et al., 2007). Recent studies have integrated future studies into SA to assess long-term sustainability perspectives. Scenario-oriented SA methods have been classified into five categories: procedural tools, analytical tools, tools for aggregating impacts, combined scenario assessment methods, and strategic environmental assessment (Fauré et al., 2017).

Further, various methods for conducting SA (broadly categorized as life cycle and indicator-based approaches) and recent developments to integrate and address effectiveness in the sustainability practice have been discussed.

SM is conventionally defined as the creation of manufactured goods that minimize negative impacts while conserving energy and resources in a most economical way (US Dept of Commerce, 2007). Methodologies developed under a particular paradigm imbibe relevant philosophical assumptions of the paradigm (Sudit, 1995). The definition of SM plays a crucial role in SA. Sustainability in the context of manufacturing has been defined and applied in different ways by various studies and organizations. There is no standard definition to date, though the definition proposed by the U.S. Department of Commerce “Sustainable manufacturing includes things such as making products using less energy and materials, producing less waste, and using fewer hazardous materials as well as products that have greener attributes such as recyclability or lower energy use,” is frequently cited (Moldavska and Welo, 2017) Table 3 lists salient SM definitions and contributions. Definitions proposed by Lowell Centre for Sustainable Production, Alting and Jøgensen (1993), Garetti and Taisch (2012), and Nahkala (2013) link SM to products and services, whereas other definitions focus on the policy aspect of SM (OECD, 2011). Other definitions emphasize social dimensions and the circular economy perspective (Nahkala, 2013; Moldavska and Martinsen, 2018). A detailed review of 89 definitions of SM has been presented, focusing on various factors (e.g., manufacturing levels and associated stakeholders) that influence the sustainability performance (Moldavska and Welo, 2017). The majority of definitions do not consider the interconnectedness among the sustainability dimensions and across product life cycle stages, which is crucial for effectiveness (Kumar and Mani, 2021b).

This paper reviews several SA methods and tools supporting manufacturing enterprises' sustainability decision-making. Despite the advances in the development of SA measures in manufacturing, the practical applicability is lacking, leading to an inadequate positive (intended) impact on the global scale. Environmental and economic dimensions are more commonly considered than the social dimension, currently limited to the working environment (worker's health and safety). Assessment methods are comparatively less focused on the near field/indoor exposures and human toxicity impacts during product use. Industry adoption of existing SA methods in manufacturing is also not been appropriately documented. Assessment measures in the industry (practice) are primarily focused on organizations' sustainability compared to absolute environmental improvements. Reporting trends largely follow a reductionist (efficiency-based) approach, where the impact of resource and energy per product is reported. The industry reports have hardly accounted for the active impacts over the use phase. Sustainability reporting analysis shows the gap between industry and academic practices in SA in manufacturing. Including more reports in the study might reveal detailed information about the trends in industrial practice. Stakeholders significantly influence such impacts, and therefore, behavioral aspects are also essential to be considered in the assessment. Potential future work includes conducting a comprehensive questionnaire industry survey that might help reveal detailed information about challenges in adopting SA measures. Future studies on SA in manufacturing involve studying the impacts and benefits of new manufacturing paradigms such as Industry 4.0 on sustainability. Bridging the industry-academia gap can address climate change and global pollution challenges due to emerging pollutants such as microplastics and endocrine-disrupting chemicals. The effectiveness of SA would eventually guide toward absolute sustainability improvements and planetary wellness.

Both authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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