Perspectives on future research directions in green manufacturing for discrete products

Manufacturing processes

Smart manufacturing

Digitization of manufacturing processes has been obtaining increasing attention with the emergence of Industry 4.0, which offers opportunities for the reduction of carbon emissions^[36]. For example, highly connected Industrial Internet of Things (IIoT) and AI-based production increase the efficiency and flexibility of production, reduce waste, and improve production capacity and logistics. Digitization of manufacturing systems provides the capability of monitoring manufacturing equipment which allows users to minimize inefficient utilization of equipment. Some researchers reported that the consequences of using electric motors with poor efficiency can be significant from the perspective of maintenance and the environment as they consume much more power over time when compared to motors running under more efficient conditions. Proper selection of maintenance strategies can improve the environmental impact of products and manufacturing processes as it lengthens the lifespan of manufacturing equipment and, therefore, minimizes waste while preserving resource and energy efficiency^[37]. Their findings demonstrate that a predictive maintenance strategy can be used to minimize the environmental impact of manufacturing plants while optimizing production performance.

Extending the lifetime of manufacturing equipment and products is a significant strategy to reduce environmental impact in many circumstances [outlined in Figure 2]. Lengthening a production equipment life cycle is often associated with the concept of prognostics and health management (PHM) or predictive maintenance in smart manufacturing^[38]. PHM aims to provide users with an integrated view of the health state of a single piece of equipment or an overall system, in which diagnostic and prognostic analyses are conducted to detect an incipient failure and predict the remaining useful life (RUL). Based on analytical results, timely and appropriate maintenance actions (such as fastening, lubricating, and replacing components) can be taken to prevent a failure that could lead to the equipment’s eventual breakdown. Lee et al.^[39] developed a speed-invariant deep learning model to identify targeted faults of rotary machinery under different operating conditions. The proposed model achieved high accuracy in detecting the target fault while quantifying the model’s uncertainties of varying speeds. Wu et al.^[40] established a new data-driven model based on the Long Short-Term Memory Recurrent Neural Networks algorithm to identify a manufacturing system’s deterioration and forecast its future health state. The method provides significant insights into the RUL of a bearing system to avoid environmental consequences. The mentioned literature provides methods and tools for decision-making for equipment lifetime extension.

Figure 2. An outline of how extending product life through maintenance strategies contributes to green manufacturing.

The other literature investigated in this review put more focus on lengthening the product life by monitoring products with sensors and analyzing the data to predict failure. Bakker et al.^[41] map the environmental impacts of refrigerators and laptops against their increasing energy efficiency over time and finds that product life extension is the preferred strategy in both cases. They hypothesized that these findings may apply to other electrical and electronic products. Morioka et al.^[42] designed and evaluated advanced loop-closing systems for the recycling of EOL vehicles and electric household appliances and found that a strategy to lengthen the product life with parts reuse improves the eco-efficiency in terms of CO₂ emissions by 4% when compared with the conventional practice of replacing of the appliance. Another trend is to address product life extension through design/redesign. Guo and Leu^[43] found that some designs of novel geometries in AM can have the benefits including increased strength and stiffness, corrosion resistance, and improved operational efficiency and functionality.

There is also great potential to reduce the environmental impact and improve production efficiency through the implementation of smart manufacturing techniques. Several studies have been reported to attempt to reduce the environmental impact of manufacturing through a smart and sustainable production system. Zhang et al.^[44] presented a data-driven analytical framework to reduce energy consumption and CO₂ emissions for energy-intensive industries. The experimental results of the proposed methodology on ball milling processes showed a 3%-4% reduction in total energy consumption. Kellens et al.^[45] demonstrated a power measurement for a metal-cutting process to reduce standby energy and optimize the process parameters. Bermeo-Ayerbe et al.^[46] presented adaptive predictive control for equipment management to enhance energy efficiency. Song et al.^[47] proposed a similar approach for an HVAC system on a factory level. Their experimental work achieved 8.6% of the total energy consumption without compromising production efficiency.

Future directions in process improvement and smart manufacturing

With regard to manufacturing processes, more research is required to understand machine tool energy consumption. Sihag and Sangwan^[19] state that research is needed to benchmark energy consumption for standard machining processes to allow for energy management and process planning. In regards to cutting fluids, continued advances are required in dry cutting, MQL, cryogenic cooling, and other alternate green cooling/lubrication strategies^[21], which will assist in the reduction of environmental impacts, including water footprint.

The adoption of smart energy management within the industry, plus decarbonization through electrification and the adoption of renewable energy, will significantly reduce the industry’s carbon footprint. Electrification can be accomplished through upgrades with electric boilers and arc furnaces, and industrial process heat pumps^[25]. One method, as pointed out by Patterson et al.^[25], is the installation of a smart energy management system (EMS). An EMS can save facility costs and energy through a low investment cost and a short payback period. This EMS should also be integrated into Industry 4.0. In addition, machines should be scheduled during non-peak hours, a shutdown management program to turn off machines when not in use should be installed, and machine power should be monitored^[28]. For AM, more work is required to produce a standard methodology to characterize the energy consumption of AM processes^[31]. A better understanding of the embedded energy of AM feedstocks is also needed. Best practices for energy-efficient AM printing need to be investigated and shared with AM communities. More research is required to provide a detailed evaluation of life cycle impacts and to integrate sustainability and cost for AM^[35].

As smart manufacturing has enabled ever-increasing productivity, new technologies, and theories can be deployed and integrated in a more holistic way for green manufacturing development. Big data and AI analytics, digital twins, cloud, Addit Manuf, and the IIoT will be used more frequently to (1) reduce direct or indirect costs, waste, and emissions at the process level, such as increasing material use efficiency and optimizing energy consumption; and (2) improve product quality and durability, as well as EOL management. It requires a strategic approach to determine how and what outcomes will be obtained by actually implementing those technologies. Future research will have to address the application strategies for smart manufacturing in each of the life cycle steps, including planning, design, manufacture, operation, and maintenance. Reference literature and guidelines will be needed on how to connect to the existing heterogeneous legacy systems in current manufacturing sites. Furthermore, smart manufacturing is mostly addressed from the perspective of effectiveness. Developing it into an effective yet sustainable growth engine still requires more human and environment-oriented consideration.

Energy mapping

With much of the environmental footprint of a machine tool being attributed to its energy consumption^[48], it has been important to understand how the energy is consumed. From previous studies, it has been found that a significant portion of the energy is not attributed to cutting but to other systems^[49]. Dahmus and Gutowski^[49] and Kordonowy^[50] have shown that with the implementation of automation and the progression of technology, the power demand for machine tools has become more complex, and the cutting power portion has become a smaller percentage of the total. Zhou et al.^[51] (along with many others) created a power profile of a milling machine by recording power throughout the use of the machine, including starting up, preparing the machine to cut, cutting, and powering off the machine. An example, a power profile for both a milling machine and a turning machine can be found in Figure 3. What was found was that basic power and spindle power are responsible for a significant portion of energy consumption. Behrendt et al.^[52] monitored the power consumption of various milling machines and divided the power-consuming systems into two categories: constant and variable. The constant category consists of items such as the main switch, hydraulics, servos, doors, and lighting. The authors found that these systems can consume the majority of the energy. Li et al.^[53] investigated the fixed energy consumption of turning machines and found that the major contributors to the fixed energy included the cooling lubricant system, hydraulic system, auxiliary system, and servo drives.

Figure 3. (A) Power profile of a milling machine; (B) power profile of a turning machine. Figure adapted from Triebe et al.^[54] with permission from Elsevier.

The variable power is accounted for by the cutting power and rapid traverse power (associated with rapid traverse) shown in Figure 3. The cutting power depends on various conditions, including the material of the workpiece, cutting depth and width, cutting speed, the use of cutting fluids, tool wear, and so on. One method of calculating the cutting energy (cutting power × time) is using the specific cutting energy (SCE). SCE can be defined as the amount of energy required to remove a unit of material (J/mm³). Li and Kara^[55] and Kara and Li^[56] investigated the relationship between SCE, various cutting parameters, and different materials. They found there is a strong relationship between SCE and the material removal rate (MRR). MRR is defined as the amount of material removed per time (mm³/s). The authors found that as the MRR increases, the SCE decreases [see Equation. (1)]. Over the years, the modeling of SCE has been investigated more, and more complex models have been proposed. Sihag and Sangwan^[19] outline the various models in their review of machine tool energy consumption.

Energy reduction

There has been much investigation into reducing the environmental impact of machine tools, which includes the scheduling of the machines, modifying process parameters, optimizing process plans, and reducing cutting fluid. However, these methods are discussed in the other sections (processes and manufacturing systems). This short section will focus on reducing energy consumption through the design of machine tools.

To reduce the environmental impact of machine tools, there has been a number of design strategies. Albertelli^[57] explored the use of a direct drive spindle system to reduce the energy consumption of the spindle. Traditionally, a gearbox-based spindle system has been used; however, Albertelli found energy savings when a chiller, oil pump, and gearbox are not required. Diaz et al.^[58] explored the energy-saving potential of a kinetic energy recovery system (KERS). This system recovers energy from the deceleration of motors. The authors found that when the KERS was implemented on the spindle of a machine tool, power savings of up to 25% was obtained. Huang et al.^[59] provided a method to reduce the energy consumption of hydraulic presses by selecting the optimal motors to drive pumps. This method ensures that the load rate of all motors is within the specified range to provide high efficiency. The authors were able to save 26.97% of the energy in a working cycle.

Lightweighting has also been explored as a method of reducing energy consumption. Kroll et al.^[60] found that a load reduction of 30% can be obtained through the lightweighting of structural components and can lead to a decrease in the electrical power losses of servo drives by up to 50%. Similarly, Triebe et al.^[54] examined the energy savings by lightweighting the slide table of a milling machine. The authors found savings of up to 38% of the table drive energy. Lv et al.^[61] explored reducing the inertia of the spindle on a computer numeric control (CNC) lathe. The material of the chuck on the spindle was changed to a lightweight aluminum which reduced the inertia of the spindle from 0.3354 to 0.2380 kg-m². This resulted in energy savings of 20.6% and a reduction in peak power by 21.2%. However, with lightweighting, there are concerns about increased vibration and decreased stiffness. To address these concerns, Dietmair et al.^[62] presented a lightweighting method that encompassed mechanical and electrical systems along with control processes. This method explored the reduction of mass for moving structural components in order to reduce inertia and friction and optimize transmission efficiency. The authors also applied high stiffness-to-mass ratio materials to ensure structural components did not have plastic deformation, wear, or break. Aggogeri et al.^[63] also explored methods for reducing vibration in lightweighting through various lightweight structures. Some of these structures included aluminum foam and corrugated sandwiches. Suh and Leeet al.^[64] examined the use of composites for slides in machine tool structures to allow for rapid acceleration and deceleration. The authors were able to decrease the mass of the horizontal and vertical slides while increasing damping and not sacrificing the stiffness.

Future directions for manufacturing equipment

To reduce the environmental impact of manufacturing equipment, the energy performance of the machine should be a higher priority during the design phase. To assist companies in this, a benchmark for energy efficiency assessment and an energy consumption index should be developed^[19,51]. The index can be included in the machine tool specification data to allow for better comparison between machines. It will help in the selection of the machine that is best suited for the application. Value stream mapping can also be used to visualize energy consumption and carbon emissions for the different systems of the machine tool^[19]. This will allow for a better understanding of how energy is consumed in the machine and assist in the design of more efficient machine tools. More investigation into key factors and their correlation to energy consumption is necessary for more accurate energy models^[51]. More experiments for different machine tools are required as well.

Other research topics pertaining to manufacturing equipment include the implementation of energy-efficient components. This includes replacing pneumatic and hydraulic components with electromechanical actuators^[65]. Another strategy includes implementing intelligent standby modes to turn off systems that are not needed. There also should be an investigation into how to retrofit existing machines with more efficient components and systems. Another design to consider for improved energy efficiency would be the implementation of hybrid manufacturing processes^[66]. There are also opportunities to improve efficiency through lightweight designs, which can include structural optimization that reduces overall mass and improves structural stability. While structural optimization may not directly lead to greater energy efficiency, it improves structural stability, which can indirectly increase energy efficiency. Other research opportunities include reducing transmission losses in drives and motors and implementing the use of energy recovery systems^[66]. In addition, currently, there is a gap in the understanding of customer requirements. Bridging this gap will allow for less over-designed machines that will reduce energy loss from inefficient loading of motors and waste energy during idling^[19].

Scheduling and process planning

Manufacturing scheduling aims to sequence a series of operations and allocate resources (e.g., machines and operators) for each operation in order to deliver products within a specified time frame. Traditionally manufacturing scheduling has been focused on productivity or throughput (for example, makespan has been used as a common performance indicator). Since the selection of machines and the order of operations have effects on energy consumption and resource utilization, manufacturing scheduling can be used as a strategy to improve energy efficiency and environmental performance. Fang et al.^[67] are one of the first to explore the possibility of including energy and carbon emissions in manufacturing scheduling. Using a hypothetical two-machine flow shop as an example, this study develops a time-indexed multi-objective mixed integer programming to demonstrate the potential trade-off among peak load, makespan, and carbon footprint. Since then, energy-efficient scheduling has attracted increasing interest. Different types of shop floors, electricity rate structures, energy sources, and storage have all been explored^[68]. Efforts have also been explored toward developing programming models and solution methods. In addition to energy-related objectives, material efficiency, water intensity, and noise level have also been considered in several studies^[14].

Manufacturing process planning chooses the sequence of manufacturing steps to build a product. It differs from scheduling in that process planning plans the processes while scheduling is focused on the machines themselves. Process planning plays a critical role in the cost and environmental footprint of manufacturing. Making proper decisions while planning can greatly affect the overall cost and footprint of a product. To assist, Munoz and Sheng^[69] constructed a model for calculating the environmental impact of machining processes. This model examined process mechanics, lubricant, and wear and calculated energy consumption, mass flow, and process rate. Zhao et al.^[70] proposed an environmentally conscious planning method that identifies impactful process steps and the associated design features within an existing process plan. Then alternative plans that can achieve the same features are generated and then compared and evaluated in terms of environmental and economic performance. These alternative plans are compared using a Pareto front to create a set of non-dominated plans. Guo et al.^[71] developed a life cycle energy analysis integrated process planning (LCEA-PP) method that considers the energy footprint of a product throughout its life cycle in order to produce energy-efficient process plans. The authors applied this method to the production of steel and aluminum component and were able to identify methods for producing these components that will reduce energy consumption during manufacturing when compared to the traditional manufacturing method. Reiff et al.^[72] proposed a method to abstract and generalize resources and part descriptions and then match the manufacturing resources to the part descriptions. The resources are ranked based on their cost, required time, and environmental impacts. Multi-criteria decision analysis is then used to evaluate all the possible combinations based on cost, required time, and environmental impact. Jiang et al.^[73] provided an approach that evaluates the environmental impact of process plans by first producing an inventory for every operation of the plan, then scoring each step, and finally, a normalized score is given for the overall process.

Enterprise

The goal of sustainable supply chains is to transform inputs into outputs with increased financial value while not affecting environmental or social factors^[74]. These supply chains take the form of forward supply chains with reduced manufacturing emissions^[75] and reverse supply chains (closed loop) that incorporate recycling and remanufacturing to reduce the overall carbon emissions of manufacturing^[76-78]. As government entities and consumer preferences lean towards sustainable practices, carbon emission reduction strategies are becoming more prevalent within supply chains. These governmental carbon emission reduction strategies affect how sustainable the supply chains look. Carbon pricing is a cost that producers incur for the number of emissions. Higher carbon pricing incentivizes supply chains to turn to a more closed-loop scenario due to a lower profit with high emissions. The volatility of carbon pricing affects the probability of a company adopting a closed-loop supply chain due to the resulting high volatility for profit^[79]. Cap and trade practices reduce emissions by setting limits on emissions for producers that can be traded within industries but never exceeded^[78]. This reduction strategy suggests that manufacturers will converge towards carbon emission reduction strategies, and retailers will switch to low-carbon products as carbon credits can be sold on the market^[78].

Even with carbon emission reduction strategies in place, supply chains are subject to uncertainties in demand and supply which are increased with the concept of the reverse supply chain. As the use of rare earth elements (REEs) increases as renewable energy practices and electric vehicle use increases, recycling these materials is crucial as some REE supplies are critical^[77]. Demand and supply of these recycled materials are subject to volatile variables such as the price of material, recycling extraction rate, feedstock flow rate, etc.^[76]. Different approaches have been made to model the demand and supply of these materials for closed-loop supply chains, such as assigning random variables or assigning the variables to behave similarly to mechanical oscillations^[76,77]. In addition, a case study in hard disk drive (HDD) recycling highlights the importance of recycling based on the demand for different components within the product. Weights are assigned to components based on the economic value that determines how much demand to fulfill depending on supply^[76].

As the public becomes more environmentally conscious due to growing connectiveness with the internet, consumer preference is shifted towards sustainable products in terms of carbon emission reductions^[80]. This phenomenon even shifts consumers to pay more for a product that has significant emission reductions. With consumer preference in mind, manufacturers and retailers have another pressure to implement emission reductions to their practices and choose lower emission products respectively^[78]. Higher profits for manufacturers selling directly to consumers with lower emissions cause competition between traditional manufacturer-to-retailer supply chains and newer direct online supply chains^[80]. This effect can be modeled with the Stackelberg game and suggests higher profits and lower overall emissions for decentralized competition.

Green supply chain optimization can be complex due to both quantitative and qualitative variables as well as having multiple objective functions, as seen in Figure 4^[81-83]. Various methods are needed to incorporate economic, environmental, and social aspects to make informed decisions. One approach is to incorporate LCAs within supply chains to include hotspots regarding emissions, land use, and water footprint along the production pathway^[83]. In addition, Geographic Information Systems (GIS) assisted data allows for spatial aspects of supply chains to be compared and quantified. In relation to green supply chains, comparing plant locations and sizes to feedstock production locations is crucial for ensuring optimal cost and environmental performance^[81,83]. GIS and LCAs implementation within supply chain management allows for complex data such as environmental performance and spatial location to be streamlined and quantitatively compared, and these studies have had success within the biofuel supply chain and plastic recycling studies^[75,81-84]. Results for green supply chain optimization problems can follow a singular optimal solution or a Pareto front, depending on the nature of the optimization problem. This adds further complexity to green supply chain problems due to potential indefinite solutions. Should both economic and environmental factors be optimized, or should environmental factors be a constraint within an economic optimization problem? Studies suggest a multi-objective approach, but this comes at the disadvantage of expert analysis of results to suggest an optimal solution^[82,85].

Figure 4. Complex interactions of supply chain multi-objective optimization are shown here. System parameters within supply networks are characterized by topological, economic, environmental, and social features that are dynamic and usually incomparable. Figure adapted from Khoo et al.^[83] with permission from Elsevier.

Future directions within manufacturing systems

To improve manufacturing scheduling, Fang et al.^[67] suggest first constructing a simplified model of the shop floor and then designing algorithms that are specialized for this simplified model. The use of a simplified model will provide acceptable schedules with a reasonable computation effort. Akbar and Irohara^[14] find through their review that future work is required to include additional sustainability indicators in the objectives of the scheduling models. These can include wastewater discharge, water reuse, harmful gas releases, and packaging material discarded. Social indicators should also be used. Schedules can also consider the various types of renewable energy and the constraints that go along with them. The authors also find that more work is required to expand current scheduling models to encompass a larger manufacturing scale.

Within the area of process planning, more comprehensive approaches should be explored^[70]. These should consider all major issues, such as tooling, operating parameters, quality assessment, and fixture design. In addition, currently, a manufacturing expert is required to model the various manufacturing processes for a part, and this can be very time-consuming^[72]. One potential opportunity is to reduce this time-consuming step to implement a feature extraction algorithm that suggests possible manufacturing processes and includes required constraints. This would allow for feedback as to the parts’ manufacturability, costs, required time, and CO₂ emissions during the design stage. Further, energy and other resource consumption should be monitored through the connection of machine control and sensor systems to automate the quantification of CO₂^[72]. This will assist in the development of more LCA databases, and validation of these databases should be scaled up to provide more applicability to real industrial settings. With the introduction of AM, there has been a new set of challenges in process planning. These challenges include choosing the right parameters, such as wire-feed rate and travel speed, as well as deposition paths and generation of layers. These choices affect the quality of the parts, such as the porosity or cracking^[86].

The future trends within enterprise focus on the idea of a circular economy (CE) and the future of carbon pricing. Carbon pricing can be viewed as the driving force behind companies adopting carbon-reducing technologies and practices^[78]. Cap and trade models adopt the mechanisms of supply and demand within a capitalistic economy and allow for carbon reductions to be made even with an uncertain, fluctuating carbon price^[78]. This price can fluctuate, but it incurs different consumer and producer behaviors at different prices^[78,80]. At higher prices, companies often choose to invest in carbon-reducing technologies or practices such as CE due to the large cost incurred by business-as-usual emissions^[78]. At lower prices, companies will be seen buying and selling carbon credits instead of immediately investing in these technologies and practices. Consumers’ demand for reduced carbon products is related to the idea of centralized versus decentralized supply chains, and demand rises as carbon pricing increases only within centralized scenarios^[80]. These trends impact the future trends of supply chain research due to the increase in carbon pricing strategies within laws and policies. In particular, the practice of a CE is featured in many journal articles and is found to be an immediate solution in lieu of high carbon prices^[76-78]. Future trends are dealing with uncertainty within demand and supplies of recovered materials. With recovery technologies still under development, the variance in material recovery for a single product is high, and the influent feedstock of materials also faces uncertainty^[76]. For example, REEs within electronics vary in amount and type, in addition to their fluctuating market value and demand which creates uncertainty for return on investment for the practice of a CE.

Disassembly and demanufacturing

Demanufacturing refers to the disassembly of a product to its individual components/parts, which will further be reused, remanufactured, or recycled^[101]. The level of disassembly is dependent on the selected downstream CE approaches. Take the recycling for Lithium batteries as an example: pyrometallurgical recycling can begin at the module level, whereas hydrometallurgical recycling requires disassembling to the cell level^[102].

According to the research done by Cong et al.^[103,104], a demanufacturing process can be characterized by a transition matrix and a succession matrix. The transition matrix presents all the possible routes a product can be disassembled through, both non-destructive disassembly and destructive disassembly. The succession matrix, on the other hand, describes possible transitions between operations. Based on the transition and succession matrices, a mathematical model can be established to optimize the demanufacturing process, where the environmental impacts can be accounted for by defining pertinent optimization criteria.

One common challenge in designing disassembly systems is the uncertain quality or state of EOL products, which heightens the need for flexible process planning^[105]. With respect to the applications in the EV industry, disassembly can be optimized through disassembly sequence planning (DSP)^[106], lean principles (line balancing)^[107], as well as other analytical tools. Deng et al.^[108] implemented a stochastic activity network (SAN) to model a remanufacturing workflow that can directly rebuild used EVs into “new” EVs, where the initial disassembly process was discovered to be a major bottleneck that hinders the operational performance at a system level.

In pursuit of an optimal disassembly sequence, environmental impact is another critical factor to consider. Alfaro-Algaba and Ramirez^[109] proposed a multi-objective optimization model that balances the trade-off between economic profit and environmental impact in disassembly processes and the recovery procedures for the disassembled components. In this study, the environmental impact of each individual step is quantified by means of the Eco-indicator 99 methodology. The environmental sub-objective function is defined as the difference between the environmental impact avoided due to CE approaches (reuse, remanufacturing, or recycled) and the environmental impact caused by the disassembly processes. The model was able to generate the optimal level for the disassembly process achieving the maximum profit while minimizing the environmental impact.

Remanufacturing

If a product reaches an EOL condition such that it cannot be directly reused, the next option is to pursue remanufacturing approaches. According to Ortegon et al.^[110], remanufacturing refers to the process of returning a used product (nonfunctional, discarded, or traded in) to at least the original design performance/specification. A remanufactured product is assembled using a combination of reused, repaired, and replaced parts^[111]. Motivations for a company to remanufacture include profit, high demand for parts, brand protection, and long lead times to manufacture new parts^[112]. In addition, remanufacturing can also reduce the environmental impact of the product.

The environmental benefits derived from remanufacturing are quantified by the energy savings, and CO₂ avoided when extending the useful life of a product^[113]. These benefits are a result of preserving both material and functional values from core components^[114]. Adler et al.^[115] found that the energy required to remanufacture metal parts for a Caterpillar diesel engine ranged from 51% to 55% of the energy required for original manufacturing. Other studies^[116-118] have reported energy savings for other industries – including automotive (68%-83%), tires (66%-68%), and appliances (14%-44%)-as well as CO₂ savings for automotive (73%-87%) and photocopiers (23%).

For clean energy technologies such as wind turbines, remanufacturing is envisioned as an opportunity to concomitantly reduce virgin material consumption, preserve embodied energy, and reduce the carbon footprint relative to new installations. In an investigation undertaken by Ortegon et al.^[113], the energy saving from remanufacturing wind turbines is expected to be 5 million MJ per unit, which is equivalent to 70% of the total energy consumption from new production. On the carbon footprint front, it was shown that remanufacturing would avoid 862,656 kg of CO₂ per wind turbine.

Recycling

Under certain circumstances, it could be too difficult or expensive to remanufacture certain EOL products compared with producing new products from raw materials. Under such circumstances, we can resort to recycling. Recycling refers to the processes that convert products back into materials that can be used for a new product, and it is by far the most practiced CE strategy^[1]. Recycling activities provide means to separate and recover individual materials from integrated EOL products, thus generating a secondary supply source of raw materials. While remanufacturing can preserve a high degree of the functional value of a product, recycling only retains the material value. Therefore, in terms of the waste management hierarchy, recycling is a less attractive option compared to reuse and to remanufacture.

Nevertheless, recycling still has a key role to play in EOL management, as it promotes economic and environmental sustainability by reducing raw material input and redirecting EOL output back into material loops^[119]. Compared to waste disposal (e.g., incineration and landfilling), recycling can not only save potentially useful and precious materials (i.e., critical materials) but also help reduce energy use and pollution (especially when primary/virgin production is energy-intensive)^[94]. A case in point would be electronics. The material recovered from secondary feedstocks, such as waste printed circuit boards (PCBs), generally entails significant reductions in embodied energy and CO₂ emissions relative to virgin materials production, which alleviate the environmental burden caused by improper e-waste management^[120].

It should be noted that the primary resources and recycled materials are only different stages in the material flow of the product life cycle, which are intercorrelated. To reduce environmental impacts, decision-makers must strike a balance between the utilization of primary and recycled resources, and the optimal distribution varies by product^[99].

Applying CE to mitigate the supply risks of critical materials

In particular, with respect to critical materials, the implementation of CE strategies is expected to present a systematic solution to alleviate potential sociopolitical supply risks, as well as the associated environmental burdens. Sutherland et al.^[121] proposed that CE strategies have a wide range of applications in clean energy industries, including recycling neodymium and dysprosium from used wind turbines and EVs, recovering tellurium, gallium, and indium from EOL PV solar panels, and reusing worn batteries to reduce the consumption of lithium, cobalt, and nickel. Gaustad et al.^[122] have conducted a comprehensive literature review combined with case studies (e.g., Volkswagen and General Electric) to provide specific business examples for integrating circularity principles.

To address the imbalance in supply and demand of critical materials, instead of only focusing on virgin material production using new sources of materials (whether they are critical materials or substitutes), another perspective to consider is to use available materials more efficiently by reducing the waste in manufacturing processes and to increase the adoption of recovery activities^[123]. Prevalent recycling and reuse of critical materials could provide additional supply resources and significantly lower the global demand for raw materials. In addition, the enormous environmental concerns associated with the extraction and processing of ores containing these materials (especially rare earths) also encourage nontraditional feedstocks such as waste streams^[124]. For example, Arshi et al.^[125] concluded that the recycling of magnets had a significantly lower environmental impact compared to virgin production as it drastically reduces the amount of wasted neodymium.

As summarized by Akcilet al.^[126], to date, only a very small proportion of the total critical materials in used and obsolete components are being recycled (i.e., 1% for REEs). Accordingly, there is a need and growing trend for researchers to investigate new technologies to recover materials from used electrical products, EOL energy storage systems, and other secondary resources through recycling. Since the acquisition of the feedstock for these approaches can serve as the waste management for other more conventional processes, successful industrial implementations of these recovery techniques can also enable or strengthen the co-production among different manufacturing sectors^[127].

The studies on REE recovery are exemplary in this direction of research endeavors. In a comprehensive review of the state of the art of rare earth recovery, Binnemans et al.^[128] stated that conventional REE recovery methods largely depend on hydro-metallurgical or pyro-metallurgical approaches. As Priya and Hait^[129] remarked, a major drawback of these conventional approaches is the generation of secondary hazardous waste streams, e.g., large amounts of acid wastewater. This kind of waste stream is not compatible with the environment and raises undesirable ecological concerns, which contradicts the original motivation behind these interventions. In response to this, recent studies have started to transition to bio-metallurgical techniques. As Brierley et al.^[130] highlighted in an investigation, microbiology approaches tend to be eco-friendly and energy efficient and are unlikely to generate secondary hazardous waste streams. As a representative study in biosorption, Brewer et al.^[131] developed a novel biosorption-based flow-through process for selective REE recovery from electronic wastes. With regard to bioleaching, Reed et al.^[132] and Thompson et al.^[133] have studied the opportunities to recover REEs from waste phosphors and cracking catalysts using biolixiviant produced from Gluconobacter-oxydans-strain.

Aside from inventing foundational processing technologies that enable material recovery, other efforts are devoted to systematically renovating the life cycle structure of products that rely on critical materials. The waste of critical materials can be alleviated by addressing any phase of a product’s life cycle, whether it be the design stage, raw material extraction, production, use, or EOL^[134]. Such efforts could involve improving product design. Cong et al.^[135] put forward a market-driven design approach to overcome the difficulties in recyclability, reduce disassembly times, ensure the quality of recovered components, and reduce the cost incurred during manufacturing and assembly. In addition, the challenge involved in the collecting, transporting, and disassembly of EOL products (which are prerequisites for downstream material recycling) also galvanizes investigations. For example, Cong et al.^[103] also inquired into the dismantling operations of used HDDs for rare earth permanent magnet (REPM) recovery.

In a more macro sense, researchers have attempted to establish integrated/comprehensive EOL infrastructures that are focused on closing the material loops within the life cycle of a certain type of product. Mathur et al.^[136] used a multi-objective optimization model to estimate the industrial symbiotic network results of EoL photovoltaic modules. In an investigation to advance EOL vehicle recovery, Kumar and Sutherland^[137] presented a model that integrates the requirements of recovery efficiency and related economic constraints to understand the complex interaction among the business entities within the automotive recovery eco-system, based on which improvement strategies are proposed to achieve better overall economic performance. In terms of the EOL management for EVs, Deng et al.^[108] put forward a holistic CE system for rebuilding/renewing used EVs and extracting maximum value from EOL components and materials. For a pioneer study in the PV industry, Choi and Fthenakis^[138] proposed a holistic framework to provide decision-making guidelines to establish an economically feasible and environmentally viable PV recycling infrastructure at a system level.

End-of-life future trends

The traditional “take-make-dispose” linear economy has been successful in generating material wealth and improving social welfare since the first industrial revolution^[139]. However, this model has been found to be hardly sustainable in the new millennium considering the ever-growing world population and the depletion of natural resources. Alternatively, CE has been proposed to fill the gap between the exhaustion of and the growing demand for materials^[140]. In this part, we will discuss the future trends of CE.

The extensive literature review done by Kara et al.^[1] led to the conclusion that CE might be helpful in addressing the material challenge, but research and innovation efforts are advised to pursue the following directions: (i) core technologies and methodologies; (ii) product development; (iii) business models; and (iv) socio-institutional change^[1]. Nevertheless, the authors indicated that mere material recycling could also be problematic due to high energy intensity and contaminated materials. In fact, the 3Rs initiatives, i.e., reduce, reuse, recycle, were introduced by Japan at the G8 Summit in 2004^[141]. Recently, these research strings have been extended into a framework with 9Rs, which are refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, repurpose, recycle, and recover^[142].

One of the most discussed topics in correlation with CE is Industry 4.0. Industry 4.0 integrates manufacturing operation systems and information and communication technologies (ICT) and is a salient route to gaining a sustainable development advantage^[142,143]. Rosa et al.^[144] argued that the combination of CE and Industry 4.0 represents two industrial paradigms that have the potential to enable new natural resource strategies. Bag et al.^[145] developed a theoretical model linking key resources for Industry 4.0 to motivate manufacturers to focus on smart manufacturing and CE in developing countries. Wu et al.^[146] decomposed the black box of deep neural networks and proposed a method with layer-wise relevance propagation (LRP) technique which can be used for predictive maintenance using vibration data.

While tackling environmental concerns, social sustainability cannot be overlooked^[94]. Padilla-Rivera et al.^[147] found that social aspects are relevant in CE since they can provide an overview of how the economic model can impact and benefit society but also how this concept is perceived. The selection of social CE indicators was proposed using the fuzzy Delphi method^[148]. The biophysical and social perspective of CE was examined by Moreau et al.^[149], revealing the lack of social and institutional dimensions in the current CE framework. Multiple strategies toward CE, such as shifting the bulk of taxes from labor to resource consumption, are reviewed. Moreover, political processes are suggested to help build a more equitable CE.

Metrics and indicators

According to Reich-Weiser et al.^[150], sustainable manufacturing is composed of 3 main components: (i) selection and application of appropriate metrics; (ii) completion of comprehensive and repeatable LCAs; and (iii) adjustment/optimization of the system to minimize environmental impacts and cost based on the chosen metrics and the LCAs. However, there is a lack of measurement science and a baseline to measure and effectively compare the environmental performances of manufacturing processes and resources with respect to sustainability^[151]. Evaluating and improving the sustainability performance of manufacturing processes and systems requires qualitative and quantitative metrics. These metrics help to improve decision-making criteria when optimizing process and system designs^[152]. One major obstacle in developing absolute measures for sustainability is the absence of a well-defined approach to characterize sustainability for manufacturing. There are a number of indicators for sustainability, which include indicators based on environmental management, economic growth, social well-being, and technological advancement. A summary of these can be seen in Table 2.

Several tools and metrics have been developed to measure the environmental impact of a product from the life cycle perspective of the whole production chain. Carbon footprint is a metric that has gained prominence^[160]. Laurent et al.^[161] investigated the validity of carbon footprint as a performance indicator for product manufacturing. Their goal was to see if there was any correlation between the carbon footprint and other types of environmental impacts like human toxicity impacts. Singh et al.^[162], in their review of broad sustainability assessment methodologies, list 41 sustainability indicators that have been proposed globally and find that only a few of the surveyed indices actually consider each pillar of sustainability, and most focus on a single pillar. They group environmental indices into (i) eco-system indices such as living planet index (LPI) and ecological footprint (EF); (ii) environmental indices for policies, nations, and regions such as environmental quality index and environmental sustainability index; and (iii) environmental indices for industries such as eco-points and eco-indicator 99.

Sarkar et al.^[163] found that numerous indicator sets have been devised to analyze and score sustainable manufacturing. However, they note that this has created difficulty in selecting the appropriate set. In their paper, they present a sustainability indicator repository, called Sustainable Manufacturing Indicator Repository (SMIR), that is based on 5 dimensions of sustainability: environmental stewardship, economic growth, social well-being, technological advancement, and performance management. The motivation for the repository is to provide an organized set of centralized, web-based, open, and neutral indicators that can be accessible by small and medium size manufacturing enterprises. Reich-Weiser et al.^[150] developed a methodology to evaluate key metrics for energy use, CO₂ emissions, and resource consumption. Their goal was to provide manufacturing engineers and scientists with a set of tools with which they can better design and characterize sustainable manufacturing systems.

Haapala et al.^[18] provide a literature review evaluation of manufacturing environmental performance metrics. They note that the most commonly used approach by manufacturers is an Environmental Management System (EMS). The authors define an EMS as a framework that allows an organization to consistently control its significant impacts on the environment, reduce the risk of pollution incidents, ensure compliance with relevant environmental legislation, and continually improve its processes and operations. They further mention that LCA is the most common method for the environmental impact evaluation of manufactured products.

Techno-economic assessment

Manufacturing processes need to be optimized towards minimal environmental impact and maximum economic value. As we have already discussed the metrics associated with environmental impacts, our attention now shifts to the economic side of sustainable manufacturing. During the early stages of technology development, there is more flexibility in adapting the technology. Therefore, a techno-economic assessment (TEA) is required. Techno-economic analysis, or techno-economic assessment, oftentimes abbreviated as TEA, is a method to analyze the economic performance of processes in industries^[164]. The prospective assessment differs at the different stages of technology development defined by the Technology Readiness Levels (TRL), ranging from 1 to 9^[165].

Although the number of published TEAs has increased steadily over the years, methodological discussions are still rare. In general, the methodology is based on cost engineering estimate practices and cost-benefit analyses. Van Dael et al.^[166] provided a general methodology for TEA based on current procedures. They describe 4 major steps of a TEA: (i) market study to determine prices and market volumes; (ii) process flow diagram and mass and energy balance calculations; (iii) economic analysis, e.g., net present value (NPV), or the internal rate of return (IRR); and (iv) risk analysis to assess the influence of uncertainty on the indicators.

Other methods, such as LCC have been used as well to assess the economic potential of product manufacturing. A major downside of LCC is that it excludes important costs such as labor and equipment costs, greatly affecting the overall results. Moreover, LCC focuses more on the total cost distribution of the product over its total life cycle, while TEA analyses the economic profitability from an investor’s perspective.

Several studies apply TEA to investigate the economic feasibility of manufacturing processes. Thompson et al.^[133] used TEA to assess the potential of applying bioleaching to the practical recovery of critical metals from industrial and post-consumer wastes. Huang et al.^[167] used TEA to evaluate the economic performance of a novel technology to extract lithium from geothermal brine using lithium-aluminum-layered double hydroxide chloride (LDH) sorbent and forward osmosis. Deng et al.^[77] acknowledged that several TEAs have been conducted to evaluate innovative approaches to recover REEs from waste streams. They note, however, that most of these studies do not consider the price volatility of REEs, which raises concern over the reliability of the analysis in an unpredictable dynamic market. They thus propose a dynamic price model to simulate a dynamic market behavior and investigate its effect on TEA.

From efficiency to effectiveness

Throughout this paper, there have been a number of examples of reducing environmental impact and energy consumption through increased efficiency; however, there is a need for a discussion regarding effectiveness and its role in green manufacturing. Much focus within green manufacturing is on efficiency, as is evident throughout this paper. However, effectiveness can be of equal importance.

Efficiency focuses on achieving the outcome with the least amount of energy, time, and effort and the lowest amount of waste, while effectiveness focuses on identifying the outcome with the best use of resources^[168]. Efficiency may focus on a specific manufacturing process, but effectiveness will have a broader focus that can include the entire system. For example, efficiency will focus on Scope 1 or Scope 2 energy (consumed onsite in a manufacturing process), while effectiveness will have a broader focus that may include Scope 3 energy (embodied energy). Below is a description of the three scopes of energy:

● → Scope 1: Energy that is generated onsite at a facility and used in a process, e.g., using methane as a fuel to heat a furnace.

● → Scope 2: Energy transported to a facility in the form of electricity or steam that is consumed in a process.

● → Scope 3: Embodied energy of all materials, chemicals, and equipment used in support of a manufacturing operation and is at least partially consumed due to the manufacturing operation.

Understanding the different scopes of energy for a product and making design and manufacturing changes based on that understanding can more effectively reduce energy consumption than when focusing on a specific process. For example, a machine tool uses electricity as the energy source, and the electricity may come from a coal power plant many miles away. To produce and transmit the power, there are a number of losses. Even to turn that electricity into useful power within the machine tool, there are a number of losses. Below is an example of a fictitious efficiency chain adapted from Shade and Sutherland^[168].

In this example, fuel, in the form of coal, is provided to a coal power plant with an efficiency of 30%. The electricity generated is transmitted to the manufacturing plant, and a number of losses occur due to chained efficiencies. In the end, 9.7% of the energy is useful. Similar losses are illustrated by the Advanced Manufacturing Office^[169], in which about 61% of all energy in manufacturing is lost. This includes sources of offsite fuel, offsite steam generation, onsite renewable energy, and offsite electricity generation. In the case of manufacturing a product with a machine tool, reducing energy consumption by improved efficiency could happen through improving the manufacturing process, while reducing energy consumption through effectiveness could occur by changing the manufacturing process or changing the design of the product.

Future directions

From earlier discussions, it is apparent that developing metrics for green manufacturing is critical to enable manufacturing companies to quantitatively measure their sustainability performance. It is evident that some of the metrics proposed for green manufacturing are quantitative and easily measured, while others are difficult to conceptualize. There is a lack of consistency in combination with several competing methods to measure even relatively simple concepts such as energy efficiency^[170]. Cohen et al.^[171] propose the compilation of all metrics in use for green manufacturing. Thereafter, develop a process to analyze and select among the variety of environmental sustainability indicators tentatively proposed by governments, corporations, and other organizations with the objective of arriving at a specific, valid, robust, and parsimonious set of measures. This would eliminate redundancy and facilitate decision-making.

The increasing competition among businesses has led to a growth in the use of TEA. One major challenge with TEA is the lack of accessibility to data required for input parameters or the lack of advanced computational tools for concise estimation. Chai et al.^[164] propose the integration of data-driven technologies, i.e., genetic algorithm (GA), blockchain, machine learning (ML), artificial neural network (ANN), Internet of Things (IoT), cyber-physical system (CPS), digital twin (DT), metaverse, cloud computing, and big data analytics to optimize both process and economic parameters concurrently.