Abstract

Decision support methods and tools have been developed to aid in improving product sustainability performance during design. However, these approaches are often developed for domain experts and not well-suited for non-expert decision makers (e.g., engineering students and engineering practitioners), who do not possess specialized knowledge in sustainability analysis of product designs and manufacturing processes. The objective of this research is to facilitate the sustainability performance analysis of manufacturing processes and systems through unit manufacturing process (UMP) modeling within an easy-to-use, publicly-available product design, and manufacturing analysis tool. To achieve this objective, a sustainability assessment framework is developed that considers a cradle-to-gate life cycle scope and has four phases: (1) product development, (2) supply chain configuration, (3) manufacturing process design, and (4) manufacturing process and system (MaPS) sustainability analysis. To implement this framework and to address the identified limitations of existing tools, a proof-of-concept MaPS sustainability analysis tool is developed as a spreadsheet software tool. The tool supports the evaluation of environmental (energy and associated carbon footprint), economic (the cost of goods sold), and social (worker safety) impacts. While this study focuses on the technical aspects of the research, the authors investigate associated educational aspects in a separate study and report tool operational performance evaluation by undergraduate and graduate engineering students. Study participants found the tool easy to use and useful in completing sustainability assessment tasks in product design and manufacturing. To build upon this research, the developed framework and tool can be expanded to consider other phases of the product life cycle. Moreover, key software tool operational characteristics and graphical user interfaces should be investigated to improve efficiency, effectiveness, satisfaction, and learnability of the MaPS sustainability analysis tool.

1 Introduction

Due to multiple reasons such as global warming, public awareness, and stricter regulations [1], recent product development requires combining sustainability assessment and product design and manufacturing [2]. While the design phase cost accounts for only 5–7% of the entire product cost, 70–80% of the total product cost including material and resource consumption is determined in this phase [3]. A parallel situation could be hypothesized for the environmental impacts [1], indicating the significant impact of the design phase on the sustainability performance. To investigate sustainable product design, activities from the supply chain level and manufacturing level should be analyzed [4]. Decision-makers are well-equipped for sustainable design and manufacturing assessment with various eco-design tools and computer-aided design (CAD)-integrated life cycle assessment (LCA) tools [5]. However, practitioners need training and education for effective use of these eco-design methods and tools [6,7].

Moreover, due to the multidisciplinary nature of sustainability [8], conducting product and manufacturing sustainability assessment is challenging for non-expert decision-makers (e.g., engineering students and engineering practitioners), who do not possess specialized knowledge in sustainability analysis of product designs and manufacturing processes. One enabler to address this issue is engineering education [913], which has been promulgated in ABET student outcomes [14]. Sustainability principles have been incorporated into engineering courses through various methodologies and ad hoc approaches by educators, as reported in previous publications by the authors [5,15]. However, methods and software tools are yet to be developed for non-expert decision-makers to conduct sustainability assessments of product design and manufacturing [16,17].

The objective of this research is to facilitate economic, environmental, and social impact assessment of different manufacturing processes and systems through unit manufacturing process (UMP) modeling within an easy-to-use, publicly-available manufacturing process and system (MaPS) sustainability analysis tool. To achieve this objective, three main tasks were undertaken herein: (1) identifying the relevant methods and software tools for sustainability assessment of product design, supply chain, and manufacturing process activities; (2) establishing a framework that integrates product design and supply chain information within a mechanistic UMP modeling approach for quantifying manufacturing sustainability performance; and (3) developing a publicly-available MaPS sustainability analysis tool for non-experts to conduct sustainability performance analysis.

While the framework developed herein supports sustainable engineering education [18], the focus of this research is on the technical aspects of implementing UMP modeling within an easy-to-use, publicly-available MaPS sustainability analysis tool. However, to assess its suitability for the classroom, the operational performance of the MaPS sustainability analysis tool in terms of ease of use and usefulness was investigated and reported in a separate publication. To do so, two dozen undergraduate and graduate engineering students across different programs, program levels, and universities (i.e., Tampere University and Oregon State University) were recruited to use and evaluate the tool. The technology acceptance model (TAM), was applied due to its broad acceptance, technical simplicity, and flexibility. TAM is a survey-based method that has been widely used in measuring user attitudes toward a particular software tool technology [19]. It was determined that study participants found the tool easy to use and useful in analyzing product design, manufacturing process, and supply chain sustainability performance. In fact, the median responses showed agreement with all 12 standard TAM indicators for the two metrics.

The remainder of the research presented herein is organized as follows. The literature review and the limitations of prior works are presented in Sec. 2. Next, the framework developed in this research to create a publicly-available MaPS sustainability analysis tool is discussed in Sec. 3. Demonstration of the application of the framework is presented in Sec. 4. Summary of the research is discussed in Sec. 5. Conclusion and directions for future study are described in Sec. 6.

2 Background

Various tools, e.g., sustainable minds [20], openlca [21], idemat [22], solidworks sustainability [23], simapro [24], and gabi [25], as well as several CAD-integrated LCA tools [2629], are developed to conduct sustainability assessment of manufactured products and manufacturing processes and systems. In addition to these software tools, a myriad of ad hoc sustainable product design and manufacturing methods are developed for experts to evaluate the economic and environmental performance of manufacturing processes during product design.

One of the main challenges is in providing educational and technical materials for training non-experts in the sustainability assessment of products and manufacturing processes and systems [15]. This need stems from multiple factors, including (1) challenges in exchanging sustainability information between stages of the product life cycle [30]; (2) prohibitive costs of existing product cost and environmental impact analysis tools [31,32]; (3) the need for domain knowledge and expertise of eco-design issues [6]; and (4) sophistication, complexity, and difficulty in working with the eco-design software tools [33].

In addition, while much recent work has been done, a critical deficiency in the ability of life cycle assessment software tools to perform manufacturing process-level analysis, identified in literature over the past two decades, was found to be yet present. Specifically, non-experts are challenged in conducting accurate manufacturing stage environmental impact assessments due to the dependency of the existing tool on the product mass [6,18]. Thus, the framework and tool emerging from this research applies manufacturing process-level assessment using the UMP modeling approach for environmental impacts and a manufacturing process design (MPD) approach for cost analysis.

Finally, while much effort has been invested in identifying and quantifying various metrics for conducting sustainability impact assessment, it was found that many analysis approaches remain focused on individual aspect(s) of sustainability [2,34]. For example, most methods and software tools quantify environmental impacts, often connected with cost analysis, and social impact analysis is less frequent. Moreover, comprehensive quantitative sustainability impact assessment during the product design phase, where all three pillars of sustainability are investigated, remains a deficiency in reported sustainability performance assessment studies, methods, and software tools.

In a prior review of the literature related to the research presented herein, Raoufi et al. [5] investigated the development of educational frameworks, product life cycle scopes, types of design repository information, sustainability characterization methodologies, and the objectives of the analyses. Their review serves as a basis to investigate the literature from other focus areas, as summarized in Table 1. Further investigation of different types of product design and four design theories, i.e., universal design, axiomatic design, TRIZ (the theory of inventive problem solving), and general design are discussed by Qiu et al. [35]. The first column in Table 1 presents the type of design information inputs. The ease of use, presented in the next two columns, indicates whether the method and software tool developed in each paper are accessible and whether a design repository is developed. The next three columns present the sustainability aspects investigated. Finally, the last two columns describe whether the manufacturing information included in each paper is experimental or modeled.

Table 1

Summary of the prior work

Input of design informationEase-of-use of the methodSustainability aspect(s) evaluatedManufacturing information obtained byReference
CAD-integratedUser entry of dataAccessibility of the toolRepository availableEnvironmentalEconomicSocialExperimentsModeling
Online[36,37]
Online[38,39]
[4042]
Online[43]
[4446]
[47]
[48]
Online[49]
*Online*****
Input of design informationEase-of-use of the methodSustainability aspect(s) evaluatedManufacturing information obtained byReference
CAD-integratedUser entry of dataAccessibility of the toolRepository availableEnvironmentalEconomicSocialExperimentsModeling
Online[36,37]
Online[38,39]
[4042]
Online[43]
[4446]
[47]
[48]
Online[49]
*Online*****

Note: *The contribution of the research presented herein.

Based on the summary of the existing literature presented herein as well as the summary of the literature reported by Raoufi et al. [5], it was found that non-expert designers should better understand the impacts of their decisions, especially on manufacturing processes and systems. Thus, an integrated sustainability assessment framework enabling non-experts to investigate the sustainability performance of manufacturing processes and systems during the design phase is developed herein and is described in Sec. 3. This work addresses a limitation of prior reported engineering and education research by enabling non-experts to gain experience in assessing economic, environmental, and social impacts of different product designs and manufacturing processes and systems through UMP-based modeling. In particular, the research reported herein develops a step-by-step assessment framework encompassing a cradle-to-gate life cycle scope, and provides a multicopter design repository. In addition, an easy-to-use, publicly-available MaPS sustainability analysis tool supporting each phase of the assessment framework is presented.

3 Framework for Integrated Design and Manufacturing Sustainability Assessment

A framework is developed herein to facilitate simultaneous analysis of economic, environmental, and social impacts of product design changes across manufacturing processes and supply chain networks by decision-makers, including non-experts in sustainability assessment. The framework developed (Fig. 1) considers a cradle-to-gate life cycle scope and has four phases: (1) product development, (2) supply chain configuration, (3) manufacturing process design, and (4) MaPS sustainability analysis.

Fig. 1
Framework for sustainable product design and manufacturing analysis
Fig. 1
Framework for sustainable product design and manufacturing analysis
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The first phase is applying a product development approach and providing design information for the next phases. In the second phase, the supply chain configuration approach has been applied to create the supply chain, which includes supplier selection and determination of transportation modes and routes. In the third phase, to provide detailed manufacturing information (e.g., the UMPs required to make the product and their associated process parameters), manufacturing process design approach is utilized. In the fourth phase, MaPS sustainability assessment is conducted using the information provided in the previous phases to quantify the impacts of all the sustainability pillars. The framework developed herein is presented in greater detail in Fig. 2. Each phase includes a set of activities (or steps), developed based on prior work and is described in the following sections.

Fig. 2
Activities comprising each phase of the framework
Fig. 2
Activities comprising each phase of the framework
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3.1 Phase 1: Product Development.

As presented in Fig. 3, the product life cycle originates from a product design. Thus, the framework developed herein starts with providing the product design information, such as materials, components, functions, and geometry, based on the product development approaches summarized in Table 2. As presented in Table 2, the typical major steps in product development are to: (1) define the product idea, (2) define product function(s), (3) generate product conceptual design, and (4) generate product detail design. Thus, the product development phase developed herein follows these major steps. First, market demand and specific needs of clients define the product idea, which results in designing a new product or redesigning an existing product. Then, based on the market and/or client requirements, product functions are defined.

Fig. 3
Product life cycle (adapted from Haapala et al. [50])
Fig. 3
Product life cycle (adapted from Haapala et al. [50])
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Table 2

Summary of product design/development approaches

Step 1Step 2Step 3Step 4Step 5Step 6Step 7Reference
Identify needPlan for the design processDevelop engineering specificationsDevelop conceptsDevelop product[3]
Clarify objectivesEstablish functionsSet requirementsDetermine characteristicsGenerate alternativesEvaluate alternativesImprove Details[51]
Develop conceptsComplete system-level designComplete detail designTest and refine detail designRamp-up production[52]
Analyze the situationFormulate search strategiesFind product ideasSelect product ideasDefine productsClarify and elaborate[53]
Step 1Step 2Step 3Step 4Step 5Step 6Step 7Reference
Identify needPlan for the design processDevelop engineering specificationsDevelop conceptsDevelop product[3]
Clarify objectivesEstablish functionsSet requirementsDetermine characteristicsGenerate alternativesEvaluate alternativesImprove Details[51]
Develop conceptsComplete system-level designComplete detail designTest and refine detail designRamp-up production[52]
Analyze the situationFormulate search strategiesFind product ideasSelect product ideasDefine productsClarify and elaborate[53]

The next two activities in product development are specific to new products and displayed with dashed boxes in Fig. 2. The third step focuses on key design specifications to capture functionality relationships between different components of the intended product. The fourth step is not mandatory in designing products. However, the design repository provides initial design alternatives and components based on the requirements of the product functionality and facilitates the design process for designers. Next, the product conceptual design is generated using functional requirements and design repository information. Finally, materials and dimensions are determined which create the detailed design.

3.2 Phase 2: Supply Chain Configuration.

One of the key pieces of information provided in the first phase is the raw material used in making the intended product. Given the product life cycle scope considered in the framework, a supply chain network should be created to connect suppliers and manufacturers for raw material delivery. To do this, the second phase of the framework applies a supply chain configuration approach [16,5458]. Raw material extraction and processing happen at the supplier facilities. Thus, the first step in this phase is selecting raw material suppliers, which is important due to the variations in the sustainability performance of suppliers in different locations [59].

After completion of raw material processing, the intermediate material needs to be transported to manufacturing facilities as presented in Fig. 3. In the second step, two types of destinations are considered in the framework: connecting and manufacturing. While manufacturing destinations implement manufacturing processes, connecting destinations are locations at which material intermediate forms are delivered to the manufacturer without any extra activity or processing. After determining the transportation routes in step two, transportation modes should be selected in the third step. The last step of this phase focuses on developing numerical equations to quantify the selected sustainability metrics. In addition to the design information, the equations need supply chain data, such as distances and transportation capacity.

3.3 Phase 3: Manufacturing Process Design.

As presented in Fig. 3, after creating the supply chain network, manufacturing is the next phase in the cradle-to-gate life cycle scope. This phase of the framework provides detailed manufacturing information through UMP modeling to be used in the last phase of the framework. Each manufacturing process flow is composed of several UMPs. To identify, evaluate, and select a sequence of UMPs for fabricating the product, the MPD approach is applied herein [60]. MPD applies a bottom-up modeling approach to define process requirements based on product design specifications (e.g., raw materials and part geometries) and to evaluate their production cost based on the desired annual demand.

After selecting the required UMPs, numerical models should be developed for each of them. Developing the UMP models is the most time-intensive step in the framework as it involves gathering data and developing a transformation equation for each of the metrics selected to quantify the sustainability performance. It is not expected that non-experts would accomplish this step by themselves. Thus, as a critical part of conducting sustainability assessment in this framework, UMP models should be provided for non-experts. The numerical models of the UMPs should describe the outputs (values for the selected sustainability metrics) as a function of inputs (design and process specific parameters). Finally, the process parameters are defined at the last step of the manufacturing process design phase.

3.4 Phase 4: Manufacturing Process and System Sustainability Analysis.

Once the product design, supply chain, and manufacturing information are provided through Phases 1–3, the framework proceeds to the last phase, which is the MaPS sustainability analysis. The main purpose of this phase is to conduct sustainability assessment through three activities. First, to implement metric quantification, the numerical models developed in the previous phases for the transportation activities and UMPs are used. Then, these model results are used to assess sustainability performance and to identify the “red flags” and “hot spots.” In this manner, decision-makers will be able to investigate the sustainability performance of different supplier locations, transportation modes and routes, and manufacturing processes. Third, design alternatives and their associated sustainability performance can be compared to facilitate the selection of the best alternative, depending on decision-maker goals. Designers and decision-makers can further enhance product sustainability performance by investigating other potential scenarios. For example, they can modify product design specifications, such as the materials and geometry, or design a new product and, accordingly, create new networks and select different UMPs for making the product. This promotes sustainable product design by allowing decision-makers, including non-experts, to investigate the impacts of product design changes on supply chain networks and manufacturing processes.

As mentioned above, one of the main challenges for the non-experts to conduct UMP-based sustainability assessment from cradle-to-gate life cycle scope is developing the numerical models for the supply chain activities and the manufacturing processes. Further, gathering the data (e.g., distances and process parameters) for each activity within the framework is another challenge. Thus, as identified in Sec. 2, to enable non-experts to conduct sustainability assessment using the UMP modeling approach, a software tool is required. The tool needs to be user-friendly and needs to include the numerical models and the required data to conduct sustainability assessment. An application of the framework, and a proof-of-concept tool developed in this work to conduct UMP-based sustainability assessment are demonstrated in Sec. 4.

4 Demonstration of the Framework

A proof-of-concept tool, MaPS sustainability analysis tool, is developed to aid the implementation of sustainability assessment using standards-based UMP models. The tool is publicly available [61], and addresses the identified deficiencies of sustainability assessment through an integrated framework for product, supply chain, and process design, and demonstrates the application of the developed framework for supporting non-expert designers and decision-makers. Elements of this tool are derived from work under the Constructionism in Learning: Sustainable Life Cycle Engineering (CooL: SLiCE) project [49]. The tool was designed to align with the four phases of the integrated sustainability assessment framework (Sec. 3), and support the investigation of environmental impacts across the cradle-to-gate life cycle. In addition, the tool can be used to evaluate economic and social performance by quantifying the cost of goods sold (COGS) and the safety in the work environment, respectively. The tool is composed of four modules that map to each phase of the sustainability assessment framework as presented in Fig. 4.

Fig. 4
Mapping of MaPS sustainability analysis tool modules with sustainability assessment framework phases
Fig. 4
Mapping of MaPS sustainability analysis tool modules with sustainability assessment framework phases
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To realize Phase 1 (product development), the part specification module captures product design information (e.g., materials and product geometry). Similarly, supply chain information (e.g., transportation routes and modes) is provided by the supply chain configuration module, which maps to Phase 2 (supply chain configuration). In addition, the manufacturing process module provides manufacturing information (key process parameters and their associated values in each UMP), realizing Phase 3 (manufacturing process design). Each of the four tool modules has two sections: information and analysis. While each module performs the sustainability assessment independently through its associated analysis section, the information sections of the modules share the required information for conducting the sustainability assessment. Numerical models for each activity are implemented within the information section of each module of a spreadsheet software tool. The analysis sections of the modules are integrated with a graphical user interface (Fig. 5) for easier access and data entry by non-experts.

Fig. 5
MaPS sustainability analysis tool graphical user interface
Fig. 5
MaPS sustainability analysis tool graphical user interface
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The remainder of this section is organized with respect to the four phases of the integrated sustainability assessment framework defined in Sec. 3. The activities (steps) comprising each phase are described and demonstrated using a case study for the sustainable design of a hexacopter. Additionally, for each phase, the application of the associated MaPS sustainability analysis tool modules is presented. It should be noted that the case study presented herein is illustrative, and not comprehensive. Moreover, this sustainability assessment is focused on the evaluation of several hexacopter designs which are each assumed to make use of the same electronic components. Thus, they are not included in the assessment presented herein. Further, the current version of the MaPS sustainability analysis tool is not suited for the evaluation of electronic component design and manufacturing.

4.1 Phase 1: Product Development.

The first phase of the integrated sustainability assessment framework focuses on product development. The activities of this phase are presented in Fig. 2. The product idea investigated herein is a multicopter for package delivery, as multicopters have become popular and it is expected that students would be interested in learning more about this technology from various regions, backgrounds, and genders. All six activities and their associated data required are investigated and reported in a previous publication by Ref. [5]. This information is input to the part specification module of the MaPS sustainability analysis tool using drop-down menus. Different types of raw materials are provided for users in this module. Polymer types include acrylonitrile butadiene styrene, cellulose acetate butyrate, and polyoxymethylene. The metals category includes aluminum, stainless steel, brass, magnesium, and carbon steel alloys. Material impacts are accounted for based on the supplier and its associated location emission factor. The information is processed by the analysis section of the module to provide feedback to the user as well as the information sections in the other modules for further processing. A screenshot of the part specification module in the graphical user interface is presented in Fig. 6.

Fig. 6
Part specification module in the graphical user interface
Fig. 6
Part specification module in the graphical user interface
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4.2 Phase 2: Supply Chain Configuration.

The second phase of the integrated sustainability assessment framework developed herein involves configuring the supply chain to connect suppliers and manufacturers for raw material delivery. The activities of this phase are presented in Fig. 2. Representative supply chain configurations for each component developed in Phase 1 are presented in Table 3. After capturing information about the product specifications and supply chain configurations, the next step is to define the UMPs required to make the products. To do this, the manufacturing process design method is applied, as described in Sec. 4.3. Next, two new indicators for the evaluation of worker safety are presented with respect to transportation processes. These indicators will supplement COGS and environmental impacts for a more comprehensive sustainability assessment. Many metrics (e.g., child labor) have been developed to quantify the social impacts of the activities within a supply chain [62,63]. Among these metrics, those such as quality of life, equity, and safety, which quantify the higher-order needs instead of human basic needs are more favorable [64]. Thus, safety level in the work environment, whether within a factory or across the supply chain (including transportation activities), is investigated herein using nonfatal occupational injuries and illnesses (NOII) and days away from work (DAW). NOII and DAW are time-based, commonly understood, and can be easily measured [65]. To quantify these metrics for transportation activities, equations reported by Ref. [32] are adopted herein.

Table 3

Representative supply chain networks for PD1 and PD2

DesignSupply chain configurationTransportation
FromToMode
PD1 (propellers)SC1ABeijing, ChinaShanghai, ChinaRail
Shanghai, ChinaSan Francisco, CADeep-sea container
San Francisco, CAChicago, ILRoad
PD1 (shells)SC1BLondon, UKNew York, NYDeep-sea container
New York, NYHouston, TXRail
Houston, TXChicago, ILRoad
PD2 (propellers)SC2AMontreal, CanadaBoston, MARail
Boston, MARiverside, CARoad
Riverside, CAIrvine, CARoad
PD2 (shells)SC2BNew Delhi, IndiaAustin, TXAir freight
Austin, TXAnaheim, CARoad
Anaheim, CAIrvine, CARoad
DesignSupply chain configurationTransportation
FromToMode
PD1 (propellers)SC1ABeijing, ChinaShanghai, ChinaRail
Shanghai, ChinaSan Francisco, CADeep-sea container
San Francisco, CAChicago, ILRoad
PD1 (shells)SC1BLondon, UKNew York, NYDeep-sea container
New York, NYHouston, TXRail
Houston, TXChicago, ILRoad
PD2 (propellers)SC2AMontreal, CanadaBoston, MARail
Boston, MARiverside, CARoad
Riverside, CAIrvine, CARoad
PD2 (shells)SC2BNew Delhi, IndiaAustin, TXAir freight
Austin, TXAnaheim, CARoad
Anaheim, CAIrvine, CARoad

Similar to the part specification module, drop-down menus are provided for users to enter the supply chain information (transportation route and transportation mode) in the information section of the supply chain configuration module. Several locations throughout the world, i.e., Beijing, Chicago, Detroit, Frankfurt, Houston, London, New York, Paris, San Francisco, Seoul, Shanghai, Singapore, Sydney, Tokyo, and Toronto are included in the module enabling users to create their intended supply chain network. In addition, drop-down menus provide users with various transportation modes, i.e., road, rail, barge, short sea, deep-sea container, deep-sea tanker, and air freight, to deliver the raw material to the manufacturing location. In case of selecting impossible delivery by the intended transportation mode (e.g., selecting a deep-sea tanker for a city without any access to water) between locations, the MaPS tool asks users to change the transportation modes. It should be noted that final material destination is assumed to be the manufacturing location. After capturing this information from the user, the analysis section provides distances as well as upstream and transportation carbon footprint (CF). CF is a key environmental metric, which correlates with energy use [66] and is calculated as a part of Phase 4 activities under the MaPS sustainability analysis module. The MaPS analysis module uses the part mass calculated by the part specification module to determine the mass of the material/product transported. A screenshot of the supply chain configuration module is presented in Fig. 7.

Fig. 7
Supply chain configuration module in the graphical user interface
Fig. 7
Supply chain configuration module in the graphical user interface
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4.3 Phase 3: Manufacturing Process Design.

The third phase of the integrated sustainability assessment framework developed herein utilizes the manufacturing process design approach to identify, evaluate, and select a sequence of UMPs for fabricating the intended product. The three main activities of this phase are illustrated in Fig. 2.

The proof-of-concept MaPS sustainability analysis tool integrates several UMP models reported in the literature as well as a metal injection molding (MIM) process model developed based on the unit process life cycle inventory (UPLCI) method. Using drop-down menus, non-experts would select appropriate values for each parameter for each UMP required to make the product. The UMPs are selected based on the materials, geometries, and functions of the components to be produced (Fig. 8). The product functionality often dictates the feasible types of materials and the geometries for the intended product. Further, requirements from the material and part geometry indicate the alternative UMPs capable of making the part.

Fig. 8
Function-material-geometry-processes relationship
Fig. 8
Function-material-geometry-processes relationship
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Injection molding has a wide range of applications in manufacturing different types, sizes, and shapes of automotive, consumer, and industrial plastic products [67] and is selected for production of the polymer hexacopter shells due to its flexibility and reliability in creating high volumes of plastic parts. MIM, a powder metallurgy process [68] is utilized for making the propellers. Similar to polymer injection molding, MIM is amenable to the repeatable production of high quality, complex geometry metal parts, usually having small geometries.

Next, to develop numerical models of the processes, the equipment to be used and their operational characteristics must be understood. Todd et al. [69] developed a taxonomy of manufacturing processes in which MIM is a mass-conserving process. MIM shares the same initial process steps as polymer injection molding [70]. The MIM numerical model and process attributes (e.g., metal powder to binder ratio, binder composition, feedstock density, and machine specifications) are reported by Raoufi et al. [70,71]. The MaPS sustainability analysis tool includes numerical input–output models for different UMPs, i.e., polymer and metal injection molding, milling, extrusion, drilling, fused deposition modeling, and laser powder bed fusion.

With the numerical models developed for the selected UMPs, the last step of this phase is to define the process parameters. Similar to the other modules, users can provide information for the manufacturing process module using drop-down menus. This module captures the values for the key process parameters used for making the product. Moreover, it uses the information provided by the part specification (e.g., mass) and supply chain configuration (e.g., location) modules to estimate the energy consumption and CF. Similar to the transportation activities, equations reported by Raoufi et al. [32] are adopted herein to quantify NOII and DAW for the manufacturing processes. A screenshot of the manufacturing process module is presented in Fig. 9.

Fig. 9
Manufacturing process module in the graphical user interface
Fig. 9
Manufacturing process module in the graphical user interface
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4.4 Phase 4: Manufacturing Process and System Sustainability Analysis.

The last phase of the integrated sustainability assessment framework developed herein is MaPS sustainability analysis, which aims to assess economic, environmental, and social impacts of the product. The activities of this phase are presented in Fig. 2. This phase starts with applying the numerical models for all the activities in the cradle-to-gate product life cycle scope using the input supply chain and manufacturing process information. However, as mentioned above, it is not expected that non-experts develop such models. Thus, the proof-of-concept MaPS sustainability analysis tool is provided to apply developed models and conduct the economic, environmental, and social impact assessments. Each module in the MaPS sustainability analysis tool has drop-down menus that provide multiple choices for each parameter. This enables non-experts to become familiarized with the parameter ranges as well as to investigate the effects of changes in the parameter values.

Similar to commercial LCA software tools, the MaPS sustainability analysis tool should be updated regularly to improve its usability and to expand the variety of raw materials, locations, transportation modes, transportation routes, and UMPs available. Currently, several types of raw materials, locations, transportation modes, and conventional and additive processes are included in the proof-of-concept, which are general and capable of making many product designs. If the intended information is not available in the MaPS sustainability analysis tool, users can apply proxies for existing materials, processes, or other activities. Integrating the required information resources into the information and analysis sections of each module in the tool is described by Raoufi [18]. In addition, Raoufi reported the details of the mathematical formulations of each stage for each pillar of sustainability are reported by Raoufi [18]. However, part of the mathematical models, which includes the modeling approach for UMPs based on the method for manufacturing process characterization specified by ASTM E3012-22 [72] is presented in the Appendix, i.e., metal extrusion [73] (Appendix  A), polymer injection molding [67] (Appendix  B), drilling [74] (Appendix  C), and milling [75] (Appendix  D). A screenshot of the tabular presentation of the results for the plastic parts is presented in Fig. 10. In addition to the tabular presentation, pie charts are provided for the three aspects of sustainability, which are presented in detail in the next subsections. Presenting the information in this way enables the user to analyze and compare the results for different design, supply chain, and manufacturing alternatives [76].

Fig. 10
Tabular presentation of the results for the plastic products in the MaPS sustainability analysis tool
Fig. 10
Tabular presentation of the results for the plastic products in the MaPS sustainability analysis tool
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4.4.1 Economic Impact Assessment.

To compare the total unit cost for the polymer and metal injection molding processes, production cost models are required. Total COGS including seven cost elements, i.e., tool, facility, labor, maintenance, raw materials, consumables, and utilities, as well as the capability and capacity analyses, market size, cost data from vendors, raw material cost, process utilization, and the cost model parameters, i.e., cost of manufacturing space, facility amortization schedule, equipment amortization schedule, annual operator wages, loaded labor cost rate, annual maintenance as a fraction of capital cost, and the electricity cost are defined and reported by Raoufi et al. [77]. The unit cost breakout by category for production volume of 1000 and 100,000 propellers per year in PD1 and PD2 using MIM is presented in Figs. 11 and 12, respectively. Economic impact analysis results demonstrate that lower size of the propellers in PD2 leads to the lower raw material cost compared to PD1. Further, the manufacturing process time in making the propellers of PD2 using MIM is shorter than the propellers in PD1. Thus, the cost of utilities is lower in PD2. It should be noted that the mold needed in the injection molding process and the solvent required for the debinding process are considered under consumables.

Fig. 11
Unit cost breakout by category using MIM for making the propellers in (a) PD1 and (b) PD2 (1000 propellers/year)
Fig. 11
Unit cost breakout by category using MIM for making the propellers in (a) PD1 and (b) PD2 (1000 propellers/year)
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Fig. 12
Unit cost breakout by category using MIM for making the propellers in (a) PD1 and (b) PD2 (100,000 propellers/year)
Fig. 12
Unit cost breakout by category using MIM for making the propellers in (a) PD1 and (b) PD2 (100,000 propellers/year)
Close modal

Since manufacturing process time and debinding time are higher for the propellers in PD1, the cost of consumables is higher compared to PD2, accordingly. However, at higher production volumes, the costs of these consumables are amortized across more products, which results in making the consumables cost lower for both product designs. At low production volume, tool cost is the same and is the main cost driver for the both product designs. However, at 100,000 propellers per year, the tool cost in PD2 becomes slightly lower compared to the propellers in PD1. This is mainly due to the shorter manufacturing process time for making the propellers in PD2, which increases the tool utilizations and consequently, reduced the number of tools required for making them at high production volume.

The production cost breakout by category for production volume of 1000 and 100,000 shells per year in PD1 and PD2 using polymer injection molding process are presented in Figs. 13 and 14, respectively. While the raw material cost is the second cost driver at low production volume, as production volume increases it becomes the main cost driver for both product designs. The raw material cost for the shells in PD2 is lower due to the smaller size compared to the shells in PD1. Thus, manufacturing process time is shorter for the shells in PD2, which results in lower cost of utilities. Moreover, it leads to higher tool utilization in PD2, which makes the tool cost slightly lower compared to PD1. While propellers are made using MIM and require three sets of tool to make the parts, shells are made using the polymer injection molding process and require only injection molding tool. Thus, due to the lower number of tools required, tool cost is not a cost driver for the shells. In addition to the lower number of tools, polymer injection molding does not require solvent debinding. Thus, solvent is not needed for making the shells. However, similar to the MIM process, mold is considered as a consumable for the polymer injection molding. Thus, as production volume increases, the cost of the mold is amortized over more products, making the consumables cost lower for both product designs. Comparing the consumables cost, making the shells in PD2 has lower cost than PD1 at both production volumes.

Fig. 13
Unit cost breakout by category using injection molding for making the shells in (a) PD1 and (b) PD2 (1000 shells/year)
Fig. 13
Unit cost breakout by category using injection molding for making the shells in (a) PD1 and (b) PD2 (1000 shells/year)
Close modal
Fig. 14
Unit cost breakout by category using injection molding for making the shells in (a) PD1 and (b) PD2 (100,000 shells/year)
Fig. 14
Unit cost breakout by category using injection molding for making the shells in (a) PD1 and (b) PD2 (100,000 shells/year)
Close modal

4.4.2 Environmental Impact Assessment.

CF from transportation activities and manufacturing processes for making the propellers and the shells in PD1 and PD2 are presented in Figs. 15 and 16, respectively. Results from the environmental impact analysis indicate that lower manufacturing process time for making the propellers in PD2 using MIM results in lower manufacturing CF compared to PD1. Similarly, transportation activities for the propellers in PD2 have shorter transportation time. This lowers the environmental impact of the transportation activities for the propellers in PD2, while the transportation mode in the supply chain of the propellers in PD2 has higher emission factor compared to the transportation modes for delivering the raw material from the supplier to the manufacturer of the propellers in PD1. It should be noted that the environmental impacts of transportation activities in the MaPS tool are calculated based on both distance and emission factor. Similar to the propellers, the shells in PD2 have shorter manufacturing process time using polymer injection molding, which lowers the CF compared to the shells in PD1. Transportation modes in the supply chain of the shells in PD1 are deep-sea container, rail, and road, while air freight and road are the transportation modes in the supply chain of the shells in PD2. Since air freight has higher emission factor compared to the other transportation modes, transportation activities for the shells in PD2 have higher environmental impacts compared to the shells in PD1.

Fig. 15
CF associated with the propellers in (a) PD1 and (b) PD2
Fig. 15
CF associated with the propellers in (a) PD1 and (b) PD2
Close modal
Fig. 16
CF associated with the shells in (a) PD1 and (b) PD2
Fig. 16
CF associated with the shells in (a) PD1 and (b) PD2
Close modal

4.4.3 Social Impact Assessment.

NOII analysis results of the transportation and manufacturing processes for the propellers and the shells in PD1 and PD2 under their associated supply chains are presented in Figs. 17 and 18, respectively. DAW analysis results of the transportation and manufacturing processes for the propellers and the shells in PD1 and PD2 under their associated supply chains are presented in Figs. 19 and 20, respectively. Results from the social impact analysis indicate that lower manufacturing process time in making the propellers of PD2 using MIM led to reduced NOII compared to PD1. For transportation activities, propellers in PD2 have shorter transportation time. However, compared to the propellers in PD1, transportation activities have higher NOII in PD2. Due to the lower rates of cases with DAW in the transportation modes selected for the propellers in PD1, NOII has lower value compared to the propellers in PD2. Similar to the propellers, the shells in PD2 are smaller compared to the shells in PD1. Thus, they have shorter manufacturing process time using polymer injection molding. This makes the value of NOII lower for the manufacturing process in PD2.

Fig. 17
NOII associated with the propellers in (a) PD1 and (b) PD2
Fig. 17
NOII associated with the propellers in (a) PD1 and (b) PD2
Close modal
Fig. 18
NOII associated with the shells in (a) PD1 and (b) PD2
Fig. 18
NOII associated with the shells in (a) PD1 and (b) PD2
Close modal
Fig. 19
DAW associated with the propellers in (a) PD1 and (b) PD2
Fig. 19
DAW associated with the propellers in (a) PD1 and (b) PD2
Close modal
Fig. 20
DAW associated with the shells in (a) PD1 and (b) PD2
Fig. 20
DAW associated with the shells in (a) PD1 and (b) PD2
Close modal

However, this is exactly opposite for the transportation activities. The transportation modes for the shells in PD1 are deep-sea container, rail, and road, while air freight and road are selected to transport the raw material from the supplier to the manufacturer for the shells in PD2. Thus, transportation time for the shells in PD2 is shorter compared to PD1. However, due to the higher rates of cases with DAW in the air freight transportation mode, NOII have higher value for the transportation activities of the shells in PD2 compared to PD1. As described above, the DAW metric is calculated based upon NOII. Thus, similar to the analysis of the NOII, manufacturing process has lower DAW for the propellers in PD2 compared to PD1. On the other hand, DAW have higher value for the transportation activities of the propellers in PD2. Similarly, manufacturing processes have lower DAW for the shells in PD2 compared to PD1. However, the value of the DAW metric for the transportation activities of the shells in PD2 is higher compared to PD1. It should be noted that the severity of injuries and illnesses is assumed to be similar across manufacturing process types and transportation modes based on the U.S. Bureau of Labor Statistics [78] data.

5 Summary

A framework and a proof-of-concept tool are developed and described above, which facilitate MaPS sustainability assessment considering the economic (COGS), environmental (CF), and social (NOII and DAW) aspects by non-experts (Tables 4 and 5). To demonstrate the application of the framework within the MaPS sustainability analysis tool, two multicopters were designed, making use of polymer and metal components. Thus, polymer injection molding and MIM were selected to make the shells and propellers, respectively.

Table 4

Sustainability assessment results for propellers and shells in PD1

CategoryEconomic
($)
Environmental
(g CO2 eq.)
Social
(NOII)
Social
(DAW)
1000100,000Trans.Mfg.Trans.Mfg.Trans.Mfg.
Propellers252242.62.42.6 × 10−41.7 × 10−33.7 × 10−39.7 × 10−3
Shells96191.0 × 1011.1 × 1017.9 × 10−49.9 × 10−31.1 × 10−25.8 × 10−2
Total348431.3 × 1011.4 × 1011.1 × 10−31.2 × 10−21.5 × 10−26.8 × 10−2
CategoryEconomic
($)
Environmental
(g CO2 eq.)
Social
(NOII)
Social
(DAW)
1000100,000Trans.Mfg.Trans.Mfg.Trans.Mfg.
Propellers252242.62.42.6 × 10−41.7 × 10−33.7 × 10−39.7 × 10−3
Shells96191.0 × 1011.1 × 1017.9 × 10−49.9 × 10−31.1 × 10−25.8 × 10−2
Total348431.3 × 1011.4 × 1011.1 × 10−31.2 × 10−21.5 × 10−26.8 × 10−2
Table 5

Sustainability assessment results for propellers and shells in PD2

CategoryEconomic
($)
Environmental
(g CO2 eq.)
Social
(NOII)
Social
(DAW)
1000100,000Trans.Mfg.Trans.Mfg.Trans.Mfg.
Propellers252212.01.12.9 × 10−48.3 × 10−44.1 × 10−34.9 × 10−3
Shells94172.8 × 1026.88.7 × 10−48.7 × 10−31.3 × 10−25.1 × 10−2
Total346382.9 × 1028.01.2 × 10−39.5 × 10−31.7 × 10−25.6 × 10−2
CategoryEconomic
($)
Environmental
(g CO2 eq.)
Social
(NOII)
Social
(DAW)
1000100,000Trans.Mfg.Trans.Mfg.Trans.Mfg.
Propellers252212.01.12.9 × 10−48.3 × 10−44.1 × 10−34.9 × 10−3
Shells94172.8 × 1026.88.7 × 10−48.7 × 10−31.3 × 10−25.1 × 10−2
Total346382.9 × 1028.01.2 × 10−39.5 × 10−31.7 × 10−25.6 × 10−2

As expected, it was found that process cycle time has significant impact on the sustainability performance. The smaller propellers and shells in product design 2 (PD2) resulted in shorter cycle time for the polymer and metal injection molding processes. In addition to the manufacturing processes, PD2 has shorter transportation time compared to PD1 due to the selected transportation mode. Thus, the energy consumption and the associated CF for transportation and manufacturing activities are lower in PD2. The shorter manufacturing cycle time resulted in the lower NOII and DAW in the manufacturing processes for PD2. However, due to the higher rates of injuries and illnesses in the transportation mode selected for PD2, it has higher social impacts.

6 Conclusions

Ultimately, the research presented herein is essential to overcome challenges in sustainable MaPS analysis faced by non-experts. The deficiencies of existing methodologies and tools for educating non-expert decision-makers about sustainable engineering, discussed in Sec. 2, include (1) challenges in exchanging sustainability information between stages of the product life cycle; (2) prohibitive costs of existing product cost and environmental impact analysis tools; (3) the need for domain knowledge and expertise of eco-design issues; (4) dependency of analysis tools solely on product mass; (5) a limited focus on one or two aspect(s) of sustainability; and (6) sophistication, complexity, and difficulty in working with the eco-design software tools.

Thus, the identified problem addressed by this research is the lack of framework to facilitate the simultaneous evaluation of economic, environmental, and social impacts of manufacturing processes and systems during product design phase by non-experts [61]. This research develops a sustainability performance assessment framework extending from the theoretical basis of UMP modeling, but also incorporating design and analysis methods from product development and supply chain configuration research. The research pursues the hypothesis that implementation of standards-based UMP models within an easy-to-use, publicly-available MaPS sustainability analysis tool will enable sustainability performance assessment of manufacturing processes and systems. The modeling approach is based on the method for manufacturing process characterization specified by ASTM E3012-22 [72].

As mentioned above, the product sustainability assessment framework developed herein is limited to the product cradle-to-gate life cycle scope. To expand the framework, the other phase(s) of the product life cycle should be included. To achieve this, first, all the steps in the use phase and the end-of-life phase should be identified. Next, a systematic literature review should be conducted to identify the existing metrics for quantifying the economic, environmental, and social impacts of the use and end-of-life phases. In addition to applying the identified metrics, the review provides opportunity to develop new metrics. Next, the numerical models should be developed for the steps in each phase and be added to the MaPS sustainability analysis tool. Then, the required information to analyze the sustainability performance of the phases should be identified. Finally, the information and analysis sections should be added to the MaPS sustainability analysis tool.

It should be noted that improving product economic and environmental performance does not necessarily guarantee a more sustainable world. Known as the rebound effect, Khazzoom [79] stated that energy efficiency improvement of products results in lower prices, eventually leading to increased demand for energy services. This concept has been expanded to investigate a number of industries (e.g., by Binswanger [80], Brookes [81], and Saunders [82]). Researchers have investigated the rebound effect in the sustainability arena (e.g., Refs. [8388]). The research reported herein does not consider how to evaluate the rebound effect during product design, and can be studied as a future research direction in sustainable design decision support.

Footnote

Acknowledgment

This material is based upon work supported by the U.S. National Science Foundation under Grant Nos. DUE-1432774 at the Oregon State University, DUE-1431481 at the Wayne State University, and DUE-1431739 at the Pennsylvania State University.

Conflict of Interest

There are no conflicts of interest.

Data Availability Statement

The data and information that support the findings of this article are freely available online.2

Appendix A: Metal Extrusion Unit Manufacturing Process Model

Graphical representation of the metal extrusion UMP model, including the transformation equations is presented in Fig. 21.

Fig. 21
Metal extrusion UMP model (based on Ref. [73])
Fig. 21
Metal extrusion UMP model (based on Ref. [73])
Close modal

Appendix B: Polymer Injection Molding Unit Manufacturing Process Model

Graphical representation of the polymer injection molding UMP model, including the transformation equations is presented in Fig. 22.

Fig. 22
Polymer injection molding UMP model (based on Ref. [67])
Fig. 22
Polymer injection molding UMP model (based on Ref. [67])
Close modal

Appendix C: Drilling Unit Manufacturing Process Model

Graphical representation of the drilling UMP model, including the transformation equations is presented in Fig. 23.

Fig. 23
Drilling UMP model (based on Ref. [74])
Fig. 23
Drilling UMP model (based on Ref. [74])
Close modal

Appendix D: Milling Unit Manufacturing Process Model

Graphical representation of the milling UMP model, including the transformation equations is presented in Fig. 24.

Fig. 24
Milling UMP model (based on Ref. [75])
Fig. 24
Milling UMP model (based on Ref. [75])
Close modal

References

1.
Ramani
,
K.
,
Ramanujan
,
D.
,
Bernstein
,
W. Z.
,
Zhao
,
F.
,
Sutherland
,
J.
,
Handwerker
,
C.
,
Choi
,
J. K.
,
Kim
,
H.
, and
Thurston
,
D.
,
2010
, “
Integrated Sustainable Life Cycle Design: A Review
,”
ASME J. Mech. Des.
,
132
(
9
), p.
091004
.
2.
Shankar Raman
,
A. R.
,
Haapala
,
K. R.
,
Raoufi
,
K.
,
Linke
,
B. S.
,
Bernstein
,
W. Z.
, and
Morris
,
K. C.
,
2020
, “
Defining Near-Term to Long-Term Research Opportunities to Advance Metrics, Models, and Methods for Smart and Sustainable Manufacturing
,”
Smart Sustain. Manuf. Syst.
,
4
(
2
), p.
20190047
.
3.
Ullman
,
D.
,
2003
,
The Mechanical Design Process
, 3rd ed.,
McGraw-Hill
,
New York
.
4.
Pham
,
D. T.
,
Pham
,
P. T. N.
, and
Thomas
,
A.
,
2008
, “
Integrated Production Machines and Systems—Beyond Lean Manufacturing
,”
J. Manuf. Technol. Manag.
,
19
(
6
), pp.
695
711
.
5.
Raoufi
,
K.
,
Manoharan
,
S.
, and
Haapala
,
K. R.
,
2019
, “
Synergizing Product Design Information and Unit Manufacturing Process Analysis to Support Sustainable Engineering Education
,”
ASME J. Manuf. Sci. Eng.
,
141
(
2
), p.
021018
.
6.
Rossi
,
M.
,
Germani
,
M.
, and
Zamagni
,
A.
,
2016
, “
Review of Ecodesign Methods and Tools. Barriers and Strategies for an Effective Implementation in Industrial Companies
,”
J. Clean. Prod.
,
129
(
Suppl. C
), pp.
361
373
.
7.
Kong
,
L.
,
Wang
,
L.
,
Li
,
F.
, and
Guo
,
J.
,
2022
, “
Toward Product Green Design of Modeling, Assessment, Optimization, and Tools: A Comprehensive Review
,”
Int. J. Adv. Manuf. Technol.
,
122
(
5
), pp.
2217
2234
.
8.
Hawkins
,
N. C.
,
Patterson
,
R. W.
,
Mogge
,
J.
, and
Yosie
,
T. F.
,
2014
, “
Building a Sustainability Road Map for Engineering Education
,”
ACS Sustain. Chem. Eng.
,
2
(
3
), pp.
340
343
.
9.
Esmaeilian
,
B.
,
Behdad
,
S.
, and
Wang
,
B.
,
2016
, “
The Evolution and Future of Manufacturing: A Review
,”
J. Manuf. Syst.
,
39
, pp.
79
100
.
10.
Garetti
,
M.
, and
Taisch
,
M.
,
2012
, “
Sustainable Manufacturing: Trends and Research Challenges
,”
Prod. Plann. Contr.
,
23
(
2–3
), pp.
83
104
.
11.
Raoufi
,
K.
,
Shankar Raman
,
A.
,
Haapala
,
K. R.
, and
Paul
,
B. K.
,
2018
, “
Benchmarking Undergraduate Manufacturing Engineering Curricula in the United States
,”
Procedia Manufacturing, in 46th SME North American Manufacturing Research Conference, NAMRC 46
,
College Station, TX
,
June 18–22
, Vol. 26, pp.
1378
1387
.
12.
Gutierrez-Bucheli
,
L.
,
Kidman
,
G.
, and
Reid
,
A.
,
2022
, “
Sustainability in Engineering Education: A Review of Learning Outcomes
,”
J. Clean. Prod.
,
330
, p.
129734
.
13.
Haapala
,
K. R.
,
Raoufi
,
K.
,
Kim
,
K. Y.
,
Orazem
,
P. F.
,
Houck
,
C. S.
,
Johnson
,
M. D.
,
Okudan Kremer
,
G. E.
,
Rickli
,
J. L.
,
Sciammarella
,
F. M.
, and
Ward
,
K.
,
2022
, “
Prioritizing Actions and Outcomes for Community-Based Future Manufacturing Workforce Development and Education
,”
J. Integr. Des. Process Sci.
,
26
(
3–4
), pp.
415
441
.
14.
ABET
,
2016
, “
Criteria for Accrediting Engineering Programs (Effective for Reviews During the 2017–2018 Accreditation Cycle)
,”
Baltimore, MD
. http://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-programs-2017-2018/, Accessed September 8, 2017.
15.
Raoufi
,
K.
,
Paul
,
B. K.
, and
Haapala
,
K. R.
,
2020
, “
Development and Implementation of a Framework for Adaptive Undergraduate Curricula in Manufacturing Engineering
,”
Smart Sustain. Manuf. Syst.
,
5
(
2
), pp.
60
79
.
16.
Kremer
,
G. E.
,
Haapala
,
K.
,
Murat
,
A.
,
Chinnam
,
R. B.
,
Kim
,
K. Y.
,
Monplaisir
,
L.
, and
Lei
,
T.
,
2016
, “
Directions for Instilling Economic and Environmental Sustainability Across Product Supply Chains
,”
J. Clean. Prod.
,
112
(
Pt. 3
), pp.
2066
2078
.
17.
Khan
,
M. T. H.
,
Raoufi
,
K.
,
Okudan Kremer
,
G.
,
Park
,
K.
,
Reza
,
T.
,
Psenka
,
C.
,
Schmidt-Jackson
,
K.
,
Haapala
,
K.
,
Okudan-Kremer
,
G.
, and
Kim
,
K. Y.
,
2017
, “
Development of Learning Modules for Sustainable Life Cycle Product Design: A Constructionist Approach
,”
Proceedings of the ASEE Annual Conference & Exposition
,
Columbus, OH
,
June 2017
, p.
14
.
18.
Raoufi
,
K.
,
2020
, “
Integrated Manufacturing Process and System Analysis to Assist Sustainable Product Design
,”
Doctoral dissertation
,
Oregon State University
,
Corvallis, OR
, https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/0c483s07g
19.
Babar
,
M. A.
,
Winkler
,
D.
, and
Biffl
,
S.
,
2007
, “
Evaluating the Usefulness and Ease of Use of a Groupware Tool for the Software Architecture Evaluation Process
,”
First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)
,
Madrid, Spain
,
Sept. 20–21
, pp.
430
439
.
20.
Sustainable Minds
.” http://www.sustainableminds.com/, Accessed December 20, 2017.
21.
GreenDelta GmbH
, “
OpenLCA
.” http://www.openlca.org/, Accessed December 31, 2015.
22.
IDEMAT
.” http://idematap.com, Accessed May 7, 2017.
23.
Dassault Systems
, “
SolidWorks Sustainability
.” http://www.solidworks.com/sustainability/, Accessed May 9, 2017.
24.
PRé Consultants
, “
SimaPro
.” https://www.pre-sustainability.com/simapro, Accessed May 9, 2017.
25.
Sphera
, “
GaBi Software—Version GaBi ts 9.5
.” http://www.gabi-software.com/america/support/gabi-version-history/gabi-ts-version-history/, Accessed March 25, 2021.
26.
Leibrecht
,
S.
, “
ecologiCAD
.” http://leibrecht.org/ecologicad/, Accessed January 19, 2023.
27.
Tao
,
J.
,
Chen
,
Z.
,
Yu
,
S.
, and
Liu
,
Z.
,
2017
, “
Integration of Life Cycle Assessment With Computer-Aided Product Development by a Feature-Based Approach
,”
J. Clean. Prod.
,
143
, pp.
1144
1164
.
28.
Jain
,
P.
,
2009
Design of an Interactive Eco-Assessment GUI Tool for Computer Aided Product Design
,”
B.Sc. thesis
,
Indian Institute of Technology
,
Kharagpur, India
.
29.
Cappelli
,
F.
,
Delogu
,
M.
, and
Pierini
,
M.
,
2006
, “
Integration of LCA and EcoDesign Guideline in a Virtual CAD Framework
,”
Proceedings of LCE
,
Leuven, Belguim
, pp.
185
188
.
30.
Brundage
,
M. P.
,
Bernstein
,
W. Z.
,
Hoffenson
,
S.
,
Chang
,
Q.
,
Nishi
,
H.
,
Kliks
,
T.
, and
Morris
,
K. C.
,
2018
, “
Analyzing Environmental Sustainability Methods for Use Earlier in the Product Lifecycle
,”
J. Clean. Prod.
,
187
, pp.
877
892
.
31.
Santolaria
,
M.
,
Oliver-Solà
,
J.
,
Gasol
,
C. M.
,
Morales-Pinzón
,
T.
, and
Rieradevall
,
J.
,
2011
, “
Eco-Design in Innovation Driven Companies: Perception, Predictions and the Main Drivers of Integration. The Spanish Example
,”
J. Clean. Prod.
,
19
(
12
), pp.
1315
1323
.
32.
Raoufi
,
K.
,
Wisthoff
,
A. K.
,
DuPont
,
B. L.
, and
Haapala
,
K. R.
,
2019
, “
A Questionnaire-Based Methodology to Assist Non-Experts in Selecting Sustainable Engineering Analysis Methods and Software Tools
,”
J. Clean. Prod.
,
229
, pp.
528
541
.
33.
Ahmad
,
S.
,
Wong
,
K. Y.
,
Tseng
,
M. L.
, and
Wong
,
W. P.
,
2018
, “
Sustainable Product Design and Development: A Review of Tools, Applications and Research Prospects
,”
Resour. Conserv. Recycl.
,
132
, pp.
49
61
.
34.
Seay
,
J. R.
,
2015
, “
Education for Sustainability: Developing a Taxonomy of the Key Principles for Sustainable Process and Product Design
,”
Comput. Chem. Eng.
,
81
, pp.
147
152
.
35.
Qiu
,
C.
,
Tan
,
J.
,
Liu
,
Z.
,
Mao
,
H.
, and
Hu
,
W.
,
2022
, “
Design Theory and Method of Complex Products: A Review
,”
Chin. J. Mech. Eng.
,
35
(
1
), p.
103
.
36.
Oman
,
S.
,
Gilchrist
,
B.
,
Rebhuhn
,
C.
,
Tumer
,
I. Y.
,
Nix
,
A.
, and
Stone
,
R.
,
2012
, “
Towards a Repository of Innovative Products to Enhance Engineering Creativity Education
,”
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Chicago, IL
,
Aug. 12–15
, Vol. 45066, pp.
207
218
.
37.
Oman
,
S. K.
,
Tumer
,
I. Y.
,
Wood
,
K.
, and
Seepersad
,
C.
,
2013
, “
A Comparison of Creativity and Innovation Metrics and Sample Validation Through In-Class Design Projects
,”
Res. Eng. Des.
,
24
(
1
), pp.
65
92
.
38.
Haapala
,
K. R.
,
Poppa
,
K.
,
Stone
,
R. B.
, and
Tumer
,
I. Y.
,
2011
, “
Automating Environmental Impact Assessment During the Conceptual Phase of Product Design
,”
Proceedings of the 2011 AAAI Spring Symposium: Artificial Intelligence and Sustainable Design
,
Palo Alto, CA
,
Mar. 21–23
, pp.
53
59
.
39.
Bohm
,
M. R.
,
Haapala
,
K. R.
,
Poppa
,
K.
,
Stone
,
R. B.
, and
Tumer
,
I. Y.
,
2010
, “
Integrating Life Cycle Assessment Into the Conceptual Phase of Design Using a Design Repository
,”
ASME J. Mech. Des.
,
132
(
9
), p.
091005
.
40.
Ramanujan
,
D.
,
Benjamin
,
W.
,
Bernstein
,
W. Z.
,
Elmqvist
,
N.
, and
Ramani
,
K.
,
2013
, “
ShapeSift: Suggesting Sustainable Options in Design Reuse From Part Repositories
,”
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
American Society of Mechanical Engineers
, Vol.
55911
, p.
V004T05A041
.
41.
Ramanujan
,
D.
,
Bernstein
,
W. Z.
,
Benjamin
,
W.
,
Ramani
,
K.
,
Elmqvist
,
N.
,
Kulkarni
,
D.
, and
Tew
,
J.
,
2015
, “
A Framework for Visualization-Driven Eco-Conscious Design Exploration
,”
ASME J. Comput. Inf. Sci. Eng.
,
15
(
4
), p.
041010
.
42.
Ramanujan
,
D.
,
Bernstein
,
W. Z.
,
Kulkarni
,
D.
,
Tew
,
J.
, and
Ramani
,
K.
,
2016
, “
ShapeSIFT: Evaluating InfoVis Tools for Eco-Conscious Design
,”
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
American Society of Mechanical Engineers
, Vol.
50145
, p.
V004T05A045
.
43.
Wisthoff
,
A.
,
Ferrero
,
V.
,
Huynh
,
T.
, and
DuPont
,
B.
,
2016
, “
Quantifying the Impact of Sustainable Product Design Decisions in the Early Design Phase Through Machine Learning
,”
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Charlotte, NC
, Vol. 50145, p. V004T05A043.
44.
Gilchrist
,
B.
,
Tumer
,
I. Y.
,
Stone
,
R. B.
,
Gao
,
Q.
, and
Haapala
,
K. R.
,
2013
, “
Comparison of Environmental Impacts of Innovative and Common Products
,”
Presented at the ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Sept. 2013
,
American Society of Mechanical Engineers Digital Collection
, pp.
825
834
.
45.
Gilchrist
,
B.
,
Van Bossuyt
,
D. L.
,
Tumer
,
I. Y.
,
Arlitt
,
R.
,
Stone
,
R. B.
, and
Haapala
,
K. R.
,
2013
, “
Functional Impact Comparison of Common and Innovative Products
,”
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
American Society of Mechanical Engineers
, Vol.
55911
, p.
V004T05A037
.
46.
Arlitt
,
R.
,
Bossuyt
,
D. L. V.
,
Stone
,
R. B.
, and
Tumer
,
I. Y.
,
2017
, “
The Function-Based Design for Sustainability Method
,”
ASME J. Mech. Des.
,
139
(
4
), p.
041102
.
47.
Raoufi
,
K.
,
Haapala
,
K. R.
,
Jackson
,
K. L.
,
Kim
,
K.-Y.
,
Kremer
,
G. E. O.
, and
Psenka
,
C. E.
,
2017
, “
Enabling Non-Expert Sustainable Manufacturing Process and Supply Chain Analysis During the Early Product Design Phase
,”
Procedia Manufacturing, in 45th SME North American Manufacturing Research Conference, NAMRC 45
,
Los Angeles, LA
,
June 4–8
, Vol. 10, pp.
1097
1108
.
48.
Raoufi
,
K.
,
Haapala
,
K. R.
,
Kremer
,
G. E. O.
,
Kim
,
K.-Y.
,
Psenka
,
C. E.
, and
Jackson
,
K. L.
,
2017
, “
Enabling Cyber-Based Learning of Product Sustainability Assessment Using Unit Manufacturing Process Analysis
,”
Proceedings of the ASME 2017 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
,
Cleveland, OH
,
Aug. 6–9
,
ASME
,
p.
V004T05A038
.
49.
Raoufi
,
K.
,
Park
,
K.
,
Khan
,
M. T.
,
Haapala
,
K. R.
,
Psenka
,
C. E.
,
Jackson
,
K. L.
,
Kremer
,
G. E.
, and
Kim
,
K. Y.
,
2019
, “
A Cyberlearning Platform for Enhancing Undergraduate Engineering Education in Sustainable Product Design
,”
J. Clean. Prod.
,
211
, pp.
730
741
.
50.
Haapala
,
K. R.
,
Rivera
,
J. L.
, and
Sutherland
,
J. W.
,
2008
, “
Application of Life Cycle Assessment Tools to Sustainable Product Design and Manufacturing
,”
Int. J. Innov. Comput. Inf. Contr.
,
5
(
3
), pp.
575
589
.
51.
Cross
,
N.
,
2008
,
Engineering Design Methods: Strategies for Product Design
, 4th ed.,
Wiley
,
Chichester, England; Hoboken, NJ
.
52.
Ulrich
,
K. T.
, and
Eppinger
,
S. D.
,
2011
,
Product Design and Development
, 5th ed.,
McGraw-Hill Education
,
New York
.
53.
Pahl
,
G.
, and
Beitz
,
W.
,
2013
,
Engineering Design: A Systematic Approach
,
Springer Science & Business Media
,
Berlin, Germany
.
54.
Tan
,
K. C.
,
Kannan
,
V. R.
, and
Handfield
,
R. B.
,
1998
, “
Supply Chain Management: Supplier Performance and Firm Performance
,”
J. Supply Chain Manag.
,
34
(
3
), p.
2
.
55.
Chaabane
,
A.
,
Ramudhin
,
A.
, and
Paquet
,
M.
,
2011
, “
Designing Supply Chains With Sustainability Considerations
,”
Prod. Plan. Control
,
22
(
8
), pp.
727
741
.
56.
Seuring
,
S.
, and
Müller
,
M.
,
2008
, “
From a Literature Review to a Conceptual Framework for Sustainable Supply Chain Management
,”
J. Clean. Prod.
,
16
(
15
), pp.
1699
1710
. 16/j.jclepro.2008.04.020
57.
Hassini
,
E.
,
Surti
,
C.
, and
Searcy
,
C.
,
2012
, “
A Literature Review and a Case Study of Sustainable Supply Chains With a Focus on Metrics
,”
Int. J. Prod. Econ.
,
140
(
1
), pp.
69
82
.
58.
Olson
,
E. C.
,
2010
, “
Integration Between Product Design And Supply Chain With Consideration For Sustainability
,” Master Thesis, The Pennsylvania State University. https://etda.libraries.psu.edu/catalog/11313.
59.
Sarkis
,
J.
, and
Dhavale
,
D. G.
,
2015
, “
Supplier Selection for Sustainable Operations: A Triple-Bottom-Line Approach Using a Bayesian Framework
,”
Int. J. Prod. Econ.
,
166
(
Suppl. C
), pp.
177
191
.
60.
Paul
,
B. K.
, and
McNeff
,
P.
,
2018
, “
A Pedagogical Framework for Manufacturing Process Design
,”
Proc. Manuf.
,
26
, pp.
1388
1397
.
61.
Raoufi
,
K.
and
Haapala
,
K. R.
,
2020
, “
Manufacturing Process and System (MaPS) Sustainability Analysis Tool
,”
Figshare
.
62.
Alsaffar
,
A. J.
,
Raoufi
,
K.
,
Kim
,
K.-Y.
,
Kremer
,
G. E. O.
, and
Haapala
,
K. R.
,
2016
, “
Simultaneous Consideration of Unit Manufacturing Processes and Supply Chain Activities for Reduction of Product Environmental and Social Impacts
,”
ASME J. Manuf. Sci. Eng.
,
138
(
10
), p.
101009
.
63.
Goedkoop
,
M. J.
,
Indrane
,
D.
, and
de Beer
,
I. M.
,
2018
, “
Product Social Impact Assessment Handbook—2018
,” The Roundtable for Product Social Metrics, Amersfoort, The Netherlands, Version 4.0. https://pre-sustainability.com/articles/2018-handbook-for-product-social-metrics-available-now/, Accessed December 10, 2018.
64.
Hutchins
,
M. J.
, and
Sutherland
,
J. W.
,
2008
, “
An Exploration of Measures of Social Sustainability and Their Application to Supply Chain Decisions
,”
J. Clean. Prod.
,
16
(
15
), pp.
1688
1698
.
65.
Eastwood
,
M. D.
, and
Haapala
,
K. R.
,
2015
, “
A Unit Process Model Based Methodology to Assist Product Sustainability Assessment During Design for Manufacturing
,”
J. Clean. Prod.
,
108
, pp.
54
64
.
66.
Haapala
,
K. R.
,
Zhao
,
F.
,
Camelio
,
J.
,
Sutherland
,
J. W.
,
Skerlos
,
S. J.
,
Dornfeld
,
D. A.
,
Jawahir
,
I. S.
,
Clarens
,
A. F.
, and
Rickli
,
J. L.
,
2013
, “
A Review of Engineering Research in Sustainable Manufacturing
,”
ASME J. Manuf. Sci. Eng.
,
135
(
4
), p.
041013
.
67.
Madan
,
J.
,
Mani
,
M.
,
Lee
,
J. H.
, and
Lyons
,
K. W.
,
2015
, “
Energy Performance Evaluation and Improvement of Unit-Manufacturing Processes: Injection Molding Case Study
,”
J. Clean. Prod.
,
105
, pp.
157
170
.
68.
German
,
R. M.
,
2012
, “Metal Powder Injection Molding (MIM): Key Trends and Markets,”
Handbook of Metal Injection Molding
,
D. F.
Heaney
, ed., Woodhead Publishing Series in Metals and Surface Engineering,
Woodhead Publishing
, pp.
1
25
.
69.
Todd
,
R. H.
,
Allen
,
D. K.
, and
Alting
,
L.
,
1994
,
Manufacturing Processes Reference Guide
, 1st ed.,
Industrial Press
,
New York
.
70.
Raoufi
,
K.
,
Harper
,
D. S.
, and
Haapala
,
K. R.
,
2020
, “
Reusable Unit Process Life Cycle Inventory for Manufacturing: Metal Injection Molding
,”
Prod. Eng. Res. Dev.
,
14
(
5–6
), pp.
707
716
.
71.
Raoufi
,
K.
,
Manoharan
,
S.
,
Etheridge
,
T.
,
Paul
,
B. K.
, and
Haapala
,
K. R.
,
2020
, “
Cost and Environmental Impact Assessment of Stainless Steel Microreactor Plates Using Binder Jetting and Metal Injection Molding Processes
,”
Procedia Manufacturing, in 48th SME North American Manufacturing Research Conference, NAMRC 48
, Vol.
48
, pp.
311
319
.
72.
ASTM
,
2022
, “
Standard Guide for Characterizing Environmental Aspects of Manufacturing Processes (ASTM E3012-22)
,”
ASTM International
,
Conshohocken, PA
.
73.
Groover
,
M. P.
,
2015
,
Fundamentals of Modern Manufacturing
,
Wiley
,
New York
, http://public.eblib.com/choice/PublicFullRecord.aspx?p=5106307. Accessed December 1, 2017.
74.
Overcash
,
M.
,
Twomey
,
J.
, and
Kalla
,
D.
,
2009
, “
Unit Process Life Cycle Inventory for Product Manufacturing Operations
,”
ASME International Manufacturing Science and Engineering Conference
,
West Lafayette, IN
,
ASME
, pp.
49
55
.
75.
Kellens
,
K.
,
Dewulf
,
W.
,
Overcash
,
M.
,
Hauschild
,
M. Z.
, and
Duflou
,
J. R.
,
2012
, “
Methodology for Systematic Analysis and Improvement of Manufacturing Unit Process Life Cycle Inventory (UPLCI) CO2PE! Initiative (Cooperative Effort on Process Emissions in Manufacturing). Part 2: Case Studies
,”
Int. J. Life Cycle Assess.
,
17
(
2
), pp.
242
251
.
76.
Raoufi
,
K.
,
Taylor
,
C.
,
Laurin
,
L.
, and
Haapala
,
K. R.
,
2019
, “
Visual Communication Methods and Tools for Sustainability Performance Assessment: Linking Academic and Industry Perspectives
,”
Procedia CIRP, in 26th CIRP Conference on Life Cycle Engineering (LCE)
,
West Lafayette, IN
,
May 7–9
, Vol. 80, pp.
215
220
.
77.
Raoufi
,
K.
,
Haapala
,
K. R.
,
Etheridge
,
T.
,
Manoharan
,
S.
, and
Paul
,
B. K.
,
2022
, “
Cost and Environmental Impact Assessment of Stainless Steel Microscale Chemical Reactor Components Using Conventional and Additive Manufacturing Processes
,”
J. Manuf. Syst.
,
62
, pp.
202
217
.
78.
U.S. Bureau of Labor Statistics
, “
Incidents Rates for Non-Fatal Occupational Injuries and Illnesses
.” https://www.bls.gov/news.release/osh2.t01.htm. Accessed December 10, 2015.
79.
Khazzoom
,
J. D.
,
1980
, “
Economic Implications of Mandated Efficiency in Standards for Household Appliances
,”
Energy J.
,
1
(
4
).
80.
Binswanger
,
M.
,
2001
, “
Technological Progress and Sustainable Development: What About the Rebound Effect?
,”
Ecol. Econ.
,
36
(
1
), pp.
119
132
.
81.
Brookes
,
L.
,
1990
, “
The Greenhouse Effect: The Fallacies in the Energy Efficiency Solution
,”
Energy Pol.
,
18
(
2
), pp.
199
201
.
82.
Saunders
,
H. D.
,
1992
, “
The Khazzoom-Brookes Postulate and Neoclassical Growth
,”
Energy J.
,
13
(
4
).
83.
Font Vivanco
,
D.
,
Kemp
,
R.
, and
van der Voet
,
E.
,
2015
, “
The Relativity of Eco-Innovation: Environmental Rebound Effects From Past Transport Innovations in Europe
,”
J. Clean. Prod.
,
101
, pp.
71
85
.
84.
Mulrow
,
J.
,
Gali
,
M.
, and
Grubert
,
E.
,
2021
, “
The Cyber-Consciousness of Environmental Assessment: How Environmental Assessments Evaluate the Impacts of Smart, Connected, and Digital Technology
,”
Environ. Res. Lett.
,
17
(
1
), p.
013001
.
85.
Norman
,
W.
, and
MacDonald
,
C.
,
2004
, “
Getting to the Bottom of ‘Triple Bottom Line’
,”
Bus. Ethics Q.
,
14
(
2
), pp.
243
262
.
86.
Polimeni
,
J. M.
,
Mayumi
,
K.
,
Giampietro
,
M.
, and
Alcott
,
B.
,
2015
,
The Myth of Resource Efficiency: The Jevons Paradox
,
Routledge
,
London
.
87.
Shove
,
E.
,
2018
, “
What is Wrong With Energy Efficiency?
,”
Build. Res. Inf.
,
46
(
7
), pp.
779
789
.
88.
Sorrell
,
S.
,
2014
, “
Energy Substitution, Technical Change and Rebound Effects
,”
Energies
,
7
(
5
), pp.
2850
2873
.