Hierarchical analysis of barriers in additive manufacturing implementation with environmental considerations under uncertainty

https://doi.org/10.1016/j.jclepro.2023.137221Get rights and content

Highlights

  • Barriers hindering LCA adoption in AM were identified and classified.

  • Hierarchical analysis of the barriers was performed using fuzzy AHP.

  • Examination of the outputs was drawn via the sensitivity analysis.

  • The most dominant classified barrier was the organizational barriers.

  • Lack of financial resources to conduct LCA study on AM was the main sub barrier.

Abstract

Notwithstanding additive manufacturing has been gaining momentum in the industry, particularly during the fourth industrial revolution, their widespread implementation as a disruptive production technology has brought impacts on the environment. Considering this issue, the research for this paper was built upon the discussion concerning life cycle assessment (LCA) adoption in implementing additive manufacturing (AM). The capability of LCA to achieve environmental goals is a vivid illustration of why it has given researchers and practitioners an extensive impetus. However, to actualize such an effective approach, it is necessary to identify and face the barriers impeding its implementation in AM. Thus, this research aims to identify, classify, and analyze the most critical barriers hindering LCA adoption in AM implementation. To do so, the fuzzy analytical hierarchy process, along with a comprehensive literature review and thorough interviews with the relevant experts, was used to achieve the research purposes. The results revealed twenty-two barriers within five classifications, where the lack of financial resources to conduct LCA study on AM is the most dominant barrier, followed by the lack of LCA expertise in the AM context and the lack of laws and directives for LCA application in AM, respectively. The findings would be useful to decision-makers to develop suitable mitigation strategies and make more informed decisions with individual and/or cluster concentrations. This study can be fruitfully exploited as a guiding reference since no article has hitherto discussed, identified, or analyzed barriers in the understudied area.

Introduction

Industrial metabolism, defined as the transformation of inputs like matter, energy, and labor into outputs like goods, services, waste, and ambient emissions, has made significant contributions in terms of value while having a detrimental effect on the environment and society due to consuming an inordinate amount of precious resources and generating hazardous wastes and emissions (Peng et al., 2018; Gholami et al., 2020). The field of additive manufacturing (AM), also known as 3D printing, has been gaining momentum in the industry, particularly during the fourth industrial revolution (Industry 4.0), which has developed to maximize industrial productivity. With the use of this technology, the creation of prototypes and proof-of-concept designs can be streamlined and accelerated for developing new products (Bourhis et al., 2013; Colorado et al., 2020). In comparison to conventional means, AM has been affirmed as a green technology (Peng et al., 2018) since it holds the potential to reduce the life cycle impacts such as material usage, energy consumption, and emissions, as well as enable greater engineering functionality by reducing the need for specialized tooling in part fabrication, speeding up tool production, and minimizing material waste (Peng et al., 2018; Bechmann, 2014; Faludi et al., 2015; Huang et al., 2016). In product-level sustainability, it is contended by Gholami et al. (2022) that AM enhances the capabilities for manufacturing sustainable products, enables shorter lead times for products, and decreases the requirement for product and component assembly and thus for customizing complex product designs.

However, AM is not necessarily more environmentally beneficial (Faludi et al., 2015; Kellens et al., 2017a; Colorado et al., 2020) since it also entails several inherent drawbacks such as energy usage (Gutowski et al., 2017; Gholami et al., 2022). According to Rejeski et al. (2018), AM carries potential environmental impacts due to the emissions generated from materials and energy consumption. Referring to Peng et al. (2018), the essential environmental factors to be examined as potential environmental impacts of AM include materials demands, process energy, life cycle energy, recyclable waste, non-recyclable waste, process emissions, and life cycle emissions. According to ISO 14001 (2015), environmental impact entails “the changes to the environment, whether adverse or beneficial, wholly or partially resulting from an organization's environmental aspects”. Therefore, it is essential to holistically evaluate the potential environmental impacts of AM so as to support decision-makers in making environmentally informed decisions. Following the viewpoints of Agustí-Juan and Habert (2017), Bekker and Verlinden (2018), and Saade et al. (2020), the current literature on assessing AM's environmental impacts appears to be scant, hence requiring movements to generate significant outlooks since, based on Arvidsson et al. (2018), such assessments have been of critical importance to sustainable development, but also difficult due to the inherent uncertainties and lack of effective appraisal tools.

Life cycle assessment (LCA), which was first developed in the 1990s, is a widely accepted approach for measuring the potential environmental impact of various business operations and products (ISO 14040, 2006; Böckin and Tillman, 2019; Gouveia et al., 2022). Contrary to other environmental impact assessment techniques such as Carbon Assessment or Design for the Environment, LCA enables quantifying the environmental impact of a global system effectively and with different criteria (Bourhis et al., 2013). Despite its growing use in the manufacturing context, studies examining the potential environmental impacts of AM using LCA are very limited (Ma et al., 2018; Saade et al., 2020; Gouveia et al., 2022), e.g., Faludi et al. (2017) conducted an LCA technique to examine the environmental impacts of AM machines and powder production. Further investigations into the environmental effects of AM using LCA are likewise called for (Frazier, 2014; Saade et al., 2020), particularly empirical investigations that offer systematic guidelines for succeeding in its adoption (Huang et al., 2017; Bekker and Verlinden, 2018). The effectiveness of LCA to meet environmental objectives exemplify why it has given researchers and practitioners an extensive impetus. However, to actualize such an effective approach, it is necessary to identify and face the barriers hindering its successful implementation (Hetherington et al., 2014; Long et al., 2016; Farooque et al., 2020).

Understanding the potential environmental impacts of AM from a life cycle perspective is found to be complicated since AM is an interdisciplinary technological field and, more importantly, there are always emerging barriers that pose serious challenges to LCA adoption in implementing AM in terms of managerial, organizational, technical knowledge, legal, and methodological aspects (Bekker et al., 2016; Pinna et al., 2018). According to Pinna et al. (2018), companies that already have management and employee commitment to sustainability values and business practices based on assumptions of potential environmental impacts will be able to successfully utilize LCA in the AM context. Meanwhile, the environmental knowledge required by LCA is rather advanced for design engineers to possess (Bhander et al., 2003). Such barriers are often interrelated or carry some significant weight. Therefore, it is critical to recognize key barriers and their level of importance to mitigate or overcome them during the appraisal process. This issue was also brought up by Dwivedi et al. (2017), who stated that in order to take the required preventative or corrective steps, it is crucial to scrutinize potential obstacles.

Thus, this research aims to identify, classify, and analyze the most critical barriers hindering LCA adoption in implementing additive manufacturing within automotive manufacturing systems. The automotive industry is an ideal setting for the analysis due to its size and impact on the ecosystem, considering that the usage of AM technology in this sector is expected to grow intensely (Böckin and Tillman, 2019). Yet, there is no research addressing the aforementioned noble aim at this moment, while, based on Schmidt and Sullivan (2002) and Farooque et al. (2020), there are significant obstacles to putting LCA into practice. To effectively implement AM with environmental considerations using this method, practitioners must first address the potential barriers to LCA adoption. (Ma et al., 2018; Saade et al., 2020). Thus, the contribution of this paper entails presenting a particular topic that is underexposed in the literature. In this regard, the fuzzy analytical hierarchy process (AHP), outlined as a fuzzy modification of AHP intended to remedy its deficiencies in handling ambiguity and uncertainty (Buckley, 1985), is applied due to its significance for determining the important weights of the identified barriers under fuzzy environments (Mangla et al., 2017).

To further showcase the research contributions, this article is systematized as follows: Section 2 presents a literature review on barriers; Section 3 explains the applied methods; Section 4 reveals the results and elaborates a discussion on the findings and implications; and, Section 5 delivers conclusions and recommendations.

Section snippets

Literature review

This study investigates a topic that has yet to be explored in the literature. Hitherto, i.e., Dec. 2022, searches performed using the academic databases Scopus and Web of Science, which are the largest indexer of global research content and include titles and abstracts from plenty more leading publishers worldwide (Lee et al., 2021; Gholami et al., 2021), found no articles clearly discussing, identifying, and/or analyzing barriers hindering LCA adoption in the context of AM, as we debate

Research methodology

In this research, a four-phase methodological approach is being considered for achieving the research purposes. It began with the literature study consisting of a review of previous studies on barriers, as discussed above. Next, the content validity was performed with an expert group. The fuzzy analytical hierarchy process (AHP), which is a fuzzy modification of AHP intended to remedy its deficiencies in handling ambiguity and uncertainty (Buckley, 1985), was further applied to analyze the

Results and discussion

Automotive manufacturing industries have been under pressure in recent decades to enhance their sustainable performance due to unsustainable products and protocols that could harm the environment and society (Gholami et al., 2022). In order to mitigate the such type of harm, it is recommended that the auto industry moves toward sustainable manufacturing practices; hence, environmental initiatives should be implemented throughout the entire production process to lessen the potential

Conclusion and recommendations

It is appeared that the investigation on assessing AM's environmental impacts is rather limited, necessitating further rhythms to develop this research stream along with environmental considerations since such an assessment has been of critical importance to sustainable development; however, due to the inherent uncertainties and lack of efficient appraisal methods, it is also observed to be challenging. In this regard, LCA is reported to be a competent methodology in measuring the potential

CRediT authorship contribution statement

Jocelyn Ke Yin Lee: Conceptualization, Methodology, Software, Data curation, Validation, Formal analysis, Investigation, Writing – original draft, Visualization, Writing – review & editing. Hamed Gholami: Conceptualization, Methodology, Investigation, Writing – original draft, Supervision, Project administration, Writing – review & editing. Khaled Medini: Project administration, Writing – review & editing. Anas A. Salameh: Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors would like to thank the professionals and participants for their contribution to this study. This work is supported by the Thomas Jefferson Fund under the SUSTAIN project, and in part by the PSAU project (Ref. no.: PSAU/2023/R/1444).

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