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Abstract

Additive manufacturing (AM) is a transformative production method with significant sustainability benefits. However, its adoption in heavy industry faces challenges due to non-flexible manufacturing processes. This study identifies and prioritizes 11 critical factors influencing AM success, analysing their causal relationships using the Grey–Decision Making Trial and Evaluation Laboratory technique with expert insights. The findings highlight technological factors (CF1) and raw material availability (CF9) as the key drivers of AM adoption. Intellectual property protection (CF3) ranked lowest among causative factors but remains crucial. Among the effect group factors, economic considerations (CF11) have the highest impact. By understanding these interdependencies, AM practitioners can strategically focus on high-impact causal factors to enhance operational sustainability. This study provides a systematic framework to guide effective AM implementation in the heavy industry, ensuring long-term success and sustainability.

Keywords

Grey theory DEMATEL critical factor additive manufacturing human–machine collaboration

Introduction

Additive manufacturing (AM) supports sustainable practices by reducing waste and enhancing production efficiency (Agrawal, 2022; Ghobadian et al., 2020; Gupta et al., 2023; Kumar et al., 2021). As a vital technology in digital transformation, AM improves manufacturing sustainability through system-level approaches (Graziosi et al., 2024). Technologies like 3D printing digitize production and supply chain operations (Priyadarshini et al., 2022; Verboeket & Krikke, 2019). AM offers substantial sustainability benefits, eliminating traditional tools and improving product development efficiency (ASTM International, 2012; Shukla et al., 2018). Consequently, AM is increasingly recognized as the future of manufacturing, indulging in offering a substantial competitive advantage to large-scale innovative firms in the heavy industries segment. AM offers numerous benefits, including the ability to create complex shapes often unachievable with traditional techniques (Cohen et al., 2014). Utilizing diverse materials such as metals, ceramics, biochemical and thermoplastics, AM encompasses various processes such as layered manufacturing and 3D printing (Eyers & Potter, 2017). This digital method enhances manufacturing efficiency, minimizes waste and reduces production time and costs. Despite its potential, AM faces constraints in mass customization (Shukla et al., 2018). Its flexibility, personalization and sustainability advantages drive its adoption (Agrawal & Vinodh, 2019). Berman (2012) identified numerous benefits and barriers of AM in industrial settings. Benefits include reduced production costs, minimized material use and enhanced consumer satisfaction through on-demand manufacturing. However, challenges such as high initial costs, the need for specialized expertise and the development of compatible materials remain significant. Ensuring the quality and reliability of AM-produced parts is crucial, especially in safety-critical industries. Environmentally, AM is beneficial as it conserves energy and resources, but its sustainability aspects are still somewhat limited.

AM technologies seem promising in terms of strategic flexibility and catering to entrepreneurial opportunities through bringing product innovation, experimenting with new product development or offering customized solutions to customers. Simultaneously, boosting manufacturing performance to attain success, AM technologies encounter inherent non-flexible manufacturing challenges (Ruiz-Benítez et al., 2018). Despite increasing adoption of AM technologies in industrial areas, especially in motor vehicles, medical and aerospace, the systematic understanding of the cause of important success factors remains limited (de Mattos Nascimento et al., 2022; S. Kamble et al., 2023; Yeh & Chen, 2018). The studies mainly focus on the identification and ranking of individual enablers or obstacles by using approaches such as integrated structural modelling (ISM), analytical hierarchy process (AHP) or fuzzy-ISM. However, some studies provide an integrated model that captures the direction and strength of the effect between factors, especially in the context of heavy industry, where the implementation is quite high due to the complexity scale, safety and lack of investment. However, previous studies extensively explored AM in different contexts, but the exploration of critical factors of adopting AM in heavy industry still remains unexplored. In this regard, the legacy of empirical knowledge on the cause-and-effect relationships among various critical factors of AM could result in enhanced sustainable practices in firms. The study focuses on 11 important structures: technical preparedness, environmental responsibility, IP protection, strategic alignment, operational efficiency, health and safety, human–machine cooperation, supply chain integration, availability of raw materials, product quality and financial feasibility. These factors were chosen on the basis of their continuous discussion in AM literature and discussion with industry experts, their relevance to make industrial decisions during stability and digitization pressure. For example, technical preparedness and IP security are necessary for digital file safety and accurate production. The availability of the supply chain and materials affects direct scalability; While the implementation of economic and quality factors is crucial in viability. This concentrated sample supports a comprehensive analysis of AM success in complex industrial surroundings. Hence, the present study aims to address the research gap with the following objectives: (a) explore the associated critical factors of adopting AM and its adoption success, (b) prioritize them and (c) identify their causal interactions, particularly about the heavy industries prone to take up new challenges and attempt to achieve the excellence in the manufacturing domain. The objectives of the study would ultimately be to respond to the following set of interrelated research questions:

RQ1: What are the potential critical factors to be encountered in adopting AM in heavy industry?

RQ2: How important is the role of the identified critical factors in order of their priorities?

RQ3: In the process of AM adoption, how do the various critical factors have causal relationships with each other?

The article is organized into six successive sections to approach the answers to these questions. The second section presents the theoretical contents from relevant literature to logically explore the critical factors crucial for achieving the success of AM adoption. The third section explains the methodological approaches to ascertain the prioritization and their inter-relationships. The fourth section explores the case implementation and results. After that, the fifth section explores the discussion of the findings. The sixth section elaborates on the management and policy implications and suggests areas for future investigation.

This study makes a significant contribution to identify and prioritize the critical factors influencing the success of adopting AM in the heavy industry by addressing the difference in understanding how important interacting factors affect the successful adoption of AM production in the heavy industry. While advanced examinations have identified individual ingredients or obstacles, the cause of these factors has received limited attention. By implementing the Grey–Decision Making Trial and Evaluation Laboratory (DEMATEL) function, this research provides a nice, system-level view that supports management decisions in resource allocation, strategic prioritization and operational preparedness. It directly supports the leadership goal to improve performance and flexibility.

Literature Review

Theoretical Underpinning

The study of AM adoption is largely focused on identifying disconnected technical and operating promoters, often without assessing their systemic interrelatedness. The technology– organization–environment (TOE) framework (Awa et al., 2017; Prakash, 2025) and resource-based views (RBV; Ristyawan et al., 2023; Shibin et al., 2020) provide a useful theoretical approach to explain how AM-related abilities appear and interact. The TOE framework suggests that organizational innovation is successful with technical preparedness, organizational structures and environmental references (Chittipaka et al., 2022; Jewapatarakul & Ueasangkomsate, 2024). When it comes to AM, factors such as IP security, the availability of raw materials and integration of the supply chain can be mapped on these dimensions. Meanwhile, RBV emphasizes the role of internal abilities, for example, technical infrastructure and efficient human resources, and ongoing competitive advantage. Within this lens, the success of AM is not only a result of external ingredients but also how the companies orchestrate internal and external resources (Chahal et al., 2020). This research integrates these approaches by identifying 11 important success factors. By positioning success factors in this theoretical structure, the study helps to understand how technical, organizational and environmental elements dynamically interact in a complex industrial environment, to determine a basis for evaluating causes and priorities.

Factors Alignment with the Sustainability Theory

In addition to the TOE and RBV frameworks, this study also draws upon sustainability theory, particularly the triple bottom line framework, which conceptualizes sustainability across three core dimensions: economic, environmental and social (Chang et al., 2017; Joseph et al., 2023). The critical success factors identified in this study align with these dimensions. For instance, economic factors (CF11), raw material availability (CF9) and product quality (CF10) correspond to the economic pillar; environmental factors (CF2) and health and safety (CF6) align with environmental and social sustainability, while human–machine collaboration (CF7) and IP protection (CF3) reflect social and innovation-related governance concerns. By mapping the causal relationships among these sustainability-aligned factors, the study contributes to operationalizing sustainability in the context of AM.

Critical Factors of AM Success: A Review

The rising interest of researchers and manufacturers in AM can be attributed to the adaptability of reduced machine prices and design freedom for complex geometries. AM has the capacity to significantly change the way production is done, especially when compared to other advanced manufacturing technologies (Ford & Despeisse, 2016; Holmström & Gutowski, 2017). For diverse purposes, AM technology has been widely adopted in the healthcare, automotive, aerospace, medicinal and consumer products industries (Mandolla et al., 2019). Sustainable AM provides enterprises with enhanced production opportunities and more sustainable business models. This is achieved through reduced energy usage, lower CO₂ emissions, decreased production costs, improved waste management and enhanced safety measures (Gu et al., 2020; Iqbal et al., 2020; Khatibi et al., 2021; Peng et al., 2018). A number of researchers (Cappucci et al., 2020; Le Bourhis et al., 2013; Suárez & Domínguez, 2020) have initiated the evaluation of the environmental impacts of AM in contrast to subtractive manufacturing, owing to the increasing concern for environmental sustainability. Examining the implications of AM to improve industrial sustainability provides a holistic perspective on sustainable AM. The existing research explores how AM can offer several advantages regarding sustainability in operations. These include decreased material toxicity, lower carbon footprints, reduced material consumption, heightened awareness of material recycling and minimized production waste (de Mattos Nascimento et al., 2022; Kellens et al., 2017). Gebler et al. (2014) conducted an extensive analysis of AM from a worldwide sustainability standpoint. Faludi et al. (2015) performed a life cycle assessment (LCA) to evaluate the environmental impacts of AM on two AM systems. The environmental benefits of AM have been investigated by Kohtala (2015) and Kellens et al. (2017). Burkhart and Aurich (2015) introduced a methodology that manufacturers can use to decrease the environmental consequences of AM, specifically in relation to enhancing automotive components. Peng et al. (2018) developed the concept of AM sustainability with a specific emphasis on the energy and environmental consequences. Kellens et al. (2017) compared the environmental aspects of traditional manufacturing and AM. They emphasized the environmental issues in several study fields. Kohtala (2015) conducted a comprehensive assessment of the environmental sustainability of distributed manufacturing, with a focus on highlighting potential future prospects and existing risks. Researchers such as Yang et al. (2019) and Bours et al. (2017) have conducted studies on the LCA of AM to improve its environmental performance. They have also provided recommendations for appropriate frameworks. Creating an LCA is crucial for accurately determining the environmental consequences of products created by AM (Mohd Ali et al., 2019). From an economic standpoint, AM is viable, particularly for producing small- and medium-sized batches (Khorram Niaki et al., 2019; Short et al., 2015). The technology delivers accelerated time to market, eliminates the requirement for tooling, enables manufacturing on demand and allows for design flexibility, resulting in cost savings. The social sustainability advantages of AM encompass enhanced labour conditions, heightened customer happiness, increased accessibility and the ability to customize on a large scale (Beltagui et al., 2020; Huang et al., 2013; Matos & Jacinto, 2019; Murmura & Bravi, 2018; Naghshineh et al., 2021). Chen et al. (2015) examined the economic, social and environmental aspects of digital manufacturing, drawing comparisons to traditional manufacturing models. Ford and Despeisse (2016) observed the advantages of AM, including enhanced resource efficiency, prolonged product lifespan and restructured value chains. An active approach is required to integrate and prioritize the key aspects determining AM’s long-term viability. This article thoroughly analyses the literature to identify critical factors for the success of AM. It then uses multi-criteria decision-making (MCDM) tools to rank the essential factors. The review comprises material sourced from reputable research journals. Table 1 provides a concise summary of the present state of the literature on the exhaustive factors contributing to the success of AM prepared based on the relevant literature review.

Table 1.

Summary of Important Literature on the Success of AM Adoption/Implementation.

S. No.	Source(s)	Purpose	Methodology	Findings
1.	Meixell and Luoma (2015)	Examines how AM technology development barriers can impact supply chain resilience	Case study	Investigated barriers to AM technology development can impact supply chain resilience
2.	Durach et al. (2017)	Provides empirical information on new AM processes and their success barriers	Multi-stage survey study	Identified 15 barriers to the success of AM
3.	Dwivedi et al. (2017)	Studies AM implementation barriers in the Indian automobile sector and their relationships	Fuzzy-ISM	Structured demonstration of Indian automotive AM implementation barriers
4.	Shukla et al. (2018)	Identifies barriers and observes their dynamic interaction	Literature review and ISM	A rigorous literature analysis, expert opinions and the structural model examined barriers
5.	Choudhary et al. (2021)	Analyses potential barriers to AM in medical SC	Hybrid ISM–analytical network process methodology	Observed the barriers to the success of AM in the medical SC context
6.	Haleem and Javaid (2022)	Develops a hierarchical model of critical colour-jet printing technology success factors	ISM and MICMAC	Provided a structural model of 12 factors towards the success of colour-jet 3D printing technology
7.	Naghshineh and Carvalho (2022)	Investigates that the AM adoption barriers impact supply chain vulnerabilities and minimize supply chain resilience	Qualitative data analysis	Proposed a framework for supply chain resilience outcomes of SC vulnerabilities and barriers to AM adoption
8.	S. S. Kamble et al. (2020)	Examines and evaluates 3D printing enterprises’ barriers to becoming localized supply chains, enabling partners	ISM and DEMATEL	Analysed the challenges that 3D printing enterprises face in becoming localized supply chain enablers
9.	Verma et al. (2023)	Observes the success barriers of Sustainable medical AM in the Indian context	Hybrid ISM-DEMATEL	Established the causal relationship between the validated barriers
10.	Patil et al. (2023)	Investigates AM’s contingent success and suggests a typology to assess its supply chain feasibility	Exploratory qualitative research approach	Explored and validated the contingent factors that influence the adoption of AM
11.	Orji and Ojadi (2023)	Assesses the severity of supply chain AM barriers and examines the impact of supply chain collaboration activities on them	Decision support model	Suggested reliable strategies for manufacturing companies to identify challenges and succeed with AM
12.	Ronchini et al. (2023)	Examines the impact of AM success on upstream supply chain design and its drivers and barriers	Literature-based framework	Demonstrated four AM adoption patterns that affected upstream SC design
13.	Sonar et al. (2024)	Evaluates the sustainable enablers of AM	DEMATEL	Studied each enabler’s influence and developed the cause-and-effect diagram
14.	Naghshineh and Carvalho (2024)	Studies the impact of SCR through resilience practices on AM adoption	Descriptive statistical analysis	Analysed the impact of AM technology on SCR, considering resilience strategies
15.	Present work	Identifies and evaluates the critical factors for the success of AM in the context of the heavy industry	Grey–DEMATEL	Investigated the causal relation between the critical factors for the success of AM

Notes: AM = additive manufacturing; fuzzy-ISM = fuzzy-integrated structural modelling; ISM = interpretative structural modelling; MICMAC = Multiplication Appliquée à un Classement; DEMATEL = Decision Making Trial and Evaluation Laboratory.

Summarization of Critical Success Factors for AM Adoption

In summary, various researchers consider the factors that influence the adoption of AM in their particular context. Ronchini et al. (2023) studied the supply chain barriers, technology barriers, strategic barriers, organizational barriers and operational barriers for the AM adoption. Non-availability of a variety of materials as barriers to the adoption of AM is highlighted in the previous literature (Choudhary et al., 2021; Naghshineh & Carvalho, 2022). Agrawal (2022) and Javaid et al. (2021) studied environmental factors for the adoption of AM. In efficiently integrating an advanced manufacturing technology such as AM into an organization, operational factors play a critical role in the successful implementation of the technology (Patil et al., 2023; Ronchini et al., 2023; Singh et al., 2024; Verma et al., 2023). The technological aspect is the most significant factor in the widespread adoption of 3D printing (Chan et al., 2018). Patil et al. (2023) reveal strategic factors influencing AM adoption. Practitioners should focus on organizational factors for creating awareness, skill development and cross-functional knowledge that influence AM adoption at a very early stage (Patil et al., 2023). The study outcomes indicate the structural barriers that exhibit the highest rank in terms of severity (Orji & Ojadi, 2023). IPR issues help create the workforce, market support and readiness to analyse unknown environmental, health and safety issues in AM implementation (Dwivedi et al., 2017). In Table 2, all the major AM success factors are listed.

Table 2.

Summary of Important Critical Factors for the Success of Additive Manufacturing.

S. No.	Critical Factors/Dimensions	References
1.	Technological factors	Dwivedi et al. (2017), Choudhary et al. (2021), Orji and Ojadi (2023), Patil et al. (2023), Verma et al. (2023), Singh et al. (2024)
2.	Environmental factors	Javaid et al. (2021), Agrawal (2022), Ronchini et al. (2023)
3.	Technical factors	Yeh and Chen (2018), Choudhary et al. (2021), Singh et al. (2024), Lalita et al. (2025)
4.	IP protection issues	Dwivedi et al. (2017), Shukla et al. (2018), Naghshineh and Carvalho (2022)
5.	Strategic factors	Shukla et al. (2018), Patil et al. (2023), Ronchini et al. (2023), Singh et al. (2024)
6.	Organizational factors	S. Kamble et al. (2023), Patil et al. (2023), Singh et al. (2024)
7.	Operational factors	Patil et al. (2023), Ronchini et al. (2023), Verma et al. (2023), Singh et al. (2024)
8.	Health and safety	Dwivedi et al. (2017), Choudhary et al. (2021), Haleem and Javaid (2022), Jimo et al. (2022)
9.	Structural competency	Naghshineh and Carvalho (2022), Orji and Ojadi (2023), Sanguineti et al. (2023)
10.	Human–machine collaboration	Dwivedi et al. (2017) Choudhary et al. (2021), Ronchini et al. (2023), Kumar and Singh (2025)
11.	Supply chain factors	Orji and Ojadi (2023), Ronchini et al. (2023), Verma et al. (2023)
12.	Lack of training and knowledge	Dwivedi et al. (2017), Chaudhuri et al. (2019), Choudhary et al. (2021)
13.	Raw materials availability	Choudhary et al. (2021), Naghshineh and Carvalho (2022)
14.	Product quality	Haleem and Javaid (2022), Naghshineh and Carvalho (2022), Singh et al. (2024)
15.	Lack of technical expertise	Choudhary et al. (2021), Haleem and Javaid (2022), Hannibal and Knight (2018)
16.	Economic factors	Durach et al. (2017), Haleem and Javaid (2022), Jimo et al. (2022)

This study used a comprehensive literature survey of peer-reviewed articles on AM adoption, uptake and implementation. A list of 16 important factors has been identified. After reviewing citations and insights from 3 experts, 11 critical factors that were strongly correlated with the success of AM in the heavy industry were carefully chosen for further study. To determine the importance of critical factors for AM success, three experts were interviewed individually in an open-ended format. These specialists have extensive experience in the field of AM, as indicated in Table 3.

Table 3.

Details of the Experts for the Critical Factors Exploration.

S. No.	Expert	Year of Research Experience	Educational Qualification	Area of Expertise	Type of Organization with Which Experts Are Associated
1.	Expert 1	35 years	MTech	Product designing and development	Heavy industry
2.	Expert 2	25 years	PhD	Operations and and supply chain	Heavy industry
3.	Expert 3	20 years	PhD	Technology development	Research and academic institutions

In a brief overview, they explained the study’s purpose and the key aspects contributing to AM’s success. The experts received a detailed list of 16 critical factors, including their definitions, suitability and references. The experts’ final suggestions were recorded individually. To finalize the important parameters for AM success, a second meeting was organized with all three specialists. According to experts, some crucial elements are not important for the AM adoption by the heavy industry. This study examined expert-confirmed critical factors. In conclusion, 11 critical factors were commonly and collectively approved for further investigation. Descriptions of the 11 critical factors are provided in the following subsections.

Technological Factors(CF1)

The success of AM depends on technological factors, including robust IT infrastructure, reliable performance and safety, and achieving repeatability through precise control over calibration, conditions and material quality (Choudhary et al., 2021; Orji & Ojadi, 2023; Patil et al., 2023; Singh et al., 2024; Verma et al., 2023).

Environmental Factors (CF2)

Environmental factors for AM success include minimizing waste, reducing energy use, utilizing sustainable materials, enhancing eco-friendliness and compliance with environmental regulations (Agrawal, 2022; Javaid et al., 2021; Ronchini et al., 2023).

IP protection Issues (CF3)

IP protection issues in AM involve securing trademarks, patents and copyrights to prevent unauthorized use and maintain competitive advantage, ensuring the protection of intellectual property (IP; Dwivedi et al., 2017; Shukla et al., 2018).

Strategic Factors (CF4)

Strategic factors for AM success include aligning technology with business goals, investing in innovation and adapting to market opportunities and industry trends for long-term growth (Patil et al., 2023; Ronchini et al., 2023; Shukla et al., 2018; Singh et al., 2024).

Operational Factors (CF5)

Operational factors for AM success include effective machine maintenance, efficient workflow management and optimized process parameters to ensure smooth production and high-quality output (Patil et al., 2023; Ronchini et al., 2023; Singh et al., 2024; Verma et al., 2023).

Health and Safety (CF6)

Health and safety are crucial for AM success, involving safe equipment operation, minimizing exposure to hazards and adhering to regulations to protect workers (Haleem & Javaid, 2022; Singh et al., 2024).

Human–Machine Collaboration (CF7)

Human–machine collaboration in AM relies on skilled operators with expertise and competency to manage processes, enhancing productivity, quality and innovation in manufacturing (Choudhary et al., 2021; Dwivedi et al., 2017; Ronchini et al., 2023).

Supply Chain Factors (CF8)

Supply chain factors for AM success include efficient integration, reliable material sourcing and effective logistics, which reduce costs, improve delivery times and support scalable production (Orji & Ojadi, 2023; Ronchini et al., 2023; Verboeket & Krikke, 2019; Verma et al., 2023).

Raw Materials Availability (CF9)

Raw material availability is crucial for AM success, impacting part quality, performance, costs and scalability by ensuring a steady supply of diverse, high-quality materials (Choudhary et al., 2021; Naghshineh & Carvalho, 2022).

Product Quality (CF10)

Product quality in AM involves precision, durability and reliability, ensuring parts meet industry standards and customer expectations, which drives adoption and market acceptance (Naghshineh & Carvalho, 2022; Singh et al., 2024).

Economic Factors (CF11)

Economic factors for AM success include cost-efficiency, investment availability, supply chain integration, customization benefits and regulatory/tax incentives (Durach et al., 2017; Haleem & Javaid, 2022).

Although many critical factors have been extensively identified in the literature, few efforts combine them into a cause-and-effect framework that captures the actual complexity managers will face in implementing AM. Current methodologies tend to be unoriented towards management and thus do not facilitate actionable prioritization or investment choices. This research addresses that gap by synthesizing not just the main factors but also simulating their interdependencies, thus providing a greater practical insight into which factors need proactive managerial attention. The prioritization matrix designed here closes the gap between theoretical impediments and practical decision-making, allowing for better alignment of strategic objectives and AM readiness within the industrial environment.

DEMATEL Integrated with Grey System Theory

The application of grey theory methodologies has been extensively utilized in literature to represent intricate decision-making difficulties. The Grey–DEMATEL technique has been employed across multiple sectors to identify the interdependencies among distinct elements. Rajesh and Ravi (2017) made an effort to identify the causal relationships among the risk factors in the electronic supply chain. A further study employed a Grey–DEMATEL methodology to ascertain the causal connection between the obstacles, hindering the adoption of sustainable supply chain management in the leather sector (Moktadir et al., 2018). G. Singh et al. (2023) examine and assess the obstacles to growth in the fresh produce supply chain. Further, C. Singh et al. (2023) analyse the causal links between the factors that influence the acceptance of conversational digital assistants. This research study aims to investigate the key aspects contributing to the success of AM using the Grey–DEMATEL process.

Methodology

This study aims to identify and prioritize the critical factors influencing the success of adopting AM in the heavy industry. Additionally, it aims to explore the causal relationships among these factors. Given the complexity of the subject, input from industry experts and the application of causal modelling techniques are essential to achieve the research objectives. In recent years, various MCDM methods such as AHP, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and analytical network process (ANP) have been widely employed to prioritize the factors they often ignore the interdependencies among them. However, the DEMATEL method focuses not only on prioritizing but also on identifying their causal relationship. The DEMATEL approach can manage and organize intricate causal interactions among the crucial components by combining graphs and matrices (Hsu et al., 2013; Sahu et al., 2025). The DEMATEL method is used for structural modelling to construct interrelationships among variables using a causal diagram, even with a small sample size. It can also measure the strength of influence among variables (Devi et al., 2025; Kumar et al., 2024). Similarly, ISM identifies interdependence among the factors but does not consider the strength of the association (Kumar et al., 2022). This study used the DEMATEL method that integrates DEMATEL with Grey theory to address uncertainty issues and limited data concerns. Grey–DEMATEL further enables the modelling of linguistic and data uncertainty through grey numbers (Bai & Sarkis, 2013; Kabir et al., 2025). Grey system theory can address the ambiguity that arises from imprecise human assessments (Li et al., 2007). Integrating grey system theory with any MCDM methodologies can effectively enhance the accuracy of judgement (Tseng, 2009). The modified CFCS (converting fuzzy values into crisp scores) technique can transform grey values into sharp ones (G. Singh et al., 2023). Compared to the fuzzy logic-based approach, the Grey–DEMATEL approach was found to be a more suitable approach for exploratory studies. This study has utilized the DEMATEL technique based on grey theory to examine the interconnection between the critical factors contributing to AM’s success. The Grey–DEMATEL procedure consists of the following fundamental steps.

Step 1: Forming of Initial Relation Matrices

Assume there are l participants and m critical factors. Using the linguistic assessment codes provided in Table 4, each expert k has assessed the strength of the critical factor i concerning the j critical factors.

Table 4.

Linguistic Terms and Corresponding Grey Values.

Grey Value	Linguistic Term
[0.0, 0.1]	No influence (NI)
[0.1, 0.3]	Very low influence (VLI)
[0.2, 0.5]	Low influence (LI)
[0.4, 0.7]	Medium influence (MI)
[0.6, 0.9]	High influence (HI)
[0.9, 1.0]	Very high influence (VHI)

Step 2: Compute the Grey-Relation Matrix

Transform the integer values into the corresponding greyscale values, using specified upper and lower limits for the values (Deng, 1989), that is,

\otimes z_{i j}^{k} = (\underline{\otimes} z_{i j}^{k}, \bar{\otimes} z_{i j}^{k}),

(1)

Where 1 ≤ k ≤ l; 1 ≤ i ≤ m; 1 ≤ j ≤ m, and $\underline{\otimes} z_{i j}^{k}$ and $\bar{\otimes} z_{i j}^{k}$ represent the lower and higher limits of the grey values for the k respondent. The initial relation matrices transform from grey-relation matrices by assigning grey values, that is,

[\otimes z_{i j}^{1}], [\otimes z_{i j}^{2}], [\otimes z_{i j}^{3}], \dots \dots, [\otimes z_{i j}^{l}] .

Step 3: Calculate the Average Grey-Relation Matrix

Evaluate the average grey-relation matrix $\otimes {\tilde{z}}_{i j}$ from l grey-relation matrices $\otimes z_{i j}^{k}$ as

\otimes {\tilde{z}}_{i j} = (\frac{\sum_{k} \underline{\otimes} z_{i j}^{k}}{l}, \frac{\sum_{k} \bar{\otimes} z_{i j}^{k}}{l}) .

(2)

Step 4: Obtain the Crisp-Relation Matrix from the Average Grey-Relation Matrix

The CFCS-modified approach is used to derive precise values from the grey value. The process of acquiring these values involves three distinct steps: calculate the normalized grey value as

\underline{\otimes} {\dot{z}}_{i j} = (\underline{\otimes} {\tilde{z}}_{i j} - {}_{j}^{min}{\underline{\otimes}} {\tilde{z}}_{i j}) / △_{m i n}^{max},

(3)

where $\underline{\otimes} {\dot{z}}_{i j}$ shows the normalized lower limit of the grey number $\otimes {\tilde{z}}_{i j}$ :

\bar{\otimes} {\dot{z}}_{i j} = (\bar{\otimes} {\tilde{z}}_{i j} - {}_{j}^{min}{\bar{\otimes}} {\tilde{z}}_{i j}) / △_{min}^{max},

(4)

where $\bar{\otimes} {\dot{z}}_{i j}$ represents the normalized higher value of the grey number $\otimes {\tilde{z}}_{i j}$ , and

△_{min}^{max} = {}_{j}^{max}{\bar{\otimes}} {\tilde{z}}_{i j} - {}_{j}^{min}{\underline{\otimes}} {\tilde{z}}_{i j} .

(5)

(i) Obtain the total normalized crisp value as follows:

y_{i j} = (\frac{(\underline{\otimes} {\dot{z}}_{i j} (1 - \underline{\otimes} {\dot{z}}_{i j})) + (\bar{\otimes} {\dot{z}}_{i j} \times \bar{\otimes} {\dot{z}}_{i j})}{(1 - \underline{\otimes} {\dot{z}}_{i j} + \bar{\otimes} {\dot{z}}_{i j})}) .

(6)

Evaluates the final crisp value as

y_{i j}^{*} = (min \underline{\otimes} {\tilde{z}}_{i j} + (y_{i j} \times △_{min}^{max}))

(7)

and

Y = [y_{i j}^{*}] .

(8)

Step 5: Calculating the Normalized Direct Crisp-Relation Matrix (S)

The normalized direct crisp-relation matrix (refer to Table A1) is obtained by multiplying the crisp-relation matrix Y with the normalization factor L:

L = \frac{1}{{}_{1 \leq i \leq m}^{max}{\sum_{j = i}^{m} y_{i j}^{*}}},

(9)

and

S = Y \times L .

(10)

Step 6: Obtain the Total Relation Matrix

The total relation matrix (T) can be calculated using Equation (11) as follows:

T = S \times {(I - S)}^{- 1},

(11)

where I represent the identity matrix.

Step 7: Calculate the Cause and Effect Parameters

Compute the sum of all elements in row (D_i) and column (R_j). The sum is computed for each row (i) and each column (j) from the total relation matrix (T) as follows:

D_{i} = [\sum_{j = 1}^{m} T_{i j}] \forall i,

(12)

R_{j} = [\sum_{j = 1}^{m} T_{i j}] \forall i .

(13)

The total (D_i + R_j) shows how much a barrier i affects the others. It shows the relative importance of the critical factor i to the other barriers in the system, while (D_i – R_j) shows its net effect. The critical factor i causes the other barriers if (D_i – R_j) is positive. When (D_i – R_j) is a negative, the critical factor i is the net effect of the other critical factors.

Step 8: Set Up Threshold Value and Plotting of the Digraph

The total relation matrix (T) elucidates how one critical factor can influence the other critical factors. In order to mitigate the relatively insignificant impacts, it is imperative for researchers, experts or observers to determine a threshold value. Establishing the threshold value indicates the occurrence of effects that surpass the threshold value on the digraph. Usually, the threshold value is calculated by averaging the total relation matrix (T). The digraph is subsequently graphed using the given values: (D_i + R_j), (D_i – R_j) Ɐ i=j.

Case Implementation and Results

Initially, 16 critical factors for the success of AM have been found after the extant literature review. The critical factors are then validated through expert opinion. Subsequently, the 11 finalized critical factors for the success of AM in the context of the heavy industry, based on the experts’ opinions, were investigated for cause-and-effect interrelationships among them. Eleven finalized critical factors and their code are given in the Factors Alignment with the Sustainability Theory section. The first survey was conducted to refine and finalize AM’s success factors through expert opinion. The Grey–DEMATEL technique was used to examine completed essential criteria for AM success for cause-and-effect relationships in the second survey.

Twelve heavy industries and academic researchers were contacted for expert opinions through a survey questionnaire. The expert group consisted of managers and practitioners to guarantee a thorough and unbiased perspective. Twelve experts with more than five years of relevant experience were selected for the analysis. All the chosen experts underwent preliminary screening, which would prove their knowledge about and familiarity with aspects of AM. In addition to this, we also provided all experts with the background information and necessary context about the study. Demographic details of the 12 experts are presented in Table 5. The steps involved in using the Grey–DEMATEL technique are as follows.

Table 5.

Demographic Details of the Experts.

S. No.	Expert	Total Experience (in Years)	Educational Qualification	Domain of Expertise	Experience in Domain of Expertise (in Years)	Type of Organization with Which Experts Are Associated
1.	EX1	24	MTech and MBA	Operations and supply chain	22	Heavy electrical and mechanical equipment manufacturing
2.	EX2	20	MTech and MBA	Production and quality	16	Optical wire and other electronics hardware
3.	EX3	16	BTech and MBA	Product design and development	14	Heavy electrical and mechanical equipment manufacturing
4.	EX4	15	MTech	Metallurgist	11	Steel and special alloy manufacturer
5.	EX5	17	MBA	Marketing and supply chain	9	E-vehicle manufacturer
6.	EX6	17	BTech	Designing of drives for heavy vehicles	13	Heavy load-carrying vehicles
7.	EX7	17	BTech	Testing and quality control	12	Certification and consultancy provider of NDT
8.	EX8	20	MTech	Metallurgist	15	Material testing and quality control organisation
9.	EX9	18	MTech	Manufacturing and planning	16	Defence equipment manufacturer
10.	EX10	12	PhD	Renewal energy	10	Research institutions and academic institutions
11.	EX11	17	BTech	Designing of electrical equipment	14	Heavy electrical and mechanical equipment manufacturing
12.	EX12	15	MTech	Designing of electrical equipment	11	Heavy electrical and mechanical equipment manufacturing

Step 1: Expert opinion on the influence of one critical factor over the other on the linguistic scale from NI to VHI have to be transformed into the equivalent grey scale for the linguistic code and grey value (see Table 4). Initial 11 × 11 relation matrices were computed for 11 critical factors.

Step 2: Equation (1) was used to obtain initial grey-relation matrices from the expert’s responses after transforming the linguistic codes into grey values.

Step 3: Experts are weighted equally to ensure judgement homogeneity. The average grey-relation matrix $\otimes {\tilde{z}}_{i j}$ is calculated using Equation (2) and Table 6 matrix.

Step 4: The modified CFCS technique was used to acquire the crisp-relation matrix Y from the average grey-relation matrix in three steps. Table 7 shows the crisp-relation matrix obtained from Equations (3)–(8).

Table 6.

Average Grey Relation Matrix of the Critical Factors for the Success of AM.

	CF1	CF2	CF3	CF4	CF5	CF6	CF7	CF8	CF9	CF10	CF11	Min /Max
CF1	0.0000	0.3500	0.4000	0.4667	0.4833	0.4167	0.5833	0.4333	0.5167	0.5333	0.6500	0.0000
CF1	0.1000	0.6500	0.6917	0.7500	0.7667	0.7083	0.8417	0.7250	0.7917	0.8083	0.8833	0.8833
CF2	0.3000	0.0000	0.3500	0.3667	0.3667	0.5667	0.4000	0.4667	0.5333	0.4333	0.4667	0.0000
CF2	0.6000	0.1000	0.6500	0.6667	0.6667	0.8417	0.6917	0.7583	0.7917	0.7250	0.7500	0.8417
CF3	0.4667	0.4167	0.0000	0.4500	0.3833	0.4500	0.4333	0.4167	0.3833	0.4167	0.4167	0.0000
CF3	0.7500	0.7000	0.1000	0.7500	0.6750	0.7333	0.7250	0.7083	0.6833	0.7083	0.7000	0.7500
CF4	0.4333	0.3833	0.3833	0.0000	0.4667	0.4500	0.4667	0.4000	0.4667	0.5000	0.5500	0.0000
CF4	0.7250	0.6833	0.6750	0.1000	0.7583	0.7417	0.7500	0.7000	0.7583	0.7917	0.8167	0.8167
CF5	0.5500	0.4833	0.3833	0.3333	0.0000	0.5000	0.4667	0.3833	0.4000	0.5167	0.5000	0.0000
CF5	0.8167	0.7583	0.6833	0.6250	0.1000	0.7750	0.7583	0.6750	0.6917	0.7917	0.7667	0.8167
CF6	0.3667	0.4500	0.3667	0.3333	0.5000	0.0000	0.5333	0.4167	0.4000	0.4500	0.4167	0.0000
CF6	0.6583	0.7333	0.6583	0.6250	0.7750	0.1000	0.8083	0.7083	0.6917	0.7333	0.7083	0.8083
CF7	0.5500	0.3833	0.4000	0.5167	0.4833	0.4000	0.0000	0.4000	0.3167	0.5333	0.5333	0.0000
CF7	0.8000	0.6750	0.6917	0.8000	0.7667	0.6917	0.1000	0.7000	0.6083	0.7917	0.8083	0.8083
CF8	0.3833	0.4333	0.4333	0.5333	0.5000	0.4167	0.5000	0.0000	0.4833	0.4000	0.4667	0.0000
CF8	0.6750	0.7250	0.7333	0.8083	0.7833	0.7167	0.7750	0.1000	0.7667	0.6917	0.7417	0.8083
CF9	0.4667	0.4667	0.4000	0.5500	0.5333	0.3667	0.3500	0.5333	0.0000	0.4500	0.5333	0.0000
CF9	0.7500	0.7417	0.6917	0.8167	0.8167	0.6583	0.6500	0.8000	0.1000	0.7333	0.7917	0.8167
CF10	0.5333	0.3833	0.3667	0.4833	0.4833	0.3833	0.5000	0.4667	0.5167	0.0000	0.5000	0.0000
CF10	0.8083	0.6667	0.6583	0.7750	0.7667	0.6750	0.7833	0.7583	0.7833	0.1000	0.7667	0.8083
CF11	0.5000	0.4500	0.4500	0.4833	0.5500	0.3667	0.5667	0.5167	0.5167	0.5833	0.0000	0.0000
CF11	0.7667	0.7333	0.7417	0.7667	0.8250	0.6667	0.8250	0.7917	0.8000	0.8417	0.1000	0.8417

Table 7.

Crisp-Relation Matrix of the Critical Factors for the Success of Additive Manufacturing.

	CF1	CF2	CF3	CF4	CF5	CF6	CF7	CF8	CF9	CF10	CF11	Sum
CF1	0.000	0.452	0.505	0.578	0.598	0.525	0.696	0.545	0.631	0.650	0.753	5.934
CF2	0.396	0.000	0.456	0.475	0.475	0.694	0.510	0.589	0.643	0.549	0.583	5.370
CF3	0.594	0.535	0.000	0.587	0.500	0.574	0.561	0.540	0.506	0.540	0.535	5.473
CF4	0.552	0.498	0.493	0.000	0.592	0.572	0.586	0.518	0.592	0.631	0.671	5.705
CF5	0.671	0.600	0.498	0.433	0.000	0.619	0.592	0.493	0.512	0.639	0.613	5.670
CF6	0.474	0.567	0.474	0.434	0.620	0.000	0.659	0.533	0.513	0.567	0.533	5.375
CF7	0.660	0.494	0.513	0.646	0.607	0.513	0.000	0.519	0.414	0.647	0.659	5.672
CF8	0.494	0.553	0.559	0.659	0.626	0.539	0.620	0.000	0.607	0.513	0.581	5.751
CF9	0.586	0.580	0.512	0.671	0.665	0.473	0.458	0.652	0.000	0.566	0.646	5.809
CF10	0.659	0.488	0.474	0.613	0.607	0.494	0.626	0.593	0.633	0.000	0.614	5.801
CF11	0.610	0.563	0.569	0.603	0.675	0.475	0.681	0.636	0.642	0.701	0.000	6.155

Step 5: Equations (9) and (10) are used to generate a normalized crisp-relation matrix (S).

Step 6: The total relation matrix (T) was calculated using Equation (11). It is shown in Table 8. The total relation matrix (T) highlights the threshold value in Table A2.

Table 8.

Total Relation Matrix of the Critical Factors.

	CF1	CF2	CF3	CF4	CF5	CF6	CF7	CF8	CF9	CF10	CF11	D
CF1	1.134	1.129	1.088	1.217	1.270	1.163	1.286	1.196	1.221	1.284	1.329	13.317
CF2	1.090	0.964	0.988	1.099	1.145	1.088	1.151	1.100	1.120	1.160	1.194	12.098
CF3	1.136	1.061	0.936	1.133	1.167	1.089	1.178	1.111	1.120	1.179	1.208	12.318
CF4	1.174	1.097	1.048	1.089	1.225	1.130	1.227	1.151	1.174	1.237	1.273	12.826
CF5	1.182	1.103	1.042	1.147	1.129	1.129	1.220	1.140	1.156	1.230	1.257	12.735
CF6	1.102	1.049	0.991	1.094	1.165	0.987	1.173	1.093	1.102	1.165	1.188	12.111
CF7	1.183	1.091	1.046	1.178	1.221	1.117	1.135	1.146	1.145	1.234	1.266	12.763
CF8	1.171	1.110	1.063	1.191	1.235	1.131	1.237	1.079	1.182	1.227	1.267	12.893
CF9	1.196	1.125	1.067	1.205	1.253	1.134	1.228	1.187	1.105	1.247	1.289	13.037
CF10	1.206	1.112	1.062	1.197	1.245	1.136	1.251	1.178	1.197	1.163	1.285	13.032
CF11	1.260	1.178	1.128	1.256	1.317	1.192	1.321	1.244	1.259	1.328	1.258	13.741
R	12.835	12.019	11.460	12.807	13.373	12.297	13.407	12.625	12.780	13.455	13.813

Table 9.

Cause/Effect Parameter for the Critical Factors.

Critical Factors	D	R	D + R	D – R
CF1	13.317	12.835	26.153	0.482	Cause
CF2	12.098	12.019	24.117	0.079	Cause
CF3	12.318	11.460	23.778	0.859	Cause
CF4	12.826	12.807	25.633	0.019	Cause
CF5	12.735	13.373	26.108	–0.638	Effect
CF6	12.111	12.297	24.408	–0.186	Effect
CF7	12.763	13.407	26.170	–0.645	Effect
CF8	12.893	12.625	25.518	0.268	Cause
CF9	13.037	12.780	25.818	0.257	Cause
CF10	13.032	13.455	26.487	–0.423	Effect
CF11	13.741	13.813	27.555	–0.072	Effect

Step 7: This step evaluated (D_i + R_j), (D_i – R_j) using Equations (12) and (13). Table 9 lists (D_i + R_j). Six critical factors were found as causal variables (D_i – R_j > 0) and five as their effects (D_i – R_j < 0).

Step 8: Plot (D_i + R_j), (D_i – R_j) dataset digraph. The digraph was plotted by taking (D_i – R_j) on the y-axis and (D_i + R_j) on the x-axis. Showing all digraph associations is tough. To draw the digraph, the threshold value (θ) was determined. Only the digraph relationship with a value greater than the criterion was taken.

Sensitivity Analysis for the Validation of Results

Sensitivity analysis can be performed by adjusting the weights assigned to various factors or assigning different weights to different experts (Kumar & Singh, 2024). This analysis involves the allocation of different weights to various experts and the subsequent examination of the resulting conclusions. The composition of the group tasked with verifying the results varied from that of the previous study. The group consists of academicians and working professionals in the industry with more than 10 years of experience in AM. The experts involved in the validation process agreed with the results and said that the study is beneficial in establishing the causal links among the critical factors for the success of AM. However, the results may be affected by biases that arise from differences in the knowledge and skill levels of the professionals. A sensitivity analysis is performed to evaluate the reliability of the results. The base case estimate assigned equal weights to all the experts. The study generated two distinct cases by providing different weights to the various experts, as depicted in Table 10.

Table 10.

The Assigned Weight to Experts for the Sensitivity Analysis.

	EX1	EX2	EX3	EX4	EX5	EX6	EX7	EX8	EX9	EX10	EX11	EX12
Case 1	0.000	0.000	0.000	0.030	0.030	0.030	0.050	0.050	0.050	0.100	0.100	0.100
Case 1	0.100	0.100	0.100	0.036	0.036	0.036	0.150	0.150	0.150	0.200	0.200	0.200
Case 2	0.030	0.030	0.030	0.000	0.000	0.000	0.100	0.100	0.100	0.050	0.050	0.050
Case 2	0.036	0.036	0.036	0.100	0.100	0.100	0.200	0.200	0.200	0.150	0.150	0.150

Table 11 presents the prominence and net effect of critical factors on the success of AM as determined through sensitivity analysis.

Table 11.

Cause/Effect Parameters for Different Cases of Sensitivity Analysis.

	D + R	D – R	Base	D – R	Case 1	D – R	Case 2
CF1	26.153	0.482	Cause	0.370	Cause	0.451	Cause
CF2	24.117	0.079	Cause	0.128	Cause	0.227	Cause
CF3	23.778	0.859	Cause	0.494	Cause	0.658	Cause
CF4	25.633	0.019	Cause	0.124	Cause	0.361	Cause
CF5	26.108	–0.638	Effect	–0.412	Effect	–0.813	Effect
CF6	24.408	–0.186	Effect	–0.210	Effect	–0.338	Effect
CF7	26.170	–0.645	Effect	–0.541	Effect	–0.960	Effect
CF8	25.518	0.268	Cause	0.154	Cause	0.501	Cause
CF9	25.818	0.257	Cause	0.458	Cause	0.120	Cause
CF10	26.487	–0.423	Effect	–0.074	Effect	–0.330	Effect
CF11	27.555	–0.072	Effect	–0.065	Effect	–0.153	Effect

Figure 1 highlights that the cause-and-effect critical factors consistently maintain a similar ranking pattern in all cases, with minor deviations. Therefore, there is no significant bias in the evaluations provided by the experts. Therefore, the results can be considered to be reliable.

Figure 1.

Sensitivity Analysis.

Findings and Discussion

This research study utilized the integration of grey system theory and DEMATEL methodologies to find the cause-and-effect relationship among the critical factors that contribute to the success of AM in heavy industry contexts. A causal diagram was created, and the outcomes are then discussed. The critical factors are prioritized based on their relative significance, determined by the (D_i + R_j) values, as outlined as follows: CF11 > CF10 > CF7 > CF1 > CF5 > CF9 > CF4 > CF8 > CF9 > CF3 > CF2 (refer to Table 7). The top five most significant critical factors for the success of AM based on (D_i + R_j) scores are Economic factors (CF11), Product quality (CF10), Human–machine collaboration (CF7), Technological factors (CF1) and Operational factors (CF5). These critical factors need more attention from the managers and stakeholders.

The influencing (driver) critical factors for the success of AM are ranked based on their (D_i – R_j) values as CF3 > CF1 > CF8 > CF9 > CF2 > CF4 > CF7 > CF11. IP protection (CF3) was found to be the crucial driving factor for the success of AM, as it influences many effect factors, followed by Technological factors (CF1), Supply chain factors (CF8) and Raw materials availability (CF9). It is evident from Figure 2 that IP protection (CF3) initiates the effects of Health and Safety (CF6), Product quality (CF10), Operational factors (CF5) and Human–machine collaboration (CF7). The results clearly show a need to tackle IP protection and technological factors to enhance productivity and efficiency. These findings align partially with prior studies such as Verma et al. (2023) and Dwivedi et al. (2017), who also emphasized the significance of technological readiness and supply chain integration in AM adoption.

Figure 2.

Digraph Showing the Causal Relations Among Critical Factors.

The resulting (driven) factors whose effects are started by other factors can be prioritized as CF5 > CF7 > CF10 > CF9 > CF6 > CF11. The result shows that operational factors (CF5) are the factors that affect various causal factors, followed by human-machine collaboration (CF7) and product quality (CF10). These critical factors in the effect group need greater attention from managers as causal critical factors can significantly influence them. The Economic factor (CF11) results in Operational factors (CF5), Human-machine collaboration (CF7), and Product Quality (CF10). Many critical factors can be overcome by economic factors (CF11) and product quality (CF10) because these are critical factors that result in the success of AM. Similarly, economic factors (CF11) were found to be more of an outcome than a causal input, in contrast to Moktadir et al. (2018), who considered cost-efficiency as a primary driver. These distinctions underscore the value of causal modelling in uncovering hidden systemic leverage points. This study contributes to the existing literature on the successful adoption of AM in the heavy industry by exploring the complex interrelationship of the critical factors.

Upon conducting a more in-depth study of the results, it becomes evident that the critical factors positioned above the x-axis (D_i + R_j) have the most influence on the success of AM, primarily falling within the causal group. The critical factors below the x-axis (D_i + R_j) belong to the effect group. For a more comprehensive analysis of their impact, the network of essential elements can be categorized into four zones, as depicted in Figure 2. Zone 1 (Z1) represents the critical factors that have the most significant influence on the success of AM. The majority of the critical factors in this zone are part of the causal group. The critical factors in Zone 1 (Z1) have a significant correlation with the critical factors in Zone 2 (Z2), as shown from the digraph plot presented in Figure 2. The stakeholders, managers and policymakers should address critical factors such as Technological factors (CF1), Raw materials availability (CF9), Strategic factors (CF4) and Economic factors (CF11) in Zone 1 of the AM. This is necessary to enhance the productivity and efficiency of the supply chain and minimize waste. Chan et al. (2018) also reveal that technological aspect is the most significant factor in the widespread adoption of 3D printing. Zone 2 critical factors have a significant role in the success of AM; yet they are categorized as part of the effect group. Operational factors (CF5), Human–machine collaboration (CF7) and Product quality (CF10) are identified as the primary critical factors in the success of AM in this zone, as determined by other causal critical factors. Patil et al. (2023) and Ronchini et al. (2023) highlight operational factors play a critical role in the successful implementation of the technology for efficiently integrating an advanced manufacturing technology such as AM into an organization. Zone 3 represents the critical factors that have minimal relationships or appear to be autonomous, and their impact on the success of AM is quite low. This zone encompasses the factor of Health and safety (CF6). This research study includes several critical factors, namely IP protection issues (CF3), Supply chain factors (CF8) and Environmental factors (CF2), which are in Zone 4. However, unlike MCDM rankings in the previous research, our Grey–DEMATEL analysis reveals that IP protection (CF3), though often underestimated, is a core driver influencing multiple dependent variables. Dwivedi et al. (2017) highlight that IPR issues support the workforce, market support and readiness to analyse unknown environmental, health and safety issues in AM implementation.

This reconceptualization challenges the conventional view of IP as merely a regulatory concern and elevates it as a strategic enabler, particularly for collaborative AM ecosystems.

Conclusion

This study was inspired by the need to understand the success of the AM in the context of the heavy industry, which is an atmosphere of scope, security sensitivity and capital intensity. Directed by the following research objectives: (a) to identify important success factors (RQ1), (b) give them priority (RQ2) and (c) mapped their reasons (RQ3), this research used an integrated Grey–DEMATEL approach, which attracts expert decisions from the industry and academia. This insight helps doctors prioritize functions that can provide systemic improvement in quality, operational efficiency and economic viability. This contribution directly supports management compulsory for transition to a durable and digitally capable production ecosystem. This combines the grey theory (Deng, 1989) and the DEMATEL technique to examine the critical factors contributing to AM’s success in the heavy industry. Grey–DEMATEL can be employed to investigate the causal relationship among the factors through digraphs. This information can aid policymakers, researchers and managers in understanding the causal connection between critical factors contributing to AM’s success. In addition, a sensitivity analysis has been performed to confirm the reliability of the findings. This study examined and assessed the critical factors of AM’s performance. The findings of this research can assist decision-makers in formulating strategic decisions to handle these essential issues effectively. The findings indicated that six elements were identified as crucial causal factors, whereas five components were identified as important impact factors. The study’s findings indicate that Technological factors (CF1) are the primary cause in the group, with Raw materials availability (CF9) following closely. Additionally, the study reveals that technological facilities contribute to increased supply chain resilience and efficiency. Within the AM, the causal group identified IP protection (CF3) as the lowest ranked factor, indicating that it is a significant factor. However, it remains a critical foundational factor, indicating that it exerts a strong influence on other downstream variables. This suggests that although it may not be prominent in the overall system, its strategic role in enabling secure, collaborative and innovation-driven AM environments cannot be overlooked. Conversely, the Economic factor (CF11) ranked first in the effect group. Several causative critical factors may influence it. By overcoming technological factors, it is feasible to enhance the deployment of AM, reducing waste and augmenting efficiency and productivity. Two more significant factors to the success of AM are Product quality (CF10) and Human–machine collaboration (CF7). The results are advantageous for the heavy industry as they aid in reducing losses and improving the efficiency of AM. Furthermore, a sensitivity analysis has been performed to evaluate the dependability of the results.

Theoretical Implications

This study identifies and analyses the critical factors that contribute to the success of AM in the heavy industry, demonstrating that these factors are interrelated and mutually influence one another. Additionally, this study contributes to the existing literature on the successful adoption of AM in the heavy industry. For researchers, this study offers new insights into the intensity factors that should be addressed to enable scalable AM adoption.

Practical Implications

Managers should prioritize the identification and analysis of causal factors that directly impact the effectiveness of AM to maximize the effectiveness of their strategies. This analysis enables managers to properly prioritize by emphasizing the most critical aspects and those of lesser importance. Furthermore, the study proposes the incorporation of social, economic and environmental sustainability into AM activities, which can potentially improve productivity and efficiency. For industrial leaders, the results give priority for investment and risk reduction. The supply chain actors can benefit from the understanding of causal factors that directly impact the effectiveness of AM that in order to increase traceability, collaboration and production.

Social Implications

Furthermore, it provides policy implications that involve enhancing supply chain management, minimizing waste and addressing risks and concerns related to IP. This strategy could support policymakers and industrial managers in improving AM techniques, enhancing production capacities and utilizing digital infrastructure to address technological factors.

Limitations and Future Research Scope

For further research, researchers may integrate supplementary critical factors into the study to evaluate the efficacy of AM. In order to improve the accuracy of the results, it is recommended that additional expert perspectives be sought for the research. Other approaches, such as TISM or fuzzy/Grey ISM, can be used to analyse further and compare data. Future research should consider using statistical analysis to investigate the proposed Grey–DEMATEL model further. While the study identifies 11 critical factors, further studies can consider additional relevant dimensions, such as regulatory policies, workforce readiness and environmental impact, which could further enrich the analysis. The findings, being based on expert input and context-specific factors, may have limited generalizability to other industries or regions.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix A: Tables of Intermediaries Results

Table A2.

Total Relation Matrix (T) Highlighting the Threshold Value.

Factors	CF1	CF2	CF3	CF4	CF5	CF6	CF7	CF8	CF9	CF10	CF11
CF1	1.134	1.129	1.088	1.217	1.270	1.163	1.286	1.196	1.221	1.284	1.329
CF2	1.090	0.964	0.988	1.099	1.145	1.088	1.151	1.100	1.120	1.160	1.194
CF3	1.136	1.061	0.936	1.133	1.167	1.089	1.178	1.111	1.120	1.179	1.208
CF4	1.174	1.097	1.048	1.089	1.225	1.130	1.227	1.151	1.174	1.237	1.273
CF5	1.182	1.103	1.042	1.147	1.129	1.129	1.220	1.140	1.156	1.230	1.257
CF6	1.102	1.049	0.991	1.094	1.165	0.987	1.173	1.093	1.102	1.165	1.188
CF7	1.183	1.091	1.046	1.178	1.221	1.117	1.135	1.146	1.145	1.234	1.266
CF8	1.171	1.110	1.063	1.191	1.235	1.131	1.237	1.079	1.182	1.227	1.267
CF9	1.196	1.125	1.067	1.205	1.253	1.134	1.228	1.187	1.105	1.247	1.289
CF10	1.206	1.112	1.062	1.197	1.245	1.136	1.251	1.178	1.197	1.163	1.285
CF11	1.260	1.178	1.128	1.256	1.317	1.192	1.321	1.244	1.259	1.328	1.258

Note: Bold value represents the values that are higher than the threshold value.

References

Agrawal

(2022). Sustainable design guidelines for additive manufacturing applications. Rapid Prototyping Journal. https://doi.org/10.1108/RPJ-09-2021-0251

Agrawal

, & Vinodh

(2019). State of art review on sustainable additive manufacturing. Rapid Prototyping Journal. https://doi.org/10.1108/RPJ-04-2018-0085

ASTM International. (2012). Standard terminology for additive manufacturing technologies [ASTM F2792-12].

Awa

H. O.

, Ojiabo

, & Orokor

(2017). Integrated technology-organization-environment (T-O-E) taxonomies for technology adoption. Journal of Enterprise Information Management, 30(6), 893–921. https://doi.org/10.1108/JEIM-03-2016-0079

Bai

, & Sarkis

(2013). A grey-based DEMATEL model for evaluating business process management critical success factors. International Journal of Production Economics, 146(1), 281–292. https://doi.org/10.1016/j.ijpe.2013.07.011

Beltagui

, Kunz

, & Gold

(2020). The role of 3D printing and open design on adoption of socially sustainable supply chain innovation. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.07.035

Berman

(2012). 3-D printing: The new industrial revolution. Business Horizons. https://doi.org/10.1016/j.bushor.2011.11.003

Bours

, Adzima

, Gladwin

, Cabral

, & Mau

(2017). Addressing hazardous implications of additive manufacturing: Complementing life cycle assessment with a framework for evaluating direct human health and environmental impacts. Journal of Industrial Ecology. https://doi.org/10.1111/jiec.12587

Burkhart

, & Aurich

J. C.

(2015). Framework to predict the environmental impact of additive manufacturing in the life cycle of a commercial vehicle. Procedia CIRP. https://doi.org/10.1016/j.procir.2015.02.194

10.

Cappucci

G. M.

, Pini

, Neri

, Marassi

, Bassoli

, & Ferrari

A. M.

(2020). Environmental sustainability of orthopedic devices produced with powder bed fusion. Journal of Industrial Ecology. https://doi.org/10.1111/jiec.12968

11.

Chahal

, Gupta

, Bhan

, & Cheng

T. C. E.

(2020). Operations management research grounded in the resource-based view: A meta-analysis. International Journal of Production Economics, 230, 107805. https://doi.org/10.1016/j.ijpe.2020.107805

12.

Chan

H. K.

, Griffin

, Lim

J. J.

, Zeng

, & Chiu

A. S. F.

(2018). The impact of 3D Printing Technology on the supply chain: Manufacturing and legal perspectives. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2018.09.009

13.

Chang

R.-D.

, Zuo

, Zhao

Z.-Y.

, Zillante

, Gan

X.-L.

, & Soebarto

(2017). Evolving theories of sustainability and firms: History, future directions and implications for renewable energy research. Renewable and Sustainable Energy Reviews, 72, 48–56. https://doi.org/https://doi.org/10.1016/j.rser.2017.01.029

14.

Chaudhuri

, Rogers

, Soberg

, & Pawar

K. S.

(2019). The role of service providers in 3D printing adoption. Industrial Management and Data Systems. https://doi.org/10.1108/IMDS-08-2018-0339

15.

Chen

, Heyer

, Ibbotson

, Salonitis

, Steingrímsson

J. G.

, & Thiede

(2015). Direct digital manufacturing: Definition, evolution, and sustainability implications. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2015.05.009

16.

Chittipaka

D. V.

, Kumar

, Sivarajah

, Bowden

, & Baral

(2022). Blockchain technology for supply chains operating in emerging markets: An empirical examination of technology-organization-environment (TOE) framework. Annals of Operations Research, 327, 1–28. https://doi.org/10.1007/s10479-022-04801-5

17.

Choudhary

, Kumar

, Sharma

, & Kumar

(2021). Barriers in adoption of additive manufacturing in medical sector supply chain. Journal of Advances in Management Research. https://doi.org/10.1108/JAMR-12-2020-0341

18.

Cohen

, Sargeant

, & Somers

(2014). 3-D printing takes shape. McKinsey Quarterly.

19.

de Mattos Nascimento

D. L.

, Mury Nepomuceno

, Caiado

R. G. G.

, Maqueira

J. M.

, Moyano-Fuentes

, & Garza-Reyes

J. A.

(2022). A sustainable circular 3D printing model for recycling metal scrap in the automotive industry. Journal of Manufacturing Technology Management. https://doi.org/10.1108/JMTM-10-2021-0391

20.

Deng

(1989). Introduction to grey system theory. Journal of Grey System, 1, 1–24.

21.

Devi

, Koshta

, & Pendyala

S. S.

(2025). Digital transformation and rural healthcare disparities: A mixed-methods approach in India. Technovation, 140, 103165. https://doi.org/10.1016/j.technovation.2024.103165

22.

Durach

C. F.

, Kurpjuweit

, & Wagner

S. M.

(2017). The impact of additive manufacturing on supply chains. International Journal of Physical Distribution and Logistics Management. https://doi.org/10.1108/IJPDLM-11-2016-0332

23.

Dwivedi

, Srivastava

S. K.

, & Srivastava

R. K.

(2017). Analysis of barriers to implement additive manufacturing technology in the Indian automotive sector. International Journal of Physical Distribution and Logistics Management, 47(10). https://doi.org/10.1108/IJPDLM-07-2017-0222

24.

Eyers

D. R.

, & Potter

A. T.

(2017). Industrial additive manufacturing: A manufacturing systems perspective. Computers in Industry. https://doi.org/10.1016/j.compind.2017.08.002

25.

Faludi

, Bayley

, Bhogal

, & Iribarne

(2015). Comparing environmental impacts of additive manufacturing vs traditional machining via life-cycle assessment. Rapid Prototyping Journal. https://doi.org/10.1108/RPJ-07-2013-0067

26.

Ford

, & Despeisse

(2016). Additive manufacturing and sustainability: An exploratory study of the advantages and challenges. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2016.04.150

27.

Gebler

, Schoot Uiterkamp

A. J. M.

, & Visser

(2014). A global sustainability perspective on 3D printing technologies. Energy Policy. https://doi.org/10.1016/j.enpol.2014.08.033

28.

Ghobadian

, Talavera

, Bhattacharya

, Kumar

, Garza-Reyes

J. A.

, & O’Regan

(2020). Examining legitimatisation of additive manufacturing in the interplay between innovation, lean manufacturing and sustainability. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2018.06.001

29.

Graziosi

, Faludi

, Stanković

, Borgianni

, Meisel

, Hallstedt

S. I.

, & Rosen

D. W.

(2024). A vision for sustainable additive manufacturing. Nature Sustainability, 7(6), 698–705. https://doi.org/10.1038/s41893-024-01313-x

30.

, Styger

, & Warner

D. H.

(2020). Assessment of additive manufacturing for increasing sustainability and productivity of smallholder agriculture. 3D Printing and Additive Manufacturing. https://doi.org/10.1089/3dp.2020.0022

31.

Gupta

, Lawal

J. N.

, Orji

I. J.

, & Kusi-Sarpong

(2023). Closing the gap: The role of distributed manufacturing systems for overcoming the barriers to manufacturing sustainability. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3059231

32.

Haleem

, & Javaid

(2022). Enablers, barriers, and critical success factors for effective adoption of color-jet 3d printing technology. Journal of Industrial Integration and Management, 7(4), 599–625. https://doi.org/10.1142/S242486221950009X

33.

Hannibal

, & Knight

(2018). Additive manufacturing and the global factory: Disruptive technologies and the location of international business. International Business Review. https://doi.org/10.1016/j.ibusrev.2018.04.003

34.

Holmström

, & Gutowski

(2017). Additive manufacturing in operations and supply chain management: No sustainability benefit or virtuous knock-on opportunities? Journal of Industrial Ecology. https://doi.org/10.1111/jiec.12580

35.

Hsu

C. W.

, Kuo

T. C.

, Chen

S. H.

, & Hu

A. H.

(2013). Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2011.09.012

36.

Huang

S. H.

, Liu

, Mokasdar

, & Hou

(2013). Additive manufacturing and its societal impact: A literature review. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-012-4558-5

37.

Iqbal

, Zhao

, Suhaimi

, He

, Hussain

, & Zhao

(2020). Readiness of subtractive and additive manufacturing and their sustainable amalgamation from the perspective of Industry 4.0: A comprehensive review. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-020-06287-6

38.

Javaid

, Haleem

, Singh

R. P.

, Suman

, & Rab

(2021). Role of additive manufacturing applications towards environmental sustainability. Advanced Industrial and Engineering Polymer Research. https://doi.org/10.1016/j.aiepr.2021.07.005

39.

Jewapatarakul

, & Ueasangkomsate

(2024). Digital organizational culture, organizational readiness, and knowledge acquisition affecting digital transformation in SMEs from food manufacturing sector. SAGE Open, 14. https://doi.org/10.1177/21582440241297405

40.

Jimo

, Braziotis

, Rogers

, & Pawar

(2022). Additive manufacturing: A framework for supply chain configuration. International Journal of Production Economics, 253(July), 108592. https://doi.org/10.1016/j.ijpe.2022.108592

41.

Joseph

S. K.

, Ralwala

, Wachira-Towey

, & Mutisya

(2023). Sustainability Theory: Synopsis, Concepts, Interpretations, and Discourses, 2(1), 2958–7999.

42.

Kabir

M. A.

, Khan

S. A.

, Gunasekaran

, & Mubarik

M. S.

(2025). Multi-criteria decision making to explore the relationship between supply chain mapping and performance. Decision Analytics Journal, 15, 100577. https://doi.org/https://doi.org/10.1016/j.dajour.2025.100577

43.

Kamble

, Belhadi

, Gupta

, Islam

, Verma

V. K.

, & Solima

(2023). Analyzing the barriers to building a 3-d printing enabled local medical supply chain ecosystem. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2022.3226658

44.

Kamble

S. S.

, Gunasekaran

, & Sharma

(2020). Modeling the blockchain enabled traceability in agriculture supply chain. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.05.023

45.

Kellens

, Baumers

, Gutowski

T. G.

, Flanagan

, Lifset

, & Duflou

J. R.

(2017). Environmental dimensions of additive manufacturing: Mapping application domains and their environmental implications. Journal of Industrial Ecology. https://doi.org/10.1111/jiec.12629

46.

Khatibi

F. S.

, Dedekorkut-Howes

, Howes

, & Torabi

(2021). Can public awareness, knowledge and engagement improve climate change adaptation policies? Discover Sustainability. https://doi.org/10.1007/s43621-021-00024-z

47.

Khorram Niaki

, Nonino

, Palombi

, & Torabi

S. A.

(2019). Economic sustainability of additive manufacturing: Contextual factors driving its performance in rapid prototyping. Journal of Manufacturing Technology Management. https://doi.org/10.1108/JMTM-05-2018-0131

48.

Kohtala

(2015). Addressing sustainability in research on distributed production: An integrated literature review. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2014.09.039

49.

Kumar

, Bhamu

, & Sangwan

K. S.

(2021). Analysis of Barriers to Industry 4.0 adoption in Manufacturing Organizations: An ISM Approach. Procedia CIRP, 98(March), 85–90. https://doi.org/10.1016/j.procir.2021.01.010

50.

Kumar

, Raut

R. D.

, Agrawal

, Cheikhrouhou

, Sharma

, & Daim

(2022). Integrated blockchain and internet of things in the food supply chain: Adoption barriers. Technovation, 118, 102589. https://doi.org/https://doi.org/10.1016/j.technovation.2022.102589

51.

Kumar

, & Singh

(2024). Underpinning risk dimensions of sponsored institutional R&D projects through cutting-edge fuzzy TOPSIS and fuzzy DEMATEL approaches. Kybernetes. https://doi.org/10.1108/K-01-2024-0026

52.

Kumar

, & Singh

(2025). Decoding supply chain 5.0 adoption by grey influence analysis: What barriers and enablers lie within? Business Strategy and the Environment. https://doi.org/https://doi.org/10.1002/bse.4209

53.

Kumar

, Singh

, & Goel

(2024). Strategic forecasting for electric vehicle sales: A cutting edge holistic model leveraging key factors and machine learning technique. Transportation in Developing Economies, 1–17. https://doi.org/10.1007/s40890-024-00213-1

54.

Lalita

, Kumar

, & Dash

M. K.

(2025). Breaking down barriers: Strategic approaches and prioritization for renewable energy adoption in MSMEs sector. Kybernetes. https://doi.org/10.1108/K-11-2024-3142

55.

Le Bourhis

, Kerbrat

, Hascoet

J. Y.

, & Mognol

(2013). Sustainable manufacturing: Evaluation and modeling of environmental impacts in additive manufacturing. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-013-5151-2

56.

G. D.

, Yamaguchi

, & Nagai

(2007). A grey-based decision-making approach to the supplier selection problem. Mathematical and Computer Modelling. https://doi.org/10.1016/j.mcm.2006.11.021

57.

Mandolla

, Petruzzelli

A. M.

, Percoco

, & Urbinati

(2019). Building a digital twin for additive manufacturing through the exploitation of blockchain: A case analysis of the aircraft industry. Computers in Industry. https://doi.org/10.1016/j.compind.2019.04.011

58.

Matos

, & Jacinto

(2019). Additive manufacturing technology: Mapping social impacts. Journal of Manufacturing Technology Management. https://doi.org/10.1108/JMTM-12-2017-0263

59.

Meixell

M. J.

, & Luoma

(2015). Analysis of barriers to implement additive manufacturing technology in the Indian automotive sector. International Journal of Physical Distribution & Logistics Management, 47(10), 972–991.

60.

Mohd Ali

, Rai

, Otte

J. N.

, & Smith

(2019). A product life cycle ontology for additive manufacturing. Computers in Industry. https://doi.org/10.1016/j.compind.2018.12.007

61.

Moktadir

M. A.

, Ali

S. M.

, Rajesh

, & Paul

S. K.

(2018). Modeling the interrelationships among barriers to sustainable supply chain management in leather industry. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2018.01.245

62.

Murmura

, & Bravi

(2018). Additive manufacturing in the wood-furniture sector: Sustainability of the technology, benefits and limitations of adoption. Journal of Manufacturing Technology Management. https://doi.org/10.1108/JMTM-08-2017-0175

63.

Naghshineh

, & Carvalho

(2022). Exploring the interrelations between additive manufacturing adoption barriers and supply chain vulnerabilities: the case of an original equipment manufacturer. Journal of Manufacturing Technology Management. https://doi.org/10.1108/JMTM-04-2022-0148

64.

Naghshineh

, & Carvalho

(2024). Towards a practice-based framework for supply chain resilience in the context of additive manufacturing technology adoption. International Journal of Computer Integrated Manufacturing, 38(3), 335–361. https://doi.org/10.1080/0951192X.2024.2333011

65.

Naghshineh

, Ribeiro

, Jacinto

, & Carvalho

(2021). Social impacts of additive manufacturing: A stakeholder-driven framework. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2020.120368

66.

Orji

I. J.

, & Ojadi

(2023). Assessing the effect of supply chain collaboration on the critical barriers to additive manufacturing implementation in supply chains. Journal of Engineering and Technology Management, 68(January 2022), 101749. https://doi.org/10.1016/j.jengtecman.2023.101749

67.

Patil

, Niranjan

, Narayanamurthy

, & Narayanan

(2023). Investigating contingent adoption of additive manufacturing in supply chains. International Journal of Operations and Production Management. https://doi.org/10.1108/IJOPM-05-2022-0286

68.

Peng

, Kellens

, Tang

, Chen

, & Chen

(2018). Sustainability of additive manufacturing: An overview on its energy demand and environmental impact. Additive Manufacturing. https://doi.org/10.1016/j.addma.2018.04.022

69.

Prakash

(2025). Evaluating the TOE framework for technology adoption: A systematic review of its strengths and limitations. International Journal on Recent and Innovation Trends in Computing and Communication, 13, 76–82.

70.

Priyadarshini

, Singh

R. K.

, Mishra

, & Bag

(2022). Investigating the interaction of factors for implementing additive manufacturing to build an antifragile supply chain: TISM-MICMAC approach. Operations Management Research. https://doi.org/10.1007/s12063-022-00259-7

71.

Rajesh

, & Ravi

(2017). Analyzing drivers of risks in electronic supply chains: A grey–DEMATEL approach. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-017-0118-3

72.

Ristyawan

M. R.

, Utomo Sarjono

, & and Siallagan

(2023). Decision making mechanism in resource based theory: A literature review, synthesis, and future research. Cogent Business & Management, 10(2), 2247217. https://doi.org/10.1080/23311975.2023.2247217

73.

Ronchini

, Moretto

A. M.

, & Caniato

(2023). Adoption of additive manufacturing technology: Drivers, barriers and impacts on upstream supply chain design. International Journal of Physical Distribution and Logistics Management. https://doi.org/10.1108/IJPDLM-12-2021-0541

74.

Ruiz-Benítez

, López

, & Real

J. C.

(2018). The lean and resilient management of the supply chain and its impact on performance. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2018.06.009

75.

Sahu

, Singh

, & Kumar

(2025). Enhancing Quality 4.0 adoption: Integrative analysis using fuzzy-TOPSIS and fuzzy-DEMATEL for strategic dimension prioritization. The TQM Journal. Advance online publication. https://doi.org/10.1108/TQM-08-2024-0324

76.

Sanguineti

, Magnani

, & Zucchella

(2023). Technology adoption, global value chains and sustainability: The case of additive manufacturing. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2023.137095

77.

Shibin

K. T.

, Dubey

, Gunasekaran

, Hazen

, Roubaud

, Gupta

, & Foropon

(2020). Examining sustainable supply chain management of SMEs using resource based view and institutional theory. Annals of Operations Research, 290. https://doi.org/10.1007/s10479-017-2706-x

78.

Short

D. B.

, Sirinterlikci

, Badger

, & Artieri

(2015). Environmental, health, and safety issues in rapid prototyping. Rapid Prototyping Journal. https://doi.org/10.1108/RPJ-11-2012-0111

79.

Shukla

, Todorov

, & Kapletia

(2018). Application of additive manufacturing for mass customisation: understanding the interaction of critical barriers. Production Planning and Control, 29(10), 814–825. https://doi.org/10.1080/09537287.2018.1474395

80.

Singh

, Dash

M. K.

, Sahu

, & Singh

(2023). Evaluating critical success factors for acceptance of digital assistants for online shopping using Grey–DEMATEL. International Journal of Human-Computer Interaction, 40(24), 8674–8688. https://doi.org/10.1080/10447318.2023.2286124

81.

Singh

, Daultani

, Rajesh

, & Sahu

(2023). Modeling the growth barriers of fresh produce supply chain in the Indian context. Benchmarking. https://doi.org/10.1108/BIJ-09-2021-0517

82.

Singh

, Misra

, & Singh

(2024). Evaluation of critical success determinants to the implementation of additive manufacturing technology in the spare parts supply chain: A grey causal modelling approach. Business Process Management Journal, 30(4), 1154–1184. https://doi.org/10.1108/BPMJ-06-2023-0456

83.

Sonar

, Ghosh

, Singh

R. K.

, Khanzode

, Akarte

, & Ghag

(2024). Implementing additive manufacturing for sustainability in operations: Analysis of enabling factors. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2022.3206234

84.

Suárez

, & Domínguez

(2020). Sustainability and environmental impact of fused deposition modelling (FDM) technologies. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-019-04676-0

85.

Tseng

M. L.

(2009). A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2008.09.011

86.

Verboeket

, & Krikke

(2019). The disruptive impact of additive manufacturing on supply chains: A literature study, conceptual framework and research agenda. Computers in Industry. https://doi.org/10.1016/j.compind.2019.07.003

87.

Verma

V. K.

, Kamble

S. S.

, Ganapathy

, Mani

, Belhadi

, & Shi

(2023). Exploring the barriers in medical additive manufacturing from an emerging economy. Production Planning and Control. https://doi.org/10.1080/09537287.2023.2275694

88.

Yang

, Min

, Ghibaudo

, & Zhao

Y. F.

(2019). Understanding the sustainability potential of part consolidation design supported by additive manufacturing. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.05.380

89.

Yeh

C. C.

, & Chen

Y. F.

(2018). Critical success factors for adoption of 3D printing. Technological Forecasting and Social Change, 132(January), 209–216. https://doi.org/10.1016/j.techfore.2018.02.003

Driving Success in Additive Manufacturing for Heavy Industry: Insights from a Grey–DEMATEL Approach

Abstract

Keywords

Introduction

Literature Review

Theoretical Underpinning

Factors Alignment with the Sustainability Theory

Critical Factors of AM Success: A Review

Summarization of Critical Success Factors for AM Adoption

Technological Factors(CF1)

Environmental Factors (CF2)

IP protection Issues (CF3)

Strategic Factors (CF4)

Operational Factors (CF5)

Health and Safety (CF6)

Human–Machine Collaboration (CF7)

Supply Chain Factors (CF8)

Raw Materials Availability (CF9)

Product Quality (CF10)

Economic Factors (CF11)

DEMATEL Integrated with Grey System Theory

Methodology

Case Implementation and Results

Sensitivity Analysis for the Validation of Results

Sensitivity Analysis.

Findings and Discussion

Digraph Showing the Causal Relations Among Critical Factors.

Conclusion

Theoretical Implications

Practical Implications

Social Implications

Limitations and Future Research Scope

Footnotes

Declaration of Conflicting Interests

Funding

Appendix A: Tables of Intermediaries Results

References