Development of a fuzzy analytic hierarchy process model,sensitivity analysis,and addressing barriers to additive manufacturing implementation in small- and medium-sized enterprises (SMEs)

Abstract

Additive manufacturing (AM) is gaining popularity worldwide due to its potential to revolutionize manufacturing processes, improve product customization, and reduce waste. Nevertheless, small- and medium-sized enterprises (SMEs) encounter major challenges when implementing AM technologies. The objective of this research is to utilize the fuzzy analytic hierarchy process (FAHP) to identify and prioritize the critical challenges that impede the implementation of AM for SMEs. The study employs a two-stage methodology. First, a thorough literature review and expert discussions identify 15 major barriers to AM adoption. Second, a systematic model is proposed to establish a ranking of these barriers, which is based on the FAHP. The results are validated using sensitivity analysis to ensure the robustness of the findings. Additionally, potential solutions to overcome these barriers are discussed. The research indicates that the two most significant barriers for SMEs are the high costs of equipment and the scarcity of skilled designers. The model that has been presented is a valuable resource for SME managers and practitioners, as it allows them to make strategic and informed decisions that facilitate the efficient implementation of AM. This research is groundbreaking in its comprehensive analysis of the prioritization and ranking of AM implementation barriers, with a particular emphasis on SMEs. It offers essential insights for future research and practical applications.

Keywords

Additive manufacturing critical barrier FAHP sensitivity analysis

Introduction

As the world’s attention has been drawn to the importance of sustainable manufacturing, it may help businesses accelerate their goals for growth, minimize their consumption of resources and pollution during every step of the process.¹ Therefore, throughout this advanced manufacturing age, industry and academics have been arguing and focusing on the adoption of smart manufacturing in their various domains of research and manufacturing.²

In the current period, additive manufacturing (AM) is a rapidly expanding technology reshaping the manufacturing business.³ Chuck Hull was the first to invent the AM machine in 1983. Prototypes and product models are the primary reason for the invention of it. Over the past 30 years, AM has developed from a tool for rapid prototyping to an industrial manufacturing technology.⁴ The technology is employed in various industries to produce components that cannot be made using alternative methods.⁵ AM is able to create intricate and effective parts.⁶ The higher the technology advances, the more widely applicable it will be for the production of complex components in serial fashion. International attention has been given to AM, with research, translation, and implementation activities taking place worldwide.⁷

The recent data shows that the fastest-growing use of AM is in the final part production sector, and this sector would soon be the largest and most significant in terms of technology. AM is disruptive in manufacturing because it helps companies innovate and adjust, and as a result, it helps them minimize their warehouse footprint and save time on assembly, which is said to be the most challenging task for manufacturers to accomplish.^{8, 9}

Leading AM application developers are among those who have progressed their application amid public scrutiny, but they don’t accurately represent the state of readiness across other businesses. The reality is that the maturity and prevalence of commercial AM products vary significantly by industry, application, and company.¹⁰

The advancement of AM has led in the development of inclusive environments through the use of 3D printing technology, which allows for the fabrication of personalized designs matched to individual physical characteristics.¹¹ Furthermore, the introduction of advanced technologies such as metal binder jet and cold spray AM has expanded the industries in which AM can be used. These approaches have been effective in resolving issues such as anisotropic mechanical characteristics and surface finish quality.¹² In addition, the notion of four-dimensional (4D) printing has emerged, which allows static three-dimensional (3D) printed items to change shape or functionality over time in reaction to external stimuli. This illustrates the inventive potential of AM technologies.¹³ The continual developments in AM techniques, such as surface modeling on three-dimensional (2D) objects, demonstrate the method’s cost-effectiveness and efficiency in producing complicated structures while minimizing material waste.¹⁴

Small- and medium-sized enterprises (SMEs) are critical sectors that serve as the backbone of every country’s economy. Every business, particularly SMEs, faces numerous global competitive challenges. As a result, numerous SMEs have used a variety of different approaches to increase production productivity, efficiency, and product quality. However, due to their limited resources and lack of familiarity with technological innovation, SMEs require more work to integrate AM into their existing processes.¹⁵ Therefore, it is vital to understand SMEs’ perspectives on AM technology adoption to assist them in maintaining a competitive edge in modern manufacturing value chains.

Numerous studies on AM implementation have been conducted, but only a few have established AM barriers critical for AM implementation in SMEs. As a result, a study that identifies and prioritizes vital barriers is critical, as it provides a consistent foundation for developing an efficient AM roadmap. Thus, the goal of this research is to discuss the barriers to AM adoption in SMEs.

Research objectives

Identify significant barriers: conduct a systematic review of the literature and expert discussions to identify key barriers to AM adoption by SMEs.

Develop and validate an fuzzy analytic hierarchy process (FAHP)-based framework to prioritize and rank the identified barriers, focusing on the most critical ones.

This study aims to identify and assess important hindrances to the implementation of AM to raise the standard of performance in that organization. We suggest suppressing it by the application of fuzzy set theory. The present work proposes a method based on FAHP for ranking the essential barrier to implementing AM. When combined with a fuzzy system, the FAHP technique generates the weight of the detected barrier.

The remainder of the article is organized similarly. The second section contains an up-to-date review of the pertinent literature and a list of identified barriers. The third section discusses the analysis’s methodology. The fourth section illustrates the proposed model with a case study. The fifth section discusses the findings and recommendations. Finally, the implications, concluding remarks, and additional scope are discussed in the sixth section.

Literature review

Today, AM is one of the most rapidly growing automated manufacturing techniques. The technology enables the conversion of computer aided design (CAD) models to finished components with little or no postprocessing required. Traditionally, 2D drawings were used to manufacture specific parts, but AM introduces complex 3D models.¹⁶ AM is defined by International Organization for Standardization (ISO)/American Society for Testing and Materials (ASTM) International¹⁷ as the ”process of joining material to create parts from 3D model data, typically layer by layer, in contrast to subtractive and formative manufacturing methodologies.”

Using AM, it is feasible to create geometrically complicated objects that are stronger, use less material, and require fewer assembly processes.¹⁸ The majority of the time, AM technologies require no tooling.¹⁹ However, a range of processes, such as curing, is sometimes required to complete the surface, or heat treatment is applied to alter the molecular structure to improve the product’s strength.²⁰ Using AM, any item may be affordably made in a single batch, increasing production flexibility and customization options.²¹ Furthermore, due to its “frictionless” nature, AM is an ideal general-purpose manufacturing method.²² Thus, AM offers tremendous opportunities for existing production processes as a viable innovation technology.²³

A survey of the literature revealed numerous studies on AM. Another excellent example is found in,¹⁸ who did a literature assessment of the current state and future potential of AM and mentioned some impediments in engineering applications. Roberson et al.²⁴ developed a multicriteria ranking model for choosing an AM printer that takes into consideration the time it takes to create the printer, the amount of material used, the accuracy of the finished print, and postprocessing. Implementing AM would free up enormous stocks of raw materials, thereby eliminating the necessity for creating big inventories of raw materials for every product.²⁵ Laser-based AM was examined in a range of industrial domains by Schmidt et al.²⁶

Almost all research on AM is done to study specific technologies, process parameters, materials, or applications^–32 Sonar et al.³³ do extensive research on AM implementation in the Indian manufacturing sector, discovering numerous influencing elements. After this, they construct a hierarchical relationship between these diverse factors. Isasi et al.³⁴ analyze the potential for AM approaches that are appropriately designed in the automotive spare parts industry. AM adoption, flexibility, and performance in the context of supply chain management are conceptualized by Delic and Eyers,³⁵ and an examination of these links is proposed.

It is noted in the literature review that no research has been done to examine barriers AM implementation poses for SMEs. Despite SMEs’ difficulties in using AM, a thorough investigation of these issues has yet to be done. AM advantages are still largely unexplored by most companies. Administrators must identify and rank the critical obstacles to AM implementation to reap the benefits of economy and customer satisfaction. These research gaps are addressed in this study by proposing a methodological approach that determines and prioritizes what is most important in enhancing AM implementation in SMEs two-stage procedure that was devised and used in this article. Stage 1: Identify the most widely recognized AM implementation barrier through literature review and discussion with experts. Stage 2: A FAHP approach is used to assign weight and rank barriers in a fuzzy environment.

AM adoption barriers

Specific significant barriers to AM adoption should be identified and evaluated correctly. It is established through a review of the literature and expert consultation. A comprehensive literature evaluation was regarded as a crucial component of any research project focused on determining the theoretical content of the topic.³⁶ In this regard, a literature search was conducted using the terms ”Barriers to Additive Manufacturing Implementation,” ”Challenges for Additive Manufacturing,” and ”Implementation Barrier for Additive Manufacturing,” among others. All essential barriers have been documented in the literature and explained with the help of eight experts from various academic and industry organizations. Fifteen barriers were classified according to their reactions for further study. These impediments are categorized according to five criteria. Table 1 details the criteria and barriers.

Table 1.

AM implementation barrier.

Criteria	Barrier	Reference(s)
Cost (C1)	High equipment cost (B1)	37; 9; 38; 39; 40
	High maintenance cost (B2)
	High material cost (B3)
Technical and production (C2)	Slow production speed (B4)	8; 9; 38; 39; 41
	Limited part size (B5)
	Scalability (B6)
	Unavailability of variety of material (B7)
	Poor surface finish and quality (B8)
Human (C3)	Unavailability of skilled designer (B9)	21; 37; 42; 43
	Unavailability of skilled operator (B10)
	Worker’s resistance (B11)
Software (C4)	Software complexity (B12)	9; 42; 43; 44
	IT integration (B13)
Organizational (C5)	Management support (B14)	45; 46; 47
	Digital infrastructure (B15)

Methodology

Research must be done using a methodical approach to be successful. The authors accomplished the defined objectives through the use of a FAHP methodology. This section explains the study flow and outlines the research methods. This study has two distinct stages of research methods. The first stage entails defining the critical barrier via a literature review and expert consultation. The second stage entails determining the weights and rating barrier using the FAHP system and performing a sensitivity analysis. The research framework is presented in Figure 1.

The 15 identified barriers found in the existing literature review were established in the current study. A panel of experts was brought in to discuss the barriers. For accurate research results, experts were carefully chosen. All eight experts have experience of at least eight years, with four holding doctorate degrees in manufacturing and four employed by reputable Indian companies.

Fuzzy AHP

Several weight computing techniques can be used for decisions involving multiple criteria.⁴⁸ Researchers have adopted the analytical hierarchy as their preferred decision-making strategy.⁴⁹ Additionally, the solutions’ accuracy was substantially improved after this technique was implemented using the fuzzy set theory. An Analytic Hierarchy Approach (AHA) was established by Saaty,⁵⁰ who used multi-criteria decision-making to make analytical hierarchy development efficient. It is a method that aids in muddling through, figuring out, and classifying a complicated problem. When trying to decide if something is correct or incorrect, AHP looks for the criteria that best serves the goal.⁴⁹ Using the AHP methodology, the weights and ranks assigned to each criterion determine how well each criterion has performed in achieving the objective. Due to the inadequacy of subjective human judgments, we combine fuzzy concepts with the AHP strategy.

The FAHP is a decision-making model that is applied to tasks where multiple parameters have an impact on the decision. For example, when fuzzy linguistic variables and corresponding fuzzy triangular numbers are combined, one can utilize the data in making decisions and resolving ambiguous problems.

A wide range of applications of FAHP and related work are applied to various studies. FAHP has been used to address challenges to lean implementation in SMEs51, supply chain performance indicators52, lean six sigma barriers48, and Arctic shipping selection 53. In addition, FAHP criteria are described and graded in their ability to lead to success in cellular manufacturing implementation.⁵⁴

The following are the steps involved in FAHP:

Step 1: Determining the objective.

Step 2: Formation of a hierarchical structure.

Step 3: A pairwise comparison.

A comparison between pairs. Triangular fuzzy numbers are used when performing pairwise comparisons. A triangular fuzzy number $\tilde{A}$ has an associated triplet (a, b, c), which describes the function using the aforementioned Equation (1).

μ_{\tilde{A}} (x) d e n o t e s = \{\begin{matrix} 0, x < a \\ \frac{x - a}{b - a}, a \leq x \leq b \\ \frac{c - x}{c - b}, b \leq x \leq c \\ 0, x > c \end{matrix}

(1)

Where a, b, and c denote the bottom, mean, and upper values of the triangle fuzzy number, respectively (see Figure 2).

To compare the barrier of AM in pairs, industry and academic experts’ perspectives were gathered. Experts were asked to make comparisons between the barriers using the linguistic terms listed in Table 2. The experts were requested to score the comparisons between each barrier in such a way that they accurately reflected the amount to which a particular barrier affects the other barrier in the pair. If, for example, the decision-maker states that ”Barrier 1 is much more important than Barrier 2,” the fuzzy triangular scale is (6,7,8). In pairwise contribution matrices for barriers, the fuzzy triangle scale (1/8, 1/7, 1/6) shall be used to distinguish between Barrier 2 and Barrier 1. Similarly, if the decision-maker asserts that ”Barrier 1 is more significant than Barrier 2,” the fuzzy triangular scale is increased in size (5,6,7). Equation (2) illustrates the matrix of average pairwise contributions.

Table 2.

Linguistic terms and a fuzzy triangular number.

Scale	Description	Fuzzy triangular number
1	Equally important	(1, 1, 1)
3	Weakly important	(2, 3, 4)
5	Fairly important	(4, 5, 6)
7	Strongly important	(6, 7, 8)
9	Absolutely important	(9, 9, 9)
2	Intermediate values	(1, 2, 3)
4		(3, 4, 5)
6		(5, 6, 7)
8		(7, 8, 9)

[\begin{matrix} \tilde{\begin{matrix} d_{11} \\ . \\ . \end{matrix}} \dots & \tilde{\begin{matrix} d_{1 n} \\ . \\ . \end{matrix}} \\ \tilde{d_{n 1}} \dots & \tilde{d_{n n}} \end{matrix}]

(2)

Step 4: Relative fuzzy weights calculation. Equation (3) determines the fuzzy weight assigned to each barrier.

{\tilde{w}}_{2} = {\tilde{r}}_{i} \oplus {({\tilde{r}}_{1} \oplus {\tilde{r}}_{2} \oplus \dots \dots \oplus \oplus {\tilde{r}}_{n})}^{- 1}

(3)

where

n ∈ N denotes the total number of barriers

${\tilde{r}}_{i}$ is the geometric mean of the comparison value of barrier i to each barrier,

$\tilde{w_{i}}$ is the ith barrier’s fuzzy weight

⊕ is the symbol of matrix plus

Three estimating steps are used to determine the fuzzy weight of the barrier. The first step is to calculate the vector sum of each ${\tilde{r}}_{i}$ . The second step is to compute the inverse of the summation vector and replace the fuzzy triangular number with increasing order. Finally, the third step is to determine the fuzzy weight associated with barrier i ( $\tilde{w_{i}}$ ), by multiplying each ${\tilde{r}}_{i}$ by this reverse vector.

Step 5: The nonfuzzy weight calculation. The center of area approach is used to defuzzify fuzzy triangular numbers $\tilde{w_{i}}$ . Refer Equation (4)

M_{i} = \frac{a w_{i} + b w_{i} + c w_{i}}{3}

(4)

Step 6: Normalized weight determination. To calculate the normalized weight of any given barrier, use Equation (5).

N_{i} = \frac{M i}{\sum_{i = 1}^{n} M i}

(5)

An application case of the proposed model

Eight professionals were appointed to an expert board, including six senior-level managers from SMEs and two academics. Experts were chosen based on ten years of experience, management abilities, and specialties like the topics. Interactive group discussions were used to collect data from experts. The method of identifying barriers could be biased because it includes human judgment and subjectivity. A FAHP program is employed to consider this advantage; the following steps provide further details.

Step 1: Prioritizing AM implementation barriers is the goal.

Step 2: Figure 3 shows the research’s hierarchical structure.

Step 3: According to Table 3, the fuzzy matrices for the significant barrier pairwise comparison are generated to estimate the aggregated fuzzy matrix.

Table 3.

Pairwise comparison matrix of the main criterion.

	Cost (C1)			Technical and production (C2)			Human (C3)			Software (C4)			Organizational (C5)
Cost (C1)	1	1	1	4	5	6	1	1	1	2	3	4	6	7	8
Technical and Production (C2)	0.1667	0.2	0.25	1	1	1	0.1667	0.2	0.25	2	3	4	4	5	6
Human (C3)	1	1	1	4	5	6	1	1	1	2	3	4	6	7	8
Software (C4)	0.25	0.3333	0.5	0.25	0.3333	0.5	0.25	0.3333	0.5	1	1	1	4	5	6
Organizational (C5)	0.125	0.1428	0.1667	0.1667	0.2	0.25	0.125	0.1428	0.1667	0.1667	0.2	0.25	1	1	1

Step 4: With the help of Equation (3), Table 4 shows the relative fuzzy weights of each barrier.

Table 4.

Fuzzy weight of the main criterion.

	Fuzzy weight
Cost (C1)	0.270006	0.3659807	0.487961
Technical and production (C2)	0.092155	0.1302719	0.184903
Human (C3)	0.270006	0.3659807	0.487961
Software (C4)	0.071499	0.1029711	0.160967
Organizational (C5)	0.026464	0.0347955	0.047827

Step 5: Each barrier’s relative nonfuzzy weight is measured by Equation (4). Table 5 shows the normalized weights of each barrier using Equation (5). Standard weights can be used to determine the barrier’s strength.

Table 5.

Nonfuzzy weight and normalized weight of each criterion.

	Average	Normalized
Cost (C1)	0.374649	0.362593
Technical and production (C2)	0.135776	0.131407
Human (C3)	0.374649	0.362593
Software (C4)	0.111812	0.108214
Organizational (C5)	0.036362	0.035192

The steps outlined above are used to determine the normalized weight of all barriers. Appendix 1 contains pairwise comparisons, fuzzy weights, nonfuzzy weights, and normalized weights for all barriers. Assuming that the key criteria and barriers have been normalized, global weight is derived by multiplying the local barriers’ weights by the main criteria’s weighting. Finally, the relative importance of barriers can be calculated from this global weight. The weights of barriers on a local and global rank are listed in Table 6.

Table 6.

Local and global rank of barriers.

Criteria	Weight	Rank	Barrier	Local weight	Local rank	Global weight	Global rank
Cost (C1)	0.3625	1	High equipment cost (B1)	0.6295	1	0.2282	1
			High maintenance cost (B2)	0.1073	3	0.0389	8
			High material cost (B3)	0.2631	2	0.0954	4
Technical and production (C2)	0.1314	3	Slow production speed (B4)	0.4423	1	0.0581	6
			Limited part size (B5)	0.0529	5	0.0069	15
			Scalability (B6)	0.1530	3	0.0201	12
			Unavailability of variety of material (B7)	0.0892	4	0.0117	13
			Poor surface finish and quality (B8)	0.2623	2	0.0344	9
Human (C3)	0.3625	1	Unavailability of skilled designer (B9)	0.5753	1	0.2085	2
			Unavailability of skilled operator (B10)	0.2841	2	0.1029	3
			Worker’s resistance (B11)	0.1404	3	0.0509	7
Software (C4)	0.1082	4	Software complexity (B12)	0.7423	1	0.0803	5
Software (C4)	0.1082	4	IT integration (B13)	0.2576	2	0.0278	10
Organizational (C5)	0.0351	5	Management support (B14)	0.7423	1	0.0260	11
Organizational (C5)	0.0351	5	Digital infrastructure (B15)	0.2576	2	0.0090	14

Sensitivity analysis

The sensitivity analysis identifies the degree to which the dimension rankings are affected by changes. Thus, sensitivity analysis is a critical part of the analysis process. Cost and human-related barriers are given the highest priority in this study because they affect the other barriers. First, cost criteria increase from 0.1 to 0.9, and each of the other criterion increases by 0.1 increments, as shown in Table 7. Following that, human criteria increase from 0.1 to 0.9, and each of the other criterion increases by 0.1 increments, as shown in Table 8. Tables 9 and 10 present a ranking of barriers following sensitivity analysis. Figures 4 and 5 illustrate the distinctions in the ranking of barriers.

Table 7.

Variation in weight of C1 criteria.

Criteria	Normalized weight	Run 1	Run 2	Run 3	Run 4	Run 5	Run 6	Run 7	Run 8	Run 9
C1	0.3625	0.100	0.200	0.300	0.400	0.500	0.600	0.700	0.800	0.900
C2	0.1314	0.197	0.172	0.147	0.122	0.097	0.072	0.047	0.022	–0.003
C3	0.3625	0.428	0.403	0.378	0.353	0.328	0.303	0.278	0.253	0.228
C4	0.1082	0.174	0.149	0.124	0.099	0.074	0.049	0.024	–0.001	–0.026
C5	0.0351	0.101	0.076	0.051	0.026	0.001	–0.024	–0.049	–0.074	–0.099

Table 8.

Variation in weight of C3 criteria.

Criteria	Normalized weight	Run 1	Run 2	Run 3	Run 4	Run 5	Run 6	Run 7	Run 8	Run 9
C1	0.3625	0.428	0.403	0.378	0.353	0.328	0.303	0.278	0.253	0.228
C2	0.1314	0.197	0.172	0.147	0.122	0.097	0.072	0.047	0.022	–0.003
C3	0.3625	0.100	0.200	0.300	0.400	0.500	0.600	0.700	0.800	0.900
C4	0.1082	0.174	0.149	0.124	0.099	0.074	0.049	0.024	–0.001	–0.026
C5	0.0351	0.101	0.076	0.051	0.026	0.001	–0.024	–0.049	–0.074	–0.099

Table 9.

Ranking of barriers after variation in weight of C1 criteria.

	Normalized 0.3625)	Run 1	Run 2	Run 3	Run 4	Run 5	Run 6	Run 7	Run 8	Run 9
Barrier	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank
B1	1	6	2	2	1	1	1	1	1	1
B2	8	14	12	10	8	6	5	5	4	4
B3	4	11	8	5	3	3	3	2	2	2
B4	6	4	5	6	6	8	8	7	7	11
B5	15	15	15	15	15	13	13	13	11	7
B6	12	10	11	12	12	11	11	10	9	9
B7	13	13	14	14	13	12	12	12	10	8
B8	9	8	9	8	9	9	9	9	8	10
B9	2	1	1	1	2	2	2	3	3	3
B10	3	3	3	3	4	4	4	4	5	5
B11	7	7	6	7	7	7	6	6	6	6
B12	5	2	4	4	5	5	7	8	13	13
B13	10	9	10	11	10	10	10	11	12	12
B14	11	5	7	9	11	14	15	15	15	15
B15	14	12	13	13	14	15	14	14	14	14

Table 10.

Ranking of barriers after variation in weight of C3 criteria.

	Normalized (0.3625)	Run 1	Run 2	Run 3	Run 4	Run 5	Run 6	Run 7	Run 8	Run 9
Barrier	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank	Global rank
B1	1	1	1	1	2	2	2	3	3	3
B2	8	8	9	8	8	8	7	6	6	6
B3	4	3	4	3	4	4	5	5	5	5
B4	6	4	5	6	7	7	8	7	7	11
B5	15	15	15	15	15	13	13	13	11	7
B6	12	10	12	12	12	11	11	10	9	9
B7	13	13	14	14	13	12	12	12	10	8
B8	9	7	8	9	9	9	9	9	8	10
B9	2	6	2	2	1	1	1	1	1	1
B10	3	11	6	5	3	3	3	2	2	2
B11	7	14	11	7	6	5	4	4	4	4
B12	5	2	3	4	5	6	6	8	13	13
B13	10	9	10	11	10	10	10	11	12	12
B14	11	5	7	10	11	14	15	15	15	15
B15	14	12	13	13	14	15	14	14	14	14

Results and discussions

Mass customization is one of AM’s most appealing features. Numerous industries, including dentistry, jewelry, and art, directly benefit from AM’s capacity to mass customize. Bigger companies like Nike and Adidas also use AM to manufacture personalized shoes.⁵⁵ However, although the technology has massive potential for usage in various industries, including SMEs, its penetration is limited. Its application has several impediments, including cost, the lack of expert designers, and consumer expectations. On this premise, it is vital to identify and remove obstacles that restrict the growth of SMEs through the use of alternative marketing methods. The barriers preventing SMEs from using AM were identified by means of a literature review and evaluated by experts.

This study aims to identify and classify important barriers to AM adoption using the FAHP method. Interactions with academic and industry experts are a primary focus of the research. As a result, experts have established and confirmed the existence of critical barriers in the literature.

Figure 2 illustrates the hierarchy of barriers that must be overcome to implement the AM. The FAHP process began with pairwise comparisons of various barriers. All barriers are prioritized in Table 6.

High equipment cost (B1) and unavailability of skilled designers (B9) hold first and second rank, respectively, as critical barriers to the implementation of AM in SMEs. The process of adopting AM is one that demands a significant amount of time and effort over time. The enormous investment that is necessary to bring AM to the production floor is the key barrier that prevents its widespread adoption. Considering that advanced AM technology can be prohibitively expensive, it might be challenging for SMEs to justify the expenditure it requires. On the other hand, the dearth of talented designers presents a further significant barrier. Creating exact and effective 3D designs—the foundation of AM processes—that constitute the backbone of the industry depends on a talented designer. These experts can create high-quality designs with little extra assistance, expediting the manufacturing process and lowering total effort requirements. Nevertheless, the lack of such skills implies that even if a SMEs invests in AM technology, they could find it difficult to apply it efficiently without the required design knowledge.

Findings contribute to the expanding corpus of research on the realities of AM for mass customization, with an emphasis on both technical and so-called ”soft” hurdles to mass customization.⁵⁶ Already, manufacturing confronts a skills crisis, but AM design, processes, machines, and technology could face an even more significant shortage of competent personnel. Comparatively, the costs of materials in AM can be relatively high compared to traditional manufacturing methods.⁵⁷

A sensitivity analysis investigates barrier rating fluctuations to identify the impact on barrier rankings of the change in the weighting of the criterion. Figures 3 and 4 show that when C1 criteria are changed, the variation in barrier ranking after the sensitivity analysis is lower. Additionally, the sensitivity analysis demonstrates that when the cost (C1) and human (C3) criteria are changed from 0.1 to 0.9, the top four barriers retain their ranking.

Proposed solution of barriers

Based on the main criteria, a solution of barriers is proposed and presented in Table 11.

Table 11.

A proposed solution for barriers

Barrier criteria	Proposed solution
Cost (C1)	Before pursuing AM as a practice, SMEs must develop a strategy. Analyze their supply chains and component manufacturing processes, develop a business case, and incorporate all of this into a strategy with a budget. If not a complete manufacturing transformation, AM is a long-term investment for an organization. While the systems and materials may appear to be expensive, they may offset or result in significant cost savings in productivity, efficiency, and part and mechanical property. AM is capable of long-term cost reductions. This enables the cost-effective production of complex geometries, tooling cost reduction, and part customization
Technical and production (C2)	SMEs require new knowledge and technological advancements to address a variety of technological and quality issues. At the same time, a spirit of adventure, exploration, and experimentation is vital to new technology
Human (C3)	SMBs must cross-train existing employees to bring them up to speed on AM. As design teams learn to design for AM, they may require additional support. Training an entire workforce on new technologies can increase employee engagement, foster creativity, and generate a greater sense of accomplishment
Software (C4)	If SMEs want to turn software fragmentation into a thing of the past, they will have to develop a whole new way of looking at it. An all-encompassing approach is required to guide the design process for all software, no matter what type of application or platform it runs on
Organizational (C5)	For management to better understand the long-term benefits of the AM, and thus help in planning processes, they must actively participate.Workflow management software is required for SMEs. This kind of software helps handle all aspects of the workflow, from managing the requests, analyzing printability, and undertaking machine analytics to making production plans, carrying out postprocessing, and talking to suppliers

6. Implications and conclusion

AM is currently receiving considerable practical consideration. This study utilizes a FAHP to delve into the inhibiting barrier limiting SMEs businesses from implementing AM methods. To identify a feasible way past every possible barrier, academic and industry specialists joined forces to explore anything that might come between them. Another distinctive aspect of this study is that the study discovered five critical criteria and 15 barriers for SMEs when it comes to AM usage. In addition, this research used FAHP methodology to analyze the recognized AM implementation barrier. As a validation of the barriers mentioned earlier, sensitivity analysis was employed. Additionally, a solution for barriers is proposed. It will assist managers in developing AM implementation systems that will improve overall performance.

Managers can use the FAHP technique to address human bias in ranking the barriers to implementing AM. Managers can effectively implement AM in their organizations by focusing on the barrier. This study has significant implications for academics and practitioners alike. Academic researchers require a better understanding of the interactions between various findings pertinent to the AM implementation barrier. Furthermore, the study presents a better knowledge of the complexities in the SME sector concerning various theoretical viewpoints. Finally, this research provides researchers with the tool they need to create bespoke AM frameworks.

The management must initially focus on the top barrier on which they must concentrate their attention are high equipment cost (B1), unavailability of skilled designer (B9), unavailability of skilled operator (B10), high material cost (B3), and software complexity (B12). This research contributes to existing literature where SMEs conflict with the implementation of AM.

There are also some pitfalls in the proposed study, which give researchers something to aim for in the future. To begin, the research focuses on experts’ perspectives, which can be improved. In addition, results can be generalized based on data from several industries from various regions around the world. Comparing the findings to other MCDM approaches yields comparable results. Another approach could be to implement a systematic action plan, perform case studies that describe the various steps of the process, and analyze the outcomes to back the conclusions and value of recommendations.

Appendix 1

Table A1.

Pairwise comparison matrix of the barrier under C1 criteria.

	High equipment cost (B1)			High maintenance cost (B2)			High material cost (B3)
High equipment cost (B1)	1	1	1	4	5	6	2	3	4
High maintenance cost (B2)	0.1667	0.2	0.25	1	1	1	0.25	0.3333	0.5
High material cost (B3)	0.25	0.3333	0.5	2	3	4	1	1	1

Table A2.

Fuzzy weight of the main barrier under C1 criteria.

	Fuzzy Weight
High equipment cost (B1)	0.430624	0.636993	0.918511
High maintenance cost (B2)	0.074649	0.1047272	0.159215
High material cost (B3)	0.170893	0.2582794	0.401197

Table A3.

Non-fuzzy weight and normalized weight of barrier under C1 criteria.

	Average	Normalized
High equipment cost (B1)	0.662043	0.6295
High maintenance cost (B2)	0.112864	0.107316
High material cost (B3)	0.27679	0.263184

Table A4.

Pairwise comparison matrix of the barrier under C2 criteria.

	Slow production speed (B4)			Limited part size (B5)			Scalability (B6)			Unavailability of variety of material (B7)			Poor surface finish and quality (B8)
Slow production speed (B4)	1	1	1	2	3	4	4	5	6	4	5	6	2	3	4
Limited part size (B5)	0.25	0.3333	0.5	1	1	1	0.25	0.3333	0.5	0.1667	0.2	0.25	0.1667	0.2	0.25
Scalability (B6)	0.1667	0.2	0.25	2	3	4	1	1	1	4	5	6	0.25	0.3333	0.5
Unavailability of variety of material (B7)	0.1667	0.2	0.25	4	5	6	0.1667	0.2	0.25	1	1	1	0.25	0.3333	0.5
Poor surface finish and quality (B8)	0.25	0.3333	0.5	4	5	6	2	3	4	2	3	4	1	1	1

Table A5.

Fuzzy weight of the main barrier under C2 criteria.

	Fuzzy weight
Slow production speed (B4)	0.28256	0.4480653	0.687124
Limited part size (B5)	0.034501	0.0513394	0.083891
Scalability (B6)	0.098734	0.1516688	0.240091
Unavailability of variety of material (B7)	0.060069	0.0882425	0.137896
Poor surface finish and quality (B8)	0.162288	0.260684	0.418022

Table A6.

Non-fuzzy weight and normalized weight of barrier under C2 criteria.

	Average	Normalized
Slow production speed (B4)	0.472583	0.442331
Limited part size (B5)	0.056577	0.052955
Scalability (B6)	0.163498	0.153032
Unavailability of variety of material (B7)	0.095402	0.089295
Poor surface finish and quality (B8)	0.280331	0.262386

Table A7.

Pairwise comparison matrix of the barrier under C3 criteria.

	Unavailability of skilled designer (B9)			Unavailability of skilled operator (B10)			Worker’s resistance (B11)
Unavailability of skilled designer (B9)	1	1	1	2	3	4	2	3	4
Unavailability of skilled operator (B10)	0.25	0.3333	0.5	1	1	1	2	3	4
Worker’s resistance (B11)	0.25	0.3333	0.5	0.25	0.3333	0.5	1	1	1

Table A8.

Fuzzy weight of the main barrier under C3 criteria.

	Fuzzy weight
Unavailability of skilled designer (B9)	0.359977	0.5841671	0.907086
Unavailability of skilled operator (B10)	0.179989	0.2808289	0.453543
Worker’s resistance (B11)	0.089994	0.135004	0.226772

Table A9.

Non-fuzzy weight and normalized weight of barrier under C3 criteria.

	Average	Normalized
Unavailability of skilled designer (B9)	0.617077	0.575388
Unavailability of skilled operator (B10)	0.304787	0.284196
Worker’s resistance (B11)	0.15059	0.140416

Table A10.

Pairwise comparison matrix of the barrier under C4 criteria.

	Software complexity (B12)			IT integration (B13)
Software complexity (B12)	1	1	1	2	3	4
IT integration (B13)	0.25	0.3333	0.5	1	1	1

Table A11.

Fuzzy weight of the main barrier under C4 criteria.

	Fuzzy Weight
Software complexity (B12)	0.522408	0.7500094	1.044815
IT integration (B13)	0.184699	0.2499906	0.369398

Table A12.

Non-fuzzy weight and normalized weight of barrier under C4 criteria.

	Average	Normalized
Software complexity (B12)	0.772411	0.742389
IT integration (B13)	0.268029	0.257611

Table A13.

Pairwise comparison matrix of the barrier under C5 criteria.

	Management support (B14)			Digital infrastructure (B15)
Management support (B14)	1	1	1	2	3	4
Digital infrastructure (B15)	0.25	0.3333	0.5	1	1	1

Table A14.

Fuzzy weight of the main barrier under C5 criteria

	Fuzzy weight
Management support (B14)	0.522408	0.7500094	1.044815
Digital infrastructure (B15)	0.184699	0.2499906	0.369398

Table A15.

Non-fuzzy weight and normalized weight of barrier under C5 criteria

	Average	Normalized
Management support (B14)	0.772411	0.742389
Digital infrastructure (B15)	0.268029	0.257611

Footnotes

Acknowledgements

The authors gratefully acknowledge the Mody University of Science and Technology Lakshmangarh for providing all facilities, infrastructure, and equipment. The authors also thank AM experts for their contributions to the model’s development.

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.

ORCID iDs

Vikrant Sharma

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