Introduction

Additive manufacturing (AM), also known as 3D printing, is set to be a game changer by offering flexibility in the manufacturing process and flourishing the supply chain by localized manufacturing [1, 2]. AM enables the production of precise geometric shapes by adding the material layer by layer directly from a digital design without the need for human intervention as opposed to the traditional manufacturing process [3, 4]. Thus, distinctly differentiating it from the conventional manufacturing process, various organizations evaluate their operations present status in an exceedingly way enabling them to implement AM, thus enhancing their competitiveness [5]. Modern manufacturing firms face challenges due to inherent competitive pressure to catch up on operational excellence and competitiveness in domestic and international markets [6]. Competing through operational excellence by leveraging AM technology is a path to achieve firm competitiveness [7].

Additive manufacturing technology has experienced significant advances. Today, AM technology is used by various industries, including automotive, medical, aerospace and consumer products [8]. The example includes titanium aerospace components that required only 10% of raw material than the original machined part using traditional manufacturing [9]. Production of race car gearboxes using AM technology significantly reduced component weight by 30% [10]. The aerospace sector has also found several applications for high-value parts. Medical devices such as dental crowns and hearing heads driven by customer requirements have also produced using AM. AM has also applied to low-volume consumer products, including high-value lighting goods and electronics.

Several researchers revealed the benefits of implementing AM into businesses [11,12,13,14]. However, considering the significant benefits of this technology, manufacturing firms cannot implement this technology. To develop competitive strategies, manufacturing managers need to understand important AM implementation factors. Although Indian industries are geared up to adopt AM technology, they are still at the initial stage. In this regard, the diffusion of this technology and studying its important factors hindering its implementation will help to understand the dynamics of this industry and competitiveness.

This work focuses on identifying comprehensive AM implementation factors. A total of 11 factors has been identified from the extensive literature review and prioritized using the analytic hierarchy process (AHP). AHP is a multicriteria decision-making tool used to evaluate and rank the alternatives concerning criteria in pairwise mode [15]. This study was undertaken in various industries in West India. Hence, this study offers a novel approach to understand and prioritize the factors hindering AM implementation from an Indian manufacturing industry perspective. This study will help industry managers for planning effective AM implementation strategy.

Factors Identification

Several researchers usually focused on methods, material and various technological aspects of additive manufacturing. However, the rate of implementation of this technology in the Indian manufacturing sector is very low. Nowadays, research is witnessing the growing interest in examining essential factors that hinder the early implementation of AM into firms [7]. Past literature reports sector-specific case studies on understanding enablers and barriers of AM implementation such as dentistry [16], automotive [17], SME’s [18] and engineering services [10]. However, these studies consider either a single case or a single domain, making it difficult to generalize them for the early implementation of this technology.

A comprehensive list of important AM implementation factors has been identified from previous literature. Detailed steps for identifying important AM implementation factors can be found in [7]. Authors have validated the factors from experts to capture industry perspective. Additionally, a detailed description of AM implementation factors can be found in [19]. In this article, authors have identified interrelationship between factors using an integrated ISM-MICMAC approach. A total of 11 such factors are summarized in Table 1.

Table 1 AM implementation factors
Full size table

Overview of AHP

The analytic hierarchy process (AHP) is adopted in the present study to prioritize the various AM implementation factors identified from the literature. Analytic hierarchy process (AHP), developed by Saaty in 1977 as a decision support tool, uses a multilevel hierarchical structure of objectives, criteria, sub-criteria and alternatives based on pairwise comparisons [15]. In this study, an AHP method is used to estimate the relative importance of the 11 AM implementation factors. In the AHP survey instrument, an assessment of each pairwise scoring was conducted. Each pairwise comparison was evaluated based on a scale ranging from 1 to 9 [35]. Figure 1 shows the hierarchy structure for all identified factors.

Fig. 1
figure 1

AHP hierarchy structure

Full size image

AHP consists of four steps:

  1. (i)

    Structuring the hierarchical model.

  2. (ii)

    Data collection through pairwise comparisons and measurement.

  3. (iii)

    Determining normalized weights of each factor.

  4. (iv)

    Analyzing the weights and deriving solutions to the problem.

A structured questionnaire was framed, and all the criteria were rated by the industry experts.

Data Collection

The sample has been selected to form a heterogeneous group of experts to reduce biases to guarantee the robustness of the model and generality of its findings in all AM implementation levels. The target companies who already implemented AM technology have been selected from diverse industries such as medical, automotive, dental and consumer products to generalize the findings. Few of the companies with more than five years of experience in AM usage are considered “former,” and those with less than five years of experience are considered “recent.” Furthermore, these companies are categorized as small, medium and large based on annual turnover. This study targeted experts in the middle or higher management levels from various companies in Maharashtra, a western part of India.

Initially, 16 experts were contacted to participate in this study; however, seven experts refused to participate. Data sensitivity issues and shortage of time are the reasons for their unwillingness to participate. Finally, nine experts from diverse areas such as R and D, production planning, manufacturing, logistics and service, with a minimum 5 years of experience in AM technology usage, have participated in this study. Only managerial-level experts are chosen for this study. The motivation behind this selection is because managers are aware of recent applications and disruptions in manufacturing sectors due to various advanced manufacturing technologies such as additive manufacturing. Additionally, managers would get the complete picture of AM implementation challenges to formulate a better AM implementation strategy. Workers or operators may not know the complete scenario of AM implementation and its advantages over traditional manufacturing. This may lead to bias in the data.

Semi-structured interviews have been conducted face-to-face, and each of these interviews lasted approximately one hour in duration. A pairwise comparison matrix sheet was provided to all the experts for giving relative importance between all AM implementation factors. Each of the two criteria was compared, and comparison results in the form of a relative scale are used to rank the factors. Experts provided their opinions based on a complete understanding of factors and their interrelationships. A sample size of 5–20 experts is adequate to obtain quality results for AHP [36, 37]. The data were collected during January and February 2020. The summary of industry experts participated is shown in Table 2.

Table 2 Summary of industry experts participated
Full size table

The selected experts carried out the pairwise comparisons of all identified factors to determine the relative importance between identified factors. The results of the AHP methodology are discussed next.

Results and Discussion

Based on the expert's relative importance, matrices are formed and priorities are synthesized using the AHP method. The obtained data were transformed into a pairwise comparison matrix to evaluate the factors consistency and compute the eigenvectors, consistency ratio and index. For computing the weights of each criterion, the geometric mean has been calculated and used in the present study. The evaluation is conducted for the criteria levels, and no alternatives were measured. Table 3 shows the pairwise comparison matrix.

Table 3 Pairwise comparison matrix
Full size table

Lambda max (λmax) is the maximum eigenvalue of the matrix, and it is needed to calculate the consistency index (CI). To calculate the value of Lambda max, all the elements of the weighted sum matrices are divided by priority vector for each criterion and then take an average of all these values.

$$ \lambda_{{{\text{max}}}} = { 12}.{4241} $$

Since pairwise comparisons were made subjectively, a consistency check must be done. Consistency index (CI) for each matrix order n is calculated by using the following formula,

$$ \begin{aligned} {\text{CI }} = \, \left( {\lambda_{{{\text{max}}}} {-}n} \right) \, / \, \left( {n{-}{1}} \right) \\ = \, \left( {{12}.{4241} - {11}} \right) \, / \, \left( {{11} - {1}} \right) \, = \, 0.{142} \\ \end{aligned} $$

Based on the CI and random consistency index (RI), the consistency ratio (CR) is calculated using the following formula,

$$ \begin{aligned} {\text{CR }} = {\text{ CI}}/{\text{RI}} \\ = \, 0.{142 }/{ 1}.{52 } = \, 0.0{9342} \\ \end{aligned} $$

Since the acceptable CR value for all larger matrices, n ≥ 5 is 0.1 [35]. It implies that the matrix's evaluation is acceptable or indicates a good level of consistency in the comparative judgments represented in that matrix. The final prioritization of the identified factors based on its criterion weights is given in Table 4.

Table 4 Ranking of factors
Full size table

Table 4 deals with the simple ranking/prioritization of factors. The analysis revealed that experts gave more weight to top management commitment (27%), followed by AM technology (17%). The proactive and visionary top management committed must promote the continual improvement culture with advanced manufacturing processes and systems toward long-term organizational goals. From the analysis, top management commitment, AM technology, technological awareness, financial capability, organization capability and human resources are more significant for AM implementation. From the critical analysis of this study, it can be inferred that reliable and robust financial support is required to develop, leverage and practice AM technologies and systems.

The literature on AM supply chain [2, 5, 38] reveals that the implementation of AM may impact different supply chain players such as suppliers, manufacturing firms and customers. Agile and responsible supply chain operations are necessary by coordination between supply chain players to leverage AM technology. However, it is ranked 6th in the hierarchy, indicating that the supply chain plays a mediating role in AM implementation strategy to improve its competitiveness further, as suggested by [19]. Besides, customer's active participation in various business facets such as product designing, customization and delivery feedback is an essential aspect of AM implementation. Thus, integrating such supply chain practices is required while formulating AM implementation strategy to gain a competitive edge for the firms.

A synchronous flow of information is necessary for effective and efficient communication. However, according to expert priorities, information sharing and education and training are not relevant factors during early implementation of AM and, thus, places at the bottom of the hierarchy. However, additional factors may be considered in the hierarchical model based on different manufacturing firm's specific requirements.

Conclusion

Most of the industries are still struggling to identify important factors hindering AM implementation. This work aims to identify important AM implementation factors and prioritize them using the analytic hierarchy process (AHP). A total of 11 factors were identified from previous literature. AHP model and prioritization of important factors responsible for implementing AM technology enhance rigor in AM implementation strategy formulation. The analysis shows that top management commitment is placed at rank 1 in the hierarchy. This finding is consistent with the previous literature [17, 19, 21, 22], highlighting that uninterrupted support from top management is essential for successful AM implementation.

This work can help understand the AM implementation process and operational and implementation challenges, which will help industry managers plan effective AM implementation strategies. The extension of this work would be prioritizing the factors using the analytical network process (ANP) or interpretive ranking process (IRP). Additionally, the prioritization of factors based on challenges faced by different departments within the organization may vary and provide a distinct perspective that further needs to be mitigated.