Investigating additive manufacturing implementation factors using integrated ISM-MICMAC approach

Abstract

Purpose The purpose of this paper is to identify various factors influencing additive manufacturing (AM) implementation from operational performance in the Indian manufacturing sector and to establish the hierarchical relationship among them. Design/methodology/approach The methodology includes three phases, namely, identification of factors through systematic literature review (SLR), interviews with experts to capture industry perspective of AM implementation factors and to develop the hierarchical model and classify it by deriving the interrelationship between the factors using interpretive structural modeling (ISM), followed with the fuzzy Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis. Findings This research has identified 14 key factors that influence the successful AM implementation in the Indian manufacturing sector. Based on the analysis, top management commitment is an essential factor with high driving power, which exaggerates other factors. Factors, namely, manufacturing flexibility, operational excellence and firm competitiveness are placed at the top level of the model, which indicates that they have less driving power and organizations need to focus on those factors after implementing the bottom-level factors. Research limitations/implications Additional factors may be considered, which are important for AM implementation from different industry contexts. The variations from different industry contexts and geographical locations can foster the theoretical robustness of the model. Practical implications The proposed ISM model sets the directions for business managers in planning the operational strategies for addressing AM implementation issues in the Indian manufacturing sector. Also, competitive strategies may be framed by organizations based on the driving and dependence power of AM implementation factors. Originality/value This paper contributes by identification of AM implementation factors based on in-depth literature review as per SLR methodology and validation of these factors from a variety of industries and developing hierarchical model by integrative ISM-MICMAC approach.

Context-specific micro-foundations and successful SME internationalisation in emerging markets: A mixed-method analysis of managerial resources and dynamic capabilities

“…Accordingly, considering the factors type (qualitative), unavailability of numerical data, and the abovementioned pro and cons among the methods, hybrid ISM-MICMAC has been implemented and presented in the following subsection. This hybrid approach is popular and reliable among scholars and has been used in many similar conditions (e.g., Sonar et al, 2020;Wang et al, 2018).…”

“…ISM-MICMAC is an expert-based method widely used for identifying the relationship between the factors and designing a stage-based conceptual model in many scientific applications (Sonar et al, 2020). These methods were debuted in the 70 s (ISM in 1973 by Warfield and MICMAC in 1971 by Godet) and since then they have been applied in many areas and recent research topics including lean management (e.g., Kaswan & Rathi, 2019); sustainability (e.g., Azevedo et al, 2019); information modelling (e.g., Tan et al, 2019); etc.…”

“…These methods were debuted in the 70 s (ISM in 1973 by Warfield and MICMAC in 1971 by Godet) and since then they have been applied in many areas and recent research topics including lean management (e.g., Kaswan & Rathi, 2019); sustainability (e.g., Azevedo et al, 2019); information modelling (e.g., Tan et al, 2019); etc. This approach has been described in six steps as follows (Kapse et al, 2018;Sonar et al, 2020).…”

Safety Failure Factors Affecting Dairy Supply Chain: Insights from a Developing Economy

“…This approach is used to draw a contextual relationship among different SSFs. It helps to demonstrate the relationships of different elements in the hierarchical structure [54]. However, various MCDM methods can perform the same analysis, e.g., data mining, TOPSIS, game theory, analytical hierarchy processing (AHP) and Bayesian theory.…”

Application of total interpretive structural modeling for analyzing factors of additive manufacturing and industry 4.0 integration

“…Driving power and dependence power of these factors are determined using MICMAC analysis (Jena et al , 2017; Sonar et al , 2020).…”