Narrowing the barriers to Industry 4.0 practices through PCA-Fuzzy AHP-K means

Abstract

PurposeThe study aims to analyze the barriers in the adoption of Industry 4.0 (I4.0) practices in terms of prioritization, cluster formation and clustering of empirical responses, and then narrowing them with identification of the most influential barriers for further managerial implications in the adoption of I4.0 practices by developing an enhanced understanding of I4.0.Design/methodology/approachFor the survey-based empirical research, barriers to I.40 are synthesized from the review of relevant literature and further discussions with academician and industry persons. Three widely acclaimed statistical techniques, viz. principal component analysis (PCA), fuzzy analytical hierarchical process (fuzzy AHP) and K-means clustering are applied.FindingsThe novel integrated approach shows that lack of transparent cost-benefit analysis with clear comprehension about benefits is the major barrier for the adoption of I4.0, followed by “IT infrastructure,” “Missing standards,” “Lack of properly skilled manpower,” “Fitness of present machines/equipment in the new regime” and “Concern to data security” which are other prominent barriers in adoption of I4.0 practices. The availability of funds, transparent cost-benefit analysis and clear comprehension about benefits will motivate the business owners to adopt it, overcoming the other barriers.Research limitations/implicationsThe present study brings out the new fundamental insights from the barriers to I4.0. The new insights developed here will be helpful for managers and policymakers to understand the concept and barriers hindering its smooth implementation. The factors identified are the major thrust areas for a manager to focus on for the smooth implementation of I4.0 practices. The removal of these barriers will act as a booster in the way of implementing I4.0. Real-world testing of findings is not available yet, and this will be the new direction for further research.Practical implicationsThe new production paradigm is highly complex and evolving. The study will act as a handy tool for the implementing manager for what to push first and what to push later while implementing the I4.0 practices. It will also empower a manager to assess the implementation capabilities of the industry in advance.Originality/valuePCA, fuzzy AHP and K means are deployed for identifying the significant barriers to I4.0 first time. The paper is the result of the original conceptual work of integrating the three techniques in the domain of prioritizing and narrowing the barriers from 16 to 6.

Developing an interplay among the psychological barriers for the adoption of industry 4.0 phenomenon

“…Furthermore, according to an analysis by Haddud, DeSouza [ 33 ], the integration of i4.0 into supply chains is associated with potential benefits and challenges. In developing economies, where cheap labor will no longer remain an added advantage, i4.0 poses various kinds of barriers [ 3 ]. However, the researchers agree that the investigation of barriers related to i4.0 implementation remains unexplored in the extant literature [ 8 , 22 , 32 , 34 ].…”

“…Step 2 : In this step, experts were solicited using a 1–9 preference scale as described in [ 88 ] to create matrices of pair-wise comparisons of all the barriers and groups. This pair-wise comparison is the strength of AHP [ 3 ].…”

“…But this phenomenal technological thrust calls for investigation in all areas to understand and facilitate the transition. Furthermore, the revolution has various opportunities and barriers, but little literature is available concerning it [ 3 ]. Notwithstanding, some researchers have identified certain obstacles that may hold back manufacturers to attune to i4.0 [ 8 , 12 , 13 ].…”

A Framework for Transforming Indian Sports Goods Manufacturing Industry

“…'Government support system (C-10)' is placed at the most significant and much sought-after 'cause' (Kumar et al, 2020c;Raj et al, 2020) to drive the transformation. 'Access to technology (C-1)' (Kumar et al, 2020a), 'information technology-IT infrastructure (C-2)' (Kumar et al, 2020c, Moktadir et al, 2018, 'standard transformation plan and role model/trendsetter industry (C-4)' (Kumar et al, 2021) and 'availability of soft loans and support from financial institutions (C-7)' (Kumar et al, 2021) are placed after that. The importance of these challenges is also reported by past studies referenced next to them.…”

“…The study focuses on developing a transformational framework of a highly fragmented industrial cluster, mainly managed by managers, having limited exposure to technological advancement and unwilling to invest in technological up-gradation and staff training. 'Management's willingness (C-11)' is rightly ranked as the most significant challenge and essential for the transformation (Kumar et al, 2020c). In the study, it is placed among the 'effect' as the managers can only be influenced by making aware and understand the benefits (ROI) (C-3) (Kumar et al, 2020c) of 4IR and by providing an environment conducive to the transformation.…”