Role of Industry 4.0 Technologies in Enhancing Supply Chain Resilience of Original Equipment Manufacturers (OEMs): A PLSSEM Based Empirical Investigation

Komal Sharma, Deepika Rathi, Jainish Bhagat and Ronak Ajaykumar Mehta
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Jainish Bhagat: P P Savani University

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Abstract: Purpose – This study examines how the adoption of Industry 4.0 (I4.0) technologies – the Internet of Things (IoT), Big Data Analytics (BDA), Artificial Intelligence/Machine Learning (AI/ML), Blockchain (BCT), and Digital Twin (DTW) – influences the supply chain resilience (SCR) of Original Equipment Manufacturers (OEMs), through the mediating roles of supply chain visibility (SCV), supply chain agility (SCA), and supply chain collaboration (SCC), and the moderating role of organizational readiness (ORGREAD). Design/methodology/approach – A structural model integrating the Resource-Based View and the Dynamic Capabilities View was tested using Partial Least Squares Structural Equation Modelling (PLS-SEM) on survey data from 260 supply chain, operations, procurement, and digital-transformation executives in OEM firms across the automotive, electronics/electrical, industrial machinery, aerospace, and consumer-durables sectors. I4.0 technology adoption (I4ADOPT) is modelled as a second-order construct formed by five first-order technology dimensions; supply chain resilience is modelled as a second-order construct reflecting robustness, redundancy, flexibility, and recovery. The structural model was estimated with 2,000-sample nonparametric bootstrapping. Findings – I4ADOPT significantly and positively predicted SCV (β = 0.487, p < .001), SCA (β = 0.254, p < .001), and SCC (β = 0.193, p < .01), and exerted a significant direct effect on SCR (β = 0.175, p < .01). SCV, SCA, and SCC each significantly predicted SCR (β = 0.237, 0.354, and 0.240 respectively, all p < .001) and significantly, partially mediated the I4ADOPT–SCR relationship. SCV additionally strengthened both SCA (β = 0.335) and SCC (β = 0.308). Organizational readiness had a significant positive direct association with both SCV (β = 0.169) and SCA (β = 0.205), but its hypothesized moderating (interaction) effect on the I4ADOPT–SCV and I4ADOPT–SCA relationships was not statistically supported. The model explained 58.2% of the variance in supply chain resilience, and all measurement-model reliability, validity, and model-fit criteria were satisfied (SRMR = 0.013). Practical implications – OEM managers should prioritise IoT- and analytics-led visibility investment as the entry point of a digital resilience strategy, since visibility significantly amplifies agility and collaboration in addition to its own direct effect on resilience, and should invest in organizational readiness (leadership commitment, digital skills, process maturity) as a parallel, direct driver of these capabilities rather than solely as a moderating lever.

Date: 2026-09-17
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:fkprh_v1

DOI: 10.31235/osf.io/fkprh_v1

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