The Role of Data Management and Automation in the Impact of Industry 4.0 On Supply Chain Performance: Empirical Analysis

Abstract

This research aims to assess the degree to which the Pakistani textile sector is ready for industry 4.0 (I4.0) and how this readiness affects Supply Chain (SC) performance. The paper seeks to provide concrete evidence of the potential benefits of implementing Industry 4.0 by the Data Management (DM) and Automation Level (AL) and how doing so can affect SC efficiency. An empirical study is conducted to evaluate the capability of the industry to implement industry 4.0 and its effects on SC efficiency. Three hundred and fifty-one workers in Pakistan’s textile sector provided the data. The research has two main categories. After determining the state of DM and AL in the business world and the sector’s overall level of preparedness, the author used a Structural Equation Model (SEM) strategy to find the impact on SC performance. The author evaluated a research framework in Spss and AMOS. By employing a CFA and SEM strategy, we learned that DM and AL significantly influence the feasibility of implementing industry 4.0. It demonstrates a significant negative relationship between the possible application of industry 4.0 and SC efficiency. The study only looked at the Pakistani textile industry, limiting its applicability to other emerging countries and manufacturing sectors like automobiles and electronics. As a result of this study, businesses will have a better idea of where they need to focus their efforts to improve. Managers and researchers can determine the readiness level of any organization or sector by assessing how well they fit the study’s components and framework. This research contributes to the literature by expanding our understanding of the interplay between DM, AL, the application of industry 4.0, and the performance of SC through the presentation of new data and empirical findings. Researchers and businesses can then coordinate their efforts to advance toward Industry 4.0 and identify the roadblocks to improved SC efficiencies.