STUDY ON STRATEGIC ISSUES PERTAINING TO THE APPLICATION OF BIG DATA ANALYTICS IN MANUFACTURING SECTOR SUPPLY CHAIN

Abstract

In the era of Industrial 4.0 all organization are moving towards digitalization of their processes. Due to the digitalization of processes, massive unstructured data is being generated in an organization from different sources. This huge amount of data is very difficult to manage with traditional decision-making tools. Therefore, Big Data Analytics (BDA) play an important role to manage/analyze such kind of data. There is lack of comprehensive and exhaustive study on implementation of BDA in manufacturing sector. In the context of the Indian manufacturing sector supply chain, the current study intends to investigate the barriers and critical success factors of BDA adoption. Many gaps need to be filled by conducting research, which gives a framework for the BDA application in the manufacturing sector. Therefore, four objectives of this research have been developed based on the research gaps identified in the literature review. The first objective is to identify and justify the benefits of Big Data Analytics applications in the context of the Indian Manufacturing Industry. The second objective is to identify and analyze the key barriers obstructing the implementation of Big Data Analytics and develop framework for evaluating the barriers intensity index. The third objective is to Identify and ranking of Critical Success Factors in Big Data Analytics implementation. The fourth objective is to explore the determinants and develop a conceptual framework for adopting Big Data Analytics in the context of Indian Manufacturing. Literature has been reviewed in the areas such as big data analytics (definitions, characteristics, application of BDA in manufacturing, identification of barriers, critical success factors, determinants, and items. The flow of this research goes as follows. Initially, there is a need to justify the Big Data enabled manufacturing over without Big Data enabled manufacturing which has been done in the study using Analytical Hierarchy Process. In the study, it was justified that Big Data enabled manufacturing is better as compared to without Big Data enabled manufacturing. Then, identification and analysis were carried out for vi the major barriers obstructing the implementation of Big Data Analytics and framework for evaluating the barriers intensity index in the context of the Indian manufacturing industry were developed. "A total 17 barriers were identified through an extensive literature review and based on the opinion of experts from industry and academia. Factor analysis is applied to factorize the seventeen identified BDA barriers into three categories viz: organizational, data management, and human barriers. Further, Graph Theory Matrix Approach (GTMA) was employed to evaluate the barriers intensity index. In the results, the organizational barrier came out to be the most important barrier in the implementation of BDA. This study is further extended by Identifying and ranking of Critical Success Factors (CSFs) in Big Data Analytics implementation. Critical Success factors for BDA application in the manufacturing sector are identified through literature. After discussion with experts, 15 factors are finalized for their ranking from a strategic perspective. A questionnaire-based survey was conducted in the context of big data analytics applications in the Indian manufacturing sector. The experts were selected from industry and academia. The experts from industries and academia were requested to respond to the questionnaire designed for this study. The CSFs have been ranked by Fuzzy TOPSIS approach. Commitment and engagement of top management, strategy development for BDA, and development of capability for handling big data are prioritized as 1st, 2nd, and 3rd in their relative importance, which is crucial for BDA implementation. In addition to this, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach categorizes the critical Success factors into cause-and-effect groups. Based on DEMATEL results, eight critical success factors are falling in the category of cause group and seven critical success factors fall in the effect group. Finally, while exploring the determinants a conceptual framework for adopting Big Data Analytics in the context of Indian Manufacturing was developed. A structural modelling was used to examine the hypothesized conceptual research model using smart partial least squares vii (PLS). All the path coefficients are positive, and the P value is in the acceptance range (P<0.005); hence the results support the hypothesis. The research work comprises the fulfilment of all objectives identified based on the research gaps. The achievement of the objectives of this research can assist managers or the top management in implementing new technologies. This thesis makes a novel theoretical and practical contribution. The significant contributions and research implications can be retrieved from the research. Recommendations, limitations, and future scope of the study have also been made". This research will help manufacturing organizations, academicians, and researchers to understand, adopt, and implement the learning based on the outcomes of the study.