The Significance of Big Data Analytics in the Procurement Process and Supply Chain Management in the Nigerian Manufacturing Industry

Itiri Idam Okpara, Patrick Chigozie Moneme, Ogbonnaya Abraham Onuaja and Kingsley Enujiofor Ikegbunam
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Itiri Idam Okpara: Department of Business Education, Federal College of Education (Technical), Isu, Ebonyi, Nigeria
Patrick Chigozie Moneme: Department of Business Administration, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
Ogbonnaya Abraham Onuaja: Department of Business Education, Federal College of Education (Technical), Isu, Ebonyi, Nigeria
Kingsley Enujiofor Ikegbunam: Economic behavior and Governance, University of Kassel, Hesse, Germany

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 10, 2850-2872

Abstract: The adoption of BDA in Nigeria faces challenges due to the absence of affordable computing, mining, reliable data, and weak institutional frameworks in many organisations. It is important to examine the significance of BDA in the procurement process and SCM in the Nigerian manufacturing industry (NMI). The study employed multiple case study sampling method, linear curve estimation (LCE), Cronbach’s alpha, and exploratory factor analysis (EFA) for the analysis of the data. The results showed that BDA contributed significantly to the increase in the procurement process by 65.3% and the increase in SCM by 48.4% in NMI. Further estimations to examine interconnectivity among the complex structures in SCM revealed that efficient procurement processes, SC visibility, and SC flexibility of NMI had positive and significant influence on SC resilience. Strong reliability was shown by the majority of scale items, which had Cronbach’s alpha coefficient values greater than 0.7 but less than 0.90. The outcomes of factor analysis indicated that the pattern matrix displayed significant factor loading and a high connection between factors. The study concluded that BDA is significant in the procurement process and SCM in the Nigerian manufacturing industry. Therefore, agreeing with knowledge management and competitive advantage theories that companies process diverse data to ensure efficient operations and market differentiation to improve competitive edge, customer satisfaction, and brand loyalty in the industry. The study provides valuable insights for practical policy implications.

Date: 2024
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