A Quantitative Analysis of Big Data Analytics Capabilities and Supply Chain Management

With the emergence of Big Data Technologies (BDT) and growing application Analytics (BDA), Supply Chain Management (SCM) researchers increasingly utilize BDA due to opportunities from BDT present. (SC) data is inherently complex results in an environment with high uncertainty, which presents a real challenge for SC decision-makers. This research study aimed investigate illustrate within existing decision-making process. allowed extraction processing data. aided further understanding inefficiencies delivered valuable, actionable insights by validating existence bullwhip phenomenon its contributing factors. Furthermore, enabled pragmatic evaluation linear nonlinear regression relationships applying machine learning techniques such as Principal Component Analysis (PCA) multivariable analysis. Moreover, more sophisticated time series forecasting Sarimax, Tbats, neural networks improved accuracy. Ultimately, demand planning forecast accuracy will reduce uncertainty effects observed phenomenon, thus creating competitive advantage all members value chain.