Utilizing Predictive Analytics to Improve Efficiency and Decision-Making in ERP-Connected Supply Chains
Keywords:
Predictive Analytics, Supply Chain Optimization, ERP Systems, ARIMA, Time Series Forecasting, Linear Regression, Logistic Regression, Random Forest, Demand Forecasting, Cost-Benefit Analysis, Inventory Management, Model Comparison, Operational Efficiency, ROC-Curve, Decision-Making.Abstract
This research study discusses that predictive analytics can be used in ERP-connected supply chains to optimize decision-making and operational processes. Some of the models that were applied in forecasting demand, inventory optimization and classifying of supply chain issues were Time Series Forecasting (ARIMA), Linear Regression, Random Forest, and Logistic Regression. These models were tested based on descriptive and inferential statistics, with the results indicating a better accuracy and cost-effectiveness after implementation. The results indicate that the incorporation of predictive analytics in the ERP systems is an effective way of enhancing the supply chain, which can produce actionable guidance, lower costs and streamline operations across various segments of the supply chain.
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