A Data-Driven Framework for Intelligent Procurement Decision-Making Using Machine Learning and Predictive Analytics in Global Supply Chain Networks
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I1P145Keywords:
Intelligent Procurement, Machine Learning, Predictive Analytics, Demand Forecasting, Supplier SelectionAbstract
Global supply chains are concurrently facing mounting demands, uncertain suppliers, price fluctuations, and low real-time visibility in procurement, making this a major challenge that leads to lack of efficiency in young decisions and an increasing cost of doing business. Conventional systems, which are based on rules, are reactive, and do not use the increasing amount of enterprise and external data to provide predictive information. The paper suggests a machine learning (ML)-based predictive analytics-powered framework to make intelligent procurement decisions. This framework has a modular structure that includes the data ingestion, preprocessing and feature engineering, and a decision engine powered by machine learning. It uses regression and ensemble cost and demand forecasts, and time-series models like LSTM and ARIMA to capture time variation. Supplier evaluation is done using multi-criteria decision-making (MCDM). Experimental findings on real and simulated data show that the accuracy of forecasting, cost reduction in procurement and selection of suppliers are better than traditional methods. The suggested framework opens the prospects of decisions that are proactive and real-time as well as enhances resilience in the supply chain. All in all, the study provides a scalable and extending answer to data-driven procurement to aid greater efficiency and strategic decision-making in intricate global supply chain settings.
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