In the current dynamic and consumer driven market environment, supply chains need to move out of reactive to the proactive ecosystems. One of the most urgent requirements is increasing procurement visibility and the development of better order planning for the inventory to meet the products’ demand with continued high customer satisfaction. This paper is an agenda of big-data-driven predictive analytics framework that is aimed at predicting low-stock items and optimization of procurement processes in real-time. With a hybrid approach that integrates data from prior years of sales figures, supplier lead times, and seasonal trends as well as real time feeds into the stock under consideration, the study uses machine learning algorithms such as Random Forest Regression and Long Short-Term Memory (LSTM) networks to determine depletion values of stock with great accuracy. The proposed system combines these forecasts into procurement dashboard that is dynamic which supports threshold-based automatic ordering and better operation agility. In order to assess the efficiency of the model, some of the Key Performance Indicators (KPIs) like Stockout Rate, Inventory Turnover Ratio, Lead Time Variance and Forecast Accuracy Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were to be used over a six-month time span of data gathered from a Results indicate 28% increase in the accuracy of inventory forecasting, 35% reduction in purchase response time and 22% upsurge in customer satisfaction metric based on Service Lags (SLAs). The study describes how big-data approaches (especially Apache Hadoop for storage, Spark for real-time, and Tableau for visualization) can raise the intelligence and regard for the responsiveness of contemporary supply chains by quite a large margin. In-short the paper addresses inefficiencies in procurement forecasting by integrating Random Forest for feature selection and LSTM for time-series prediction. Real-time and historical data are fused using Apache Spark to provide timely insights, while a dynamic procurement dashboard uses forecast thresholds to trigger automated reordering, improving accuracy and reducing delays. This research closes the gap between procurement planning and predictive inventory control while paving the way for the emergence of a customer-centric model of a supply chain based on data availability and insights.
Pathak et al. (Thu,) studied this question.
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