Predictive Analytics for Inventory Optimization in Manufacturing

Abstract—Accurate demand forecasting is important in the biscuit manufacturing business to optimize inventory management, maintain operational efficiency and reduce costs. Currently, the scenario subjected company has produced over 50 products under different brand names as well as SKUs. To get demand forecasting, they rely on the manual methods conducted by the sales and administration and marketing departments. These manual methods are performed using tools like excel, they are prone to errors, data duplication, and inefficiencies which are leading to unreliable forecasts and dependencies on individuals. To address these challenges, this project aims to develop predictive analytics forecasting models. To build this model, machine learning techniques will be employed such as Random Forest, Time series and Regression which will be used to analyze historical sales data to identify the trends and patterns, capture complex relationships and find external factors influencing demand. Model creation will enhance the accuracy of demand, inventory management, and improve the decision-making process of the company. The future outcome of this project, the company will be able to meet customer demands in a better manner, reduce inventory costs, and maintain a competitive edge in the market.