Managing Uncertainty in Production Planning for Fast-Moving Consumer Goods: A Linear Programming and Monte Carlo Simulation Framework

Abstract

The production planning of Fast-Moving Consumer Goods (FMCG) industries is a challenging task due to uncertainty and risk. This study proposes a novel algorithm that combines linear programming and Monte Carlo simulation methods to manage uncertainty and minimize costs in FMCG production planning. The algorithm uses simulation to forecast the various possible production units and quantify the impact of risk and uncertainty on each production decision. The output is an annual production plan that satisfies minimum customer requirements while optimizing profits. The proposed model was tested on an FMCG factory problem, and it was found to improve the company's expected profit by 2.06%, reduce variable costs by 7.65^%, and decrease the contribution of inventory holding costs to the total system cost from 4.43% to 1.39%. The study highlights the high cost incurred due to inventory holding costs and demonstrates that the proposed model provides a more efficient approach to determining production and inventory quantities. The combination of linear programming and Monte Carlo simulation methods provides an efficient way to manage uncertainty and minimize costs while meeting customer requirements. These findings have important implications for FMCG production planning, and the proposed model can help companies make more informed decisions.