A Hybrid Neural Network and Discrete Event Simulation Approach for Dairy Production Line Optimization

Operational production planning has become a key factor in a company’s competitiveness, enabling efficient resource management and a rapid response to fluctuations in demand. However, many manufacturing organizations base their decisions on a conceptual perspective, creating imbalances between supply and demand. In this context, this study proposes a hybrid framework that integrates demand forecasting techniques and discrete-event simulation to optimize production planning at a dairy plant under scenarios involving random demand. First, a predictive model was developed using ARIMA as a baseline for comparison, and then parameters were adjusted in neural networks to estimate demand. The results showed that neural networks performed better, specifically the RN2 model, in generating future projections. Subsequently, the estimates were used as inputs for the simulation model to represent the company’s current system, enabling the identification of bottlenecks and the evaluation of improvement scenarios. The findings indicated that the company’s current capacity is insufficient to meet current and projected demand. Similarly, the “Assembly” and “Quality Control” stations were identified as the main operational constraints. The proposed improvement increased production capacity by 27.7%, enabling the company to meet demand. The evidence shows that integrating forecasting with simulation is an effective tool, as it provides data and scenarios that closely reflect reality, allowing companies to anticipate constraints and, most importantly, reduce risks and make more informed investment decisions.