Predictive Sales and Operations Planning Based on a Statistical Treatment of Demand to Increase Efficiency: A Supply Chain Simulation Case Study

Forecasting is the basis for planning. Good planning is based on a good prediction of what is going to happen to prepare a company, a department, and their environments for certain future developments and their intermediate states. In this context, resources are allocated to these future states in the most efficient way, given a certain set of resource conditions. Although market volatility demands the high adaptability of companies' operations, dynamic planning is still not widespread. As a result, the alignment of planning processes with potential scenarios is not given, leading to a lack of solution preparation in the long term, suboptimal decision-making in the medium term, and corrective measures in the short term, with higher costs and a lower service level. Therefore, the aim of this research is to propose a predictive approach that will help managers develop sales and operations planning (S&OP) with higher accuracy and stability. For this purpose, a methodology combining demand scenarios, statistical analysis of the demand, forecasting techniques, random number generation, and system dynamics was developed. The goal of this predictive S&OP is to predict the supply chain system's behavior to generate plans that prevent potential inefficiencies, thereby avoiding corrective measures. In addition, to assess the methodology, the model is applied in the software Vensim, for an automotive producer ' s supply chain, to compare the predictive S&OP model with a classical approach. The results show that the proposed predictive approach can increase a manufacturer's efficiency by increasing its adaptability through the identification of potential inefficiencies and can also be used to prepare solutions.

Automated summary

Forecasting is essential for planning, but dynamic planning is not common in practice, resulting in insufficient preparation of solutions in the long term, suboptimal decision-making in the medium term, and the need for corrective measures with higher costs and lower service levels in the short term.