Robust Sales and Operations Planning model under Demand and Lead Time uncertainty: a real-life case study from a packaging company

Sales and Operations Planning (S&OP) is a key process for aligning supply and demand, but in today’s fast-changing markets it is increasingly difficult to rely on stable and predictable conditions. Companies often face fluctuations in demand and variations in lead times, reducing the effectiveness of traditional planning methods. This thesis addresses these challenges through a real-life case study in the packaging sector, where uncertainty plays an important role in operational decisions. The objective of the work is to design and compare two distinct models for S&OP. The first is a deterministic model, which assumes complete knowledge of all parameters and represents the traditional approach. The second is a robust optimization model that explicitly incorporates uncertainty in demand and lead time. Both models have been implemented and tested using real industrial data, allowing a realistic evaluation of their performance and the analysis of their respective advantages and limitations. The results highlight a clear trade-off between cost efficiency and robustness. The deterministic model achieves lower costs under stable conditions, but is vulnerable to variations in demand and lead time. In contrast, the robust model finds solutions that are more resilient to market fluctuations, although at a higher cost. Overall, the study contributes both to academic research on robust optimization models and to practical decision-making by offering a structured approach to balance efficiency and robustness in the S&OP context.