Leveraging Dynamic Simulation Modeling for effective Master Production Scheduling: a food processing case study
ABSTRACT
This case study explores how dynamic simulation modeling can significantly enhance Master Production Scheduling (MPS) in complex manufacturing environments, using the example of a small-to-medium enterprise in the fish processing industry. The company specializes in harvesting and processing fresh salmon for the Horeca sector, facing the challenge of aligning daily raw material inflows with production constraints and market demands.
The study demonstrates how simulation enables the creation of a responsive and realistic MPS that not only meets demand but also respects operational constraints such as storage capacity, processing throughput, and waste minimization. Unlike traditional MPS methods, which often rely on static, mono-objective optimization, the simulation approach allows for the modeling of stochastic variables—like harvest variability—and supports multi-objective analysis, including profitability and sustainability.
The simulation model incorporates detailed structural, operational, and economic data to replicate the current planning logic and test alternative strategies. By adjusting inventory control parameters (reorder points and quantities), the model evaluates over 240 planning scenarios, identifying configurations that improve both profitability and waste reduction.
One scenario, in particular, demonstrated potentialities to strengthen the industrial margin while reducing waste, validating the model’s ability to guide strategic planning decisions. The study also highlights the exploratory nature of simulation: rather than seeking a single optimal solution, it provides a framework for testing and refining planning policies under realistic conditions.
Key insights include the model’s transparency, adaptability, and ability to simulate real-world constraints, offering decision-makers a powerful tool for continuous improvement. The case concludes by comparing simulation with traditional MPS methods, emphasizing the former’s advantages in flexibility, interpretability, and alignment with managerial goals.
Ultimately, this study underscores the value of simulation as a practical and effective approach to master production planning, especially in dynamic and resource-constrained environments like food processing.
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