Quantitative Dynamic Simulation and Analytical Optimization
Modern supply chains and manufacturing systems are complex and subject to a wide variety of risks. Making decisions based on average and deterministic values (typical Excel computations) is far too risky as it does not represent the real-world random behavior. Quantitative dynamic simulation provides decision makers with a powerful tool that assures sound decisions in awareness of all the consequences and risks.
Quantitative Dynamic Simulation supports decisions on all levels in the Supply Chain and Manufacturing system:
The robustness of your supply chain or manufacturing system can be assessed and reinforced to prevent future disruptions by testing different evolution scenarios (e.g. optimistic, pessimistic, etc.)
Quantitative Dynamic Simulation highlights which parts of the system are underperforming or where bottlenecks may appear. By supporting the optimal dimensioning of the supply chain and manufacturing system (inventories, manufacturing capacity, lot-sizes, etc.) Quantitative Dynamic Simulation ensures peak performances and minimal cost.
Quantitative Dynamic Simulation allows testing and selecting the best routing or scheduling on the shop floor. Combined with optimization it becomes a very powerful support for efficient scheduling in complex manufacturing systems characterized with several difficult constraints.
In summary, Quantitative Dynamic Simulation is a must to:
Evaluate the robustness of your supply chain
Assess cost and service level trade-offs
Dimension system’s parameters such as stock levels and lot sizes
Study what-if scenarios such as: capacity investment, modifications in the industrial and distribution networks, changes in the management policies for procurement, manufacturing, inventory, delivery and transport
When coupled with analytical optimization, quantitative dynamic simulation provides an excellent decision-making tool to support business leaders and supply chain professionals in their toughest supply chain and operations challenges.