Simulation vs Optimization
Inventory Management has become significantly more complex in the "on-demand" economy
we are now experiencing. High product complexity has yielded low forecastibility
and high inventory risk.
Traditional management methods are too simplistic. However, two potent mathematical
methods are available to "optimize" inventory.
|
Feature
|
Optimization
(linear program)
|
Simulation
(Monte Carlo)
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|
Horizon |
Point in time |
12 to 18 months |
|
|
|
|
|
Demand |
Average |
Daily Variability |
|
|
|
|
|
Capacity |
Constrained |
Model variable |
|
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Inventory |
Presumed |
Calculated |
Both seek an optimum solution:
- Optimization calculates the Least Cost deployment
- Simulation calculates the Least Inventory required at each deployment point
Both methods have "what if" capability:
- Optimization considers a series of point-in-time scenarios with fixed capacity constraints
- Simulation considers the peaks and valleys of demand over many months and flexibility
required in capacity to balance inventory.
Used together, the two methods provide a least cost solution and the inventory policies
which minimize inventory investment risk.