Turn Distribution Planning into an Optimization Model — and Know When Not to Trust It

This tutorial explains how to translate a distribution-planning problem into an optimization model and validate the result before operational use.

1. Define the decision

State exactly what the model controls: quantities by origin, destination, product and period. Separate decision variables from observed data and policy rules.

2. Encode the objective

Specify what the model minimizes or maximizes, such as transport cost, shortage cost or service penalties. Avoid hiding critical business rules inside arbitrary weights.

3. Model the constraints

Represent demand, capacity, inventory availability, lane limits, lead times and mandatory business rules explicitly.

4. Test feasibility

Before trusting an optimum, verify that the model can represent real operational states and clearly reports infeasible scenarios.

5. Run sensitivity tests

Change capacity, cost and demand assumptions and examine whether the solution behaves plausibly. Extreme instability is a signal to review assumptions or constraint design.

6. Compare with the real plan

Measure the proposed allocation against the incumbent plan and investigate large differences before execution.

Failure controls

• Constraint coverage review.
• Feasibility checks.
• Sensitivity analysis.
• Assumption ownership.
• Manual plausibility review.