Built a decision-support engine that, per SKU per planning month, recommends how much of a plant's output ships to each regional distribution center to meet demand at lowest total landed cost — as a transparent, auditable linear program a planner can trust and override.
The hard part of decision-support isn't the maths — it's getting planners to trust a new engine over the spreadsheet they've run for years. Instead of asking them to, I built a cross-check that runs the live solver on the exact inputs their spreadsheet used and returns a per-SKU diff of both the demand-split step and the allocation step. The engine could be proven apples-to-apples against the incumbent process, one SKU at a time, on their own numbers — so adoption became a matter of evidence, not faith.
For decision-support that replaces human judgment, being validatable against the old way matters more than being clever.
- ExplainableEvery recommendation traceable to a named business rule
- SKU-by-SKUProvable against the incumbent spreadsheet, on the planners' own numbers
- RepeatableSame inputs, same answer — every run auditable after the fact
- ExtensibleNew business constraints slot in without rework