Use cases → Materials and reliability engineer
You are asked to cut inventory without risking a stockout. OptimalSPARES™ lets you do both, by stocking to criticality on clean, harmonised data.
The problem
Duplicate parts and messy data hide the real holding, so capital sits on a slow-moving tail while the odd critical, long-lead part is still missing when it is needed.
AI-assisted classification and fuzzy de-duplication turn a messy catalogue into one trusted record per part.
Consequence of a stockout, lead time and demand set a service-level target per part.
EOQ, reorder point and safety stock size the stock, and pooling shares it across sites.
The outcome
Ranges confirmed across Optimal client engagements. Illustrative, not a guarantee.
"Half the store had not moved in three years, and two critical parts were not stocked at all. Fixing both cut value and cut stockouts."
Start a 14-day trial and optimise your real storeroom on real data.
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