OptimalSPARES™ Wiki / Value and deployment

Value and config

Value and deployment

What spares and materials optimisation returns, what data it needs, and how it is configured to deliver, in general terms.

Potential value

Across Optimal client engagements, spares and materials optimisation returns value in these ranges, shown for context, not as a guarantee:

The headline is the paradox it resolves: inventory value falls while availability rises, because the capital comes out of the slow-moving tail that never gets used while the critical parts are actually covered. Working capital is released and the stockouts that cause the expensive shutdowns are prevented. Across a multi-site network, pooling shared parts and redeploying excess from one store to another compounds the saving, because the group holds one buffer instead of many.

What it takes: the inputs

Config to deliver value (general)

In general terms, the programme earns out when: the data is cleansed and de-duplicated first, so the analysis runs on reality; criticality drives the service levels, so stock is spent where it matters; the demand model fits the pattern, normal for fast movers, Poisson or one-for-one for slow and insurance spares; the min, max and reorder points are written back to the ERP and kept live, not left in a report; and the slow-moving tail is reviewed for obsolescence, redeployment to the sites that need it and disposal. The result holds only if the optimisation stays synchronised with the ERP rather than drifting back.

A typical path

Assess the storeroom with the consultancy wizard; cleanse, classify and de-duplicate the materials data; build the bills of materials and inherit criticality from the assets; fit the demand model and compute the stocking policy per part; write the min, max and reorder points back to the ERP; and run the permanent optimisation so the policy tracks demand and criticality, publishing spares health to the ARaaS® Dashboard.