The situation
A mid-size discrete manufacturer operating three production facilities and a network of contract suppliers produces assemblies for industrial and commercial customers with contractual on-time delivery requirements. The production planning system, the warehouse management system, the ERP, the supplier portal network, and the customer demand signal are all live systems, all producing useful operational data, and none of them have ever been connected into a single trusted picture. The production planner reconciles between them manually, every week, in a spreadsheet that takes two days to build and is partially out of date by the time it is distributed.
The consequences accumulate across the operation. Safety stock builds up as the demand signal is too uncertain to trust at planning horizon. Expedited freight becomes the normal response to supply disruptions that were visible in the data weeks before they hit the floor — just not surfaced or trusted enough to act on. Equipment downtime is managed on a calendar rather than a condition basis because the condition data from the production floor has never been connected to the maintenance planning system. OEE runs below industry benchmarks not because the assets are inadequate but because the data environment they operate in is fragmented.
The COO had two specific questions she needed answered: why does the production schedule slip by an average of 8% in the final two weeks before shipment, and where is the excess inventory that her finance team believes is trapped in the operation? Both answers were in the data. Neither was surfaced by the existing systems.
The solution
The PolyPhaze Knowledge Fabric™ connected the production planning system, WMS, ERP, supplier portal network, and customer demand signal. Entity resolution reconciled product identifiers, supplier records, and customer codes across all five systems into canonical Trust-Scored records. Every production signal — scheduled versus actual output, material availability, supplier lead time, equipment health, customer demand — now carries a PolyPhaze Trust Score™ and full lineage. The same trusted foundation serves the production planner’s workbench, the COO’s executive view, and every AI agent running against the operational data.
The Signal Intelligence capability surfaced the answer to the COO’s first question in the first week of deployment. The 8% schedule slip traced consistently to three root causes: a specific component category with a 23-day actual lead time against a 14-day planned lead time, a production cell whose downtime pattern was not captured in the planning system, and a customer demand signal that had a 12-day update lag built into the integration. All three were visible in the data. None had been connected before.
The Optimize capability deployed against inventory, procurement, production, and supplier dimensions. Inventory optimization surfaced excess safety stock by location and product category — $3.2 million in carrying cost concentrated in 14 SKUs where demand uncertainty had driven over-ordering. The Trust-Scored demand signal allowed safety stock to be right-sized with confidence rather than judgment. Supplier performance was ranked by on-time delivery accuracy weighted by production impact, enabling constrained suppliers to be identified and routed around before their delays became line-stop events.
The Asset Twin capability built condition-based maintenance models for the highest-utilization production assets, connecting equipment sensor feeds, maintenance history, and OEM life limits into Trust-Scored maintenance recommendations. Calendar-based schedules were replaced on the primary production lines. The first cycle detected a spindle bearing on a CNC machining center trending toward failure 19 days before the scheduled maintenance interval, and 31 days before the point at which the failure would have caused an unplanned line stop.
The production planning cycle that previously required two days of manual spreadsheet reconciliation now runs from a live, governed operational picture. The planner’s time shifted from data assembly to decision-making. The schedule slip narrowed from 8% to under 2% within the first operating quarter.