Manufacturing
Operational OEE, supply chain logistics, demand analytics
Standard Overall Equipment Effectiveness (OEE) KPI definitions, a supply chain process taxonomy from procurement through fulfillment, and demand forecasting benchmarks by sub-sector -- so your current-state assessment starts against known-good comparison data, not generic benchmarks.
The patterns that show up again and again in this vertical.
OEE is quoted as a single plant-level number, which averages away the specific losses -- changeover, minor stops, quality rework -- that are actually costing you.
Procurement, production, and fulfillment each hold a piece of the same process, and nobody owns the handoffs between them.
Throughput is measured at the line but never reconciled against demand, so overproduction on one SKU hides underproduction on another and the plant hits its numbers while the business misses its orders.
Maintenance runs to a calendar rather than to condition -- you pay for interventions you didn't need and still take the breakdowns you didn't see coming.
What the accelerator gives you a head start on.
An accelerator is the same QBPES™ architecture every engagement runs on, shaped for this vertical -- so Discovery starts from a working taxonomy and a defined KPI set rather than a blank page. Each one deepens with every engagement we run in the vertical.
Standard OEE definitions -- availability, performance, quality -- decomposed to loss category, so the number points at a cause instead of just reporting a score.
A supply chain process taxonomy running procurement through fulfillment, with the handoffs between functions already mapped.
Demand forecasting benchmark ranges by sub-sector, so current-state is scored against comparable operations rather than a generic industry average.
Maturity scoring rubrics for planning, scheduling, and maintenance governance, ready to apply against evidence from day one of Discovery.
The difference it makes to how the business runs.
Discovery starts against known-good comparison data, so the assessment phase is shorter and the findings are harder to argue with.
Losses are attributed to a named cause and a named owner, rather than absorbed into a plant-wide average nobody can act on.
The KPI architecture outlives the engagement, because it is defined once and governed -- not rebuilt from scratch for each month's report.
AI-native execution lands where it earns its keep -- forecasting, scheduling, exception handling -- instead of being spread thin across everything at once.
Good fit if...
You have plenty of plant data but no agreed definition of what good actually looks like.
Improvement projects keep getting delivered and then quietly decaying back to the old way.
Production and supply chain are optimising against numbers that quietly conflict.
Ready to talk about manufacturing?
A first conversation is a diagnostic, not a pitch -- we'll tell you plainly whether the accelerator fits your operation, and where the highest-leverage place to start actually is.