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Value

OEE you can act on

Availability, performance, and quality unified across lines and plants — with the drill-down the maintenance and operations teams will actually use during a shift.

Predictive maintenance, not predictive theater

Sensor telemetry, work-order history, and failure modes joined into models that flag the failure before it lands — and tell the planner which spare to stage.

Supply chain visibility end-to-end

Inbound, in-plant, and outbound flows in one model. Suppliers, logistics, and finance work from the same numbers instead of three reconciled spreadsheets.

Quality analytics that close the loop

SPC, scrap, and warranty data linked back to lot, line, and shift. Root-cause analysis stops being an archaeology project.

Energy as a controllable cost

Submeter and utility data correlated to production schedules so peak-shaving and tariff optimization become engineering decisions, not surprises on the bill.

Make better decisions using data

Manufacturing generates an extraordinary volume of data — PLC tags, MES transactions, ERP postings, quality readings, supplier EDI, energy meters, and warranty claims. The hard part is rarely capturing it. The hard part is making it converge into a single set of numbers that operations, maintenance, supply chain, quality, and finance all trust.

Canopy Analytic helps manufacturers close that gap. We build analytics that survive contact with the shop floor, AI models that augment human judgment on the asset and quality decisions that matter, and automation that removes the manual reconciliation work eating planner and engineer capacity.

Where we help

OEE that operations actually uses

Availability, performance, and quality calculated consistently across lines and plants, broken down to the shift and the asset. Designed so a line lead can answer “what cost us output this shift?” in under a minute — and so plant management can compare lines without arguing about definitions.

Predictive maintenance that pays back

Vibration, temperature, current, and cycle-count signals joined to work-order history and known failure modes. We start with one asset class where the failure cost is documented, prove the model on real history, and only then scale. The deliverable is fewer surprise failures and better-staged spares — not a dashboard that says “anomaly detected.”

Supply chain visibility across the flow

Inbound material, WIP, and outbound shipments in one model with the same definitions of on-time, in-full, and at-risk. Buyers, planners, and logistics work from a shared picture of where the next bottleneck is forming, and finance gets working-capital visibility without a month-end scramble.

SPC trends, scrap, rework, and warranty events joined back to the lot, line, shift, and supplier. Pareto analysis stops being a quarterly slide; it becomes a continuous signal that quality engineers can use to prioritize the next investigation.

Energy as an engineering decision

Utility bills, submeter data, and production schedules in one place so the cost of running a line at a given time is visible alongside the throughput it produces. Peak-shaving, tariff optimization, and load-shifting become decisions made with numbers — not after the bill arrives.

How we work

Canopy Analytic works primarily in the Microsoft ecosystem — Power BI, Fabric, Azure, and Dataverse — and integrates with the MES, historian, ERP, and EAM systems your plants already run. We engage in scoped pilots tied to a measurable plant outcome, prove the model in production, and train your team to own it. The goal is leverage that compounds in your organization, not consulting hours that compound in ours.

Related case studies

Common questions

We already have an MES and a historian. Where does Canopy Analytic fit?
On top of them. Your MES and historian are the systems of record for the shop floor; we build the analytics, AI, and reporting layers that turn their output into shift-level, plant-level, and enterprise-level decisions. We integrate, we don't replace.
Do we need a perfect data foundation before we can start?
No. We start with the highest-leverage decision — usually OEE or unplanned downtime — and build the data plumbing required to answer it well. Foundation grows from real use, not from a six-month modeling exercise.
How quickly does predictive maintenance pay back?
On well-instrumented critical assets, the first model usually pays for the engagement inside one prevented failure. We scope the pilot around an asset class where the failure cost is documented so payback is measurable, not anecdotal.
Can this work in a multi-plant environment with mixed equipment vendors?
Yes — and that's where the leverage is highest. A shared analytics layer over heterogeneous PLC, SCADA, and MES estates is the only way to compare lines and plants apples-to-apples and roll best practices forward.
Who runs the analytics after you leave?
Your team. Power BI training for engineers, line leads, and operations managers is part of every engagement so the work compounds inside your organization instead of inside ours.