Manufacturing

Manufacturing Analytics & AI for Uptime, Planning, and Performance

Manufacturing AI delivers ROI when it reduces downtime, improves planning accuracy, and makes constraints visible early. We build the data and decision systems operators trust.

Book a Manufacturing Strategy Call

Key problems

Downtime + reactive maintenance cycles

Failures repeat because event history, work orders, and utilization signals aren’t analyzed together.

Forecast volatility + planning gaps

Planning relies on inconsistent inputs; forecast error isn’t tracked by the right hierarchies.

Fragmented asset + production reporting

Multiple systems and spreadsheets create conflicting KPIs and slow decisions.

Slow root-cause analysis + exception handling

Teams spend time explaining problems instead of getting an actionable risk queue.

What we deliver

Predictive maintenance analytics (events + telemetry where available)

Failure patterns, MTBF/MTTR drivers, and early warning signals tied to work-order actions.

Planning systems (demand / inventory / capacity)

Forecast baselines, error metrics, bias tracking, and conversion into buffers and service-level targets.

Plant performance dashboards (exceptions-first)

OEE-style views, throughput, downtime reasons, and constraint visibility for weekly cadence.

Governance: KPI layer + monitoring + adoption

Semantic model, definitions, freshness checks, and adoption playbook for operators and leaders.

Deliverables

Architecture + roadmap + MVP plan (plant/asset class scope)

Curated models (assets, maintenance events, production, planning hierarchies)

Dashboards for plant + leadership cadence (exception-first)

Model evaluation + monitoring approach (accuracy, drift, overrides)

Frequently Asked Questions

What data is needed for predictive maintenance?

Real-Time IoT sensor data, historical maintenance logs, asset information(make, model and specifications) and environmental or operational conditions.

What is a “digital twin readiness” approach?

Establishing the asset hierarchy, event model, and KPIs so telemetry and maintenance data becomes actionable.

How long for a Pilot/MVP?

Typically 4–8 weeks for a scoped asset class or single plant.

How do you ensure adoption?

Exception-first dashboards, named KPI owners, and embedding reviews into weekly operating cadence.

How do you measure success?

Improved OEE , faster interventions, increased forecast accuracy and reduced manual reporting effort.