Data & AI Strategy for Manufacturing

80%

of OT data is left unanalysed at the edge — never reaching the systems that could turn it into a decision.

The Friction

You're drowning in signal. You're starving for decisions.

Every modern line generates terabytes of telemetry. Most of it dies on the PLC. The fraction that reaches the historian is rarely joined to quality, maintenance, supply, or finance data — which is where the operational decisions actually live.

Meanwhile, the COO is asked about AI. The plant manager is asked about OEE. And the data team is asked to deliver both, on top of a backlog of report requests, with no single answer for what to prioritise.

The Strategic Vision

Where data & AI move the needle in manufacturing.

A curated set of use cases — chosen for the impact they've had in real industrial estates, not the breadth of a vendor catalog. Each one earns its place on OEE, quality, cost, or resilience.

Predictive quality

Inline vision and process-parameter models that catch drift before it becomes scrap. Setpoints tuned against downstream defect data.

Predictive maintenance

Failure-mode forecasting on critical assets. Scoped honestly — deployed only where the cost of failure justifies the cost of the model.

Energy & carbon optimisation

Energy intensity per unit produced — instrumented and tuned against real-time price signals, ESG targets, and production constraints.

Digital twin

A virtual representation of process, line, or product — kept current with reality. Used from engineering through production to service.

Supply network resilience

Supplier health, commodity prices, and logistics signals monitored continuously. Get ahead of shortages — not behind them.

Data & knowledge landscape — the foundation

A unified semantic layer — where OT and IT silos break down.

Part, asset, site, and process mean the same thing whether you're in PLM, MES, ERP, or the historian. The unlock isn't another integration project — it's a shared semantics layer that lets AI work across the plant network instead of inside each system.

PartAssetSiteProcess

Our Framework

From data chaos to AI roadmap — in 90 days.

A four-step methodology that gets you from a fragmented OT/IT estate to a funded, sequenced AI portfolio.

01 · Weeks 1–3

Readiness Assessment

Independent maturity scorecard across OT/IT integration, data platforms, operating model, and AI literacy.

02 · Weeks 4–7

Strategy & Portfolio

A prioritised AI use case portfolio with business cases, plant-level applicability, and a 12-month sequence.

03 · Weeks 8–10

Pilot Blueprint

A detailed blueprint for the first pilot line or plant — scope, success metrics, team, vendors, and rollout criteria.

04 · Week 11+

Execution Handover

Internal owners staffed, governance in place, network-level standards drafted. We hand over — and stay on call.

FAQ

Common questions from manufacturing leaders

We have a historian and an MES. Why do we need another assessment?

A historian stores data; an MES runs operations. Neither answers whether your estate is ready to support AI decisions at the line, the plant, and the network. That's what the readiness assessment tells you.

Will this work for a multi-plant network?

Yes. The assessment explicitly looks at plant-level autonomy versus network-level standards, and the portfolio is sequenced to balance lighthouse pilots with scalable rollouts.

Can you work with our existing automation and IT vendors?

Yes. We're vendor-independent. We assess your current estate as-is and recommend the smallest viable change to get you to your target posture.

How long until we see results?

The assessment delivers a usable roadmap in 4–6 weeks. The first pilot typically goes live within two quarters. Measurable business impact tracks the pilot's success metrics.

Turn OT data into operational intelligence.

The Friction

You're drowning in signal. You're starving for decisions.

The Strategic Vision