Manufacturing: making the losses visible where they happen

Overall Equipment Effectiveness is availability times performance times quality, and each factor requires data that most plants do not capture reliably. Availability needs actual run and stop times with reasons. Performance needs actual cycle rate against ideal. Quality needs first-pass yield at the machine, not the final inspection figure.

We capture these from machine signals where instrumentation exists — a simple run signal, a part counter, a reject signal — and from operator entry on a tablet where it does not. The operator entry is designed to take a few seconds: a stop happens, the operator taps a reason from a short list that they helped define, and it is recorded with a timestamp.

The output is the loss waterfall: calendar time reduced step by step through planned stops, breakdowns, setup, minor stops, speed loss and quality loss, with each step attributed to a machine, a shift and a reason. That waterfall is what turns "our OEE is 63 per cent" into "we lose eleven points to setup on the two smaller presses, mostly on changeovers between three specific part families".

The main reason shop-floor systems fail is that the interface was designed in an office. Screens assume clean hands, good light, a mouse and unlimited time. The reality is gloves, glare, noise, and an operator who has thirty seconds between cycles.

We design for that: large touch targets, high contrast, minimal typing, sensible defaults, and a workflow that mirrors the physical sequence. Rugged tablets or industrial panels rather than office PCs. Offline tolerance, because plant Wi-Fi has dead spots. And critically, the new process must be faster than the paper register it replaces, which we verify with a stopwatch before rollout rather than assuming.

We also design for the supervisor: an approval and correction workflow, so an entry made in error can be fixed with a trail rather than by editing a database. This matters because the credibility of the whole system depends on operators believing that a mistake can be corrected without consequence.

Most planning in mid-market manufacturing happens in a spreadsheet maintained by an experienced planner who holds a great deal of undocumented constraint knowledge — which machines can run which parts, which changeovers are expensive, which customer will accept a split delivery.

We do not try to replace that judgement with an algorithm, because the algorithm will not know those things and the planner will stop trusting it in week two. Instead we build a planning tool that captures the constraints explicitly, does the arithmetic the planner currently does by hand, shows the consequences of a change immediately, and leaves the judgement with the human.

The result is that planning takes an hour instead of a day, scenarios can actually be compared, and — importantly — the constraint knowledge is finally written down somewhere other than one person's head.

For manufacturers supplying automotive, pharmaceutical, food or export markets, traceability is not optional and audits are frequent. The typical state is that traceability exists on paper and reconstructing a chain takes days.

We build forward and backward traceability as a queryable structure: from a dispatched batch back to every raw material lot, machine, operator and quality result that contributed to it, and forward from a suspect raw material lot to every customer who received product containing it. A recall scope that took three days to establish becomes a query.

Alongside that, the routine quality apparatus: incoming inspection with vendor rating, in-process checks with control limits and alerting, final inspection, non-conformance handling with corrective action tracking, and automated certificates of analysis. For plants with IATF, ISO or FSSAI obligations, we make the audit evidence a report rather than a preparation exercise.

Downtime data captured for OEE has a second use: it is the input to a real maintenance strategy. Once breakdowns are recorded with machine, duration and cause, patterns emerge — a machine with recurring failures of one type, a component whose mean time between failures is dropping, a shift-correlated fault that suggests a practice issue rather than an equipment one.

We build preventive maintenance scheduling on top of that, driven by running hours or cycles rather than calendar where the equipment justifies it, with spare parts consumption tracked against equipment so the true cost of ownership per machine becomes visible.

This is usually the third or fourth module rather than the first, because it depends on the downtime data being trustworthy — which takes a few months of disciplined capture.

The value compounds when shop-floor data joins commercial data. Actual material consumed against a work order gives real product cost rather than standard cost. Machine hours give a defensible overhead allocation. Yield and rejection give the true cost of quality. Delivery performance against promise gives the customer-facing metric that actually matters.

We build this join either into a custom ERP or as an integration layer against your existing one. Either way the effect is the same: a management meeting where production, quality, cost and delivery are discussed from one set of numbers rather than four departmental versions.