Custom OEE Software Development

OEE software that explains which loss deserves action.

An OEE number is only useful when every line agrees on planned time, ideal cycle, good count, and loss reason. We build focused OEE software for manufacturers that already have accessible production signals but cannot get trusted, actionable reporting from spreadsheets, a generic dashboard, or their current MES.

See our work

Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.

The brief

Start with what is not working.

Good software decisions begin with the constraint, not a list of features or a preferred technology.

01

Do shifts debate the OEE calculation because events, reason codes, and production context do not line up?

02

Are supervisors looking at a dashboard that cannot trace a loss back to the machine event, order, product, or operator response?

Plain answer

OEE software calculates availability, performance, and quality from production events, then helps teams investigate the losses behind the score. RaftLabs builds custom OEE applications when plant data exists but a standard MES or analytics product cannot match the line's definitions, context, or workflow. Focused releases start around $30,000.

The number is 61 percent. The argument starts immediately.

Maintenance excluded the changeover. Production counted it. The good quantity arrived after the shift closed, while the machine counter reset at midnight. Nobody can explain the loss.

OEE software should make the calculation inspectable. A supervisor needs to move from score to event, correct the reason, assign a response, and see whether the loss returns. That differs from collecting generic telemetry or building a broad manufacturing platform.

Delivery record

shipping production software
Since 2015
RaftLabs delivery record
average client rating
4.9/5
Clutch, verified reviews
post-launch support included
8 weeks
Every RaftLabs engagement

RaftLabs does not publish a named OEE implementation or verified plant result. These are company-wide facts. Use your reporting effort and loss baseline, then measure results after the data path is trusted.

A custom OEE layer fits when production signals exist but the loss model still does not.

Reliable definitions and event context come before more dashboards.

A fit
01

One or more lines expose usable state and count signals, and the plant can identify the required order, product, quality, and schedule context.

02

Current reports are delayed, disputed, or too disconnected from downtime reasons and improvement action.

03

Operations and engineering can own definitions, review exceptions, and validate the result during a parallel run.

Not a fit
01

The MES already produces trusted OEE and the real problem is review discipline or action ownership.

02

Machines lack the required signals and there is no instrumentation, gateway, or operator-capture plan.

03

The team wants predictive maintenance, simulation, or plant-wide transformation without first naming an OEE decision and baseline.

Is this OEE, IoT, MES, or a digital twin project?

IoT work establishes a dependable telemetry path. MES work controls and records production execution. OEE work defines loss calculations and the investigation workflow around them. A digital twin adds a continuously updated asset or process model for monitoring, prediction, or simulation. One programme can include several layers, but each needs its own acceptance test.

Specialist OEE product vs focused custom layer

Specialist product or MES moduleFocused custom OEE layer
Best fitSupported connectors and a standard loss modelAccessible data with proprietary definitions, context, or workflow
Time to valueFaster when configuration is sufficientLonger because event meaning and operation must be built and owned
CalculationProduct model with available configurationVersioned rules tied to your planned time, cycle, count, and exclusions
Commercial modelLicence by site, line, device, user, or moduleFixed build phases plus support and infrastructure ownership
Main riskWorkarounds or licence growthBuilding a bespoke metric layer before the input data is ready

Scope

What belongs in a credible OEE release

  • 01

    Loss definitions and calculation governance

    Define planned production time, ideal cycle, total and good count, exclusions, micro-stops, changeover, rework, and missing events. Store calculation versions and effective dates so changes do not rewrite past reports.
  • 02

    Machine and production context

    Ingest machine states and counters, then add order, product, shift, schedule, and quality context from MES, ERP, a historian, files, or operators. Keep timestamps, source identity, freshness, and lineage.
  • 03

    Reason capture and correction

    Present a short, governed reason hierarchy at the right point in the shift. Allow authorised correction with history and distinguish automated from manual classification. Prevent overlapping events, and route unresolved time to an owner rather than hiding it under "other."
  • 04

    Line, shift, and loss views

    Show availability, performance, quality, duration, and count by line, shift, order, product, and reason. Support Pareto analysis, trends, drill-through, and comparison while flagging incomplete data.
  • 05

    Response and operations

    Connect material losses to review, ownership, and follow-up. Monitor gateways, freshness, missing tags, duplicate events, integration failures, and reconciliation queues. The dashboard and data path need named owners.

How it works

From disputed events to trusted OEE

  1. Phase 1
    01

    Define the loss model

    Agree planned production time, ideal cycle, good output, exclusions, reason hierarchy, users, decisions, baseline reports, and acceptance tests for one or two lines.

  2. Phase 2
    02

    Prove the event path

    Replay representative machine, order, product, quality, and operator events through the proposed model and expose missing signals, timing conflicts, and manual context.

  3. Phase 3
    03

    Build calculation and action views

    Deliver governed calculations, event lineage, reason capture, shift and line views, alerts, reporting, reconciliation, and support tooling against production-like data.

  4. Phase 4
    04

    Validate and expand carefully

    Parallel-run the results with existing reports, resolve differences, train owners, monitor data freshness, and add lines only after the definitions and event path hold.

Risk

What makes an OEE score untrustworthy

Definitions change by team
Name owners and effective dates for planned time, ideal cycle, good count, exclusions, and reason codes. Resolve differences before automating them.
Events lack context
A run signal cannot explain the product, order, target rate, scrap, or planned stop. Join operational context without inventing precision when it arrives late.
The dashboard hides missing data
Expose gaps, stale feeds, clock drift, duplicates, resets, and manual overrides. An incomplete score should look incomplete.
Measurement has no response
Define who reviews losses, how reasons are corrected, when action is assigned, and how recurring loss is followed. Reporting alone does not improve the line.

Scope and price

A focused OEE release starts at $30,000.

Start with one or two lines, governed definitions, a proven event path, reason capture, drill-through reporting, reconciliation, and operational handover.

This is an indicative starting point, not a promised savings result. We price after testing the data path and agreeing definitions, line scope, acceptance, production windows, and ownership.

Starting investment

Starts at $30,000

A focused release usually takes 10 to 14 weeks. Hardware, gateways, several protocols, poor historical data, and multi-site rollout add time and cost.

Numbers stay traceable

The scope includes lineage from the reported score to source events, calculation rules, data-quality state, and authorised corrections.

The first lines prove the model

Expansion follows a parallel run and signed acceptance, rather than copying disputed definitions across a plant.

Related plant data services

Common questions

OEE software combines availability, performance, and quality into an overall equipment effectiveness measure and helps teams inspect the losses behind it. A dependable system keeps the source events, calculation versions, exclusions, reason codes, product or order context, and data-quality state visible so the score can be challenged and improved.

The first release needs reliable signals for equipment state and production output, plus the context required for planned time, ideal cycle, and good count. Those signals may come from PLCs, SCADA, a historian, MES, counters, files, or operator input. We audit availability and meaning before promising automation.

Buy or configure a specialist product when its connectors, loss model, operator workflow, and licence economics fit. Build a focused layer when data is already accessible but proprietary event meaning, product context, calculations, integrations, or action workflows require recurring workarounds. Custom software should not duplicate a capable MES report.

A focused release for one or two lines starts around $30,000 and typically covers the data audit, calculation model, event lineage, reason workflow, dashboards, and a bounded integration path. More protocols, sites, manual context, historical repair, edge infrastructure, complex genealogy, or advanced alerting increase the scope.

A focused first release usually takes 10 to 14 weeks after access and definitions are ready. Production windows, gateway procurement, undocumented tags, inconsistent timestamps, poor historical records, or several product and shift models can extend the plan. We parallel-run calculations before operations depend on them.

Work with us

Bring one line and one OEE report nobody trusts.

We will trace the inputs, definitions, and decisions, then tell you whether configuration, a specialist product, or a custom layer is warranted.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.