OEE Software Development Services
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.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Do shifts debate the OEE calculation because events, reason codes, and production context do not line up?
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 it. 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.
What to remember
- OEE software should make the calculation inspectable: move from score to event, correct the reason, assign a response, see whether the loss returns.
- The score is only as good as its inputs: production events, not generic telemetry.
- OEE, IoT, MES, and digital twin are different layers; each needs its own acceptance test.
- Build custom OEE when plant data exists but a standard MES or analytics product cannot match the lines.
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.
One or more lines expose usable state and count signals, and the plant can identify the required order, product, quality, and schedule context.
Current reports are delayed, disputed, or too disconnected from downtime reasons and improvement action.
Operations and engineering can own definitions, review exceptions, and validate the result during a parallel run.
The MES already produces trusted OEE and the real problem is review discipline or action ownership.
Machines lack the required signals and there is no instrumentation, gateway, or operator-capture plan.
The team wants predictive maintenance, simulation, or plant-wide transformation without first naming an OEE decision and baseline.
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
- 01
Buy
Use the MES or analytics product's OEE module when it matches your lines.
- 02
Repair
Keep the current reporting when the loss picture is clear enough to act on. Start with evidence: whether anyone can explain the loss.
- 03
Build
Own the OEE application when plant data exists but no standard product matches the lines. Start with one line and a trusted data path.
- 04
Wait
Do not build OEE before the data path is trusted. A score nobody believes changes nothing.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
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 module | Focused custom OEE layer | |
|---|---|---|
| Best fit | Supported connectors and a standard loss model | Accessible data with proprietary definitions, context, or workflow |
| Time to value | Faster when configuration is sufficient | Longer because event meaning and operation must be built and owned |
| Calculation | Product model with available configuration | Versioned rules tied to your planned time, cycle, count, and exclusions |
| Commercial model | Licence by site, line, device, user, or module | Fixed build phases plus support and infrastructure ownership |
| Main risk | Workarounds or licence growth | Building a bespoke metric layer before the input data is ready |
Scope
What belongs in a credible OEE release
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.
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.
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."
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.
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
- Phase 101
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.
- Phase 202
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.
- Phase 303
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.
- Phase 404
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
Start with a focused OEE release.
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 $10000
Hardware, gateways, several protocols, poor historical data, and multi-site rollout add time and cost. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
Numbers stay traceable
The first lines prove the model
Related plant data services
- 01
Manufacturing Software Development
Close broader production workflows and cross-system handoffs.
- 02
Digital Twin Development
Build a live asset model when monitoring, prediction, or simulation is the decision.
- 03
IoT Development
Establish device, gateway, telemetry, and application operations.
- 04
Business Intelligence
Govern wider operational metrics, semantic models, and decision reporting.
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.
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.
Recovered capacity equals OEE uplift times running hours times contribution margin. Prove the number on one line before the argument starts. Reliable definitions and event context come before more dashboards. 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.
Capture events at the machine, then push summaries back into the ERP: good count, scrap by reason, downtime against work orders. A custom OEE layer fits when production signals exist but the loss model still does not. The ERP is the system of record; OEE is the measurement layer.
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.
Yes, but changeover duration and job-specific ideal cycle times matter more than fixed line rates. The loss model must handle order-specific definitions rather than assuming one rate per line.
A focused release for one or two lines 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 follows access and definitions being 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.