OEE is known, but causes and ownership are debated after the useful period.
Industrial leadership guide
Connect schedule, execution and loss causes to manage industrial performance
Choose the data and routines that explain the gap between what was feasible, planned and actually produced.
For: Industrial, plant, production, methods, quality, maintenance and finance leadersWhat to clarify before discussing a solution
Which dominant loss prevents the schedule from being met, and which decision can genuinely reduce it?
The answer does not lie in an isolated feature: it is built by connecting rules, actors, data and evidence.A management system connecting schedule, minimum reporting, variance causes and daily decisions.
Four decisions to make before configuration
These decisions make the scope testable and prevent structural choices from being discovered during the project.
Define the dominant loss by horizon
The decision must specify the rule, its owner, the required data and the evidence used to accept it.
Limit causes to an actionable catalogue
The decision must specify the rule, its owner, the required data and the evidence used to accept it.
Connect each variance to a decision and owner
The decision must specify the rule, its owner, the required data and the evidence used to accept it.
Measure the effect at the next routine
The decision must specify the rule, its owner, the required data and the evidence used to accept it.
A readable end-to-end flow
Scope is not a list of modules. It is a chain of events, ownership and decisions.
1Feasibility
2Schedule
3Execution
4Loss causes
5Decision routine
In the first scoping
- One representative flow and its useful variants
- Authoritative events and minimum data
- Roles, decisions and escalation times
- Acceptance criteria and baseline measurement
To sequence in waves
- Additional sites, activities or populations
- Rare variants that do not condition the pilot
- Automations whose rule is not yet stable
- Advanced dashboards after source reliability
To decide explicitly
- Define the dominant loss by horizon
- Limit causes to an actionable catalogue
- Connect each variance to a decision and owner
- Measure the effect at the next routine
From direction to measured improvement
- 1Navigator
- 2Personalised review
- 3Scoping workshop
- 4Wave-based deployment
- 5KPI measurement
Each stage produces a decision or evidence reusable in the next. Deployment remains business-led and verifiable.
Measure to decide, not to fill a dashboard
Every KPI needs a definition, a source, a cadence and an associated decision.
KPIFormulaSourceDecision
OEEAvailability × performance × qualityMachine time, speed and qualityTarget the dominant loss rather than a symptom
Schedule adherenceOrders completed as planned ÷ planned ordersSchedule, orders, declarations and disruptionRebalance capacity, materials and sequencing
Work in progressValue or quantity of open production ordersOrders, consumption and declarationsReduce waiting, queues and immobilisation
Scrap rateScrapped quantity ÷ quantity producedProduction, inspections and quality causesAddress root causes by product and operation
06 · Business scenario
A routine centred on the dominant loss
- Situation
- Teams have many figures, but daily decisions remain reactive.
- Approach
- The pilot chooses one line, one horizon, a small set of causes and a routine connecting each variance to an action.
- Target outcome
- Performance becomes explainable and actions are assessed on their real effect.
Useful answers before the first discussion
01Where should we start in practical terms?
Choose a representative scope spanning “Feasibility” and “Schedule”, then document one normal case and one frequent exception. The first flow should be important enough to matter but contained enough to observe end to end.
02Do all existing systems need replacing?
No. Scoping starts with decisions, authoritative events and ownership. It then determines what should be retained, integrated, replaced or simply better governed, wave by wave.
03What data should be prepared before a workshop?
Prepare a few real cases, the volumes shaping the operation, the main variance reasons and the sources used to measure OEE. The quality of examples matters more than the quantity of documents.
04How should the right pilot scope be chosen?
Choose a scope with an available owner, accessible data, a meaningful exception and a measurable outcome. Avoid both an overly simple case that proves nothing and an overly broad scope that dilutes learning.
05How can scope changes during the project be limited?
Make assumptions, interfaces, variants, exceptions and acceptance criteria explicit. Every new request can then be classified: essential to the wave, suitable for a later wave, or outside the objective. Discussion focuses on impact rather than intuition.
Next step
Turn this guide into a personalised review
Complete Navigator to position your context, then use the review as the starting point for a scoping workshop.
- 1Navigator
- 2Personalised review
- 3Scoping workshop
- 4Wave-based deployment
- 5KPI measurement
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