Manufacturing
OEE, unplanned downtime and predictive maintenance. The shop floor generates data constantly the problem is that it never arrives intact at the decision.
Talk to a specialistThe machine knows what happened. The board finds out the following month.
Manufacturing usually has data in abundance and information in short supply. The PLC records, the SCADA shows it in real time, the downtime log is filled in during the shift and none of it comes together into an indicator that supports an investment decision.
The classic symptom is an OEE each department calculates differently. When production and maintenance disagree on the number, the conversation becomes a methodology dispute instead of an action plan on the downtime that costs most.
The questions we answer
- What is the real OEE per line, with the same calculation rule for everyone?
- Which downtime consumes most capacity: setup, failure, lack of material or lack of operator?
- Which equipment gives a signal before it fails, and how far in advance?
- What is the real cost per unit produced, including rework and scrap?
- How much of the shop-floor reporting is still typed by hand?
The indicators of this industry
The ones that show up in most projects in this industry. The final set is always agreed with you an indicator nobody uses is dead weight on a dashboard.
- Efficiency. OEE by line and by shift, with the three components broken out availability, performance and quality. Capacity utilisation, cycle time, setup rate.
- Downtime. Planned and unplanned downtime, MTBF, MTTR, downtime by root cause, availability of critical equipment.
- Quality. Scrap rate, rework, first pass yield, cost of poor quality, complaints per batch.
- Cost. Cost per unit produced, material consumption against standard, maintenance cost per machine, energy per unit.
The solutions we use most here
- Data Governance. Before the indicator: a single definition of downtime, scrap and availability.
- Business Intelligence. OEE by line and by shift, in the same place for production and for the board.
- Data Analytics. Predictive maintenance built on failure history and process parameters.
How we run it
We start by defining the indicators with production and maintenance in the same room without that, the dashboard is born contested. Then we integrate the sources: MES, ERP, manual reporting and, where it exists, automatic collection from the equipment. Predictive maintenance comes later, and only when there is enough failure history for the model to learn from.
How we measure results
The most frequent indicators are availability gained, reduction in unplanned downtime, lower scrap and manual reporting hours given back to the shift.
We now have a far more robust, fast and visual view of each management area’s results.
Sales director national fuel distributor
About this industry
Our equipment is old and has no automatic data collection.
It is the most common scenario and it does not block the project. Manual reporting comes in as a source, and the first improvement is usually simplifying that reporting to reduce error. Automatic collection, when it comes, comes later and for critical equipment, not for everything.
Each department calculates OEE differently. How do we fix that?
That is the first deliverable, not a detail. We run the single-definition exercise with production and maintenance together, write the rule into the metrics dictionary, and the dashboard becomes the only source. The discussion leaves methodology and returns to action.
Does predictive maintenance actually work?
It works when there is enough failure history and variation for the model to learn from and there is not always. The assessment measures that before any promise. When the basis is not there, we say so: starting with well-measured downtime indicators already delivers more than a model trained on bad data.
Do you integrate with our MES or ERP?
Yes. ERP, MES, SCADA and reporting spreadsheets all come in as sources. Where the source is internal, we configure the necessary bridge without exposing the network.
How much capacity is your plant losing?
Thirty minutes of conversation is usually enough to point at the biggest source of downtime.
We understand processes before recommending technology. Automation, data, artificial intelligence, and custom software for companies seeking efficiency, control, and scale.