Skip to content

Not more data. More effective improvements.

Five typical starting points from manufacturing – and what production.AI makes of them.

Five use cases

Where is productive machine time being lost – and why?

Starting point
Availability is below target, and the reasons for stoppages are only roughly recorded.
Insight
A large share of the downtime comes down to a few recurring causes on particular machines and in particular shifts.
Action
The team prioritises the top causes, names owners and sets up the actions in IDA.
Benefit
Less unplanned downtime, better use of the capacity you already have.
Illustration: empty mesh pallet box in its bay next to the roller infeed of a press, the floor marking is green

Which feedback is missing or implausible?

Starting point
Order feedback deviates from the machine data. Planning and costing work with uncertain figures.
Insight
Particular workstations and order types systematically produce incomplete or late reports.
Action
Feedback at the workstation is simplified, implausible reports stand out immediately, and reconciliation with the ERP runs alongside in the system.
Benefit
Less rework, robust order data, more reliable costing.
Illustration: clipboard with a tally sheet on the side of a machine, a green pencil hangs on a string, a loose sheet lies on the floor

Which deviations occur under which conditions?

Starting point
Scrap fluctuates, and the causes are sought case by case.
Insight
Deviations cluster around particular combinations of tool, material and process parameters.
Action
The team addresses the critical combinations, adjusts process windows and documents what it has learnt.
Benefit
Causes narrowed down faster, less scrap, knowledge retained.
Illustration: scrap bin full of stamped parts, a calliper on its rim, next to it a single good part in green on a tray

Which history explains failures or recurring defects?

Starting point
Tool failures come as a surprise. Knowledge about the tools sits with individual people.
Insight
The tool history shows patterns: failures follow particular usage counts, maintenance intervals or tryout results.
Action
Maintenance and tool use follow the history, and decisions on repair or replacement can be justified.
Benefit
Fewer recurring defects, longer tool life, knowledge retained.
Illustration: pegboard with tools in their outlines, one outline is empty, a green maintenance tag hangs next to it; below, a punch with a broken tip lies on a cloth

Which improvements are open – and what has worked?

Starting point
Improvements are agreed in meetings and managed in lists. Whether they work is rarely checked.
Insight
Many actions remain open, others do not work as expected, and nobody notices.
Action
Every action in IDA gets an owner, a due date, a status and a linked metric.
Benefit
Clear responsibilities, visible progress, continuous improvement as routine.
Illustration: tall stack of papers and folders, a foldback clip on top, a green sticky tab protrudes from the middle

Where it pays to start in your production

The examples show different starting points. The shared approach: understand causes, implement targeted actions and check their impact. Where that makes the biggest difference for you depends on your production.

Which of these situations do you recognise? In the discovery call we look at your equipment, order structure and data situation and work out together which use case is the right place to start.

Request a discovery call