Questions process mining can answer
- Which process variants actually occur?
- Where do cases wait, loop or deviate from the intended path?
- Which products, sites, suppliers or order types explain the deviation?
- Are apparent bottlenecks operational or caused by missing and delayed bookings?
Data requirements
A classic event log needs a case identifier, activity and timestamp. Manufacturing and logistics processes often involve several interacting objects such as order, material, handling unit, batch and delivery. Their relationships and the semantic meaning of status changes are clarified before analysis. Time zones, duplicates, cancellations and missing events are tested explicitly.
Approach
- Define the decision and process boundary.
- Identify source systems and create a documented event-data model.
- Validate completeness, chronology and object relationships.
- Discover variants and performance patterns.
- Check conformance against intended rules where a reference model exists.
- Confirm causes with process owners and prioritise measures.
Results and deliverables
- Event-log and data-quality specification
- Process and variant map
- Lead-time, rework and waiting-time analysis
- Conformance and root-cause findings
- Prioritised improvement backlog and measurement concept
Technical basis and limitations
A reliable event log needs at least a meaningful case or object assignment, an activity and a time reference. Data origin, time zone, status changes, duplicates, missing events and the treatment of parallel work are documented as well. A directly-follows graph shows observed adjacency, but it is not a complete process model and can obscure concurrency or rare variants.
The service combines established process-mining foundations with current object-centric analysis. Further reading: data requirements and pitfalls in production and logistics, event logs, conformance checking and object-centric process mining.