Process Mining for the Manufacturing Industry

Find and Test Manufacturing Process Improvements

Process mining in the manufacturing industry uses event data from the systems you already have to show how orders, production and maintenance actually flow. Find bottlenecks from order to shipment, then simulate changes to capacity, shift patterns or batch rules before you commit. Compare predicted and actual performance after rollout, including cycle-time reduction. This is process analysis and operational process simulation, not plant or chemical simulation. It does not provide machine-level or PLC analytics.

What manufacturing processes can you analyze?

Analyze order, production and maintenance cases using event data from systems such as ERP, MES, maintenance, quality and warehouse platforms. Process mining connects recorded events into an end-to-end view, so you can see how work moves across teams and systems instead of relying on a single department’s perspective.

For example, an order-to-shipment analysis can show when an order was released, when production began, where quality checks or rework occurred, and when the shipment was completed. You can also compare production process paths by plant, product, line, supplier or shift when those details are available in your data.

The analysis reflects the events your systems record. It does not monitor machines or PLCs. To prepare an event log, see the documentation on process mining data.

Where do bottlenecks occur from order to shipment?

A long order-to-shipment cycle time can have several causes. Process mining helps you locate delays in the recorded flow and see which cases or process variants are affected.

• Order release: Orders may wait for material availability, credit checks or engineering review before production planning can act.
• Production and changeovers: Repeated steps or rework can use capacity without increasing output.
• Quality checks: Holds and inspection queues can add waiting time between production steps.
• Maintenance: Maintenance cases can reveal delays in request handling, planning or completion.
• Shipping: Partial shipments and downstream handoffs can extend the time from order to delivery.
• Supply chain dependencies: Late inbound materials can affect schedules, even when internal steps run as planned.

Compare these patterns with KPIs such as cycle time, throughput, cost and on-time delivery. The data can show where a delay occurs, but it cannot establish the cause on its own. Use operational context to validate what the analysis suggests.

For a practical approach to finding delays, read how to analyze a process.

How does process mining help the manufacturing industry?

Process mining reconstructs actual process paths from event data. Compare those paths with the intended process to identify variation and measure waiting time, rework and handoffs.

Plant and production managers, industrial engineers and continuous-improvement teams can use the same view of what is happening. Instead of debating which step is responsible for a delay, examine the cases, timestamps and KPIs behind the pattern.

The findings can help you decide which issue to investigate first. They do not replace engineering judgment or plant-level analysis.

What can you test in process simulation?

Once you have identified a bottleneck, use process simulation to test changes to the process model before implementing them. For manufacturing workflows, compare scenarios involving:

• Capacity and resource availability
• Shift patterns
• Batch rules
• Routing or approval paths
• Maintenance schedules

Simulation estimates how a change may affect process outcomes, including cycle time, throughput and waiting time. It does not simulate physical equipment, chemical processes or machine behavior.

See the simulation capability to learn how process simulation can support scenario analysis. You can also review the simulation documentation to learn more about comparing what-if scenarios.

How do you compare predicted and actual improvement?

Before rollout, record the baseline KPI and the assumptions used in simulation. After the change, use updated event data to measure actual performance against that baseline.

Compare the same process scope and KPI in both views. If predicted and actual results differ, check whether you implemented the change as modeled, whether operating conditions shifted, and whether the event data captures the relevant steps. Use this comparison to refine the next scenario with evidence from your operation.

Results depend on your data, process scope and the changes you implement. Process mining and simulation support analysis and planning; they do not guarantee a particular improvement.

How do you get started?

Choose one process with a clear operational impact, such as order-to-production, production scheduling, maintenance or quality inspection. Define the KPI you want to understand, then identify the systems that record the relevant events.

Prepare a focused event log with case identifiers, activity names and timestamps. Check that the events represent the process you want to analyze, and include useful context such as plant, product, line or shift where available. Then use the process view in ProcessMind to locate a bottleneck, model a change and compare scenarios.

How does this fit continuous improvement?

Process mining and simulation can add operational data to familiar improvement methods. Use the analysis to find where performance varies, investigate the cause with the people who know the work, and test a proposed change before rollout. Then compare predicted and actual performance to guide the next improvement cycle.

Read the Lean Six Sigma and DMAIC guide for a structured approach to data-driven process improvement.

Test a bottleneck scenario

Choose an order, production or maintenance flow and see what your event data reveals. Start with a focused analysis, test a change in simulation, then compare the prediction with actual performance after rollout.

Test a bottleneck scenario