Company:

High-tech equipment manufacturer

• Production of low-volume highly complex machines consisting of many subassemblies.
• ach machine needs testing prior to acceptance by the client.
• The test process is a complex, knowledge-intense process, and testing takes several weeks at both sites.

Challenge:

As the company operates in a market where time-to-market of system enhancements and new system types is critical, the goal was to significantly reduce the test period.

Approach:

• Process mining was conducted on a batch of 24 machines.
• As the goal was to shorten the test process, the focus of the analysis was on idle times and rework in the process event log.
• A performance analysis was conducted to find unnecessary idle times:
  Based on the logged test sequences, a process model was constructed automatically that showed how the test process had been executed for these 24 machines.
• The resulting visualisation of the rework provided the insights on which the test process was organised differently.

Company:

Contract Manufacturer of durable consumer goods, distributed across Western-Europe

• Production volume 2 million units in 24 product families.
• Very rigid client quality requirements.
• Final product store is a 24/7 operation on weekdays.

Challenge:

Check compliance with formal inventory management procedures and guidelines.

Check compliance with quality assurance procedures.

Check compliance with First In–First Out (FIFO) procedure.

Check work distribution across shifts.

Approach:

• The existing Warehouse Management System (WMS) was used to extract data for the analysis.
• 554,745 events over a 5-month period were included in the analysis.
• Together with warehouse representatives the insights generated by Axisto Process Mining® were discussed and conclusions were drawn.

CHALLENGE

Leading manufacturer of customised precision parts

• Complex production process comprising multiple process steps across various pieces of equipment.
• Product quality is made by the first process step, but can only be determined at the last.
• This feedback cycle (i.e., the production cycle) needs to be quicker to prevent production losses.

Reduce the production time by 50% from 4 to 2 weeks.

The company is highly skilled in Six Sigma, which had already helped them to reduce the cycle time from 11 to 4 weeks. However, the traditional Six Sigma suite of tools could not help them any further.

APPROACH

• The first action was to analyse the production with process mining: Huge variations in takt times between workstations were uncovered.
• Workstations were recombined to rebalance the takt times: A consequence was that the heartbeat of the subcontractor’s process had to be synchronised.
• Further, the available time for preventive maintenance increased.

CHALLENGE

Global chemicals company

• Supplies products to a wide range of industries: raw materials, additives and process aids.
• The various global operations coordinated their own Order-to-Cash efforts, which led to processes looking very different.

Create an overview of the all the Order-to-Cash processes:

• Map the different process variants.
• Identify the root causes for difference in lead time between different processes.

APPROACH

• The actual Order-to-Cash processes from each of the regions were visualised with process mining. This included all the variants of each of the processes. The event logs from the ERP systems were used.
• From various viewpoints (cycle time, performance, organisation) the processes were analysed:
  • For the various bottlenecks and rework loops, root causes were identified.
  • In a next step the best practices were used to improve each of the processes.