CHALLENGE

A high-tech industrial equipment manufacturer has a complex supply chain with many SKUs and long lead times. It was struggling to match supply with demand, which was fluctuating strongly. Multiple engineering changes.

APPROACH

Through data analytics we mapped the characteristics of the business and identified key levers for improvement. This led to the design and implementation of a dedicated S&OP application – a decision-support tool.

CHALLENGE

A high-tech and consumer electronics company wanted to train and coach green belts and black belts in Lean Six Sigma.

APPROACH

  • Carry out in-house training with focus on personal and value-add projects of the participants.
  • Coach the participants over a longer period to make sure knowledge is transferred into changed behaviour.
  • Coach black belts to become master black belts and thus anchor Lean Six Sigma in the organisation.

CHALLENGE

A high-tech and consumer electronics company wanted to train and coach engineers and developers in Design for Six Sigma.

APPROACH

  • Carry out in-house training with focus on personal and value- add projects of the participants.
  • Coach the participants over a longer period to make sure knowledge is transferred into changed behaviour.
  • Coach the engineers and developers and thus anchor Design for Six Sigma in the organisation

CHALLENGE

Global chemical compounds company with multiple factories with multiple production lines.

Medium term decisions on allocation of products to product lines.

Strong influence of batch size and product sequence on output of production lines.

Need to plan allocation of products to production lines at least one year up front.

Uncertain demand and strong influence of market fluctuations.

Complex global supply chain cost and turnover picture including tax regimes.

APPROACH

  • Build a mathematical optimisation model of the global supply chain.
  • Embed the model in a user-friendly software system to support decision making.
  • Involve future users and management to guarantee quality and acceptance of decision support system.
  • Transfer system to the organisation and remain available for support.

CHALLENGE

Design of optimal and feasible integral supply chain for various companies in High-tech, Consumer Goods, Food and Building Materials

APPROACH

  • Determine goal, scope, decision criteria and decision-making process.
  • Involve all stakeholders and experts during the process to get commitment and create trust.
  • Make sure all relevant data on actual situation and future trends are available.
  • Tailor and validate a mathematical optimisation model of the global supply chain.
  • Organise decision preparation workshops in which scenarios are developed and discussed.
  • Organise session to discuss recommended options as basis for a decision on the design of choice.

CHALLENGE

Maintenance contractor for Rail Infrastructure executes maintenance based on human inspection of rail track videos.

Introduce predictive maintenance technology to streamline and improve inspection.

APPROACH

  • Gather data on inspections executed to create a large test set of observations.
  • Develop a deep learning algorithm (based on neural networks) to automate the inspection.
  • Minimise false positives (judged okay but in fact not okay) and false negatives (judged not okay but in fact okay).
  • Test and validate the software in the live environment.
  • Transfer the software to the maintenance organisation.

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.

CHALLENGE

Manufacturer of complex components

  • Strong market position and the ambition to grow significantly.
  • However, their New Product Development and Introduction (NPI) lead times were way to slow to enable the growth.

Take a leap step in NPI-cycle time reduction.

Lean Six Sigma (the company is highly skilled in this) didn’t enable them to tackle the problem.

APPROACH

  • Engineering, Operations and Supply Chain formed an integrated team for the assignment.
  • The plan was to visualise the customer journey for a sample request from the customer’s point of view. This was a challenge as the data was scattered across multiple sources. Once the case identifier to track individual cases was identified, the full customer journey could be visualised through process mining.
  • The bottlenecks were identified and a new NPI-process was designed and introduced without adversely affecting the on-time production performance of the normal production.

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.