Improve productivity in the automotive industry – a success story

Improve productivity in the automotive industry – a success story

The company

This North American group has a turnover of $15 billion, is present in more than 20 countries, with 100 factories and 50,000 employees. It is a supplier to the automotive industry and manufactures parts and components for combustion, electric and hybrid engines, which contribute to making engines more sustainable and less polluting.

The challenge

The goal of this transformation was to increase the production capacity of an assembly line for an electric compressor used to improve the performance of diesel combustion engines. Despite working in continuous operation (7 days a week, 24 hours a day), their production line was reaching its maximum capacity and delivery times were starting to increase. With an Overall Equipment Efficiency (OEE) of 50%, the challenge was to increase the weekly output of the line by 23%.

The approach

Current state mapping

The project began with a study of the initial situation, through the mapping of the tasks performed along the line stations. The line in question is operated by 7 or 8 operators and has two bottleneck machines which dictate the production cadence of the entire line as they are the slowest. Improving the line's efficiency, optimising the cycle time and the number of operators were the focus of the project.

Improvement of line efficiency (Kobetsu KAIZEN™ & SMED)

We followed the Kobetsu KAIZEN™ step-by-step approach, a structured problem-solving methodology. To do this, we started by identifying the main causes for the efficiency losses through a Pareto analysis, i.e. an analysis of the main reasons in terms of duration and frequency of the stoppages and micro stoppages. It was concluded that the main reasons for losses were breakdowns, substandard speed, and stoppages for shift changes. Next, the Ishikawa Diagram was used to identify possible causes for each of the main reasons for losses.

After this analysis, the team started to discuss possible solutions to solve the main losses. Some of these solutions involved increasing the reliability of the equipment, readjusting the supply to avoid frequent stoppages to change materials and optimising the changeover to ensure that the line did not stop.

To increase the reliability of the equipment, some of its components were redesigned so as to have less wear and tear and breakdowns, and autonomous and planned maintenance routines were also implemented in the lines. The optimisation of the shift changeover was achieved by defining a standard to manage this. This standard identified all the tasks to be carried out and the information to be communicated. One of the breakthrough solutions for this optimisation was to ensure that the previous shift left the machines with raw materials instead of being emptied.

To improve the line changeover time between product references, the Single Minute Exchange of Die (SMED) tool was used. The workshop started by observing and studying the changeover process, mapping the critical steps of each machine during the changeover, and separating external and internal work, i.e.what can be done with the machines running and what cannot. Then a new operating mode was defined for the changeover process, where there is a clear distribution and sequence of tasks. Tool trolleys were also developed to support the changeover process.

Cycle time optimisation (standard work)

To improve the cycle time, i.e. the time between each part produced, it is necessary to work on the bottlenecks of the line. Standard work was used to define a standardised working operation. We began by identifying the current cycle time through video analysis of the machines in operation. This analysis divided the video into microsteps and identified opportunities to improve machine movements. Finally, we worked together with the suppliers of the machines to improve the software code.

Optimisation of the number of operators (work levelling)

The work levelling tool begins with the study and measurement of the work time per operator and the elimination of waste - such as unnecessary movements and waiting times. Once the tasks are optimised, they are distributed among the different operators in an equivalent manner and the number of operators is optimised according to the target cycle time. In the following image, you can see the distribution of work among the different operators (yamazumi) before and after the project.

graphs automotive

Project management

This project was developed in three-week sprints with a review of the ongoing projects and the results that were being achieved at the end of each sprint. All improvements were managed through weekly meetings with the project teams supported by visual boards.

The results

The initial goal of increasing production by 23% was exceeded with the project managing to increase the line's output by 30%. With the different initiatives described, it was also possible to improve line efficiency by 15 p.p., reduce changeover time by 33% and improve cycle time by 16%.

These results have made it possible to reduce the cost of product production, since in the same opening time it is possible to produce 30% more products than before. On the other hand, this increase in capacity has also been reflected in an increase in sales. The annual financial benefit is around €6m.

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