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Where next for proactive maintenance in automotive?

Eric Whitley offers a deep dive on proactive maintenance for automotive production facilities

Modern car manufacturing is a complex process, and today highly automated robots perform most of the repetitive tasks. These tasks include metal stamping, welding, assembly, painting, etc. Electric vehicle (EV) manufacturing follows a similar path, albeit with some differences due to its unique components such as large batteries and the sophisticated electric drive systems.

To avoid sudden downtime and production losses in a car manufacturing plant, machines must be kept in excellent working condition. This means that modern car manufacturing facilities should implement proactive maintenance strategies.


Preventive maintenance involves routing the manufacturing process. While condition-based maintenance (see below) involves collecting data on equipment performance inspections, servicing, and replacement of components based on predetermined schedules, preventive maintenance helps minimise unexpected breakdowns and ensures that equipment operates at peak efficiency. Car manufacturers maintain and improve the productivity of their paint booth by keeping it clean and establishing a preventative maintenance schedule. Preventative maintenance decreases the likelihood of equipment failing. It’s increasingly used in EV manufacturing, as it can optimise maintenance programmes, reduce downtime, and improve equipment reliability.

To avoid sudden downtime and production losses in a car manufacturing plant, machines must be kept in excellent working condition

Condition-based maintenance (CBM) involves monitoring equipment using sensors and other tools to detect changes in equipment performance or condition. This allows managers to address potential issues before they lead to downtime. CBM involves collecting data on equipment performance, analysing the data to detect changes and developing maintenance tasks based on the analysis. It is frequently used during the production of car engines to ensure their performance and reliability. Vibration analysis, oil analysis, and thermography are used to monitor key components such as bearings, pistons, valves, and cooling systems.

Predictive maintenance uses data and analytics to predict when maintenance might be needed based on equipment performance and usage. By analysing data from maintenance records, manufacturers can anticipate and address potential issues before they cause downtime. Predictive maintenance relies on advanced analytics and machine learning algorithms to identify patterns in equipment failure. This data can then be used to predict when the equipment needs maintenance and plan maintenance activities accordingly.

BMW uses predictive maintenance as an early warning system in its production facilities. It is often used to monitor the condition of conveyor belts, motors, rollers, and other components in the factory.

The increasing digitalisation of maintenance has made a predictive approach more important

Prescriptive maintenance goes beyond preventive and predictive maintenance by not only predicting when equipment needs maintenance but also prescribing the most effective course of action to address issues. The approach optimises equipment performance in car manufacturing. By analysing data from sensors and other sources, prescriptive analytics can identify opportunities to improve equipment efficiency. This involves combining real-time data, equipment logs, historical maintenance records, and external factors with advanced analytics algorithms to generate actionable insights.

Total productive maintenance (TPM) is a maintenance strategy that promotes a culture of continuous improvement involving all employees. Under TPM, manufacturers establish a maintenance programme that emphasises operator involvement in maintenance tasks, training operators to perform routine maintenance activities and empowering them to take ownership of their work areas. TPM was introduced by Toyota and adopted by various other manufacturers. It uses performance metrics to monitor equipment performance and identify areas for improvement. This strategy helps identify and address the root causes of equipment failure to prevent them from recurring.

Reliability centred maintenance (RCM) is a data-driven maintenance strategy used in car manufacturing to identify the most critical equipment and develop an effective maintenance programme that focuses on preventing or mitigating potential equipment failures. It is used in car manufacturing facilities to maintain critical infrastructure components such as HVAC, electrical distribution systems and other utilities. Car manufacturers can optimise their maintenance programme by prioritising the most critical assets and focusing on the most effective tasks, thereby enhancing maintenance efficiency.

Establishing a proactive maintenance strategy

When selecting a maintenance strategy for a car manufacturing factory, it’s important to consider several factors to determine the most suitable approach. To start with, it’s important to assess the criticality of different equipment and systems in the manufacturing process. Companies need to identify the assets that have the highest impact on production, safety, and quality. This evaluation will help prioritise maintenance strategies based on the level of importance and the potential consequences of failure.

The selection of a maintenance strategy…should be based on a comprehensive assessment of equipment criticality, failure modes, data availability, cost-benefit analysis, organisational capabilities, and expert advice

Then there is the need to conduct a thorough analysis of failure modes for each critical asset. Here, companies must identify the common failure modes, their causes, and potential consequences. This analysis will provide insights into the appropriate maintenance strategies needed to address specific failure modes effectively.

It’s also pivotal to evaluate the availability and quality of data needed for different maintenance strategies. Some strategies, such as condition-based and predictive maintenance, rely heavily on real-time sensor data and historical records. Companies must check if all the required data is available and if they have the analysis infrastructure to support these strategies effectively.

Conducting a cost-benefit analysis of different maintenance strategies is another necessary step. Here, players evaluate the potential costs of implementing each strategy, including equipment upgrades, software systems, training and additional resources. They need to compare these costs with the expected benefits such as reduced downtime, improved equipment reliability and optimised maintenance costs.

It’s also key to assess the skills, resources, and capabilities of any maintenance team. Some strategies, such as prescriptive maintenance or predictive maintenance, may require advanced technical skills and specialised knowledge. Players will want to ensure that the team is ready to adopt and implement the chosen strategy effectively.

Finally, they should consider conducting pilot tests or trials of different maintenance strategies on a small scale before implementing them across the entire factory. That entails evaluating the results, gathering feedback, and improving the chosen strategy based on real-world performance and feedback from all stakeholders.

Ultimately, the selection of a maintenance strategy for a car manufacturing factory should be based on a comprehensive assessment of equipment criticality, failure modes, data availability, cost-benefit analysis, organisational capabilities, and expert advice. Taking all these factors into account, and then aligning the chosen strategy with specific factory requirements and goals, establishes an effective and efficient proactive maintenance programme.

About the author: Eric Whitley is as the Director of Smart Manufacturing at L2L

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