Electric vehicles are redefining the auto industry. Within this new paradigm, carmakers have to rethink their business strategy and adopt new technology to remain competitive. Analytics is one of the most important tools for any organization, helping them get the pulse of their business and take corrective actions before things go south.
With manufacturing plants being chiefly data-driven these days, the use of analytics is expanding across all industries and fueling growth for many businesses. In the case of the electric vehicle market, data analytics can help car manufacturers make their production process more dynamic and cost-effective.
Here are some examples of how you can leverage data analytics solutions in your manufacturing facility to achieve better results:
Data Collection and Analysis
The first step of any data analytics system is to collect data from all areas of the business. Data collection can happen in many ways, from manual input to sensors and automated software. A robust system that supports data collection from diverse sources is crucial for success in any manufacturing industry.
For example, electricity usage data from the plant’s energy grid might be used for setting production schedules. Data about raw materials and supplies used for manufacturing can be used for scheduling procurement. Other data points can be used to ensure the health and safety of employees in the plant, as well as to optimize product dimensions and designs.
In EV manufacturing facilities, data can be collected from many sensors and machines. Sensors can be used to track things such as temperature, humidity, airflow, vibration, and noise levels. This data can be used to ensure the quality of raw materials, work areas, and finished products.
Continuous Improvement of Assembly Line Processes
As manufacturing is more and more data-driven, production processes are being optimized based on the data collected. This has been made possible by Industry 4.0 and the Internet of Things, which are interconnecting machines and devices to improve operational efficiency. This also extends to the assembly line where the manufacturing process is automated.
The same data that is collected for operational optimization is then used for continuous improvement of assembly line processes. In the case of the EV industry, assembly line processes are particularly important because they are responsible for the vehicle’s battery pack. This is where raw materials are put together to form the battery pack and then are assembled into the car. If the manufacturing process is inefficient and takes too long, the whole process is negatively affected, resulting in lower production rates. By optimizing the assembly line and making the process more efficient, production rates can be increased and costs are lowered.
Predictive Maintenance
Some manufacturing plants are doing predictive maintenance. This is based on data collected from machines, sensors, and other devices that generate data. This data is then analyzed to identify potential issues that can reduce the efficiency of the equipment. Once identified, the system can send notifications to the team responsible for maintenance so they can take action.
In some cases, the system can even take corrective actions that include automatically shutting down the machine and notifying the operators. This type of data analytics solutions is particularly useful for EVs since they are often manufactured using machines that use high pressures and temperatures. By using predictive maintenance, the likelihood of a machine breakdown is reduced, reducing downtime and increasing plant efficiency.
Quality Assurance
Quality assurance is about catching defects and errors before the end product is shipped. Quality assurance is an important part of the manufacturing process, especially for EVs where the batteries are particularly sensitive. This is where data analytics can help identify quality issues with the batteries.
The same data that is used for operational optimization is then used to detect quality issues. By using the data to look for potential quality issues, such as unusual temperatures, defective wire connections, and more, the chance of a faulty battery is reduced. The more data that is collected, the more issues can be detected before the battery reaches the end customer.
For example, a temperature sensor in the battery can send data to the team responsible for quality assurance. If the temperature is too high, it can be a sign of an issue.
Conclusion
As the manufacturing industry becomes increasingly digitized, the role of data analytics services will grow. EVs are a particularly interesting field to study because they are a new type of vehicle and rely on a lot of new technology. This can be both a challenge and an opportunity for car manufacturers. On the one hand, EVs are more complex and require new skills for manufacturing. On the other hand, EVs are more connected and generate more data, which can be used for operational optimization and continuous improvement. This new manufacturing process also challenges data analytics.
This is because data is initially generated from disparate sources, making it difficult to use for operational optimization and continuous improvement. In order to maximize the benefits of data analytics in the EV manufacturing process, carmakers must tie together data from diverse sources, including sensors, machines, the Internet of Things, and other data sources.
