Leveraging asset analytics for automotive robotics
For more than a decade, automation and robotics have been increasing the efficiency of manufacturing plants, improving quality and aiding production workers in the complex assembly of cars and trucks. Increasingly we see humans working alongside robots, both contributing what they do best to make the most sophisticated cars on the roads.
The International Federation of Robotics indicates that 39 percent of industrial robots are deployed in the automotive industry, the largest share in any industry the organization tracks. Automotive applications of robotics equipment nearly double those in electronics manufacturing, the industry next most dependent on robotics.
The synergy between humans and robotics in manufacturing has occurred so naturally that in the recent Automotive 2025 study, only 33 percent of automotive executives indicated manufacturing as a source of significant disruption in the industry over the next decade, the lowest percentage for any functional operation in the study.
This doesn’t imply, however, that manufacturing operations simply run on autopilot and should be taken for granted. Robotic equipment must be tracked, monitored and maintained. Effective performance of robotic equipment is critical to the production and assembly of high-quality parts at very precise tolerances. Equipment uptime is also crucial for the efficiency of a highly synchronized manufacturing operation, the costs of production line shutdowns being very high.
Both robotics equipment and the products produced by such equipment generate large volumes of data useful for ensuring optimal ongoing operation of production processes. Data derived from equipment include metrics such as torque, vibration, pressure or viscosity, can be combined with other measurements from the surrounding environment, such as humidity or temperature, as well as with worker information, such as operator skill and experience. These indicators, combined with data from the product itself, can help ensure optimal operation of expensive production equipment.
Statistical data mining results can be used to directly process improvement recommendations. The health of production assets can be monitored, using all data generated across manufacturing to improve the process. Asset analytics for robotic equipment generates several benefits, including the following:
- Forward visibility into equipment, process and quality performance
- Ability to understand, monitor, predict and control process variability
- Enhanced equipment and process diagnostics capabilities
- Optimized maintenance intervals
- Minimal unscheduled maintenance
- Identification of incorrect operating practices
- Identification of improper maintenance procedures
- Ability to perform in-depth root cause failure analysis to decide on corrective action procedures
- Reduced total cost of ownership
Leveraging analytics in the automotive manufacturing industry is leading to improved operations, less downtime and increased profits. The ultimate goal of these results is to help manufacturers build a better product for less money. As Industry 4.0 ushers in new and better technologies, make sure you're leveraging them to stay ahead of competition. For more on how IBM can help you lead the pack, check out these automotive manufacturing and supply chain solutions.