Plan to attend The IIC 2018 Conferences to gain deeper insights into new manufacturing technologies and ideas!
About the topic
The recent explosion of data generated by the growing adoption of the Internet of Things (IoT) has companies searching for ways to best harness this information to reduce costs and drive improvements. Like most manufacturers, BMW had been seeking a more proactive approach to maximize operational efficiencies and identify performance issues before they turned into production downtime. The strategy they chose for their facility in Spartanburg, South Carolina used a cloud-based solution to mine their robot data for analytical insights that drove improvements while laying the ground work for future enhancements.
This presentation will center on a case study of how BMW went about adopting these measures, the challenges they faced, along with lessons learned from the implementation. BMW initiated its analytics implementation incrementally, focusing on a specific set of parameters: gear boxes, motor drives, and counter balances. The smaller subset provided them with the confidence of knowing that they would be able to read the data from controllers while understanding the cause and effect relationship of the information coming from the robots to predict what could happen next. Using three key performance indicators – motor temperature, motor current, and motor torque – for each joint, they developed an acceptable parameter of activity for each category that was tracked via dashboards, complete with anomaly notifications for when performance levels went outside of approved ranges. The system also tracked all past, current, and future maintenance tasks for each robot. BMW faced several challenges during the implementation of the software. First, they had to survey production lines to identify which robots were equipped to gather information from the controllers. Success depended on every point of their IoT being able to pass data to the cloud for analytical interpretation. Leveraging the cloud also brought questions about security and data exposure to the outside world. They adopted a multi-faceted strategy to protect their information by encrypting all data through virtual and physical firewalls within the architecture. They also implemented an identity and access management program that enforced role-based access for all approved end-users.
The adoption of an incremental IoT analytics strategy provided BMW with greater insight into their data and the factors that affect robot performance. By starting small, they were able to implement the solution faster and quickly track key operational indicators. BMW now has the ability to identify performance issues before they turn into more serious problems, such as unplanned downtime, which they plan to expand. They also use a cloud-based software that allows them to build upon it for future enhancements. By taking the proper security precautions in concert with these actions, they were able to enjoy the gains while minimizing the risk of exposing their data and intellectual property.
While sharing BMW’s story, this session will:
- Explain how IoT and analytics can be leveraged to reduce downtime and improve operational efficiencies
- Discuss the benefits of adopting an incremental approach to implementing analytic solutions for proactive maintenance
- Introduce cloud integration strategies for overcoming the silo environments in manufacturing
- Identify key security considerations when using cloud strategies for analytic data gathering and reporting
Meet your presenter
Andy Chang is the director of Product Marketing Americas at KUKA. In his role, Chang focuses on introducing cloud, web, and mobile technologies that augment traditional operating technologies. Leveraging a platform-based approach, Chang has helped create holistic workflow solutions that set the industry standard for capability and ease-of-use in product lifecycle management. Before joining KUKA in September 2015, Chang held various professional positions ranging from a controls engineer to a global program manager for academic research responsible for driving strategic partnership, product management, product strategy, and field enablement. Chang has written more than 100 scientific articles and publications. He graduated from the University of Michigan with a master’s degree in mechanical engineering, and received his bachelor’s degree in mechanical engineering from the University of California, San Diego.