When considering the implementation of AI and how a manufacturer can work towards achieving autonomous manufacturing, a few questions come to the forefront. This paper explores how manufacturers can begin the process of digitization and put the foundations in place for autonomous manufacturing.
The topics covered in this paper include:
- How much data is required for AI?
- How does your data need to be structured to enable an Expert Execution System?
- Why is real-time data not necessary to support an Expert Execution System?
- The importance of quality data
- How do you know which sensors to place next?
- Who needs to be involved with your Expert Execution System?
- Ensuring adoption and trust
Digitizing for autonomous manufacturing can be a daunting task. Through socialization of the results and of our models across the various teams, we spend time talking through our prescriptions and how we arrive at them, and we demonstrate that our prescriptions remain self-consistent. Manufacturers receive value from an EES through improved plant efficiency, reduced scrap, reduced cost of non-quality, and an improvement in the overall efficiency of plant equipment.
Read the full paper here.