Smart operation recommender system digitalizing OT knowledge to improve productivity
In the manufacturing industry, production methods can be categorized into two types: discrete manufacturing and process manufacturing. Discrete manufacturing creates products through mechanical processes and assemblies, e.g., automobiles. On the other hand, with process manufacturing, the reactions between gas or liquid materials and ingredients create products, e.g. chemicals. The ability to visualize what is happening in process manufacturing is falls short of discrete manufacturing as it is a composite and continuous process taking place in plants or pipes. Thus, when a defect is generated, operators often need to go through a process of trial and error to identify an effective countermeasure because it is difficult to discriminate the factors contributing to the defect. As a result, when there is a defect there is often a long production downtime while countermeasures are being sought. To address this issue, we developed an operation recommender system that automatically discriminates the factors contributing to the defect and determines the appropriate countermeasures using data from sensors installed in the production line together with past countermeasure records.
