Current Situation
The lag between asset performance issues occurring, their detection, and action being taken is too long and leads to valuable productivity/ material losses.
Goals and Objectives
Near automation results in faster detection of asset performance issues and validation against digital twin, and action needs to be taken to maintain product quality and reduce maintenance costs.
Technology Deployed
Hardware: Services, storage, IoT, smartphone, and tablets
Software: Big Data/analytics, cognitive/AI, machine learning, cloud, mobile, ERP, MES, APM, and SLM
Services: Business services and IT services
Use Case Summary
Assets are monitored continuously, and issues are diagnosed seamlessly by simulating on digital twin. Asset performance/settings can be adjusted in real time to address variability and ensure final product quality. Maintenance can be automatically triggered and launched depending on the severity of the issue, type of asset, criticality, and so forth.