The lag between the asset performance issues and the issues being detected is considerably large and leads to valuable productivity losses.
Goals and Objectives
Near automation results in faster detection of fault states, resulting in reduced maintenance costs and improved line visibility.
– 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. Maintenance resources and activities are automatically triggered and launched depending on the severity of the issue, type of asset, criticality, and so forth.