The lag between asset performance issues occurring, their detection, and action being taken is considerably large and leads to valuable productivity/material losses.
Goals and Objectives
Near automation results in faster detection of asset performance issues and determines if action needs to be taken to adjust the process, maintaining product quality and reducing maintenance costs.
Hardware: Servers, storage, IoT, smartphone, and tablets
Software: Big Data/analytics, cognitive, computer vision, AI (conversational, document, generative), machine learning tools, cloud, mobile, ERP, MES, APM, and SLM
Services: Business, IT, and connectivity services
Use Case Summary
Assets are monitored continuously and issues are diagnosed seamlessly. 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.