With more than $1 trillion USD per year lost on unplanned downtime, owner operators in heavy process industries seeking to improve uptime and reduce costs have much to gain from improved operations and maintenance strategies. Typically, companies monitor equipment behaviour based on operational data but are unable to combine that with knowledge of risk and how to apply actionable insights, jeopardising the effectiveness of the interventions and outcomes. This creates a compelling case for introducing greater use of artificial intelligence and machine learning capabilities to draw insights from operational data combined with solid engineering expertise and the right remediation tasks to deliver better results.
In this webinar, we’ll show you how to identify your most critical equipment and combine that with a plug-and-play solution for predictive production operations – giving you actionable insights that not only help you identify and prevent imminent events but also create an ideal maintenance strategy for your facilities. The resulting Digital Twin solution gives you a seamless view of risk and real-time conditions based on operational data to create an end-to-end improvement loop. This enables you to expedite decision making to optimise asset health and ongoing maintenance activity - optimising productivity and profit by reducing unscheduled downtime.
Key Learning Objectives
- How to identify your most critical equipment
- How to gain a seamless view of risk and real-time conditions based on operational data
- How to generate a holistic plan for ongoing maintenance activity based on new operational conditions
Target Audience
- Maintenance Manager
- Reliability Manager
- Shutdown Manager
- Operations Manager
- Asset Manager
- Pre-Operations Managers
- Late Life Asset Managers
- Reliability Engineer
- Maintenance Engineer
- Production Engineer
- Shutdown Coordinator
- Plant Manager
- Site Coordinator
- Manager and Director level of each
