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How to Use Historical Data to Find Opportunities to Improve the Effectiveness of Equipment Reliability Programs, Optimize MRO Inventory Operations, and Enhance MRO Workflow Management Processes

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Original date: 
Thursday, April 28, 2022

Recent developments in AI, ML and related techniques see wide adoption in many industries. However, in the asset management area, such technical advances are still in their infancy, especially in the maintenance, repair, and operations (MRO) area. Part of the reason is that, contrary to production, MRO data has its unique characteristics (e.g. incompleteness, inconsistency and heterogeneous), and most organizations are still planning to introduce diagnostic sensors dedicated to maintenance and equipment reliability. We face both challenges and opportunities in advancing data-driven continuous improvements within the asset management world. This presentation shares the findings of our current research and development focus. Titled “How to use historical data to find opportunities to improve the effectiveness of equipment reliability programs, optimize MRO inventory operations, and enhance MRO workflow management processes”, we will first examine the characteristics of MRO data as their uniqueness to a specific company, plant or equipment and their commonality across all sectors. Then we evaluate the feasibility of applying AI/ML techniques with MRO history for better operational efficiencies. We need to understand what data is related to human knowledge, human interaction and process, and what data is associated with the actual condition of the asset, and if there are patterns and models that can be learned. Last, we will demonstrate that AI/ML can find equipment agnostic models and patterns which help continuously improve MRO operations across different industries. Based on the findings, we will also show how AI/ML models learned from historical MRO data can be translated into prescribed actions for improvements in equipment reliability, MRO inventory and workflow operations for individual organizations.

BoK Content Source: 
MainTrain 2022
BoK Content Type: 
Presentation Slides
Asset Management Framework Subject: 
01 Strategy & Planning, 1.02 Asset Mgmt Strategy & Objectives, 04 Asset Information, 4.03 Asset Information Systems, 4.04 Data & Information Management, 06 Risk and Review, 6.05 Assets Performance & Health Monitoring, 6.06 Asset Management System Monitoring
Maintenance Management Framework Subject: 
02 Maintenance Program Mgmt, 2.7 Program Measurement / KPIs, 03 Asset Strategy Management, 3.2 Performance Measurement & Optimization, 04 Tools and Tactics, 4.8 Predictive Maint. Techniques, 05 Maintenance & Reliability Engineering, 5.1 Stats Analysis / Analytical Methods, 09 Information Management, 9.0 Information Management General, 9.1 Information Systems, 10 Continuous Improvement, 10.0 Continuous Improvement General, 10.4 Maintenance Practices Improvements
Author Title: 
VP & Cofounder
Author Employer: 
Perspect Analytics. Inc
Author Bio: 

Stan Shantz: Stan is a veteran in asset management. With 30 plus years of experience in advising multinational corporations around the world to improve asset reliability and efficiency with data and culture change, he is a recognized expert in utilizing predictive technologies to drive sustainable improvements.

Author 2 Title: 
Author 2 Employer: 
Perspect Analytics Inc.
Author 2 Bio: 
Dr. Wang is a pioneer in applying AI/ML technologies in traditional industries. Over the last 25 years, he co-founded multiple companies to commercialize AI/ML solutions for efficiency and productivity improvement in various industries. He is active in academic research and providing advices to international companies.