Best Practices: Transitioning To Predictive Maintenance For Enhanced Asset Management

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Executive Summary

This report provides industrial operations managers and executives across asset-intensive industries with an overview of best practices for implementing predictive maintenance (PdM) solutions. To capture best practice insights, Verdantix conducted in-depth interviews with 10 predictive maintenance experts at industrial, service providers and technology firms and surveyed 256 managers in operations, maintenance and engineering roles. Verdantix research finds that while predictive maintenance solutions have been around for some time, many firms have found it difficult to prove their benefits and successfully implement them. However, experience from industry frontrunners shows that success is possible, by prioritizing critical assets, laying strong data management foundations, getting buy-in as well as proving return on investment (ROI), and integrating predictive maintenance solutions into wider digital ecosystems.

Table of contents

Predictive Maintenance Advances Asset Management Capabilities 
‘If It Ain’t Broke, Don’t Fix It’ Is A Dated Method Of Asset Maintenance Management 
Predictive Maintenance Strategies Deliver Significant Benefits 
Digital Solutions Are Essential For Predictive Maintenance, But Barriers To Adoption Exist 

The Recipe For Predictive Maintenance Success - Strategize, Digitize, Scale 
Prioritize Predictive Maintenance For Critical Assets In Your Firm 
Implement Digital Solutions To Achieve The Predictive Maintenance Gold Standard 
Scale Up Returns By Expanding To Similar Assets And Demonstrating Added Sustainability Gains 

Table of figures

Figure 1. The Evolution Of Maintenance Strategies
Figure 2. Level Of Digital Maturity For Asset Management
Figure 3. Sophistication Of Predictive Maintenance Solutions
Figure 4. Industry Usage Of Predictive Maintenance Software
Figure 5. Step By Step Guide On Implementing A Predictive Maintenance Tool
Figure 6. Considerations When Selecting The Right Predictive Maintenance Vendor
Figure 7. Types Of Maintenance Strategies

About the authors

Malavika Tohani

Research Director, Operational Excellence
Malavika leads the Verdantix Operational Excellence practice. Her current research agenda focuses on digital technologies for Operational Excellence including digital twins and software solutions for industrial risk and asset management. Malavika has over 15 years’ experience in research and strategy consulting. Malavika previously worked at Frost & Sullivan, managing and delivering advisory projects for clients involving expansion, acquisition, benchmarking and product development strategies. Malavika holds a MSc in Economics from Madras School of Economics.

Kiran Darmasseelane

Senior Analyst
Kiran is a Senior Analyst in the Verdantix Operational Excellence practice. His current research agenda focuses on emerging solutions and global market trends across the process industries, with a particular interest in APM, AIP and process simulation solutions. Prior to joining Verdantix, Kiran worked at Siemens DI, where he gained experience in advanced process modelling and digital twin technologies. Kiran holds an MEng in Chemical Engineering from the University of Nottingham.

Henry Kirkman

Henry is an Analyst in the Verdantix Operational Excellence practice. His current research agenda focuses on connected worker solutions, technologies for industrial asset maintenance, and the industrial applications of AI, including generative AI and computer vision. Prior to joining Verdantix, Henry completed a Masters degree in Civil Engineering at the University of Exeter.

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