We often – narrow-mindedly – tend to restrict the AI use case discussion in maintenance to the more sophisticated maintenance strategies (Predictive (PdM) or Prescriptive (RxM)), with a focus on predicting equipment failure. Yet, there are other ways AI can benefit maintenance objectives even under more basic maintenance models (reactive and preventive).
While Part 1 of this blog series looked at how more sophisticated maintenance strategies powered by AI can create substantial value for asset-intensive businesses, in this (second) part, I will review the constraints of the advanced maintenance strategies and highlight some pragmatic ways in which traditional maintenance models can still benefit from the use of AI (and digital).
In the third part of this blog series, I will review some common problem definitions and ML techniques used in advanced PdM, and give some tips on how to best get started with AI.
Managing Director, Prosperitree Consulting
June 2020
Recap of Advanced PdM
As shown in Part 1, Predictive maintenance allows the maintenance frequency to be as low as possible to prevent unplanned reactive maintenance, without incurring costs associated with doing too much preventive maintenance. It uses condition-based indicators and alerts to surface maintenance needs only when machines are at risk of breaking down — optimizing maintenance cadence and maximizing machine availability. It uses data from various sources like historical maintenance records, sensor data from machines, and weather data to determine when a machine will need to be serviced. Leveraging real-time asset data plus historical data, operators can make more informed decisions about when a machine will need a repair.
Decisions on when to intervene and perform a maintenance activity based on the condition of the equipment can be rule-based or AI-powered, latter typically denoted as advanced PdM. In latter, advanced AI algorithms learn a machine’s normal data behaviour and use this as a baseline to identify and alert to deviations in real-time. The system will then be able to predict when a breakdown is likely to occur and can run automated root cause analysis to help improve operating and preventive maintenance activities for the future.
Predictive maintenance often allows for the detection of impending failures that could never be detected by human eyes – take, for example, imaging that looks for microcracks in heavy machinery, even while in use.1
Advanced PdM is not for everyone
When evaluating whether Advanced PdM is the right maintenance strategy for a particular asset class or business, there are four areas of considerations that need to be investigated beforehand:
- Business rationale
- Predictability and data availability
- Operational deployability
- Human mindset & behaviour
These factors may either limit the relevance of advanced predictive maintenance for a particular business or asset class, or shall induce some specific design components and transformation planning to ensure realizing the benefits.
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Part 1 of this blog series looked at how more sophisticated maintenance strategies powered by AI can create substantial value for asset-intensive businesses.
Part 3 of this blog series reviews some common problem definitions and ML techniques used in advanced PdM, and highlights some tips on how to best get started with AI.
About the Author
Dr. Adam Flesch is the Managing Director of Prosperitree Consulting, and a former McKinsey Jr. Partner. He has been in the management consulting space for more than 15 years serving Clients across a wide range of industries on strategy, risk management, and operation effectiveness topics. He is a strong advocate for the wider use of AI in business, from decision-making to front-line automation. He advised an integrated international Oil and Gas company on designing and implementing a lean transformation program across its refinery operations, including the entire maintenance function.
About Prosperitree
Prosperitree Consulting is a boutique strategy consultancy focusing on AI-centered business solutions and traditional management consulting. It helps businesses build future-proof strategies and establish smarter day-to-day decision-making routines while turning their data assets into actionable insights and enhancing their respective capabilities.
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