Success at scale has been low... but there is a path forward
While many companies have launched isolated pilots, however, few have been able to deploy Predictive Maintenance (PdM) at scale across their operations.
WHY IT MATTERS: Several factors can stand in the way of a successful large-scale PdM program, and most companies face issues in one or more of the following broad categories:
Data is insufficient, inaccessible, or of low quality
Technology is inadequate, with too few sensors or poor IT/OT infrastructure
Prioritization is difficult, as companies lack a clear view of which assets to include in their PdM programs
Capabilities are missing, especially the skilled data engineers and data scientists required to build advanced AI models
Change management is weak, often because of user-unfriendly design
Economic return (ROI) is low, due the high cost of developing models to cover diverse assets and numerous potential failure modes
Leadership is not committed, either they have been burned on other PdM projects or don't see the competitive advantage gained from a PdM program successful at scale.
Overcoming these challenges requires a systematic and holistic approach to the design, development, and implementation of your PdM program. That approach begins with a clear understanding of the organization’s asset base and its reliability goals. Companies also need to recognize that PdM encompasses a wide range of analytical and technological approaches, with differing levels of complexity, costs and benefits.
FINDING SUCCESS: While PDM deployments are rare today, successful deployments share several characteristics:
Multiple assets or plants sharing a degree of similarity, which enables scale advantages such as replication of models and sharing of data and best practices
Asset-constrained growth, with no commercial limitation to selling more product—so that additional production converts into additional sales
A large and diverse range of downtime root causes that need to be addressed to achieve sizeable impact
High-value failure modes (such as in critical equipment) that occur at low frequency each year, making them harder for traditional methods or typical AI approaches to predict accurately
Platform(s) that scale and integrate multiple use cases.
Understand that today's high-availability operations are costly as they are supported by very high reliability maintenance costs
Enlist vendors and consultants who have made the journey to successful PdM programs at scale.
THE BOTTOM LINE: New systems are being introduced to solve these problems. The Amazon Monitron PdM platform is a good example. It deploys fast and requires no data science staff to setup and scale. Bringing in a partner with a proven track record substantially reduces the cost of deployment and probability of success. There is a growing ecosystem of PdM partners, like DecisionIQ, that have deep industry knowledge and practical operational experience to advance your program rapidly.
PRIOR POST: AI Project Success: Your Seven Risk Factors
NEXT POST: 10 Ways to Boost the ROI of your PdM Program
GetIQ.ai is a blog about building AI solutions for augmenting decision making and empowering people that make them. It's authored by the engineers at DecisionIQ.
ABOUT DECISIONIQ: We are "boots on the ground" factory engineers expert at the adoption of AI and machine learning into operations. As consultants and system integrators we bring our experience in a mix of a well structured programs that enable our clients to produce winning results and maximum ROI. You will learn to use AI to build competitive advantage by increasing plant uptime, quality and yield. With 24 POCs and 16 deployments we have achieved for our client’s ROIs as high as 60x and a cumulative $22 million in savings.
AN AWS PARTNER: We provide capability in design of AI solutions for industrial applications. We work with the AWS Industrial IoT services stack, cloud migrations and Amazon Monitron POCs and deployments. For qualifying customers, many of these capabilities are eligible for AWS subsidized funding.
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