In the world of asset management acronyms—CMMS, EAM, APM—the approach that can most profoundly influence your organization but is least likely to be in place already is APM, or asset performance management.
It has taken time for the asset management profession, the people who practice it, and the technology that supports it to come together to a place where all are in sync and focused on not just asset maintenance, but more holistically on overall organizational health. We have now reached an inflection point where the technology to make precise predictions and recommendations based on unique operational factors is widely available and accessible. There is no turning back the clock on business intelligence; there is only the question of who will take advantage of it and transform their business performance based on information already within reach.
CMMS’s role, historically, has been tactical: to optimize day-to-day maintenance processes by managing work, spare parts, and labor skills as they pertain to the asset register.
With the advent of enterprise asset management, organizations began looking more strategically at the cost of owning an asset in the first place, the risks of not owning it, and its value throughout its lifecycle as compared to other capital investments the organization could make. EAM manages risks as well as assets in order to achieve the organization’s long-term plan.
APM takes this tactical and strategic information and combines it with risk and cost data from across the enterprise. The result is a set of contextualized operational data that allows asset management professionals to not only extend asset life but do so in ways that align with strategic business initiatives and contribute to long-term success.
The key to an effective APM program is tying together data sources from across the organization, such as ERP, inventory management, and quality systems, for a complete picture of not only asset health but progress towards overall corporate goals. Deloitte says, “Because asset performance is affected by variables in operations and material supply, companies that fail to connect APM with other technologies and data in the enterprise-wide digital supply network (DSN) will not be able to harness its full value.”
IFS uses AI and machine learning to analyze and interpret this data through time series analyses and other forecasting models, resulting in anomaly detection, failure prediction, and recommended actions. A continuous feedback loop ensures that the prediction algorithm gets smarter and more precise over time.
An important part of this process, once failure is predicted, is direction on the appropriate next step and the timing of that step. Do you repair or replace the asset? What is the optimal time to do that to maximize output and minimize cost and risk, based on your unique operating parameters? Many APM solutions provide predictions and anomaly detection, but without the accompanying suggestions for how to proceed based on this new information.
Your organization’s long-term performance, and asset management’s contribution to it, can both grow with the help of advances in business intelligence. To learn more about the current reality of predictive maintenance and how you can move towards the next level of efficiency with APM, read our eBook: