Are The “Squeaky Wheels” Diverting Attention From Underlying Infrastructure

AI-driven analytics can ensure high-priority infrastructure issues are addressed before surface-level issues

By Kevin Price

As the saying goes, “It’s the squeaky wheel that gets the oil.” Small, troublesome maintenance issues can keep technicians busy as they rush from one highly visible “emergency” to the next. However, this reactive approach to maintaining infrastructure assets can keep the larger, underlying issues from rising to the top of the priority list. Fortunately, Artificial Intelligence (AI) driven analytics can help asset maintenance teams focus on their true priorities, squeaky wheel or not.

Further Defining the Problem

A Matter of Perception.

Asset management teams monitor and maintain a wide range of asset types. Judging which assets are high-priority depends on the person you ask. However, neither of these “minor” issues compare to the risk to public safety that something like storm-damaged traffic lights may cause.

Delaying Non-Essentials.

Some maintenance teams face large volumes of service requests. Incorporating preventive maintenance into the dispatch calendar can be difficult if the break-fix emergencies start to dominate time and use of resources. The preventive tasks can be classified as non-essential, causing them to be pushed back repeatedly—until they are forgotten completely or escalate into emergency status.

Rushed, Surface-Level Responses.

Continually responding to “fire drills” is an inefficient use of a technician’s time. Because there was no pre-planning, replacement parts may not be in stock, adding to the delays and frustration. When the repair is considered a critical emergency, the rushed response is not the response that is ideal—or most cost-effective. Quick fixes may solve the immediate problem, but not address the underlying issue that may repeat itself soon. Teams that try fixing behemoth-sized issues with kid-sized bandaids will likely cause more work for themselves as symptoms keep returning.

Lack of Intelligence on Asset Conditions.

For some teams, assets may be spread over a wide area or there might be limited intelligence on condition of assets, service history, expected life expectancy and costs associated with repair or replacement. Without sound data to support funding requests, investment in the asset’s repair or replacement may be delayed.

Is There a Solution?

Upgrading Enterprise Asset Management Solutions (EAM).

Advanced EAM solutions will help manage assets in a more logical, prioritized, and proactive manner. Modern solutions will also help track Condition Assessment and State of Good Repair for infrastructure assets. Advanced software solutions—purposely built for asset management—can help provide a holistic view, from Facility Condition Assessment (FCA) to Remaining Useful Life (RUL) and Estimated Replacement Cost. Service history, as well as related costs and resolution tactics, can also be tracked, giving technicians valuable information about the history of the assets.

Risk Assessment.

A risk assessment will help determine which assets have the biggest impact. This helps prioritize the order in which assets will be addressed. As part of the assessment, the “value” to the stakeholders is defined. This means more than replacement costs Convenience to the public, usability, and safety are factored in as well.

Predictive Analytics.

Innovative Business Intelligence (BI) solutions, powered by Artificial Intelligence (AI) provide added insight to analytics, offering advice and recommendations. The analytics use algorithms and data science to identify patterns and project next likely outcomes. The layer of AI augments BI with insight and recommendations. Users can explore “what if ” scenarios and obtain forecasts of likely costs and likely demands.

Setting Priorities.

Predicted needs can be juxtaposed against projected cash cycles and budgets. Managers can then prioritize major capital investments when funding and political backing are in place. Plans for stop-gap fixes may be needed when funds are limited.

Critical Issues.

AI-driven analytics will help identify time-sensitive issues which will affect health and safety of constituents. Using data science and algorithms, the BI tools will monitor for issues that demand immediate response, including those that may incur costly fines for noncompliance. An example of this is ADA accessibility, building code compliance, OSHA or EPA mandates, and workforce or public safety issues.

In today’s environment, maintenance teams are often stretched thin. They can easily fall into the trap of responding to pressures and addressing the highly visible assets first—even if that means larger underlying issues do not receive maintenance or repair when needed. Service technicians and facility managers must juggle many demands and limited budgets. Smart software solutions with Augmented Analytics will provide the insights needed to make sound decisions based on priority, risk assessment, and data about cost impact.

Kevin Price is the Technical Product Evangelist & Product Strategist for Infor EAM. He may be reached at www.infor.com.

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