A typical high-rise office building contains about 1,000 maintainable assets, like electrical equipment, backup power systems, elevators, pumps, and air handling units. For a facility management team to locate, identify and record them all could take weeks depending on the number of engineers involved in the inventory and validation process. Even more follow-up time would be required to find and review mechanical drawings, OEM manuals and work orders.

This is why facility managers have historically had so much trouble obtaining quality asset data. It requires painstaking effort and diligence and often results in incomplete or incorrect data.

Without quality data, facility management (FM) teams struggle to make smarter maintenance and capital replacement decisions. They’re exposed to greater risks of asset failure and unscheduled downtime and can’t accurately measure performance or improve operations. Quality data impacts all aspects of FM operations, maintenance and renewal throughout the asset lifecycle.

And that’s why it’s never been more critical to make sure your asset data is of the highest quality possible.

An asset inventory is a storehouse of data for assets and relies on the 5Cs of quality data, which states that the data must be:

• Complete – All targeted maintainable and renewable assets are collected for the inventory. 

• Comprehensive – All required attributes of the targeted assets are collected (e.g., asset class, manufacturer, model #, capacity, voltage, tonnage, etc.). 

• Consistent – Nomenclature for asset names and attributes is consistent throughout the inventory. 

• Correct – Asset IDs and descriptions must correctly ID the asset, be accurate and avoid syntax errors, etc. 

• Current – Assets must be correctly designated as active (in service), inactive, abandoned-in-place, etc.

High-quality data in an asset inventory overcomes the risks and limitations cited above and allows teams to focus on assets deemed important based on cost, criticality, regulatory compliance, safety, or operational impact.

New technologies can substantially reduce the traditional challenges of asset inventories by slashing the labor, time and expense involved and improving overall data quality.

An AI-enabled mobile app, like JLL Serve, identifies equipment type using content-based image retrieval (CBIR). Taking a photo of an asset enables a connected, private cloud to correctly identify the asset type as an air handling unit, for example.

Optical character recognition (OCR) can read a photo of an asset’s nameplate, decode the model number, and populate attributes (e.g., RPM, tonnage, capacity, voltage, filter sizes, refrigerant type, etc.) directly into the asset inventory, ensuring accurate and comprehensive data. The technology brought all of an asset’s attributes into a single view as well as OEM manuals, mechanical drawings, work orders, troubleshooting guides, even do-it-yourself YouTube videos.

In a traditional audit, an engineer would spend 8-10 minutes per asset for an initial visual inspection and nameplate recording. With the new technologies above, a field technician with a mobile app can accelerate the process and get higher quality data in far less time compared to a traditional audit.

Because new AI-powered capabilities can automatically retrieve equipment attributes, manuals, drawings, etc., there’s no need for weeks of follow-up research and review and there’s greater assurance that the data approaches 5C-quality.

Apps like JLL Serve promote efficiency through automation and deliver accurate, quality data for better-informed lifecycle asset management decisions. Automation and AI ensure that data is complete, comprehensive correct, and current, saving valuable time for technicians.

Conducting a traditional asset inventory wasn’t always a smooth process. Locked doors and missing keys led to incomplete asset inventories.

Engineered rules, which can be created by FM teams, asset management software providers, industry associations, or asset management consultants offer predefined criteria for classifying assets within a building based on type, use, square footage, number of floors, etc. 

For example, most restaurants have rooftop packaged AC units, kitchen exhaust hoods/upblast fans, Ansul fire protection systems, refrigerators, freezers, and microwaves, so these assets should be found and recorded for a complete asset inventory with quality data.

Engineered rules ensure that asset data is complete, comprehensive, and current by providing a valuable checklist of assets expected to be found on site during the asset inventory. JLL Serve, with its connection to a private cloud and the immense Corrigo asset database, gives exceptional insights into asset types. Combined with AI-enabled algorithms and machine learning on the quality data, JLL Serve can predict expected inventory data and prescriptively fill in gaps even before setting foot in the actual buildings.