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.

The asset inventory, also known as an asset registry, is a database of assets and their attributes like an asset’s brand, model, serial numbers, purchase date, warranty information, and location.

Creating an asset inventory has historically been labor-intensive, lengthy and expensive, which is why facility managers had to be selective about which assets they onboarded into the inventory. Assets have varying degrees of importance to the organization. While the inventory ideally focuses on all maintainable assets, costs and resources may influence which ones get included. In all cases, the value of the inventory rests on quality asset data.

For example, an organization with a portfolio of 250 locations, each with critical 400 assets, was looking at a total inventory of 100,000 assets. Managing that many assets demands 5C-quality data. Anything less will fall far short of the purpose and value of the inventory.

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. 

Asset data proves its value by allowing FM teams to drive actionable insights for their lifecycle asset management plans. Accessing complete, consistent, correct, current, and comprehensive data generates immediate gains in efficiency.

Asset data quality compliant with the 5Cs allows for more intelligent facilities management and helps maximize uptime, asset life and asset performance. The ability of current technology to populate asset attributes in real-time, while including other data resources—like OEM manuals and mechanical drawings—brings all asset details into a single pane-of-glass for technicians, eliminating the need to access multiple technologies.

AI-enabled capabilities and mobile apps accelerate asset onboarding while saving time, cost and labor. With CBIR and OCR, asset onboarding gives more reliable, complete, and accurate data than traditional manual processes.

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