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.