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Getting Started with Data-Driven Asset Management

Decide the scope and platform of an asset management solution with these steps

by
Todd McEvoy, PhD

Todd McEvoy, PhD, is the senior director of laboratory services for Azzur Labs at their Schnecksville, PA location. In this role he is accountable for all aspects of the business...

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Aside from ensuring that high-quality science is being performed, laboratory managers have many non-technical responsibilities. Of particular importance is ensuring that the laboratory’s assets are actively managed for the benefit of the business. Well-developed asset management programs allow lab managers to optimize asset productivity, monitor instrument life cycles, develop evergreen capital plans, and optimize spending of the lab. In today’s connected lab, all of this is easily done with the availability of an array of data streams. Integration of these data streams allows for smarter decisions based on facts rather than opinions. Following are the steps needed to formulate a winning plan for creating a robust asset management solution that fits your lab’s needs.

Getting started

Whether at the beginning of the journey or deciding how to improve an existing laboratory asset management program, there are several key steps to get started:  

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1.    Determine what asset management means for your organization. What is an asset, and how broad is the program?

While lab managers must control their operating costs, they should remember that more costly enterprise systems often deliver a significant turn on investment.

2.    Decide why you want to manage your assets in a controlled manner. Are you driven by operational excellence improvements, cost controls, or regulatory factors?

3.    Consider what asset management classes are important. Will you want to manage IT, fixed assets, and/or financial transactions?

4.    Evaluate the data you currently track. What data makes sense to continue collecting, and what can be discontinued?

5.    Brainstorm what data you can obtain easily—manually or electronically. Raise questions as to what data would be useful to help manage the asset fleet more easily.

6.    Decide which data make sense to collect, manage, and can lead to action.

7.    Know your budget. 

Asset management platforms

After establishing which asset classes are included in the management program, you must then decide the platform on which to manage. The platform can be simple or complex depending on the type and amount of data collected. While lab managers must control their operating costs, they should remember that more costly enterprise systems often deliver a significant return on investment.

    Spreadsheets and databases: Basic electronic systems, like spreadsheets or simple databases, are often good places to start as they have built-in applications to visualize data. However, significant time is often required to customize each spreadsheet per data type. These systems likely do not integrate to hardware objects; thus, they may not be amenable to automatic data updates.

    Asset management systems: Software and hardware products are available that monitor various types of data including calibration/qualification/maintenance, instrument usage, and facility monitoring data. Monitoring is highly customizable and lab managers can decide which data is most important and invest in that first. However, managing multiple systems may lead to logistical and data integration challenges.

    Fully integrated asset management platforms: Integrated enterprise systems provide data capture, visualization, and trending for all aspects of the laboratory environment. While these are the costliest option, they may provide a singular view of the entire laboratory workflow and output inclusive of all equipment, instruments, hardware, software, and finances. 

With carefully selected data on hand, lab managers can make more informed decisions to positively impact the management of their lab’s assets. These decisions can more easily be communicated and rationalized with lab scientists and senior management because data is available to support them.