Monitoring 2.0 Technology featured in Lacompare
A thought leadership article featured in Labcompare, a Buyer's Guide for Laboratory Equipment

Monitoring Technology 2.0: Replacing Cold Storage Monitoring’s Fail-and-Fix Model with Predict-and-Prevent™, a thought leadership article featured in Labcompare, a Buyer's Guide for Laboratory Equipment.

Remote laboratory monitoring systems have saved billions of dollars worth of samples by sending critical temperature alarms when a freezer or other lab equipment fails. When this happens, almost inevitably at 2 a.m. on a weekend, the engineers and managers must report to the lab to move and secure the samples. However, sometimes by the time the alarm sounds, it is too little too late, resulting in the loss of priceless research or production inventory. But this doesn’t have to be the reality for the cold storage industry. 

Today’s Fail and Fix Method

Remote monitoring has been a broad category that includes simple remote dialers that send a signal if they sense a temperature change to more advanced integrated enterprise-wide deployment of monitoring systems. These systems come with standard graphs and alarm features to alert the team – whether on-site or remote – of changes in the equipment’s performance. However, they are only triggered after a failure has occurred, requiring a reactive approach and putting valuable products in jeopardy.  

Moving to Predict and Prevent Technology

Wi-Fi sensing devices have started to gain popularity as they are relatively low-cost and cloud applications have continued to mature. These devices leverage existing IT infrastructure and have been widely deployed throughout the life science industry for asset monitoring. However, these devices are not all the same.

Some recent advancements have added features that help companies transition from a fail-and-fix approach to predict-and-prevent. Predict-and-prevent monitoring systems – also referred to as monitoring 2.0 systems – leverage powerful statistical models to identify and flag asset failures before they occur. Using these proactive asset management tools allows companies to reallocate maintenance budgets and resources to the assets needing repair – saving costs and time.

The Role of Monitoring 2.0 in Cold Storage

Monitoring 2.0 is changing the game, particularly in the cold-storage industry. All cold-storage laboratory equipment uses novel and often different mechanical techniques to produce cooling, but they all compress gas and all have failure points. This means monitoring 2.0 systems can use machine learning algorithms and industry benchmarks to predict the onset of most failures.

As an example, mechanical compressors typically pull more current when they are under stress due to environmental conditions, aging or deferred maintenance. So, a monitoring 2.0 system would apply machine learning algorithms on temperature, vibration and other data to benchmark the performance of the freezer so it can be compared to its peers in a larger database providing actionable analytics that distinguish a healthy from a failing refrigeration system, and a clear call-to-action.

Adopting monitoring 2.0 with predictive analytics can help life science companies drive operational and capital savings in the following ways:

  • Sample protection. Advanced machine health warning and cloud-based systems offer extra layers of assurance. If the compressor fails, users will know hours before temperature alarms fire allowing them to address the problem or move the assets.

  • Repair savings. Monitoring 2.0 systems can indicate or tag poorly running units which can often be corrected with routine or low-cost maintenance versus waiting for a larger breakdown.

  • Increased service life. Addressing maintenance issues early can help increase asset life an average of two years for a 10-year asset.

  • Energy savings. Independent research from one monitoring 2.0 device has indicated that a conventional ultra-low temperature freezer consumes the equivalent energy of a whole house, and 50 percent waste an average of 22 percent of all electricity consumed. Monitoring 2.0 systems that can detect and manage repairs to restore energy efficiency can qualify for utility incentive payments in some areas.

  • Responder savings. Unplanned maintenance events, especially those that occur outside of business hours are not only very expensive, but product losses occur at a higher rate due to dispatching delays.  

  • Global sourcing and purchasing intelligence. The capabilities of some systems include benchmarking techniques that enable the comparison of different brands for energy efficiency, cooling capacity and stability. When combined with failure analytics, the data can be used as an objective basis for making purchase, retirement and repair decisions. Knowing what to buy and how to plan your next operational expansion is best achieved when you know which products are the best performing for your application.

The next generation of laboratory monitoring, monitoring 2.0 systems, has helped make predict-and-prevent a reality in the cold storage industry. By leveraging powerful machine learning techniques, life science companies can realize organizational efficiencies and feel confident that their biological samples are protected.

Chris Wilkes serves as the chief commercial officer at KLATU Networks, Inc. 

The Case for Cold Chain and Monitoring 2.0
A conversation with Harbor Research