How to Prevent Inventory Loss with Automated Data Collection and Predictive Alarms

Automated Data Collection Inventory Loss

How to Prevent Inventory Loss with Automated Data Collection and Predictive Alarms

What’s better than automated data collection? An automated data collection system that provides early warning notifications.

fast food kitchen

Imagine you’re a grocery store owner 30 years ago. You get an early morning call from one of your opening employees, who reports that one of your freezers failed overnight. You rush to the store to assess the damage and realize you can’t sell the affected food. Your pay is based on the profitability of your store, so you need to offset this loss somehow. You end up hauling home large amounts of thawed and semi-melted ice cream, TV dinners, and various other foods to refreeze and feed to your own family. Sometimes “free food” isn’t as great as it sounds!

Equipment failure and product loss can result in significant costs for Food & Beverage operations. A modern example is a sandwich shop franchise with a walk-in cooler that fails—which can result in a loss of more than $10,000 for even a modest operation. To prevent this kind of loss, today’s stores and quick service restaurants (QSRs) use early warning predictors powered by statistical process control (SPC)

Equipment temperatures are automatically captured by data loggers, as well as supplemented by manual verification checks. The risk of equipment failure and food loss is still a reality, but the risk is reduced because of the frequent checks.

However, to balance these risk mitigation improvements, USDA and FDA regulations surrounding food safety have also escalated dramatically in the past 30 years. How long food can remain in the “danger zone” temperature range is clearly defined and enforced. So, when equipment does fail, the potential for food loss is increased if not caught in time. Quickly responding to equipment failures and triggering food loss mitigation workflows is the name of the game in today’s warehousing, retail, and restaurant environments.


Good: Manual Data Collection

Rather than waiting until the next morning to discover that a cooler went down, implementing manual equipment temperature checks a few times per day is a good practice. This activity regularly verifies that equipment is operating within standards. And while the temps are being recorded, there’s time to perform quick visual inspections to verify cleanliness and storage housekeeping as well.

Whenever a noncompliance condition is found, the event is documented on a form, along with any corrective actions taken. Storing these forms for easy retrieval provides evidence that the checks were performed. If further analysis is warranted, the data from these forms can be entered into a computer spreadsheet.


Better: Adding Automated Data Collection

What about introducing automated data collection? Rather than pulling someone off a revenue-generating task to walk around and collect temperatures at specified times, why not make a modest investment in data logger devices that connect to the store’s Wi-Fi network and collect the temperatures automatically? There are a few things that make these systems superior to manual checks:

  • Sampling intervals can happen as often as you like. Rather than a couple of times a day, the logger can collect readings several times an hour. You can get data as often as you want or need.
  • Data is collected 24/7. Over time, you have a complete history of the equipment’s temperature profile as the unit(s) run through their cooling cycles.
  • The loggers can automatically send alerts whenever a reading falls outside the acceptable range. This way you can respond the instant a failure occurs.

Any equipment that needs to maintain a temperature range is a candidate for automatic temperature monitoring. The only challenge is finding the hardware to do the job.


Best: Early Warning from Automated Data Collection

What’s better than automated data collection? An automated data collection system that provides early warning notifications. 

Getting a real-time notification when a piece of equipment isn’t meeting standards is great, but how much more valuable would it be if you were forewarned when a piece of equipment was about to fall outside of standards?

Even if the heads-up was only an hour or two ahead of time, just imagine how much cost and risk could be avoided. Being notified at 3:00 a.m. that your cooler is close to failing is so much better than a 3:00 a.m. notification that your cooler is already out of temperature range. Because these early warning notifications are based on sound statistical rules, you can rely on the accuracy of the alarm and never be inundated with false alarms.


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