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What is Operations Data?

Since we started OpsAnalitica we have been talking about Operations Data(Ops Data) and how powerful it is and it dawned on me last week that we haven’t taken the time to define Operations Data for our readers.  This is an oversight on our part but one that I hope to correct in this week’s blogs.

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Here are some examples of Ops Data:

  • Ticket Times
  • Temperatures
  • Taste of Soup of the Day
  • Cleanliness of Bathroom
  • Staff Readiness
  • Pre-shift meeting completed
  • Line Pars
  • Staffing % (were you staffed to your schedule or were you short staffed)
  • Food Cost
  • Labor Cost
  • Waste
  • Appearance of Restaurant
  • Sales
  • Day Dots being followed (FIFO)

This list is far from being complete; these items are just a small sampling of the items that good restaurant managers are checking.  Each one of those Ops Data items can affect your operations and ultimately your profitability.  Some of them are easier to track, sales because your cash register does that for you.  Some are harder to track, day dots being followed, because you physically have to go into your walk-in, coolers, and pantry to inspect what you expect.  All of those operations data items help tell the story of your restaurant.

It is in the story of your restaurant where you find out who is a good employee and a bad employee, you start to understand the patterns that you have intuitively known but have never been able to quantify because you couldn’t back them up with numbers. More importantly when you have data, you can disprove assumptions as data sheds light on what is happening.  You may have thought your issue was slow sales because of a holiday weekend, and the real answer might have been that the kitchen was short staffed, you had the sales but couldn’t execute on ticket times.  The operations data when it is consistently recorded in an application like OpsAnalitica can be one of the most powerful tools in your restaurant.

For the next couple of days, I’m going to document Ops Data use cases that will show you how tracking and analyzing data points can help you run better operations.

Day Dots:

We all know that we should be using Day Dots in our operations to ensure that we are following the FIFO inventory methodology, serving safe food, and reducing waste.  If you don’t check your walk-in every day and record that the day dots were in use and that FIFO was being followed; will you remember that you may have had a bad week because of some training issues at the end of the quarter?  Maybe maybe not.  When you are looking at those cost numbers you may attribute the higher waste to some other issue and spend a lot of time trying to solve a problem that has already been solved or wasn’t the cause of your waste issue.  If you had that operations data and you were able to compare it to your cost data easily you would be able to see that you had some new cooks who weren’t following FIFO and you had increased waste until you were able to train them.  After the training, they were fine but during that week before you were able to train them a lot of extra food went bad and was thrown away.

This is a simple example, but it illustrates how important it is to have the complete picture of what is going on in your restaurant each and every shift in a digital format that you can use to compare to other data to make good data-driven decisions.

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Written by

I've been in the restaurant industry for most of my adult life. I have a BSBA from University of Denver Hotel Restaurant school and an MBA from the same. When I wasn't working in restaurants I was either doing stand-up comedy, for 10 years, or large enterprise software consulting. I'm currently the Managing Director of OpsAnalitica and our Inspector platform was originally conceived when I worked for one of the largest sandwich franchisors in the country. You can reach out to me through LinkedIn.

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by Operations Data Use Cases | Opsanalitica , post on 22 July 2015 | Reply

[…] yesterday’s post, What is Operations Data. We discussed the importance of Operations Data (Ops Data) and how it could be used to make better […]

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