Using Big Data to Pinpoint Restaurant Theft

30 Sep

Theft is a huge problem in the restaurant business. There’s a laundry list of studies that show what a huge impact theft has to the bottom line. The National Restaurant Association estimates that 75% of inventory shortages are caused by theft. How is theft manifested? Employees give away free food, ring up false transactions, take money from registers or steal tips – all just a few examples of employee theft.


Not all theft can be caught. However, the point of sale (POS) is a huge advantage for the restauranteur. The POS maintains a repository of transaction data that can be used to determine what was stolen, when it was stolen, and how much it impacted the bottom line. In more technology-forward industries, data mining for shrinkage has been in use for decades. In fact, the statistical techniques employed to find suspicions transactions are not all that complicated.

In the restaurant industry these techniques are rather new, and are something that should be employed at every restaurant given their low cost of implementation. Many POS companies will call these solutions “exception based reporting” or “loss prevention”and charge $50 – $100 per month. Here’s how they work:

A line of code crawls over all the transactions that were recorded in the POS. The code starts to assemble relationships between transactions and builds a database. The results are lists of items that were sold, and items that sold with those items. For example, the relationship could be that for every 10 hamburgers sold, 1 coke sells. Using these types of relationships a confidence matrix is built, giving the probability of theft for transactions made on the POS. All of these activities can be drilled down to shift and employee.

In this way you can use data to determine suspicious activity, and what employees to monitor more closely. Just don’t pay $100 a month for the solution – it’s not that complicated.

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