Loss prevention continues to be a major priority for restaurant operators, with nearly half (48%) of those queried for Hospitality Technology’s 2019 Restaurant Technology Study deeming it a must-have component of point-of-sale (POS) software. Additionally, through integration with other applications, including next-wave technology such as predictive analytics and artificial intelligence (AI), POS data itself becomes a loss prevention linchpin.
360-Degree View of Operations Protects Against Profit Loss
For certain restaurant players, integrating inventory and related applications with the POS is an integral part of loss prevention. COPA (https://copadurham.com), a Cuban farm-to-table restaurant in Durham, N.C., utilizes the Upserve (https://upserve.com) POS platform, which integrates with a module called Upserve Inventory. This syncs the restaurant’s recipes with the POS system, minimizing waste and subsequent loss because ingredient usage is automatically tracked.
“Because recipes sync to the POS, every time we sell a mojito, [we know] exactly how much rum and lime juice was supposed to leave the bar,” says Elizabeth Turnbull, co-owner of COPA with her husband Roberto Copa Matos.
She adds that she also uses Upserve Inventory’s recipe costing feature, which lets the system recommend a price for each menu item based on the cost of individual ingredients and a desired, pre-determined profit margin. Turnbull recalls a situation where she was attempting to set a price on a new drink that was to be added to COPA’s menu. With three ingredients, one of which was very expensive, the drink was complicated to cost out despite being rather simplistic in structure. Turnbull utilized the system to realize that she had to boost the libation’s price in order to meet the restaurant’s margins.
Get Proactive Rather than Reactive
Some operators leverage loss prevention tools that integrate with and apply predictive analytics to POS data to assess the likelihood of employee scams and address them before they become major problems. These range from improper handling of/failure to ring up guest checks, granting unauthorized discounts and more. Clyde’s Restaurant Group (https://www.clydes.com) integrated its Avero(www.averoinc.com) POS system with an Avero predictive analytics module that monitors and scores every front-of-house employee across seven “watch types,” including comps, tip inflation, auto gratuity, check reuse, voids/promotions, transfers, and POS authorization.
In addition to check-level detail, the module analyzes employee patterns, performs peer comparisons across POS data and isolates historic trends. It apprises operators about which employees trigger the most alerts and where problems are most likely to be occurring or will occur given isolated patterns. According to Avero, access to predictive analytics gleaned from POS data has allowed Clyde’s to identify several “check-level” control areas. In turn, the chain has altered its procedures and policies — for example, its procedure for voids — reducing the risk of loss.
Predictive analytics is also being used to aid loss prevention by minimizing the food waste that stems from purchasing excessive quantities of perishable ingredients to preparing too much food for a particular day, offering the wrong menu items, or assigning too many staff members to work a certain shift.
Tenzo (https://www.gotenzo.com) takes historic sales data from the POS and combines it with other data such as weather predictions, events and labor schedules. Then real-time push notifications containing forecasts and alerts are conveyed to management. For example, the operator of an Italian restaurant might receive a message that making eight pans of lasagna for the following day, as planned, would not be a good idea based on the snowy weather forecast and/or historic traffic patterns, or a restaurateur might be informed that its labor expenditure is always too high on a certain day or during a particular time of day throughout the week.
AI Gains Ground
AI-based solutions and tools that integrate with POS systems are beginning to play an equally critical role in loss prevention, according to TouchBistro (www.touchbistro.com). Some solutions and tools thwart fraud and/or other loss by bringing to light server performance issues. Examples include waitstaff pouring too much alcohol or giving a customer a “break” on his bill by ringing up the price of a less expensive drink.
In some instances, an AI component can find patterns indicative of waitstaff misconduct. Tenzo gives the example of a waiter with consistently very low average spend per guest (when compared to other employees) or an unusually high number of credit card transactions compared to cash transactions. Either could mean a server is pocketing cash, according to the company.
WingHouse Bar and Grill (www.winghouse.com) integrated its Aloha POS (https://alohancr.com) system with BeerBoard’s (https://beerboard.com) BeerBoard BOSS (Back-Office-Support-System) at two of its 24 locations, with further rollout slated to begin shortly. While one of the primary functions of BeerBoard BOSS is to ensure that employees pour the proper quantity of beer for each guest, keeping waste from over-pouring at a minimum, the integration of the POS system with BeerBoard curbs shrinkage by ferreting out and alerting managers to anomalies.
“We can see situations such as 120 ounces of a particular draft were poured at 2:45, but nothing was rung up in the POS until 3:07, or that two guests were served at the bar at 5:30, but only one glass was recorded in the POS system,” states WingHouse Bar and Grill CEO Dennis Prescott. “The intelligence in the system finds the information. It also tells us whether we’re within the guidelines for what we call a ‘perfect pour,’ which is less than 5% waste. Then we can address the issues.”
Prescott notes that since the technology was implemented earlier this year, WingHouse Bar and Grill has saved $600,000 on beer that would otherwise have been lost due to over-pouring or the actions of dishonest employees. Two-thirds of the savings stem from dissuading employees from “giving away beer in one form or another” because of the integrated system in place or because dishonest actions have been curtailed, Prescott says.
Aloha parent NCR (www.ncr.com) also offers a loss prevention and employee performance monitoring tool called Restaurant Guard. It utilizes AI to perform functions like analyzing POS data for suspicious transactions, such as transfers, voids after check close, and comps after check close. Instant notifications of significant events are sent to a manager’s mobile device.
In another application of AI to loss prevention, cloud-based POS solutions are being integrated with video surveillance and data platforms such as Envysion (https://envysion.com) and Solink (https://solink.com). The platforms work by first examining existing videos of restaurant operations to garner an understanding of operational patterns, frequent transactions, and customer and employee behavior. These video extracts are then analyzed by AI and machine learning algorithms in order to detect repetitive events and recognize them as normal or suspicious activities. POS transactions are matched with AI-based video analytics, and when such suspicious activities occur in real time — for instance, an employee uses the same check twice — a notification is sent to the restaurant owner. According to Poster (https://joinposter.com), matching transactions, date and time with AI-based video analytics also allows restaurant operators to easily predict suspicious actions with a high degree of reliability.