ISE Magazine

DEC 2017

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December 2017 | ISE Magazine 37 Aiming to please seafood lovers Red Lobster, a full-service seafood restaurant, wanted to change the cooking platforms used to prepare a variety of their dishes to improve the taste and texture of the food the chain was serving its guests. At the same time, the service time and labor deployment had to be similar or better than the baseline. This was a collaborative effort where the culinary team provided different metrics, including cooking and assembly times for each of the options being considered. For three dif- ferent cooking platform combinations, different kitchen op- tions were tested. Each cooking platform had different menu item routings, cooking times and hands-on labor time for each dish. Another challenge came from the fact that menu promo- tions affect kitchen performance a great deal. These promo- tions are cyclical, as seasons change, and could affect stations differently. So each promotional period had to be tested to ensure success for the entire year. Some of the key metrics, among others, that were tracked in the simulation were: • Labor utilization to balance out the production responsibilities in the design for the different deployment schemes (e.g., two to seven employees in the line) • Equipment utilization to understand the level of equipment resources needed and ensure that the employee had access to the equipment when he or she needed it • Product routing to balance the demand on the station and ensure that the work demand was spread through the labor deployment that was being tested Using simulation allowed the team to test several equip- ment lineups and deployment strategies until a good solution was found. The final solutions were then rolled out in the system. Simulation enabled the team to take the construc- tion philosophy of "measure twice, cut once" to a whole new level. With simulation in food service, you actually can "measure hundreds of times" and account for hundreds of variables. Only then do you have to "cut once." 30 percent higher throughput In this case, a fast-casual res- taurant chain wanted to test different service systems, along with kitchen layouts and labor deployment setups, that would enable its franchisees to process more guests using the same number of employees. Fast casual restaurants gener- ally refer to restaurants that offer customer service similar to fast-food restaurants but often with higher quality and more expensive menu items. The service systems tested affected the inside customers as well as the drive-through guests, making testing in a real res- taurant expensive, time-consuming and very limiting in the number of options that could be examined. For this particu- lar concept, the time of the year (season) and the part of the country (region) significantly affected customer orders, so the options had to be tested for those variables as well. The team used simulation software to create the model, which provided quantifiable differences between tested op- tions and types of guests for service times, resource utilization and labor utilization. The simulation also was used to do sen- sitivity analysis to determine the impact of how well the guests would accept the new technology that was being applied. The model was used to determine the ideal number of order points needed as well as the number of new equipment pieces re- quired to meet a targeted throughput volume, all while main- taining or improving service times. The simulation was used to run options for different labor staffing levels ranging from two to eight people, along with different task assignments or slide deployment practices to de- termine the best use of the labor resource for new and existing stores. According to the simulation's results, an alternative or- dering system could achieve up to 30 percent higher through- put with the same number of employees, all while maintaining or improving service times. One key metric tracked during the simulations was the time from when the guest placed the order until the order was ready for pickup, as shown in Figure 4. This is a key metric in res- taurants that for the most part can be managed with the right design. FIGURE 4 Order placed to pickup The time from when customers order until they can pick up their food is a key metric in food service simulations, particularly in the case of this fast casual restaurant chain.

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