[LH] Understanding cafeteria users’ preferences and satisfaction with a Scan
Use Case of Hall Manager Solution
A menu that satisfies everyone’s taste
“What’s on the menu today?”
This is a question that many people ask when they head to the cafeteria during mealtime.
If a favored menu is offered, the experience of getting the food is more enjoyable and the meal tends to be more satisfying. On the contrary, when there are no preferred options, the experience leads to disappointment and a comparatively less satisfying meal.
So many think it would be nice if their favorite menus were offered more frequently.
Likewise, those who plan and prepare the menu also face challenges.
Nutritionists design menus considering essential nutritional elements, and chefs put their best effort into preparing these dishes. However, problems arise when people do not choose these meals or leave a lot of food left over.
It would be ideal if the cafeteria users’ preferences for certain menus were known, but conducting satisfaction surveys regularly is not easy.
LH’s Seoul Regional Headquarters, which promotes the improvement of people’s residential life and the efficient use of land, utilized Nuvilab’s solution to understand the preferences of users and operate a more satisfying cafeteria environment.
Scan completed in the blink of an eye
The cafeteria at LH’s Seoul Regional Headquarters in Nonhyeon, Gangnam-gu, Seoul, is responsible for serving three meals a day — breakfast, lunch, and dinner — to about 500 employees.
After each meal, users are encouraged to dispose of leftovers and clean up their own used utensils while exiting.
The route was designed so that users could first throw away general waste, such as used tissues, before disposing of leftovers and dishes.
We installed an AI Food Scanner that can scan the trays above the disposing area where users pass by with their waste.
Food trays can be scanned in just 1 second, allowing users to naturally scan while disposing of general trash or waiting for their turn without taking any special action.
Additionally, our team installed a dashboard near the exit to help increase awareness of users so that less leftover food waste is left behind.
The dashboard easily informs users of the total number of scans, the number of times ‘Zero Leftovers’ were achieved without leaving any leftovers on the user’s plate, and the amount of greenhouse gases reduced through ‘Zero Leftovers’ in terms of straw usage, number of pine trees planted, etc.
This menu would be good to offer again
Based on the served amount and the leftovers they leave behind, you can estimate which menus users prefer and are satisfied with. This could be classified into four different categories.
Category 1. High Served Amount with Low Leftovers
: This indicates that a large number of people who chose the menu were satisfied with their meal. Therefore, it is advisable to offer the same menu again in the future.
Category 2. Low Served Amount with Low Leftovers
: This reflects that although not many people chose the menu, those who did were satisfied with their meal. Due to the lower number of selections, it is recommended to change the menu to a different composition.
Category 3. Low Served Amount with High Leftovers
: This indicates that few people chose the menu and were not satisfied with their meal. Among the four categories, the menus classified in this sector require the most significant change.
Category 4. High Served Amount with High Left Overs
: This reflects a scenario where many people chose the menu, but were not satisfied with their meal. It suggests that the menu quality may have been low, or the portion sizes were too large, necessitating a review and possible adjustments.
Additionally, the categorized chart can help identify which menu has achieved the most ‘Zero Leftovers’. These insights allow nutritionists and chefs to review and consider the most favorable menus to serve again in the future and provide a satisfying service.
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