※Translated by machine.

Challenge to reduce food loss with DX

DX reduces food loss and ensures customer safety

Challenge to reduce food loss with DX
“Kaiten Sushi Management System”

The average cost rate of the products offered by SUSHIRO is about 50%, which is one of the highest in the industry. The reason is that we always want to deliver high quality and delicious products. In order to achieve this high cost rate, it is essential to reduce the amount of food waste. SUSHIRO has been working to reduce food loss by incorporating IT into store operations for about 20 years.

Analyze data of 1 billion dishes in one year and forecast demand with high accuracy

“SUSHIRO” has introduced a “Kaiten Sushi Management system” that utilizes IT to reduce waste ingredients by managing sales trends and forecasting demand. An IC tag is attached to each dish to grasp in real time which material was taken from the lane and when, and forecast demand with high accuracy based on that data. Number of data to be analyzed = The number of dishes on which products are placed reaches 1 billion in one year. We predict the number of dishes that customers will request 1 minute and 15 minutes after they are seated, and we will use that number as a reference when loading products into the lane.

By starting this measure, we have also succeeded in reducing the amount of food that is discarded. We have adopted a mechanism to automatically dispose of dishes that have moved a certain distance in the lane. For example, tuna is automatically disposed of at 350 meters (about 40 minutes). This ensures that only fresh products are always flowing in the lane.

Reducing food loss is a contribution to the global environment, and at the same time, it is an essential initiative for the food service industry when considering the sustainability of business activities. It was about 20 years ago, in 2002, that the Kaiten Sushi Management system was introduced. Actually, before that, all the predictions about which material would sell and how many dishes were decided by the store manager who works at the store. The point is intuition. Also, when disposing of the products flowing through the lane, the dishes that the human eye felt “this material is dry” were thinned out from the lane.
By promoting DX to change such a situation and recording the movement of each dish as data, it has become possible to standardize the management of products flowing through the lane without relying on human senses. Not only that, by grasping the needs of customers numerically, we will purchase products with high needs firmly and prevent them from running out of stock, while increasing the motivation to reduce the amount of discarded ingredients, and the store manager will be concrete. It’s easier to take action.

Precisely predict the number of store visits and the amount of purchases
System transformation utilizing AI

Kaiten Sushi Management system consists of various subsystems, including freshness management of ingredients, guidance management of customers, supply instructions indicating what kind of products should be provided, and order management of receiving orders from customers. increase. Of these, for example, order management was initially a mechanism for receiving orders via an intercom, but in 2008 we started switching to a touch panel. In 2016, we introduced a mechanism to deliver ordered items in a dedicated lane at some urban stores. In addition, guidance management was changed to utilize smartphone apps in 2015. In 2019, we also started to introduce a dedicated kitchen lane to streamline work in the kitchen. In this way, we are constantly updating to improve the system by flexibly responding to customer needs, changes in the environment, and new technologies.

Demand forecasts have been calculated based on two analysis, one is analysis based on data related to product management such as the number of sales obtained by The Kaiten Sushi management system, and the other is analysis derived from past statistics such as the number of visitors by day of the week. I was giving instructions according to the store. In the future, we are working on evolution using AI to further improve prediction accuracy. We will also work to further reduce food loss by expanding its use not only to stores but also to purchasing and inventory management that the head office collectively carries out.

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