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Best AI technology company from Silicon Valley in 2022!

 

Silicon Valley

Silicon Valley

Palo Alto Insight LLC (Headquarters: Silicon Valley, CEO: Yuai Ishizumi) and Ringer Hut Co., Ltd. (Headquarters: Shinagawa-ku, Tokyo, President and CEO: Moronobu Sasano), which operates Nagasaki Champon specialty store “Ringer Hut” , jointly developed a demand forecasting system to respond to emergencies.

In August 2022, we will start test operation of the “automatic ordering app” and “store shift management app” based on this system at two stores, Shoppes Ichikawa store and Kawasaki Inadatsutsumi store. In addition, from 2022 to 23, we plan to introduce it at about 700 “Ringer Hut” and “Tonkatsu Hamakatsu” nationwide.

By introducing a store shift management app that applies this demand prediction system, we aim to predict consumer demand that has changed due to the corona crisis and solve the labor shortage and food loss that the restaurant industry faces.

Table of contents

  • Systematization of shift management and demand forecasting
  • Background of development of demand forecast system for emergency situations
  • Regarding the development of a demand forecast system for emergency situations
  • Realization of solutions to all kinds of problems faced by the food and beverage industry

Systematization of shift management and demand forecasting

Ringer Hut has been developing and operating an AI system that predicts sales for three years since 2018, and built the system by having the AI ​​learn sales data. In the emergency demand forecast system jointly developed with Palo Alto Insight LLC, in addition to data for the past 3 years, changes in sales data for the last 4 to 5 days will be modified to reflect changes in the AI ​​system. This makes it possible to respond to changes in demand due to emergencies such as natural disasters and infectious disease pandemics, which were difficult in the past.

The operation of the “automatic ordering app” and “store shift management app” is that when a date is selected, the forecasted sales and number of visitors for that day are displayed, and the demand forecast is an hourly demand forecast for each store. It is a periodic system. In accordance with this demand forecast, the optimal number of orders and staff allocation are displayed, and by systematizing food ordering and shift management, which was previously done by individual workers, it is possible to reduce the burden of work and improve the accuracy of judgment. connect.

Background of development of demand forecast system for emergency situations

The spread of the new coronavirus has changed consumer behavior. This has caused confusion in international logistics and inflation, making the traditional demand forecast model obsolete. Therefore, in preparation for various situations that may occur in the future, the development of this system is based on the belief that it is essential to develop a demand forecasting model that can flexibly respond to any environment.

Regarding the development of a demand forecast system for emergency situations

In addition to conventional systems that predict consumer demand, this system utilizes AI to predict demand that changes under various emergencies, such as natural disasters such as earthquakes and typhoons, and infectious disease pandemics. As a result, based on data such as sales results, weather information, and regional information, we can predict consumer demand, calculate the appropriate number of orders, manage inventory, forecast shipping volumes, and allocate staff. Waste can be reduced. In addition, since it is possible to instantly switch to emergency demand forecasting while using normal demand forecasting, it is a technological platform that eliminates operational delays and realizes smooth operational efficiency.

Realization of solutions to all kinds of problems faced by the food and beverage industry

Improving operational efficiency leads to solving the problem of “lack of human resources” that the restaurant industry has had for many years. It is also possible to reduce “food loss” by optimizing inventory. Optimizing demand and supply through demand forecasting is thought to lead to the realization of solutions to all the problems faced by the food and beverage industry.

Palo Alto Insight LLC (Headquarters: Silicon Valley, CEO: Yuai Ishizumi) and Ringer Hut Co., Ltd. (Headquarters: Shinagawa-ku, Tokyo, President and CEO: Moronobu Sasano), which operates Nagasaki Champon specialty store “Ringer Hut” , jointly developed a demand forecasting system to respond to emergencies.

In August 2022, we will start test operation of the “automatic ordering app” and “store shift management app” based on this system at two stores, Shoppes Ichikawa store and Kawasaki Inadatsutsumi store. In addition, from 2022 to 23, we plan to introduce it at about 700 “Ringer Hut” and “Tonkatsu Hamakatsu” nationwide.

By introducing a store shift management app that applies this demand prediction system, we aim to predict consumer demand that has changed due to the corona crisis and solve the labor shortage and food loss that the restaurant industry faces.

Table of contents

  • Systematization of shift management and demand forecasting
  • Background of development of demand forecast system for emergency situations
  • Regarding the development of a demand forecast system for emergency situations
  • Realization of solutions to all kinds of problems faced by the food and beverage industry

Systematization of shift management and demand forecasting

Ringer Hut has been developing and operating an AI system that predicts sales for three years since 2018, and built the system by having the AI ​​learn sales data(Silicon Valley). In the emergency demand forecast system jointly developed with Palo Alto Insight LLC, in addition to data for the past 3 years, changes in sales data for the last 4 to 5 days will be modified to reflect changes in the AI ​​system. This makes it possible to respond to changes in demand due to emergencies such as natural disasters and infectious disease pandemics, which were difficult in the past.

The operation of the “automatic ordering app” and “store shift management app” is that when a date is selected (Silicon Valley), the forecasted sales and number of visitors for that day are displayed, and the demand forecast is an hourly demand forecast for each store. It is a periodic system.

In accordance with this demand forecast (Silicon Valley), the optimal number of orders and staff allocation are displayed, and by systematizing food ordering and shift management, which was previously done by individual workers, it is possible to reduce the burden of work and improve the accuracy of judgment. connect.

Background of development of demand forecast system for emergency situations

The spread of the new coronavirus has changed consumer behavior. This has caused confusion in international logistics and inflation, making the traditional demand forecast model obsolete.

Therefore, in preparation for various situations that may occur in the future, the development of this system is based on the belief that it is essential to develop a demand forecasting model that can flexibly respond to any environment.

Regarding the development of a demand forecast system for emergency situations

In addition to conventional systems that predict consumer demand, this system utilizes AI to predict demand that changes under various emergencies, such as natural disasters such as earthquakes and typhoons, and infectious disease pandemics.

As a result, based on data such as sales results(, weather information, and regional information, we can predict consumer demand, calculate the appropriate number of orders, manage inventory, forecast shipping volumes, and allocate staff. Waste can be reduced.

In addition, since it is possible to instantly switch to emergency demand forecasting while using normal demand forecasting (Silicon Valley), it is a technological platform that eliminates operational delays and realizes smooth operational efficiency.

Realization of solutions to all kinds of problems faced by the food and beverage industry

Improving operational efficiency leads to solving the problem of “lack of human resources” that the restaurant industry has had for many years. It is also possible to reduce “food loss” by optimizing inventory. Optimizing demand and supply through demand forecasting is thought to lead to the realization of solutions to all the problems faced by the food and beverage industry.

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