#Japan #AI Solutions #Tokyo #Just Planning #OGIS Research

Introduction

In the restaurant industry, the increasing reliance on technology has not completely mitigated the challenges posed by workforce shortages, particularly in kitchen operations. Just Planning Co., Ltd. and OGIS Research, Inc. have joined forces to introduce a groundbreaking solution aimed at streamlining kitchen operations through artificial intelligence. The new system, named 'Makete AI Deshap', was officially launched on August 28, 2025, signaling a new era for restaurants coping with operational challenges.

Background and Challenges

As restaurants become more advanced with the implementation of self-ordering systems, automated cooking robots, and self-service registers, one crucial area continues to depend heavily on human judgment— the dispatch of dishes during peak service times, known as 'deshap'. This operation requires a skilled approach to coordinate cooking timelines and the use of kitchen equipment, especially when handling set menus that include multiple dishes that must be served simultaneously.

However, the reliance on experienced staff for these tasks has posed significant difficulties, especially in light of a persistent labor shortage in the industry. Specifically, the accuracy of dish timing, quality of service, and overall customer satisfaction are heavily influenced by the deshap operations, which traditionally require personalized skills that are difficult to train and retain.

Solution Overview

The 'Makete AI Deshap' system merges a mathematical optimization AI engine from OGIS Research with a kitchen display application developed by Just Planning. This integrated approach enables real-time management of cooking schedules based on the orders received through the Order Entry System (OES). The AI engine evaluates cooking times, kitchen capacity constraints, and menu policies, ultimately providing a tailored cooking schedule that aligns with the unique requirements of each kitchen position. This user-friendly application displays the cooking schedule according to each position's specifications, ensuring that staff can prepare and serve dishes at optimal times.

Key Features

Real-Time Cooking Schedule

The AI engine assesses various constraints such as dish preparation times and equipment availability, rapidly calculating the optimal cooking timing for each order. This immediate planning capability significantly simplifies and enhances kitchen workflow, allowing staff to focus on executing their tasks efficiently.

Intuitive Kitchen Display

With interfaces customized for different cooking positions—whether it be for noodles, grilled items, or fried dishes—'Makete AI Deshap' provides a clear view of upcoming tasks. This feature allows kitchen staff to plan better and minimize delays, ensuring a smoother operation during busy periods.

Enhanced Customer Satisfaction

By improving the accuracy of serving dishes simultaneously—especially for set menus or course meals—'Makete AI Deshap' helps maintain dish quality and prevent delays. This not only enriches the dining experience but also aids in reducing waste and increasing restaurant turnover during peak hours, subsequently enhancing customer satisfaction and operational efficiency.

Future Prospects

Looking ahead, 'Makete AI Deshap' is currently being prepared for full-scale deployments among early adopter restaurants. Feedback from these implementations will drive further enhancements to the system. Efforts are also underway to expand the service to other restaurant chains facing similar operational challenges, thereby broadening the deployment of this innovative AI solution.

Conclusion

The collaboration between Just Planning and OGIS Research offers a promising pathway to transforming kitchen operations in the restaurant industry. As they continue to refine 'Makete AI Deshap', it stands to revolutionize how restaurants manage dish preparation and service schedules in the face of ongoing workforce challenges.

For more information on Just Planning's offerings, visit Just Planning.