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Logistics operations require continuous improvement in daily productivity to promote white logistics. In recent years, approaches using AI to determine delivery routes have gained attention, but incorporating various conditions such as stacking methods based on products and driver skills makes calculations difficult, leading to variations in planning accuracy. The method of finding optimal solutions from a vast number of patterns considering these many conditions is called "combinatorial optimization problems," which are addressed in logistics operations through themes such as load optimization, shift optimization, and route optimization. A technology that is gaining attention as a solution to this problem is "quantum computing." By utilizing the "MAGELLAN BLOCKS" quantum computer solution provided by Groove Notes, it has been confirmed in verifications with major delivery companies that delivery efficiency can be improved by over 50%. Additionally, there are various cases in internal logistics, transportation between bases, and last-mile delivery, achieving maximization of loading rates, reduction of the number of trucks, leveling of cargo volumes, and improvement of turnover rates, contributing to logistics cost reductions on the scale of tens of millions to hundreds of millions of yen annually, as well as reductions in CO2 emissions.

• Other production management systems

In today's rapidly changing market and customer needs, production and manufacturing sites are required to improve productivity more than ever before. However, even in production scheduling, precise design is necessary for order and equipment allocation, and there are limits to how much skilled workers can manually create and modify plans in response to demand forecasts and fluctuations in production capacity. The latest technology that excels in planning production schedules and plans, entangled with these complex constraints, is quantum computing. By utilizing the "MAGELLAN BLOCKS" quantum computer solution provided by GrooveNotes, it is possible to automatically derive the optimal plans, such as the work sequences that require the least setup changes according to various manufacturing items and tool allocations that consider the workers' dominant hands. This enables the reproduction and enhancement of decisions made by skilled workers, which was difficult to achieve with traditional systems. There are also examples of business automation where planning tasks that previously took more than half a day can now be completed in less than an hour. We will introduce examples of the use of quantum computers in manufacturing sites.

• Production Scheduler