In the manufacturing and wholesale sectors, demand forecasting and supply-demand judgments often rely on the experience and intuition of a few veteran personnel, leading to a situation where the basis for decisions and adjustment know-how become personalized. Furthermore, there is a challenge in that judgments based on factors such as weather, promotions, days of the week, and seasonal influences are not documented, making it difficult to reproduce and pass on this knowledge. As a result, the transfer of knowledge to younger staff does not progress, and the quality of operations becomes unstable due to personnel transfers or retirements, which can lead to insufficient utilization of AI even when it is implemented. In this seminar, we will introduce the concept of a "Supply-Demand Inheritance Model" that combines the AI demand forecasting engine "Deep Predictor" with the business platform "Pleasanter" to reproduce and accumulate the insights of veterans as a "system." We will explain in an easy-to-understand manner, from a practical perspective, how to record and share the basis for judgments and exception handling while considering external factors, leading to the resolution of personalization, standardization of operations, and continuous improvement. For more details, please check the "Details & Application" link below.