The report that you interpret this time is the "Artificial Intelligence Industry AI Empowerment Punctual Inventory Management Research Report: Demand** + Strategy", the report has a total of 36 pages, for more important content and core views, please refer to the original report, and there is a full version at the end of the article to obtain it.
This research report focuses on the application of artificial intelligence (AI) in JUSD's inventory management, especially in the two core areas of demand and strategy. By building advanced AI inventory management models and algorithms, we aim to improve inventory turnover efficiency, reduce inventory costs, and significantly improve customer satisfaction, thereby enhancing the market competitiveness of enterprises. The report elaborates on the construction process of the model, the results of the experimental verification, and its potential application impact in the actual operation of the enterprise.
1. Project background and objectives
In view of the complexity, dynamics and randomness of chain inventory management, traditional management methods have been difficult to meet the needs of modern enterprises. Therefore, JUSDA decided to use AI and data-driven management decision-making technology to develop a set of intelligent inventory management models and algorithms.
The core objectives of the project are as follows:
Improve the efficiency of inventory management and reduce inventory costs.
Improve customer satisfaction, trust and loyalty, and in turn, enhance your company's competitiveness in the market.
Second, the demand module
In order to solve the complex challenges of chain inventory management, we have built a multi-dimensional (daily, weekly, monthly) and multi-cycle dynamic model based on AI algorithms. The model uses historical demand data to construct the requirement feature set, and selects the features through the ranking importance test to ensure the accuracy and effectiveness of the model. At the same time, we have integrated a variety of AI algorithms to improve the accuracy and robustness of the requirements**. In addition, in order to deal with **chain emergencies or demand fluctuations, we have introduced SMAPE error calculation to enhance the stability of the model in these cases.
3. Strategy module
In terms of strategy, we have adopted an intelligent strategy with demand as the core. By constructing the distribution of demand and using the data-driven newsboy model as a theoretical guide, we are able to achieve flexible adjustment of the highest volume. In addition, the module also provides recommended strategies and safety stock levels based on multiple cycles in the future to help traders make more scientific and accurate decisions.
Fourth, experimental results and analysis
In order to verify the actual effect of the model, we selected product data with a large amount of data for experiments. On the premise of satisfying the quality of the data, we found that the average weekly** accuracy rate of the top 80% of products can reach more than 70%. At the same time, after adopting the intelligent ** strategy, the satisfaction rate of commodity demand can reach more than 95%, and the average inventory level will drop by more than 10. These experimental results fully demonstrate the great potential and practical application value of AI in the field of inventory management.
V. Conclusions and prospects
Through in-depth research and experimental verification, this report shows the important application and remarkable results of AI in just-in-time inventory management. This set of intelligent inventory management models and algorithms not only improves the efficiency of inventory management and customer satisfaction, but also brings substantial economic benefits to enterprises. In the future, we will continue to optimize and improve this model to better adapt to market changes and meet the needs of enterprises, and provide strong support for the sustainable development and competitiveness of JustStar.
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