| 摘要: | 在當今全球商業環境迅速變化的背景下，企業面臨著前所未有的挑戰與機遇，庫存管理做為連接供應鏈上下游的關鍵環節，對於提升企業營運效率和顧客滿意度扮演著極為重要的角色。傳統的庫存管理依賴於人工判斷與經驗操作，不僅耗時費力，且容易因人為失誤導致庫存準確性的波動。隨著人工智慧技術的快速發展，特別是機器學習與深度學習技術的應用，為解決傳統庫存管理中的難題提供了新的途徑。
本研究聚焦於T公司，一家在其行業內擁有多個分公司的大型企業，致力於探討AI技術在提高分公司庫存管理下單預估準確性方面的實際應用。透過對T公司分公司的庫存管理實踐的深入分析，本研究識別出一系列的挑戰和機遇，其中包括傳統人工作業在處理日益增長的大量SKU(Stock Keeping Unit)時，工作效率無法提升的問題。為了克服這些挑戰，研究採用了人工智慧技術，透過蒐集和分析大量的庫存相關數據，訓練多種機器學習和深度學習模型，研究結果顯示，所採用的決策樹(Decision Tree)模型在所有測試中的表現較為出色。這一成果不僅證明了AI技術在提升庫存管理預測準確性方面的有效性，也顯示出機器學習模型的強大潛力。通過這種方式，T公司能夠更準確地預測每個SKU的下單量，以致實現庫存的最佳化，降低過剩或缺貨的風險，並顯著提高工作效率和客戶滿意度。
本研究的實施過程還揭示了數據質量和完整性在應用AI技術進行庫存管理中的重要性。研究的成功進行，得益於T公司高層的支持，以及能從分公司的管理系統中獲取高品質數據集。這一點強調了在應用AI技術進行企業管理創新時，企業內部支持和資源提供的重要性。整體來看，這項研究不僅為T公司開闢了提高庫存管理效能和精確度的新途徑，也為廣泛的企業界展現了如何運用AI技術創新庫存管理的實踐路徑。隨著AI技術的持續發展和其在更多領域的應用，預期將有更多的企業從中獲益，達到庫存管理的自動化和最佳化，以確保在日益加劇的市場競爭中保持或提升其競爭力。
In the rapidly changing global business environment of today, companies face unprecedented challenges and opportunities. Among these, inventory management, as a critical link in the supply chain, plays a vital role in enhancing operational efficiency and customer satisfaction. Traditionally, inventory management relied on manual judgment and experience, which was not only time-consuming and labor-intensive but also prone to human error, leading to fluctuations in inventory accuracy. With the rap-id development of artificial intelligence (AI) technology, especially the application of machine learning and deep learning techniques, a new path has been provided to solve the difficulties in traditional inventory management.
This study focuses on T Company, a large enterprise with multiple branches within its industry, aiming to explore the practical application of AI technology in im-proving the accuracy of inventory management and order forecasting at the branch level. Through an in-depth analysis of the inventory management practices of T Com-pany's branches, this study identified a series of challenges and opportunities, includ-ing the issue of inefficiency in handling an increasingly large number of SKUs with traditional manual work. To overcome these challenges, the study adopted AI technol-ogy, collecting and analyzing a vast amount of inventory-related data and training various machine learning and deep learning models. The results showed that the Deci-sion Tree (DT) model performed relatively well in all tests. This outcome not only proves the effectiveness of AI technology in improving the accuracy of inventory management forecasts but also demonstrates the strong potential of machine learning models. In this way, T Company can predict the order quantity for each SKU more ac-curately, thereby optimizing inventory levels, reducing the risk of surplus or shortage, and significantly improving work efficiency and customer satisfaction.
Furthermore, the process of implementing this study also revealed the importance of data quality and integrity in applying AI technology to inventory management. The successful conduct of the research benefited from the support of T Company's top management and the ability to obtain high-quality datasets from the branch's man-agement system. This emphasizes the importance of internal support and resources when innovating enterprise management with AI technology. Overall, this study not only opened new avenues for T Company to improve the efficiency and accuracy of inventory management but also showed the broader business community how to inno-vate inventory management practices using AI technology. As AI technology continues to develop and find applications in more fields, it is expected that more companies will benefit, achieving automated and optimized inventory management, and thereby maintaining or enhancing their competitiveness in an increasingly fierce market com-petition. |
