近年來半導體公司持續擴廠中，伴隨著所需要的廠房設備及機台物料也日益增加，為避免機台及物料造成不必要的耗損，且尋找與撿料也花費諸多時間，連帶庫存品持續積壓過高，導致撿料人員在時間與成本上的浪費。本研究主在探討該如何以最短的時間撿料。此外，對於新廠房的機台和物料的持續增加，而造成舊廠房滯留的閒置物料，該如何有效被推薦給使用者加以利用。

實際使用69種類別之物料，約13,560筆領料單資訊，透過分出課別並利用關聯分析找出關鍵物料，依FP-growth演算法得知相關聯的物料放置一起，省下約10分鐘-15分鐘，可以加速撿料的時間，表示此結果可以提供給檢料人員進行物料管理，讓公司可以縮減人事成本前提下，採取主動出擊方式推薦給相關使用者選用並找到比較屬於需求的物料，除了減少閒置物料的管理、有效率的領料，更可降低庫存水位之目標。

Semiconductor companies have continued to expand their factories in recent years, and the spending amount on the required plant equipment and machine materials is thus increasing. In order to avoid unnecessary waste on those resources, the continuous backlog of associated inventory is generally high, which leads to the waste of time and cost in picking materials. This study is aimed at reducing inventory cost that can be achieved by proper material recommendation for users and good layout of materials for inventory managers.

Sixty-nine key categories of materials for semiconductor companies are considered in this study, and 13,560 material requisition forms are collected for analyzing their usage associations. The instances are divided into four subsets based on the department of material requisition. The ten association rules with the highest lift found by the FP-growth for each department are analyzed to filter the departments for material recommendation. The associations among materials are also used to reallocate the store position of each material so that storekeepers can pick up necessary materials efficiently.

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