經濟部工業局(2019)根據統計，台灣製造業者中，逾 146 萬家為中小企業，根
據統計資料，紙本作業及人工作業占了 32.3%，可看出目前台灣中小企業數位化不
足。傳統安全帽公司也是紙本作業為主，但隨著工業 4.0 興起，少量多樣的模式成
為常態，繁多的品項讓倉儲管理系統（Warehouse Management System, WMS）受到
重視，因此紙本作業轉型成數位化變成勢在必行的選擇。
本研究以安全帽成品倉庫為實例，且揀貨作業占了倉庫營運成本的 55%以上，
因此需找出揀貨作業的關鍵因子，並給予案例公司建議。案例公司揀貨作業是高度
依照現場揀貨員的經驗去判斷揀貨位置，但當揀貨員並未參與補貨時，該揀貨員是
無法得知該類商品位在何處，其儲位分配是沒有相對應的規則及邏輯，造成存放位
置不定，導致揀貨員找不到商品，並重複作業沒有價值的活動。訂單分配的部分也
是根據辦公室人員的經驗去分配，其邏輯為將算好每張銷貨單的頂數，並平均分配
給五位揀貨員，再由揀貨員去分配如何揀取銷貨單上的商品，其邏輯為，先找出相
同的商品，並根據自身的經驗去安排這次的揀貨行程。因此本研究提出儲位分配、
揀貨策略、揀貨路徑的三因子組合，設計不同水準分析，並建構田口式實驗設計分
析，找出最小化揀貨距離的最佳組合因子，最終建構數位揀貨系統。
最後透過模擬實驗結果可看出，實驗的最佳因子組合為 ABC 分類-銷售量儲
位、批量揀貨-座標、蟻群演算法路徑，其總揀貨距離平均值為 474 公尺，而現況總
揀貨距離平均值為 1905 公尺，其改善效益為 75%。

According to the Bureau of Industry, Ministry of Economic Affairs (2019), more than
1.46 million Taiwanese manufacturers are SMEs, and according to the statistics, paperbased operations and manual work account for 32.3% of the total, which shows the current lack of digitization of Taiwan's SMEs. Traditional safety helmet companies are also paper-based operations, but with the rise of Industry 4.0, a small amount of multiple models become the norm, a wide range of items so that the warehouse management system (Warehouse Management System, WMS) is valued, so the transformation of paper-based operations into digital becomes an inevitable choice.
In this study, the case study takes a finished helmet warehouse as an example, and the picking operation accounts for more than 55% of the warehouse operation cost. The case company's picking operation is highly based on the experience of the on-site picker to determine the picking location, but when the picker is not involved in replenishment, the picker is not able to know where the goods are located, and there is no corresponding rule and logic for the storage allocation, resulting in uncertain storage location, resulting in the picker not finding the goods and repeating the operation with no value. The logic is that the top number of each sales order is calculated and distributed equally to five pickers, and then the pickers are assigned how to pick the goods on the sales order.Therefore, this study proposes a three-factor combination of storage allocation, picking strategy, and picking path, and designs different levels of analysis, and constructs Taguchi-type experimental design analysis to find the best combination of factors to minimize picking distance, and finally constructs a digital picking system.
Finally, the experimental results showed that the best combination of factors was ABC classification - sales volume storage, batch picking - coordinates, and ant colony algorithm path, and the average total picking distance was 474 meters, while the average total picking distance was 1905 meters. The improvement benefit is 75%.

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