隨著商業環境及客戶要求提高，產品的新世代或新產品開發上市時間明顯縮短，且因全球化趨勢及科技進步，使得企業若在原物料採購上受到地域限制而被迫選擇地理方便的廠商，將對企業競爭力有重大影響。市場上的競爭對於企業在選擇供應商時，一方面要考慮如何選出更有利的合作對象，一方面要確認新興產品原物料品質穩定。因此需要一個系統化的方法來解決上述問題，以提升企業對供應商搜尋及評選能力。
本研究主要將大數據分析(網頁探勘，web mining)應用於供應商搜尋，並採用層級分析法建立供應商評選機制。本研究使用R程式語言，針對關鍵字及CSS（Cascading Style Sheet）進行編碼以解析網頁，抓取資料時間為2019年6月至2020年4月。研究中首先載入搜尋網站版第一頁的網址，從載下來的頁面取出公司名稱、產品名稱與聯絡資料，接著重複此方式以抓取網頁網址，並將整理好的資料進行資料串接。共抓取網頁160萬則，透過新興產品屬性特徵(密度、熔融指數)、操作條件等比對最接近需求內容為42萬則，經網路爬重建立篩選表格比對品名、新興產品現用原料物性(稀稠度、軟化點)，篩選後共1457則。本研究依據最終抓取資料1457則進行初步人工整理，將重複供應商與無關之內容皆剔除，總分析資料則數為324則。
完成上述供應商海選後，接著透過文獻探討找出篩選適合新興產品供應商的相關評估因素，再以層級分析法進行供應商評估因素的優勢度排序與分析，結果顯示優先順序依序為「服務品質」、「專業能力」、與「客訴處理」。透過此機制，可建立一套新興產品來源供應商良好評選機制。
研究結果顯示，利用web mining搜尋供應商對於個案公司在尋找新興產品原料供應有顯著的幫助，在個案企業過去一年所新增供應商不到10家，新興產品供應商更是只有一家，相較於過去供應商搜尋方式需要耗費許多人力物力，本研究其結果顯示搜尋到324有效資料中，對個案公司在主要市場有15家供應商在工廠附近，其中有6家是個案公司過去未曾發現之供應商且經評選後是可合作之供應商，幫助個案公司在發展新興產品有其他選擇及提升競爭力。
關鍵字：供應鏈管理、層級分析法、供應商評選、大數據分析

Time to market for new products have to be consistently shortened, for companies to increase competitiveness, which resulting a manufacturer to choose appropriate associated suppliers being more and more difficult. To choose an outsourced manufacturing partner, an enterprise must confirm that it has stable quality, scalable capacity and technical support capability. A systematic approach is necessary to solve the above-mentioned problems.

This work has combined big data analysis (web mining) and AHP (Analytic Hierarchy Process) to identify new suppliers when developing new products. In applying big data analysis for suppliers search, R programming language is used to encode keywords and CSS (Cascading Style Sheet) is applied to parse web pages, which extracts the company name, product name, and contact information from the downloaded page, and then repeats this ay to crawl the web and concatenate the sorted data. During the search, 1.6 million web pages were crawled, and 420,000 were sorted out through the characteristics such as density, melt index, operating conditions, A screening table to go one step further to compare product names and physical properties (thinness, softening point) of required raw materials for emerging products. After screening, 1457 items were extracted. The final data set was further filtered and 324 items are identified for the potential suppliers list.

Based on the literature review and opinions from experts, important characteristics of new suppliers were listed. AHP method was then applied to rank and analyze the ranking of the supplier evaluation factors. The results show that the priority is "Service Quality", "Professional Ability", and "Customer Complaint Management ". Supplier selection mechanism is then developed based on the preferred characteristics. After this mechanism has been applied to the potential suppliers list, 15 new suppliers have been selected which support the new product commercialization process being faster and more effective.

Keywords: Supply chain management, AHP, Supplier selection, Big data analysis

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