台灣工具機廠商教育訓練績效分析-模糊資料包絡分析法之應用

Abstract

工具機不僅在機械產業占有一席之地，在製造業更是扮演舉足輕重的\n角色，生活周遭所使用的物品，許多都是由工具機所製造。工具機產業的\n核心技術，仰賴資深員工的經驗傳承，因此教育訓練是工具機廠商營運的\n流程中重要的一環。傳統DEA 要求所有的投入、產出資料必須為量化的數\n據，然而現實生活中有很多重要的變數難以量化，Likert 量表發明後，學\n者們大量使用Likert 量表量化這些變數，但是Likert 量表尺度的公平性\n是許多學者質疑的重點。模糊DEA 透過模糊理論，改善Likert 量表尺度\n公平性的問題，因此本研究使用模糊DEA 進行工具機廠商教育訓練績效的\n分析，並比較Likert 量表尺度下的DEA 與模糊DEA 實證結果的不同，研\n究對象為民國96 年的台灣地區85 間工具機廠商。\n 幾項重要的實證分析結果如下：\n1. 整體技術效率差者，多為訓練積極的廠商。\n2. 部分規模效率差的廠商，純技術效率表現尚在中等水平，表示這些廠商\n只是因為訓練較為積極，使得投入在生產規模遞減的水準。\n3. 工具機產業的教育訓練普遍有浪費的現象，顯示只要投入少許資源即可\n達到相當的成效，若要突破某個瓶頸，就要增加相當多的資源。\n4. 整體而言使用Likert 量表尺度或是模糊DEA 的結果沒有顯著差異，但\n是對於效率較佳的廠商來說，模糊DEA 具有較高的鑑別力。

The machine tool plays an important role in not only machinery industry but also\nthe whole manufacturing industry. Most of goods around us are made by the machine\ntool. The core technologies hold by experienced employees depends on training to\npass down. Therefore, training is an essential process in machine tool factories. In\ntraditional DEA, all inputs and outputs need to be numeric data. But in reality, there\nare many key variables are hard to be measured in numeric. After Likert scale being\ninvented, the scholars widely use the scale to evaluate these key variables. However,\nthere is criticism of the equity in Likert scale. Fuzzy DEA corrects this disadvantage\nof Likert scale with fuzzy theory. As a result, this study evaluates the training\nefficiency of 85 Taiwanese machine tool manufacturers in 2007 with fuzzy DEA and\ncomparers the result between fuzzy DEA and the method of Likert scale.\n Some important conclusions are shown as follow:\n1. Most of the inefficient DMUs are active about training.\n2. Although some DMUs are bad in scale efficiency, they are fair in pure technical\nefficiency. It explains that these DMUs just input at decrease return to scale\n(DRTS) level.\n3. Generally, there is waste of training in this industry. The empirical result tells us\nthey can obtain certain effects with little input. If they want to break through some\nbottle neck of training effects, they have to input tremendous resources.\n4. As a whole, there is no difference between the result of fuzzy DEA and the\nmethod of Likert scale. However, there is better discrimination of efficient DMUs\nin fuzzy DEA.