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    题名: 營造業標竿學習對象決定模式之研究
    作者: 蘇能歆;Su,Neng-Hsin
    贡献者: 營建管理研究所
    关键词: 標竿學習;相似性分析;群體智能技術;多目標決策;Benchmarking;Similarity Analysis;Collective Intelligence;Multi-objective Decision Making
    日期: 2016-07-25
    上传时间: 2016-10-13 15:24:12 (UTC+8)
    出版者: 國立中央大學
    摘要: 營建專案之品質缺失對營建工程的影響極大,輕則影響外觀、延誤專案,重則危及人命,因此,提升品質便成為營建專案首要任務。營造廠的整體品質良窳是左右專案品質最關鍵的因素,故提升營造廠之品質亦有助於提升專案品質,而標竿學習則是可達成提升營造廠品質目的之品管技術。標竿學習已廣泛應用於各行各業用以改善整體績效或是部門績效,學術上亦被證實具有卓越成效。選擇標竿學習對象為標竿學習流程中為最重要的步驟之一,然而,本研究歸納文獻後發現目前選擇標竿學習對象之方法主要有幾點不足:(1)現行標竿學習技術決定學習對象的方式缺乏量化評估、(2)現行決定標竿學習學習對象的量化方法評估面向不符需求、與(3)缺少考量營建企業特性之標竿學習流程或模式。本研究透過文獻回顧、問卷調查以及專家訪談彙整,提出一個以相似性為基礎之標竿學習對象選擇方法,此方法考量22項選擇因子以量化表示營造廠商之特性,並以兩種最基本的相似性辨識工具(歐氏距離與夾角餘弦)計算其相似性,而後排序出學習對象的優先順序,達到改善以往的主觀判定或是現有方法無法排序的結果,得到符合標竿學習意涵的學習對象順序。本研究採用兩個案例來驗證所提出方法的適用性,案例一為小公司學習大公司的學習對象選擇狀況,而案例二為同等級公司的學習對象選擇狀況,並比較主觀排序與量化排序的差異原因,最後再以數據層面提出可能的學習策略。研究成果顯示本研究所提出的方法確實能有效輔助選擇標竿學習對象,進而提升此一步驟之效率。;Construction quality defects significantly affect a construction project by presenting poor appearance, delaying the project and even threatening workers or users’ lives. Promoting quality, therefore, becomes a top priority for a construction project. Because contractors’ quality is the most critical factor contributing to projects’ quality, projects’ quality can be promoted by improving contractors’ quality and benchmarking is a quality management technique for contractors’ quality improvement. Benchmarking has been widely used in different industries to improve company-wide and department-wide performance, and has also been proved in academic research to be effective in promoting quality. Selecting best practices (i.e., target companies to be learned) for a learning company (i.e., a company to learn from the best practices) is an important step in a benchmarking process; however, current methods for selecting best practices in literature have certain insufficiencies. First, these methods are mostly qualitative and hence, the results of best practice selection are subjective and vulnerable to human misjudgment. Second, even though some methods are quantitative but their considerations for evaluating best practices are not suitable for the nature of benchmarking. Last, current methods did not consider the characteristics of construction industry. There, this research proposes a similarity-based approach to benchmarking best practice selection. The proposed approach considers 22 selection factors that are determined through literature review, questionnaire survey and expert interviews and represent the characteristics of construction companies. Two similarity measurement techniques, Euclidean distance and cosine similarity, are adopted in the proposed approach to measure the similarity between a learning company and any of the best practice candidates; the best practice candidates are then prioritized according to the similarity values. Two case studies are conducted to validate the proposed approach. The differences between the measurement results by using traditional subjective decision and the proposed approach in the case studies are analyzed, and possible benchmarking learning strategies are also suggested according to the analysis results and the original data. The research results show that the proposed approach can benefit a benchmarking process by facilitating selecting benchmarking best practices, and further improve the efficiency of this important step.
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