供應鏈管理之灰色多準則決策

Supply Chain Management Using Grey Multiple Criteria Decision Making

蔣鵬(Peng Jiang)

指導教授 ： 嚴奇峰 ； 胡宜中

中原大學/商學院/企業管理研究所/博士(2017年)

https://doi.org/10.6840/cycu201700149

摘要

隨著中國社會主義市場經濟體系的逐漸完善，越來越多的企業開始引入現代企業制度來應對新興市場的需求。現代企業管理範式的一項重要轉變即為企業不再依靠一己之力來進行實體競爭，而是轉變為網路競爭，因此供應鏈管理的觀念應運而生。近年來，環境議題越來越受到全球的關注。作為中國最重要的產業之一，汽車製造業理應注重綠色供應鏈管理，仔細思考如何由環境面評選與綠色管理息息相關的零組件供應商。另外，作為重要的生產性服務業之一，物流業的發展高度依賴於製造業。因此，物流業和製造業的互動發展是至關重要的，是提高核心競爭力並促進製造業產業升級的關鍵。決定影響供應商評選以及兩業互動的關鍵因素，並分析其績效表現，是據以提出改善策略於提升廠商或產業競爭優勢的重要基石。
供應鏈管理是典型的多準則決策議題，而基於決策實驗室法的網絡程序分析法(DANP)在關鍵因素與因果關係的決定上扮演了相當重要的角色。然而，DANP在應用上通常需要發放「成對影響比較問卷」，而大量的題項很有可能降低受訪者的填答意願，也會干擾最後的研究結果分析，這一方面限制了該方法在次級資料上的使用，另一方面也讓受訪者承擔了填寫大量題項的負荷，嚴重限制了DANP的應用。因此，本文結合了灰關聯分析提出稱為「基於灰關聯的決策實驗室與網絡程序分析」(GDANP)的創新決策分析方法，其具體特點即在於利用德爾菲的調查問卷或是次級資料，即可衡量準則之間的影響關係與因果關係。
為彰顯GDANP在供應鏈管理上的應用與發展潛力，本文將所提出之GDANP模型應用於兩個重要的實務案例。案例一透過德爾菲問卷發放，分析影響中國汽車製造商遴選綠色零部件供應商的關鍵因素。研究結果顯示製造商在選擇供應商時注重的是成本、品質、技術和污染源管控。研究結果還表明，相對於環境面的準則，中國的汽車製造商雖然所關注者仍多屬經濟面的準則，但在供應商評選上已逐漸重視綠色管理，強調供應商對污染源的管理。
案例二則選取中國國家統計局所公開的物流業與製造業的統計資料來識別出影響兩業互動的關鍵因素。研究結果顯示，影響兩業互動的主要物流業指標為運輸業就業總人數、貨運量和運輸線路總長度；影響兩業互動的主要製造業指標為製造業總產值及工業附加值。因此，政府部門應該協助物流業者提高運輸業的就業人數、提升貨運量，並在公路等基礎設施的建設上加大投入。
由實際分析可知，本研究提出之GDANP與傳統DANP相較有以下兩個主要優勢：
一、GDANP允許使用歷史統計資料作為系統的輸入，而非僅僅依賴受訪者所填答的問卷，在面臨全為量化準則的情況下，顯示出GDANP在次級資料上的應用優勢。而且GDANP允許準則的績效值可為負值，也較傳統的熵值更有彈性。
二、GDANP可直接產生成對影響比較矩陣，大幅降低計算複雜度，可使受訪者完全避免填答成對影響比較矩陣所衍生的大量題項。由受訪專家對於GDANP所得到的研究結果的高度認可，更突顯本研究所提出方法在供應鏈管理實務上的有效性與良好發展潛力。

關鍵字

供應鏈管理 ； 綠色供應商 ； 汽車製造業 ； 供應商選擇 ； 製造業與物流業互動 ； 多準則決策分析 ； 灰關聯分析 ； 決策實驗室法 ； 網路程序分析法

並列摘要

With the gradual improvement of the socialist market economy system in China, increasingly more enterprises are introducing modern enterprise systems to meet the needs of emerging markets. One of the most substantial paradigm shifts of modern business management is that individual manufacturers no longer compete as solely autonomous entities, but rather as internetwork competition. Therefore, new concepts of supply chain management (SCM) are arising. Global environmental issues have attracted increasing public attention in recent years. As one of the most crucial industries in China, the automotive industry should focus on green SCM, and consequently, environmental factors for supplier selection should be considered. In addition, as a crucial part of producer services, the logistics industry is highly dependent on the manufacturing industry. In general, the interactive development of logistics and manufacturing industries is essential; in particular, it is a key to improving core competitiveness and promoting ongoing improvement among manufacturing firms.
Supply chain management is a classic case of multiple criteria decision-making (MCDM). The decision-making trial and evaluation laboratory–based analytic network process (DANP) has played a paramount role in identifying key factors and causal relationships. However, the DANP usually requires pairwise comparison questionnaire surveys. Respondents may be unwilling to fill out a large number of questionnaire items; this can interfere with the final outcome analysis and limits the application of the DANP and the use of secondary data. Therefore, this dissertation proposes a novel MCDM model combining grey relational analysis and the DANP, namely the GDANP. The GDANP can measure influence relationships and causal relationships through Delphi surveys and secondary data.
To highlight the application and development potential of the GDANP in SCM, it was applied in two real-world cases. Case 1 identified key factors affecting Chinese automotive manufacturers in their green parts’ supplier selection by Delphi questionnaires. The results showed that the key factors are cost, quality, technology, and pollution control, respectively, in supplier selection. They also showed that Chinese automotive manufacturers consider economic factors to be much more important than environmental factors and gradually attach greater importance to green management by emphasising pollution control.
In Case 2, statistical data for logistics and manufacturing industries in the China Statistical Yearbook (2006–2015) were used to identify the key factors for interaction between these two industries. The results showed that the key logistics criteria for interaction development are the total number of employees in the transport business, volume of goods, and total length of routes. The key manufacturing criteria for interaction development are gross domestic product and value added. Therefore, stakeholders should increase the number of employees in the transport industry and freight volumes. Also, the investment in infrastructure should be increased.
Based on the practical results, the GDANP has two clear advantages over the traditional methods.
1. The GDANP allows the use of historical statistical data as input, rather than respondent questionnaires, which enables the GDANP to achieve superior results with secondary data. Because negative performance values can also be analyzed in the GDANP, it is more flexible than the traditional entropy method.
2. The GDANP can automatically generate comparison matrices of pairwise influence, greatly reducing computational complexity and obviating the requirement for respondents to fill out numerous items derived from a tedious comparison matrix of pairwise influence. The results obtained by the GDANP are consistent with expert opinions, which highlights the effectiveness and excellent development potential of the proposed method in the practice of SCM.

並列關鍵字

supply chain management ； green supplier ； automotive manufacturing ； supplier selection ； interaction between logistics and manufacturing industries ； multiple criteria decision making (MCDM) ； grey relational analysis (GRA) ； decision making trial and evaluation laboratory (DEMATEL) ； analytic network process (ANP)

參考文獻

Hu, Y. C., 2014, Neighborhood-Based Collaborative Filtering Using Grey Relational Analysis, Journal of Grey System, 6(1): 99-114.

Baraldi, E., Proenca, J. F., Proenca, T., de Castro, L. M., 2014, The Supplier's Side of Outsourcing: Taking over Activities and Blurring Organizational Boundaries, Industrial Marketing Management, 43(4): 553-563.

Becker, G. S., Murphy, K. M., 1992, The Division of Labor, Coordination Costs, and Knowledge, The Quarterly Journal of Economics, 107(4): 1137-1160.

Burgess, R., 1998, Avoiding Supply Chain Management Failure: Lesson from Business Process Reengineering, International Journal of Logistics Management, 9 (1): 15-23.

Carter, C. R., Dresner, M., 2001, Purchasing's Role in Environmental Management: Cross-Functional Development of Grounded Theory, Journal of Supply Chain Management, 37(2):12–27.