鐵路運輸生產效率、服務效果、生產力與銷售力衡量

Abstract

傳統的資料包絡分析法(DEA) 並未加計環境因素或資料誤差的影響，此等模式在比較各決策單位的相對效率時，亦未考慮針對差額的多寡予以修正效率值，致未能找出影響效率的因素，也使得績效評估的結果似有偏誤。為改善這些缺失，本研究建議採用四階段的DEA 模式，不僅環境因素與資料誤差的影響均納入模式中，且評估結果亦已考量投入或產出差額。本研究並應用此一方法於評估鐵路運輸之績效，由於鐵路公司之產品（運輸服務）具有不可儲存之特性，評估績效時宜區分「效率」與「效果」兩個層面，同時，「生產力」 與「銷售力」兩指標亦宜分別衡量， 評估之樣本包括四十四家七年間 (1995-2001) 之營運資料。評估結果顯示傳統的DEA 模式因為考慮環境因素與資料誤差，以致低估了效率和效果值，但卻高估了生產力與銷售力。 根據四階段DEA模式的實證分析結果，得到若干政策意涵，本研究最後提出改善鐵路經營績效之建議，亦具體指出若干未來可能的研究方向。

Conventional data envelopment analysis (DEA) models do not consider the effects of environmental factors or data noise; nor do these models adjust the “slacks” while making comparison of the relative efficiency for the decision making units (DMUs). As a consequence, factors causing the inefficiency are hardly to be recognized and such performance evaluation is likely biased. To remove these shortcomings, this research proposes a four-stage DEA model in which the effects of environmental factors and data noise are accounted for and the input excesses or output slacks are adjusted. The proposed model is then applied to the evaluation of railway performance, with distinctive measurements between efficiency and effectiveness and between productivity and sales force due to the non-storable nature of the railway transport services. A panel data composed of 44 worldwide railway systems over recent seven years (1995-2001) are used as our tested DMUs. The results of the proposed four-stage DEA have concluded that conventional DEA models understate the efficiency and effectiveness scores but overstate the productivity and sales force measurements. Finally, the implications of the four-stage DEA results are drawn to propose strategies for ameliorating the railway performance. Some possible extensions in the future research are presented.