188 / 330
考量買方風險接受態度的供應商選擇：結合效用函數的簡單多屬性評比方法
188
buyer’s risk-taking behavior into the process of supplier evaluation and selection. Utility can
be defined as preference of the buyer to the alternatives (suppliers) under uncertainty.
Therefore, by computing utilities of suppliers, the proposed approach can provide a more
realistic and reliable supply portfolio to the buyer.
This research applies the proposed approach to the supplier selection of a large
semiconductor assembly and testing company (the buyer) in Taiwan, and compares the
results with the company’s current practice. To provide a more comprehensive analysis, three
different scenarios of market demand are considered when performing the approach. The
purpose is to examine whether or not the buyer’s selection of suppliers changes with market
demand (demand increases, decreases or remains unchanged). The major contributions of
this paper to the literature are as follows: incorporating the buyer’s risk-taking behavior in
supplier evaluation, selection to make a supply portfolio, investigating supplier evaluation,
and selection under different scenarios of market demand.
The remainder of this paper is organized as follows. Section 2 provides the literature
review. Section 3 presents the methodology of this research. Section 4 describes the case
study. Concluding remarks are included in Section 5.
2. Literature Review
Various multi-criteria decision-making (MCDM) approaches have been adopted to
address the supplier evaluation and selection problem. According to the survey conducted by
Ho et al. (2010), data envelopment analysis (DEA) is the most popular methods in supplier
evaluation and selection, followed by mathematical programming, analytic hierarchy process
(AHP), analytic network process (ANP) and simple multi-attribute rating technique
(SMART).
2.1 Data Envelopment Analysis
DEA is a well-established performance evaluation approach in operations research and
has been extensively applied in the performance assessment and benchmarking of schools,
hospitals, bank branches, production plants, nations, etc. (e.g., Charnes, 1994; Cooper,
Seiford, and Tone, 2007; Lu and Yu, 2012; Lu and Wu, 2014). As a non-parametric approach
developed using linear programming (LP) methodology, DEA is employed to measure
empirically the production efficiency of multiple decision-making units (DMUs) when the
production process presents a structure of multiple inputs and outputs. The feature of