Using Data Envelopment Analysis to Support Best-Value Contractor Selection

Abstract

Selecting an appropriate contractor or supplier is essential to the successful implementation of a public procurement project. The Taiwan government frequently applies the best-value (BV) tendering method, a multi-criteria evaluation method, to procure projects. However, the selection process of the winner for a BV-based procurement project is generally subjective and thus is easily accused of corruptions. To develop a systematic method to support contractor selection, this study proposes using the Data Envelopment Analysis (DEA) to facilitate the criteria evaluations for each bidder during the short-listing stage. The evaluation results of using the DEA are a list of potential BV winners who are then suggested to enter into the final selection stage. Based on three case studies related to service procurement projects, this research finds that the DEA is suitable of assessing the relative efficiencies among bidders when the BV approach is applied. Lessons learned here should be helpful in applying the DEA to aid bid evaluations in other supplier selection problems.

Fuzzy AHP Application for Supporting Contractors’ Bidding Decision

“…Analytic Hierarchy Process (AHP) + PROMETHEE subcontractor selection for main contractor 13 [8] Data Envelopment Analysis (DEA) subcontractor selection at short-listing stage 5-6 selected depending on the specific tender [9] Fuzzy AHP; The method of entropy; Method of criterion impact loss (CILOS); Integrated Determination of Objective CRIteria Weights (IDOCRIW) method; The SAW method; The TOPSIS method; The COPRAS method comparing quality assurance in different contractor contracts 7 [10] The EDAS method comparing quality assurance in different contractor contracts 7 [11] hybrid MCDM model of discrete zero-sum two-person matrix games with grey numbers delays in Design-Bid-Build projects 8 [12] Table 1. Cont.…”

Criteria Evaluation for Contractor Selection in Cultural Heritage Projects Using Multiple Criteria Approach

“…The most common criteria presented in the studies are financial soundness (Fong & Choi, 2000;Polat, 2016;Gao, 2018;(Vahdami, Meysam Mousavi, Hashemi, Mousakhami, & Tavakkoli-Moghaddam, 2013), technical capability (Niento-Morote & Ruz-Vila, 2012;Zavadskas et al, 2015;Zhang et al, 2018;Vahdami et al, 2013), management ability (Niento-Morote & Ruz-Vila, 2012; Chiang et al, 2017;Kaklauskas et al, 2006), health and safety (Chiang et al, 2017;Taylan et al, 2018;Gao, 2018;Holt, 1998) and reputation (Kaklauskas et al, 2006;Keung & Yiu, 2015;Enshassi, Mohamed, & Modough, 2013;Darvish, Yasaei, & Saeedi, 2009). Furthermore, along these criteria was proposed additional criteria, namely, previous performance (Hadidi & Khater, 2015;Cheng & Li, 2004;Ulubeyli & Kazaz, 2016), quality (Martin et al, 2018;Marzouk, El Kherbawy, & Khalifa, 2013;Juan, Perng, Castro-Lacouture, & Lu, 2009); and experience (Yang, H. Wang, W. Wang, & Ma, 2016;Topcu, 2004).…”

Performance Evaluation of Czech Innovative Companies: Data Envelopment Analysis Approach

“…This property is ideal for applications particularly in the field of mass services and the evaluation of units within the branch network. Therefore, this method is quite frequently used in banking and insurance sectors (for example Grmanová 2013;Hajiagha et al 2013;Ho, Zhu 2004;Kočišová 2014;Kumar, Gulati 2010), real estate and construction sectors (Jin et al 2015;Horta et al 2016;Cheng, Chen 2014;yang et al 2016;Hu, Liu 2016) when evaluating universities (for example nazarko, Šaparauskas 2014; Rosenmayer 2014; Žižka 2015), hospitals (for example Dlouhý 2015) and transport enterprises (for example Klieštik 2009;Lin et al 2012).…”