An Integrated Approach for Supplier Evaluation and Selection using the Delphi Method and Analytic Hierarchy Process (AHP): A New Framework

Supplier selection is one of the most critical processes in supply chain management (SCM). Most small and medium enterprises (SMEs) face difficulties choosing the best supplier using conventional methods. A hybrid multi-criteria decision-making (MCDM) approach is proposed in supplier selection. This proposed framework integrates the Delphi technique as a data-gathering tool and Analytic Hierarchy Process (AHP) as the MCDM methodology for data analysis; both were used to select an effective supplier. This project applies the Delphi technique, allows experts to select the main criteria, and compares the trade-offs between the available alternatives depending on the main criteria. The criteria selected were price, delivery time, online ranking, rejection rate, and flexibility. Using the AHP approach, the criteria's weights were then assigned. The highest was for the price (43.84%), followed by the rejection rate (21.81%), online ranking (19.27%), delivery time (9.44%), and flexibility (5.64%). Lastly, a new framework was suggested using the weighted criteria collection for supplier selection.

AHP; Delphi; MCDM; Supplier selection; Supply chain

    The multi-criteria decision-making (MCDM) approach is a decision aid framework that can evaluate multiple conflicting criteria (Shukor et al., 2018). It is a method of operational research in which various criteria are included in decision-making conditions to give optimal solutions (Anaokar et al., 2018). MCDM looks at the paradigm in which an individual decision-maker or a group of experts contemplate a choice of action in an uncertain environment. MCDM methods were highly efficient at solving selection problems (Chatterjee et al., 2014). One of the critical selection problems is supplier selection, which involves conflicting criteria such as price, quality, and delivery time. Therefore, the need for an efficient MCDM method is required. Many MCDM approaches have been proposed to deal with such problems. Velasquez and Hester (2013) analyzed the MCDM techniques and their applicability to different areas. They identified 11 MCDM methods that have been widely applied, highlighting the need for an efficient MCDM method. In the literature, many researchers have used MCDM methods in the supplier selection process, such as Multi-Attribute Utility Theory (Shaik and Abdul-Kader, 2011), Analytic Hierarchy Process (AHP) (Yadav and Sharma, 2016), fuzzy set theory (Chen et al., 2006), fuzzy AHP (Chan et al., 2008), case-based reasoning (Zhao and Yu, 2011), data envelopment analysis (DEA) (Garfamy, 2006), Simple Multi-Attribute Rating Technique (Ng, 2008), Goal Programming (Choudhary and Shankar, 2014), ELECTRE method (Fahmi et al., 2016), Simple Additive Weighing (Kaur and Kumar, 2013), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) (Zouggari and Benyoucef, 2012). Other researchers prefer to integrate two methods and techniques to yield more robust decisions: fuzzy AHP and TOPSIS (Jain et al., 2018); AHP and Delphi (Su and Zhan, 2020); AHP and Monte Carlo Method Approach (Kristy and Zagloel, 2020); goal programming and AHP (Khorramshahgol, 2012); AHP and VIKOR (Büyüközkan et al., 2019); Analytical Network Process (ANP) and VIKOR (Abdel-Baset et al., 2019); fuzzy TOPSIS and MCGP (Liao and Kao, 2011); ELECTRE and fuzzy clustering (Azadnia et al., 2011); AHP and ELECTRE II (Wan et al., 2017); utility function and ELECTRE (de Almeida, 2007); fuzzy AHP and fuzzy multi-objective linear programming (Shaw et al., 2012); ANP and DPA (Kuo and Lin, 2012); and ANP and linear programming (Ghodsypour and O'Brien, 1998).

One of the most used methods is AHP, which was developed in 1970s by Thomas Saaty. Various researchers have implemented the AHP method in supplier selection. For example, Chan and Chan (2010) used AHP for their supplier selection to evaluate four suppliers in different countries, considering five levels. Kahraman et al. (2003) used fuzzy AHP for the supplier selection problem, using data from one Turkish enterprise, considering the most important criteria determined by a questionnaire. Ramanathan (2007) used the hybrid of AHP-DEA-TCO as his methodology in supplier selection, integrating the total cost of ownership (TCO), AHP, and DEA.

The Delphi method is a “structured group communication” developed by Dalkey and Helmer (1963). This technique was defined as the method used for data gathering from subjects within their domain of expertise. Its goal is to converge their opinions about the specific issue (Hsu and Sandford, 2007). Generally, the Delphi method collects data using a series of questionnaires delivered by the investigator through multiple iterations, looking for a consensus of opinions regarding the topic at hand. An agreement is considered when 80% of the participants vote in favor of the case.

After a comprehensive review of the existing literature in the field, it was identified that different researchers used different sets of criteria. In our research, the traditional criteria (price and delivery time), semi-traditional criteria (flexibility to change and the average number of rejected parts), and nontraditional factors (online ranking) were merged. The modern era and changes in people's attitudes toward to technological developments and globalization have rendered these factors critical in selecting the suppliers. Moreover, the integration between the qualitative approach afforded by the Delphi method and the quantitative approach afforded by the AHP method will corroborate the results and reduce the risk of selecting inappropriate suppliers. 

A new framework was developed by integrating the Delphi method and the AHP method. Five main criteria were identified: price, delivery time, rejection number, flexibility, and online ranking. The questionnaire given to the experts was designed in a specific way to develop the pairwise matrix. Saaty's Scale of Relative Importance was used to prioritize the factors. Two runs were conducted using the Delphi method for the experts to reach a consensus. The results show the effectiveness of the integrated framework, and the factors were ranked by percentage as follows: price (43.84%), rejection rate (21.81%), online ranking (19.27%), delivery time (9.44%), and flexibility (5.64%).

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