Selection of Suppliers for Speech Recognition Products in IT Projects by Combining Techniques with an Integrated Fuzzy MCDM

Abstract

In today’s environment, as the complexity of actual events develops, products become increasingly complicated. As a result, companies should collaborate to integrate disparate technologies while developing a product or service. Additionally, collaborating with the right supplier helps a company increase the flexibility, competitiveness, and profitability of its goods or services. The goal of this study is to look into the factors that influence supplier selection for speech recognition. Twelve sub-criteria for quality, affordability, maintenance, and adaptability are used to evaluate prospective providers. Two separate hybrid methodologies for finding the best supplier of an information technology product are presented. intuitionistic Fuzzy Due to the uncertainty of the data, VIKOR operates as the decision-making matrix and solves the issue by determining the ideal alternative for group utility using VIKOR. The second technique, Q-ROF TOPSIS, selects suppliers by utilizing q-rung orthopair fuzzy sets, which provides decision makers with greater expression flexibility than the majority of uncertainty-related strategies. To demonstrate the effectiveness of the recommended measures, a case study is conducted. The outcomes of various strategies are compared, as well as the associated advantages.

Criteria Clustering and Supplier Segmentation Based on Sustainable Shared Value Using BWM and PROMETHEE

“…These factors may guarantee the high number of suppliers, competitive atmosphere between suppliers and producers, and issues related to the technology growth speed and the short life-cycle of products [21][22][23]. Since suppliers provide organizations with different services, and considering that they are active in their product innovation and even if environmental and ethical issues occur, they are faced with a wide range of variables that have made old methods of evaluating and selecting and working with suppliers more complicated [9,24,25].…”

Performance Evaluation of ICT Companies Using Hybrid MCDM Method in Iran

“…Taghipour has used two diferent methodologies for selection of suppliers for speech recognition products in IT projects, IF-VIKOR, and q-ROF TOPSIS for speech recognition software supplier selection and then he compared these methods [16]. Moreover, Piya has shown us that the green score is calculated based on the integrated fuzzy AHP-TOPSIS method, which is an efective approach in MCDM [17].…”

Artificial Intelligence Supported Aircraft Maintenance Strategy Selection with q-Rung Orthopair Fuzzy TOPSIS Method

“…Regarding the uncertain nature of the maintenance operations, q-ROF TOPSIS method (Pinar & Boran, 2020) is applied which is successful in modeling and quantifying human subjective decision making. Then Pinar et al (2021) and Taghipour et al(2022) used q-ROF TOPSIS method in their research on supplier selection and speech recognition product selection. In this section first, the basics of fuzzy sets are mentioned, then the methodology is briefly given and finally, a case study is presented.…”