Abstract
Nowadays, with the development of information management infrastructures in organizations and the improvement of the data storage process, managers are looking for appropriate decision-making methods based on large volumes of data. Therefore, it is crucial to choose the right approach to make the right decisions based on the volume of available data. The present study seeks to provide a comprehensive framework for the decision-making process using big data, even when it is incomplete. The framework of multiple criteria decision making (MCDM) consists of criteria and alternatives, whereas in real-world cases, decision-makers may face several criteria and alternatives. In this study, the Principal Component Analysis (PCA) approach was selected for the criteria clustering. Later, the K-means algorithm is used to cluster the alternatives, which estimates the optimal number of clusters using the Elbow method. The Fuzzy TOPSIS (TOPSIS-F) and Ordinal Priority Approach (OPA) have been used to rank clusters. Ultimately, the best alternative in the top cluster has been identified with the aid of the OPA, which has a unique function to solve MCDM problems with incomplete data. For evaluating the performance of the proposed approach, first, a pilot testing has been executed on a real-world case, and then a practical study was conducted at a refinery equipment manufacturing company with a project-oriented organizational structure. The approach is flexible, interactive, intelligent, and integrative, and significantly reduces the time and computation costs for the decision-makers. The results confirmed the soundness of the proposed approach, which can be used by managers of different companies with confidence.
Published Version
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