The current research addresses the inventory classification problem of community pharmacies, which have a dual role as both a vital component of the pharmaceutical supply chain and a typical retail store. Despite the existing literature indicating that pharmacists may lack knowledge on inventory management, it seems that the MCIC literature is weak in explaining how pharmacists can benefit from MCDA techniques in all aspects. To bridge this gap, the study aims to demonstrate that pharmacists can utilize MCDA techniques to gain deeper insights beyond mere classification in the context of inventory management. Real-world data from a community pharmacy in Turkey was classified using the EDAS method. Sensitivity analysis was performed for MCDA inputs, about which pharmacists may lack information. Scenario findings based on criterion weights and threshold values offer important managerial implications for pharmacists. This study provides a critical contribution to the literature on inventory management in community pharmacies by highlighting the potential of MCDA techniques to support decision-making beyond mere classification. The sensitivity analysis also sheds light on areas where pharmacists may lack knowledge and suggests ways to address these gaps. Overall, the study underscores the need for pharmacists to have a deeper understanding of inventory management and highlights the potential benefits of MCDA techniques in addressing this challenge.

E-commerce is one of the industries most affected by big data, from collection to analytics in the highly competitive market. Previous research on big data analytics in E-commerce focused only on particular applications, and there is still a gap in presenting a framework to evaluate big data applications from a challenges-values point of view. This study employs a three-phase methodology to evaluate big data applications in E-commerce with respect to big data challenges and values using a hybrid multi-criteria decision-making technique that combines BWM and fuzzy TOPSIS. The results showed process challenge and the strategic value obtained the highest weight for challenges and values criteria. Financial fraud detection is relatively the most challenging, and online review analytics is the most valuable application of big data in E-commerce among identified applications. Evaluating big data applications based on cost and benefit criteria is practical for E-commerce managers and experts to make decisions on implementation priorities to overcome the challenges and make the most of values. Moreover, the proposed approach is not only limited to big data analytics in E-commerce and can also be applied in other industries to evaluate emerging technology applications.

This article introduces a comprehensive analysis of 20 leading companies, scrutinized through their financial metrics across various sectors. By deploying multi-criteria decision-making (MCDM) techniques, we aim to offer investors a clear and objective perspective on which companies stand out as the best investment options. Among the MCDM techniques, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized, renowned for its efficiency in handling complex decision-making scenarios which is conducted by two clauses. 1) Implementing TOPSIS with assigning equal weights and same share to every chosen metrics as criteria and 2) employ BWM (Best Worst Method) to calculate these weights base on their significance and relevancy to the prosses of ranking. According to the Result gained from the computation, ranks 1 to 5 belong to the similar companies with both assumptions which are Ford Motor Co, BP plc, Tesla Inc, General Motors Co and Exxon Mobil Corp. The consistency in rankings across two different weighting assumptions highlights the robustness of the criteria used, ensuring stable and reliable outcomes. This enhances the credibility of the findings, making them more trustworthy and citable for those who seek reliable and robust methodologies for informed investment decisions.

Public security is an area of increasing importance in Brazil, as society requires that public resources are managed more efficiently and effectively. Criminalistics is an integral and vital part of the Brazilian public security system and requires new management tools to optimize human resources, equipment, and facilities allocation. Faced with a challenging scenario of budgetary constraints in several areas in public administration, the search for innovative methods should be a priority for the forensic service sector managers. The current article presents a multicriteria decision model to evaluate the operational viability of 23 forensic units within the Federal Police of Brazil (PF). The framework used the hybrid approach BWM and R-TOPSIS. The proposed model led to the complete ranking of 23 local forensic units. Amongst the last positions in the ranking, it was possible to recommend merging or shutting down some units. The sensitivity analysis performed did not show abrupt variations in the original positions, confirming the robustness of the proposed solution. It was concluded that the model allowed resources optimization whilst not compromising the quality of the services provided to society.