This study presents a tailored fuzzy multi-criteria decision-making (FMCDM) framework, integrating Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL), Fuzzy Analytic Hierarchy Process (F-AHP), and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) for optimized truck selection under uncertainty. While these methods are established in MCDM, their customized integration for fleet selection, incorporating both conventional and electric trucks, enhances practical decision-making. Applied to a Swiss transportation company, the framework systematically captures criteria interdependencies, assigns dynamic weights, and ranks alternatives based on operational, financial, and environmental factors. The results identify efficiency, truck capacity, and fuel consumption as key drivers. This structured approach provides a scalable, data-driven tool for logistics operators, supporting sustainable and cost-effective fleet transitions.

This study addresses the role of implicit attitudes toward risk in inventory management, specifically in the context of the newsvendor problem (NVP). Using the Implicit Association Test (IAT), we explore how implicit attitudes toward risk influence newsvendors’ ordering decisions, focusing on deviations from profit-maximising solutions. Our methodology combines the NVP inventory exercise with IAT measurements. We find a negative correlation between participants’ implicit attitudes toward risk and the absolute deviation from the optimal order quantity, indicating that individuals with implicit attitudes toward risk are closer to neutrality deviate less from the profit-maximising solution. This supports our hypothesis that implicit risk attitudes impact such deviations. Our findings emphasise the importance of considering implicit attitudes toward risk in decision support systems for inventory management. Future research directions should explore the interplay of implicit attitudes toward risk with cognitive factors and its applicability in diverse contexts.

Data from social media is increasingly being utilized to better understand consumer preferences and prospective future demand. In this paper, the Decision Making Trial and Evaluation Laboratory (DEMATEL) and the Interpretive Structural Modeling (ISM) approaches are used to identify the interdependencies and cause-effect between social media attributes centered around better understanding the impact of attributes on product sales. The methodology is demonstrated on the social media and sales data from a large food and beverage company. Results show that the “followers” and “comments” are interdependent and influenced by the “posts”, “impressions” and “videos”. The ISM and DEMATEL results are validated with Pearson's correlation coefficient.