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.

This paper proposes a new Multiple-criteria decision-making (MCDM) method called MUltiple-TRIangles ScenarioS (MUTRISS) with two scenarios respecting different levels of access to complete information for material selection problems. MUTRISS calculates the areas occupied by alternatives in n-dimensional space, employing analytic geometry and converting each alternative into n-edges forms. The paper applies MUTRISS to three material selection case studies, with Ti-6Al-4V, Material 4, and AISI 4140 Steel- UNS G41400 emerging as the best materials for the three examples with the highest overall scores of 0.036, 4.540 and 0.427 respectively. The results are compared with various MCDM methods through four statistical measures, including relative closeness ratio, robustness analysis, compromise ranking coefficient, and similarity degree. The measures focus on different aspects of MCDM methods in solving problems and their results. The paper concludes that MUTRISS offers a more robust and reliable approach for material selection problems compared to other MCDM methods, with the first scenario of MUTRISS being more reliable than the second scenario. The paper also emphasizes the importance of validating results in material selection problems due to the potential irreversible consequences of selecting the wrong material.

Supplier evaluation is a complex multi-criteria decision-making (MCDM) problem that deals with assessment of suppliers as the potential alternatives against various types of criteria. We consider the context where decision makers (DMs) have complete information about the suppliers and criteria. To address the needs of decision makers, a multi-criteria evaluation method named Ranking based on optimal points (RBOP) is developed in this paper. By imitating and simulating human decision-making behavioural patterns, the developed MCDM method selects the best alternative that is closer to what the DM desires. Furthermore, a novel subjective MCDM weighting methods called win-loss-draw (WLD) method is also developed, which is also based on human behavioural pattern. A real case study of domestic cheese brands is considered to apply the developed methods to select the best cheese supplier for an Iranian hypermarket. Compared to other MCDM methods, outputs of the RBOP method show some differences due to the impact of WLD method, which intensified divergence and optimal points during the decision-making process.

Open Education (OE) as a concept has been around for some years. Yet, a part from Open Educational Resources and Open Science, teachers and researchers are usually not aware of it. The aim of this paper is to conceptualise OE from the perspective of supply chain management (SCM), implicitly positioning it in the world of opens, the commons, the state and the market. Within a design-based approach, the concepts related to OE and SCM are presented, discussed and integrated in a novel framework dealing with the management of OE ecosystem. Findings show that keywords of the Open Education Supply Chain are cocreation, agile design and authority. The framework invites to create value from resources in a holistic way, balancing the commons, the state and the market in each stakeholder.

The collaborative planning and the management of production and storage processes are important components in supply chain management. The goal of this paper is topresent the reliability of genetic algorithms on solving bi-objective models compared to mono-objective models. To do this we will be based initially on the mono-objective Dudek's model and then we propose a division of the objective function in two objective functions. Finally we compare the results given by the genetic algorithms with the optimality result obtained using the LINGO solver on the mono-objective Dudek's model. This model aims at simultaneously minimizing the total production cost and the total holding cost. To solve the proposed model, we use a genetic algorithm NSGA-II. The proposed several test provide results that demonstrate and validate the effectiveness of the multi-objective approach and elitists genetic algorithms in solving this type of problem, compared to the literature in the proposed test.The validation of our approach will allow us later to use this algorithm in solving complex multi-objective models approaching the real context.

Summary form only given. So far, human factors in industrial environments have been considered from the safety perspective. However, beyond this specific field, it has been the need to address human factors as an important element impacting decisions in Operations Management. Within this frame, the talk addresses the research status on identifying, modelling and integrating human factors in Operations Management. The talk is supported by results from case studies and current work on the development of demand forecasting techniques. Indeed, we present a collaborative judgemental approach for demand forecasting in which the mathematical forecasts are adjusted by a structured technique that combines knowledge from different forecasters. The approach is based on the identification and classification of four types of judgemental events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence and the consistency of the collaborative aspect of the technique.

This paper is dedicated to the quantitative analysis and description of the adoption and assimilation phases of e-sales. The focus is placed on quantitative models of e-sales adoption relying upon Diffusion Of Innovation (DOI) mathematical models. Various models have been compared in order to determine that better describing e-sales adoption evolution, in terms of the number of adopting companies, for various industrial sectors in Europe. The Bass model, resulting to be the most suitable one for modelling first adoption, is then applied to estimate the parameters describing the e-sales evolution for the various industrial sectors. Then, moving to e-sales assimilation, the impact of e-sales adoption experience on e-sales adoption intensity is explored. This allows to investigate the evolution after the first adoption and if a link can be established between the maturity of the e-sales solution and its overall economic impact, reached after assimilation.