Examinando por Autor "Mediavilla Guisasola, Miguel"

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  • Miniatura
    Ítem
    How to negotiate with dominant suppliers?: a game-theory perspective from the industry
    (Universidad Politécnica de Madrid, Centro de Estudios de Postgrado de Administración de Empresas, CEPADE, 2019-03-20) Mediavilla Guisasola, Miguel ; Mendibil, Kepa; Rivera Hernáez, Olga
    The negotiation with dominant suppliers usually drives to locked-in situation in which buyers have no choice but to accept the given conditions. Commonly found in the industry, there is a need to provide new insights to practitioners to leverage competition. Specifically, researchers apply and test concepts from Game-Theory in a real supplier selection process in the port cranes industry. Our research shows that existing literature in Game-Theory is mostly descriptive, very focused on auctions and has still limitations regarding the design, application and impact of these supplier selection concepts. Therefore, it is presented one of the first field studies presenting the application of game-trees and backward induction (tools from Game-Theory) for the design and execution of a real bargaining, including the hows and whys of our decisions. The results suggest that using Game-Theory can enhance the chance to have better negotiation outcomes by predicting the possible outcomes and prescribing the best fitting game to be design in order to increase competition among suppliers.Keywords: Purchasing; Game-theory; negotiation; supplier selection
  • Miniatura
    Ítem
    A proposal for future research agenda on automated disruption management in intralogistic systems
    (Institute of Electrical and Electronics Engineers Inc., 2025-06-23) Olaizola Arregui, Imanol; Mediavilla Guisasola, Miguel; Onieva Caracuel, Enrique
    This paper presents a systematic literature review on Automated Disruption Management (ADM) in intralogistic systems, analyzing 1.406 papers between 2018 and 2024. The review examines current approaches to managing disruptions in modern intralogistic environments, focusing on system architectures, adaptation capabilities, decision-making methods, and implementation aspects. Through a structured analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we identify several critical gaps in current research, particularly in handling multiple simultaneous disruptions, integrating predictive capabilities with real-time adaptation, and validating theoretical developments in real-world settings. Our findings reveal a significant trend toward Reinforcement Learning (RL) approaches and an observable evolution from traditional Automated Guided Vehicles (AGV) to more flexible Autonomous Mobile Robot (AMR) solutions. Also, our work revealed the need for more integrated approaches that can handle multiple disruption types simultaneously while maintaining system performance, particularly in complex industrial environments. Moreover, the analysis also shows a considerable gap between theoretical development and practical implementation, with very few papers reporting real-world testing results. This review contributes to the field by providing a comprehensive taxonomy of current approaches, identifying critical research gaps, and proposing a specific research agenda in the field for future research.