20 research outputs found

    A conceptual framework for crop-based agri-food supply chain characterization under uncertainty

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    [EN] Crop-based Agri-food Supply Chains (AFSCs) are complex systems that face multiple sources of uncertainty that can cause a significant imbalance between supply and demand in terms of product varieties, quantities, qualities, customer requirements, times and prices, all of which greatly complicate their management. Poor management of these sources of uncertainty in these AFSCs can have negative impact on quality, safety, and sustainability by reducing the logistic efficiency and increasing the waste. Therefore, it becomes crucial to develop models in order to deal with the key sources of uncertainty. For this purpose, it is necessary to precisely understand and define the problem under study. Even, the characterisation process of this domains is also a difficult and time-consuming task, especially when the right directions and standards are not in place. In this chapter, a Conceptual Framework is proposed that systematically collects those aspects that are relevant for an adequate crop-based AFSC management under uncertainty.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015Alemany Díaz, MDM.; Esteso, A.; Ortiz Bas, Á.; Hernández Hormazabal, JE.; Fernández, A.; Garrido, A.; Martin, J.... (2021). A conceptual framework for crop-based agri-food supply chain characterization under uncertainty. Studies in Systems, Decision and Control. 280:19-33. https://doi.org/10.1007/978-3-030-51047-3_2S1933280Taylor, D.H., Fearne, A.: Towards a framework for improvement in the management of demand in agri-food supply chains. 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    Digital technology enablers for resilient and customer driven food value chains

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    Food production chains have to respond to disrupted global markets and dynamic customer demands. They are coming under pressure to move from a supply to a demand-driven business model. The inherent difficulties in the lifecycle management of food products, their perishable nature, the volatility in global and regional supplier and customer markets, and the mix of objective and subjective drivers of customer demand and satisfaction, compose a challenging food production landscape. Businesses need to navigate through dynamically evolving operational risks and ensure targeted performance in terms of supply chain resilience and agility, as well as transparency and product assurance. While the industrial transition to digitalised and automated food production chains is seen as a response to such challenges, the contribution of industry 4.0 technology enablers towards this aim is not sufficiently well understood. This paper outlines the key features of high performing food production chains and performs a mapping between them and enabling technologies. As digitalisation initiatives gain priority, such mapping can help with the prioritisation of technology enablers on delivering key aspects of high performing food production chains

    Influence of Losing Multi-dimensional Information in an Agent-Based Model

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    Control Charts to Support Trust Monitoring in Dynamic Logistics Networks

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    Part 14: Dynamic Logistics NetworksInternational audienceNowadays, companies to be competitive must develop capabilities that enable them to respond quickly to market needs. According to some managers, the strategy is the development of dynamic logistics networks based on a collaborative environment. However, the absence of mechanisms to detect and even anticipate potential opportunistic behaviour is an obstacle to the proliferation of this way of working. The article aims to understand the role of trust to sustainability of collaborative processes. The paper begins by discussing the trust properties. It is then discussed how statistical control charts can be used to support the trust monitoring of each member within a collaborative ecosystem. The control charts’ tools suggested in this paper are the Z control charts for trust level monitoring and the Zi capacity index. Finally, it is discussed how this approach can be applied to dynamic logistics networks within the context of a collaborative ecosystem

    A Multi Perspective Framework for Enhanced Supply Chain Analytics

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    Supply chain analytics, especially in the field of food supply has become a strategic business function. Monthly executive sales and operation planning meetings utilize supply chain analytics to inform strategic business decisions. Having identified gaps in the strategic management of food supply chains, a multi perspective supply chain analytics framework is developed incorporating process and data attributes to support decision making. Using Design Science as the research methodology, a novel framework with a supporting IT artefact is built and presented with early evaluation results. The resulting multi perspective supply chain analytics framework equips practitioners to identify strategic issues, providing important decision support information. The case study further illustrates the framework has applicability across all integrated food supply chains. This research has highlighted gaps in the application of process science to the supply chain management domain, particularly in the area of simultaneous assessment of process and data. The outcomes contribute to research in this domain providing a framework that will enhance the significant reference modelling and operational management work that has occurred in this field.</p
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