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dc.contributor.authorBisson, Christophe
dc.contributor.authorDiner, Öznur Yaşar
dc.date.accessioned2019-06-27T08:01:23Z
dc.date.available2019-06-27T08:01:23Z
dc.date.issued2017
dc.identifier.issn0016-3287
dc.identifier.issn1873-6378
dc.identifier.urihttps://hdl.handle.net/20.500.12469/364
dc.identifier.urihttps://doi.org/10.1016/j.futures.2017.01.004
dc.description.abstractThis paper presents a new approach for developing a Strategic Early Warning System aiming to better detect and interpret weak signals. We chose the milk market as a case study in line with the recent call from the EU Commission for governance tools which help to better address such highly volatile markets. Furthermore on the first of April 2015 the new Common Agricultural Policy ended quotas for milk which led to a milk crisis in the EU. Thus we collaborated with milk experts to get their inputs for a new model to analyse the competitive environment. Consequently we constructed graphs to represent the major factors that affect the milk industry and the relationships between them. We obtained several network measures for this social network such as centrality and density. Some factors appear to have the largest major influence on all the other graph elements while others strongly interact in cliques. Any detected changes in any of these factors will automatically impact the others. Therefore scanning ones competitive environment can allow an organisation to get an early warning to help it avoid an issue (as much as possible) and/or seize an opportunity before its competitors. We conclude that Strategic Early Warning Systems as a corporate foresight approach utilising graph theory can strengthen the governance of markets. (C) 2017 Elsevier Ltd. All rights reserved.
dc.language.isoEnglish
dc.publisherElsevier
dc.subjectStrategic Early Warning System
dc.subjectScenario analysis
dc.subjectGraph theory
dc.subjectCorporate foresight
dc.subjectScanning
dc.subjectMilk market
dc.titleStrategic Early Warning System for the French milk market: A graph theoretical approach to foresee volatility
dc.typeArticle
dc.identifier.startpage10
dc.identifier.endpage23
dc.relation.journalFutures
dc.identifier.volume87
dc.identifier.wosWOS:000399625100002
dc.identifier.doi10.1016/j.futures.2017.01.004
dc.contributor.khasauthorBisson, Christophe
dc.contributor.khasauthorDiner, Öznur Yaşar


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