Bisson, ChristopheDiner, Öznur Yaşar2019-06-272019-06-272017110016-32871873-63780016-32871873-6378https://hdl.handle.net/20.500.12469/364https://doi.org/10.1016/j.futures.2017.01.004This 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.eninfo:eu-repo/semantics/closedAccessStrategic Early Warning SystemScenario analysisGraph theoryCorporate foresightScanningMilk marketArticle102387WOS:00039962510000210.1016/j.futures.2017.01.0042-s2.0-85011342778Q1Q1