Abstract
A new approach is provided in our paper for creating a strategic early warningsystem allowing the estimation of the future state of the milk market as scenarios. This is inline with the recent call from the EU commission for tools that help to better address such ahighly volatile market. We applied different multivariate time series regression and Bayesiannetworks on a pre-determined map of relations between macro-economic indicators. Theevaluation of our findings with root mean square error (RMSE) performance score enhancesthe robustness of the prediction model constructed. Our model could be used by competitiveintelligence teams to obtain sharper scenarios, leading companies and public organisations tobetter anticipate market changes and make more robust decisions.
Highlights
As globalisation, deregulation and the Big Data phenomenon (Bendler et al, 2014) are rendering our economy gradually more complex and uncertain, the interest in competitive intelligence (CI) is growing (Bisson, 2014; Hugues, 2017). Calof and Skinner (1998, p.38) define CI as “the art and science of preparing companies for the future by way of a systematic knowledge management process
We aim to address this scientific gap by applying for the first time different multivariate time series regressions and Bayesian networks following the three first steps of the general frame of SEWS to predict the impacting scenario(s) that would help to be better prepared for the future
To evaluate the accuracy of our framework, we ran some tests on different parts of the machine learning system, and we report performance scores in the following
Summary
Deregulation and the Big Data phenomenon (Bendler et al, 2014) are rendering our economy gradually more complex and uncertain, the interest in competitive intelligence (CI) is growing (Bisson, 2014; Hugues, 2017). Calof and Skinner (1998, p.38) define CI as “the art and science of preparing companies for the future by way of a systematic knowledge management process. Calof and Skinner (1998, p.38) define CI as “the art and science of preparing companies for the future by way of a systematic knowledge management process. It is creating knowledge from openly available information by use of a systematic process involving planning, collection, analysis, communication and management, which results in decision-maker action.”. In order to address challenges such as Big Data, highly volatile and uncertain environments, an era where anticipation is more important and more difficult than ever, “traditional” CI systems based on scanning appear to be limited (Accenture, 2013; Gilad, 2008). The general framework of SEWS (Bisson, 2013; Gilad, 2008) for a market is: 1) define the scope, i.e
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
