A Bayesian approach to developing a strategic early warning system for the French milk market
DOI:
https://doi.org/10.37380/jisib.v7i3.277Keywords:
Bayesian networks, competitive intelligence, forecasting, milk market, strategic early warning systemAbstract
A new approach is provided in our paper for creating a strategic early warning system allowing the estimation of the future state of the milk market as scenarios. This is in line with the recent call from the EU commission for tools that help to better address such a highly volatile market. We applied different multivariate time series regression and Bayesian networks on a pre-determined map of relations between macro-economic indicators. The evaluation of our findings with root mean square error (RMSE) performance score enhances the robustness of the prediction model constructed. Our model could be used by competitive intelligence teams to obtain sharper scenarios, leading companies and public organisations to better anticipate market changes and make more robust decisions.
References
Andaluz, D. J., & Sánchez J. (2006). Nanotecnología en España. http://www.madrimasd.org/revista/revista34/tribuna/tribuna4.asp (Accessed: 05.02.2015)
Abu-Mostafa, Y. S., & Atiya, A. F. (1996). Introduction to financial forecasting. Applied Intelligence, 6(3), 205–213.
Accenture (2014). Accenture and MIT Form Alliance for Advanced Analytics Solutions. Retrieved on 20-April-2015, from http://newsroom.accenture.com/news/accenture-and-mit-form-alliance-for-advanced-analytics-solutions.htm.
Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, N. (2010). An empirical comparison of machine learning models for time series forecasting. Econometric Reviews, 29(5-6), 594–621.
Alpaydin, E (2010). Introduction to machine learning. Cambridge, Massachusetts: The MIT Press.
Bisson, C. (2013). Guide de gestion stratégique de l’information pour les PME. Montmoreau: Les2Encres.
Bisson, C. (2014). Exploring Competitive Intelligence Practices of French Local Public Agricultural Organisations. Journal of Intelligence Studies in Business, 4(2), 5-29.
Bisson, C., Guibey, I., Laurent, R., & Dagron, P. (2012). Mise en place d’un Système de détection de Signaux Précoces pour une Intelligence Collective de l’Agriculture applique aux filières de l’élevage bovin. Proceedings of the PSDR symposium, Clermont Ferrand, France.
Bendler, J., Wagner, S., Brandt, D.-V. T. & Neumann, D. (2014). Taming uncertainty in big data. Business & Information Systems Engineering, 6(5), 279– 288.
Cahen, P. (2010). Signaux faibles, mode d'emploi. Paris: Eyrolles.
Calof, J.L. & Skinner, B. (1998). Competitive intelligence for government officers: a brave new World. Optimum, 28 (2), 38-42.
Day, G.S. & Schoemaker, P.J.H. (2008). Leadership –Are you a ‘‘vigilant leader’’? MIT Sloan Management Review, 49, 43.
De Gooijer, J. G. & Hyndman, R. J. (2006). 25 years of time series forecasting. International journal of forecasting, 22(3), 443–473.
Durbin, R., Eddy, S. R., Krogh, A.,& Mitchison, G. (1998). Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge: Cambridge university press.
Engle, R. F. (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987–1007.
European Commission (2010). Evolution of the market situation and the consequent conditions for smoothly phasing-out the milk quota system second soft landing report. Report from the commission to the European parliament and the council, Brussels.
Fuld, L., (2010). The Secret Language of Competitive Intelligence: How to See Through & Stay Ahead of Business Disruptions, Distortions, Rumors & Smoke Screens. Indianapolis: Dog Ear Publishing.
Gilad, B. (2003). Early warning: using competitive intelligence to anticipate market shifts, control risk, and create powerful strategies. New-York: Amacom.
Gilad, B. (2008). Business War games: How large, small, and new companies can vastly improve their strategies and outmaneuver the competition. Pompton Plains: Career press.
Heckelei, T., & Britz, W. (2000). Concept and explorative application of an EU wide regional agricultural sector model (capri-project). In: Agricultural sector modeling and policy information systems. Proceedings of the 65th EAAE Seminar (pp. 281–290), Bonn, Germany.
Huettel, S., & Jongeneel, R. (2008). Structural change in the dairy sectors of Germany and the Netherlands–a markov chain analysis. Proceedings of the 12th EAAE 395 Congress (pp. 26–29), Bonn, Germany.
Hugues, S. (2017). A new model for identifying emerging technologies. Journal of Intelligence Studies in Business. 7(1), 76-86.
Jansson, T., & Britz, W. (2002). Experiences of using a quadratic programming model to simulate removal of milk quotas. Proceedings of the 10th EAAE-Conference, Ghent, Belgium.
Kiiveri, H., Speed, T. P., & Carlin, J. B. (1984). Recursive causal models. Journal of the Australian Mathematical Society. 36(01), 30–52.
Lapedes, A., & Farber, R. (1987). Nonlinear signal processing using neural networks: Prediction and system modeling. Technical report, Los Alamos National Laboratory, Los Alamos.
Lloyd, S. P. (1982). Least squares quantization in pcm. IEEE Transactions on Information Theory, 28 (2), 129–137.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1 (pp. 281–297), California, USA.
Momagri (2012). Chiffres clés de l’agriculture. Retrieved on 15-May-2016, from http://www.momagri.org/FR/chiffres-cles-de-l-agriculture.http://www.momagri.org/FR/chiffres-cles-de-l-agriculture.html
Muller, K.-R., Smola, A. J., Ratsch, G., Scholkopf, B. Kohlmorgen, J. & Vapnik, V. (1997). Predicting time series with support vector machines. In: Artificial Neural Networks. Proceedings of the ICANN’97 (pp. 999–1004), Springer.
Murphy, K. P.( 2002). Dynamic bayesian networks: representation, inference and learning. Ph.D. thesis, University of California, Berkeley.
National Institute of Statistics and Economic Studies (2015). Macro-economic database. Retrieved on 18-November-2015, from http://www.bdm.insee.fr/bdm2/choixCriteres?codeGroupe=1466.
Patton, M., Binfield, J., Moss, J., Kostov, P., Zhang, L., Davis,J., & Westhoff, P. (2008). Impact of the abolition of EU milk quotas on agriculture in the UK Proceedings of the 107th EAAE Seminar, Sevilla, Spain.
Pole Economie & Prospective Normandie (2014). Quels élevages laitiers en Normandie 2020, Synthese.2. Retrieved on 12-November-2014, from http://www.chambre-agriculture-27.fr/toutes-les-publications- normandes/detail-publication/actualites/quels-elevages-laitiers-en-normandie-2020/
Rahelizatovo, N. C., & Gillespie, J. M. (1999). Dairy farm size, entry, and exit in a declining production region. Journal of Agricultural and Applied Economics, 31(2), 333–347.
Reed, A. J. (1992). Expectations, demand shifts, and milk supply response. Journal of Agricultural Economics Research, 44(1), 11-21.
Robert, A. (2015). Milk crisis drives wedge between France and Germany. Retrieved 15.September-2015, from: https://www.euractiv.com/section/agriculture-food/news/milk-crisis-drives-wedge-between-france-and-germany/
Saravanakumar, V., & Jain, D. (2009). Evolving milk pricing model for agribusiness centres: an econometric approach. Agricultural Economics Research Review, 22 (1), 155–160.
Schwarz, J. O. (2005). Pitfalls in implementing a strategic early warning system. Foresight, 7(4), 22–30.
Vedaldi, A., & Fulkerson, B. (2008). VLFeat: An open and portable library of computer vision algorithms. Retrieved 25.September-2016, from http://www.vlfeat.org/
Werbos, P. J. (1988). Generalization of back propagation with application to a recurrent gas market model. Neural Networks, 1(4), 339–356.
Wright, S. (1921). Correlation and causation. Journal of agricultural research, 20(7), 557–585.
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