Sales Forecasting Models in the Fresh Food Supply Chain

Gabriella Dellino, Teresa Laudadio, Renato Mari, Nicola Mastronardi, Carlo Meloni

2015

Abstract

We address the problem of supply chain management for a set of fresh and highly perishable products. Our activity mainly concerns forecasting sales. The study involves 19 retailers (small and medium size stores) and a set of 156 different fresh products. The available data is made of three year sales for each store from 2011 to 2013. The forecasting activity started from a pre-processing analysis to identify seasonality, cycle and trend components, and data filtering to remove noise. Moreover, we performed a statistical analysis to estimate the impact of prices and promotions on sales and customers’ behaviour. The filtered data is used as input for a forecasting algorithm which is designed to be interactive for the user. The latter is asked to specify ID store, items, training set and planning horizon, and the algorithm provides sales forecasting. We used ARIMA, ARIMAX and transfer function models in which the value of parameters ranges in predefined intervals. The best setting of these parameters is chosen via a two-step analysis, the first based on well-known indicators of information entropy and parsimony, and the second based on standard statistical indicators. The exogenous components of the forecasting models take the impact of prices into account. Quality and accuracy of forecasting are evaluated and compared on a set of real data and some examples are reported.

References

  1. Andrews, B. H., Dean, M. D., Swain, R., and Cole, C. (2013). Building arima and arimax models for predicting long-term disability benefit application rates in the public/private sectors. Technical report, Society of Actuaries and University of Southern Maine.
  2. Armstrong, J. S. (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. Springer.
  3. Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (2008). Time Series Analysis: Forecasting and Control. Wiley, 4th edition.
  4. Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer.
  5. Höglund, R. and O stermark, R. (1991). Automatic arima modelling by the cartesian search algorithm. Journal of Forecasting, 10(5):465-476.
  6. Hyvärinen, A. and Oja, E. (2001). Independent Component Analysis. Wiley.
  7. Jacobs, F. and Chase, R. (2014). Operations and Supply Chain Management. McGrawHill/Irwin, 14th edition.
  8. Makridakis, S. and Hibon, M. (2000). The m3-competition: results, conclusions and implications. International Journal of Forecasting, 16(4):451-476.
  9. Makridakis, S., Wheelwright, S. C., and Hyndman, R. J. (2008). Forecasting Methods and Applications. Wiley India Pvt. Limited, 3rd edition.
  10. Najarian, K. and Splinter, R. (2005). Biomedical Signal and Image Processing. Taylor & Francis.
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Paper Citation


in Harvard Style

Dellino G., Laudadio T., Mari R., Mastronardi N. and Meloni C. (2015). Sales Forecasting Models in the Fresh Food Supply Chain . In Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-075-8, pages 419-426. DOI: 10.5220/0005293204190426


in Bibtex Style

@conference{icores15,
author={Gabriella Dellino and Teresa Laudadio and Renato Mari and Nicola Mastronardi and Carlo Meloni},
title={Sales Forecasting Models in the Fresh Food Supply Chain},
booktitle={Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2015},
pages={419-426},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005293204190426},
isbn={978-989-758-075-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Sales Forecasting Models in the Fresh Food Supply Chain
SN - 978-989-758-075-8
AU - Dellino G.
AU - Laudadio T.
AU - Mari R.
AU - Mastronardi N.
AU - Meloni C.
PY - 2015
SP - 419
EP - 426
DO - 10.5220/0005293204190426