A reliable decision support system for fresh food supply chain management

Abstract

The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning. The DSS selects the model to apply according to user-defined performance criteria. Then, it considers sales forecasting as a proxy of expected demand and uses it as input for a multi-objective optimisation algorithm that defines a set of non-dominated order proposals with respect to outdating, shortage, freshness of products and residual stock. A set of real data and a benchmark – based on the methods already in use – are employed to evaluate the performance of the proposed DSS. The analysis of different configurations shows that the DSS is suitable for the problem under investigation; in particular, the DSS ensures acceptable forecasting errors and proper computational effort, providing order plans with associated satisfactory performances.

Keywords:

• fresh food supply chain
• forecasting
• order proposal
• optimisation
• decision support systems

Acknowledgements

Authors would like to thank the editor and the anonymous reviewers for their insightful and constructive comments.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the E-CEDI project, funded by Apulia Region under the POR FESR 2007-2013 grant, Asse I, Linea 1.2 - Azione 1.2.4. The work of the second author was partly supported by PRIN 2012 n. 2012MTE38N.