×

zbMATH — the first resource for mathematics

Microforecasting methods for fresh food supply chain management: a computational study. (English) Zbl 07316203
Summary: We address the problem of forecasting sales for fresh and highly perishable products, in the general context of supply chain management. The forecasting activity refers to the single item in a given store and started from a pre-processing phase for data analysis and normalization. Then data was used as input for a forecasting algorithm designed to be user interactive. We implemented three forecasting methods: ARIMA, ARIMAX and transfer function models. The exogenous components of the forecasting models took the impact of prices into account. The best configuration of these models is dynamically chosen via two alternative methods: (i) a two-step procedure, based on properly selected statistical indicators, (ii) a Sequential Parameter Optimization approach for automatic parameter tuning. The user or the decision maker at the store level should not be exposed to the complexity of the forecasting system which - for this reason - is designed to adaptively select the best model configuration at every forecast session, to be used for each item/store combination. A set of real data based on 19 small and medium sized stores and 156 fresh products was employed to evaluate both quality of forecasting results and their effects on the order planning activity, where sales forecasting is considered as a proxy of the expected demand. Some examples are reported and discussed. Our results confirm that there is no ‘one-size-fits-all’ forecasting model, whose performance strictly depends on the specific characteristics of the underlying data. This supports the adoption of a data-driven tool to automate the dynamic selection of the most appropriate forecasting model.
MSC:
62 Statistics
90 Operations research, mathematical programming
Software:
forecast; Forecast; SPOT
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Andrews, B. H.; Dean, M. D.; Swain, R.; Cole, C., Building ARIMA and ARIMAX Models for Predicting Long-Term Disability Benefit Application Rates in the Public/Private Sectors. Tech. Rep. 08/2013 (2013), Society of Actuaries and University of Southern Maine
[2] Arminger, G., Sales and order forecasts in CPFR, ECR J., 4, 1, 55-67 (2004)
[3] Armstrong, J., Principles of Forecasting: A Handbook for Researchers and Practitioners (2001), Springer
[4] Babai, M.; Ali, M.; Boylan, J.; Syntetos, A., Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis, Int. J. Prod. Econ., 143, 2, 463-471 (2013)
[5] Bartz-Beielstein, T., Experimental Research in Evolutionary Computation - The New Experimentalism (2006), Springer
[6] Bartz-Beielstein, T., SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization. Tech. Rep. 05-10 (2010), Department of Computer Science, University of Applied Sciences, Cologne
[7] Bartz-Beielstein, T.; Chiarandini, M.; Paquete, L.; Preuss, M., Experimental Methods for the Analysis of Optimization Algorithms (2010), Springer
[8] Borade, A.; Sweeney, E., Decision support system for vendor managed inventory supply chain: a case study, Int. J. Prod. Res., 53, 16, 4789-4818 (2015)
[9] Box, G. E.P.; Jenkins, G. M.; Reinsel, G. C., Time Series Analysis: Forecasting and Control (2008), Wiley
[10] Burnham, K. P.; Anderson, D. R., Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2002), Springer
[11] Cardoso, J., High-order contrasts for independent component analysis, Neural Comput., 11, 1, 157-192 (1999)
[12] Chatfield, C., Prediction intervals for time-series forecasting, (Armstrong, J., Principles of Forecasting. Principles of Forecasting, International Series in Operations Research & Management Science, vol. 30 (2001), Springer, US), 475-494
[13] Ciavotta, M.; Detti, P.; Meloni, C.; Pranzo, M., A bi-objective coordination setup problem in a two-stage production system, European J. Oper. Res., 189, 3, 734-745 (2008)
[14] Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms (2001), John Wiley & Sons
[15] Dellino, G.; Kleijnen, J.; Meloni, C., Robust optimization in simulation: Taguchi and Response Surface Methodology, Int. J. Prod. Econ., 125, 1, 52-59 (2010)
[16] Dellino, G.; Kleijnen, J.; Meloni, C., Robust optimization in simulation: Taguchi and Krige combined, INFORMS J. Comput., 24, 3, 471-484 (2012)
[18] Dellino, G.; Laudadio, T.; Mari, R.; Mastronardi, N.; Meloni, C., Demand forecasting algorithms and supply chain optimization for fresh and perishable biological foods, (Mansutti, D.; Spitaleri, R. M., MASCOT 2015 - IMACS. MASCOT 2015 - IMACS, Computational and Applied Mathematics, vol. 20 (2017)), 61-70
[19] Dellino, G.; Laudadio, T.; Mari, R.; Mastronardi, N.; Meloni, C.; Vergura, S., Energy production forecasting in a PV plant using transfer function models, (Proceedings of the 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 (2015), IEEE), 1379-1383
[20] Dellino, G.; Lino, P.; Meloni, C.; Rizzo, A., Enhanced evolutionary algorithms for multidisciplinary design optimization: a control engineering perspective, Hybrid Evol. Algorithms, 75, 39-76 (2007)
[21] Detti, P.; Meloni, C.; Pranzo, M., Minimizing and balancing setups in a serial production system, Internat. J. Prod. Res., 45, 24, 5769-5788 (2007)
[23] Dotoli, M.; Fanti, M.; Meloni, C.; Zhou, M., A multi-level approach for network design of integrated supply chains, Internat. J. Prod. Res., 43, 20, 4267-4287 (2005)
[24] Dotoli, M.; Fanti, M.; Meloni, C.; Zhou, M., Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing, IEEE Trans. Syst. Man Cybern. A, 36, 1, 62-75 (2006)
[25] Ehrgott, M., Multicriteria Optimization (2005), Springer-Verlag
[26] Fleischmann, B.; Meyr, H.; Wagner, M., Advanced planning, (Stadtler, H.; Kilger, C., Supply Chain Management and Advanced Planning (2002), Springer: Springer Berlin), 71-96
[27] Granger, C.; Newbold, P., Forecasting Economic Time Series (1986), Academic Press
[28] Höglund, R.; Östermark, R., Automatic ARIMA modelling by the Cartesian search algorithm, J. Forecast., 10, 5, 465-476 (1991)
[29] Huang, M.-G., Economic ordering model for deteriorating items with random demand and deterioration, Int. J. Prod. Res., 51, 18, 5612-5624 (2013)
[30] Hyndman, R.; Khandakar, Y., Automatic time series forecasting: The forecast package for R, J. Stat. Softw., 27, 3, 1-22 (2008)
[31] Hyvärinen, A.; Oja, E., Independent Component Analysis (2001), Wiley
[32] Jacobs, F.; Chase, R., Operations and Supply Chain Management (2014), McGrawHill/Irwin
[33] Jolliffe, I., (Principal Component Analysis. Principal Component Analysis, Series in Statistics (2002), Springer-Verlag: Springer-Verlag New York)
[34] Ma, Y.; Wang, N.; Che, A.; Huang, Y.; Xu, J., The bullwhip effect on product orders and inventory: a perspective of demand forecasting techniques, Int. J. Prod. Res., 51, 1, 281-302 (2013)
[35] Makridakis, S.; Hibon, M., The M3-Competition: results, conclusions and implications, Int. J. Forecast., 16, 4, 451-476 (2000)
[36] Makridakis, S.; Wheelwright, S. C.; Hyndman, R. J., Forecasting Methods and Applications (2008), Wiley India Pvt. Limited
[38] McKay, M.; Beckman, R.; Conover, W., A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21, 2, 239-245 (1979)
[39] Meloni, C.; Naso, D.; Turchiano, B., Setup coordination between two stages of a production system: A multi-objective evolutionary approach, Ann. Oper. Res., 147, 1, 175-198 (2006)
[40] Meyr, H., Forecast methods, (Stadtler, H.; Kilger, C., Supply Chain Management and Advanced Planning (2002), Springer: Springer Berlin), 379-390
[41] Nahmias, S., Perishable Inventory Systems (2011), Springer
[42] Naso, D.; Turchiano, B.; Meloni, C., Single and multi-objective evolutionary algorithms for the coordination of serial manufacturing operations, J. Intell. Manuf., 17, 2, 251-270 (2006)
[43] Sürie, C.; Wagner, M., Supply chain analysis, (Stadtler, H.; Kilger, C., Supply Chain Management and Advanced Planning (2002), Springer: Springer Berlin), 29-44
[44] (Trienekens, J.; Top, J.; van der Vorst, J.; Beulens, A., Towards Effective Food Chains. Models and Applications (2010), Wageningen Academic Publishers)
[45] van Donsellar, K.; van Woensel, T.; Broekmeulen, R.; Fransoo, J., Inventory control of perishables in supermarkets, Int. J. Prod. Econ., 104, 2, 462-472 (2006)
[46] Wagner, M., Demand planning, (Stadtler, H.; Kilger, C., Supply Chain Management and Advanced Planning (2002), Springer: Springer Berlin), 123-141
[47] Wagner, M.; Meyr, H., Food and beverages, (Stadtler, H.; Kilger, C., Supply Chain Management and Advanced Planning (2002), Springer: Springer Berlin), 371-388
[48] West, K., Forecast evaluation, (Eliott, G.; Granger, C.; Timmermann, A., Handbook of Economic Forecasting, Volume I (2006), Elsevier), 100-134
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.