A Bayesian Model for Optimum Supply of Spare Parts

Abstract:

The need to have a stock of spare parts in a production company is very important in order to contract a continuity of service assurance. The costs of the stock are the costs of ownership, the costs of purchases and the costs of breaking the stock. In summary, the estimation of the needs of the stock is done mostly through quantitative methods while minimizing the costs.In this paper, we will first present our Bayesian model developed taking into account the obsolescence risk that is related to the life of parts, in order to determine the combination of new and recycled parts in quantities. After, we will expose some of the decision results of the simulation in order to clarify the operation of the bayesian networks. Finally, we will compare our model with the existing reference model and the bootstrap method for the purpose to see the influence of the parameters considered.

• Periodical: International Journal of Engineering Research in Africa (Volume 42)
• Pages: 160-171
• DOI: https://doi.org/10.4028/www.scientific.net/JERA.42.160
• Online since: April 2019
• Authors: Oumaima Bounou*, Abdellah El Barkany, Ahmed El Biyaali
• Keywords: Bayesian Networks, Forecasting, Inventory Management, Obsolescence, Optimization, Risk, Spare Parts, Stock Shortage

References

[1] Zermati P., Pratique de la gestion des stocks, 4th edition, Dunod, (1990).

[2] Javel G., Organisation et gestion de la production, 4e édition, Dunod, (2010).

[3] Component Technical Work group – Technical Commission of the GEAD, Guide de management des obsolescences de composants électroniques, Electrical and Electromechanical, group French Industries Aeronautics and Space, Réf. GIFAS/5203/2005, October (2005).

[4] Pillet M., Martin- C., Bonnefous P.,Courtois A., Gestion de production, 5th edition, Eyrolles, (2011).

[5] Lasnier G., gestion des approvisionements et des stocks dans la chaine logistique, hermes science publications, octobre (2014).

[6] Kostas-Platon A., Magou I., Dekker R., Tagaras G., Inventory control of spare parts using bayesian approach: case study, European journal of operational research,154 (2004) 730-739.

DOI: 10.1016/s0377-2217(02)00837-8

[7] Panagiotidou Sofia, Joint optimization of spare parts ordering and maintenance policies for multiple identical items subject to silent failures, European Journal of Operational Research, 235 (3) (2014) 300-314.

DOI: 10.1016/j.ejor.2013.10.065

[8] Topan E., Bayndir Z. P., Tan T., Heuristics for multi item two echelon spare parts inventory control problem with batch ordering in the central warehouse, beta research school for operation management and logistics, beta working paper 321 (2010).

DOI: 10.1016/j.orl.2010.05.006

[9] Bounou O., El Barkany A., El Biyaali A., Inventory Models for Spare Parts Management: A Review, International Journal of Engineering Research in Africa, 28 (2017) 182-198.

DOI: 10.4028/www.scientific.net/jera.28.182

[10] European Consumer Center, L'obsolescence programmée ou les dérives de la société de consommation, september (2010).

[11] Bergman J. J., A BAYESIAN APPROACH TO DEMAND FORECASTING, Master's thesis, University of Missouri-Columbia, December (2014).

[12] Can Wan, Zhao Xu, Yelei Wang, Zhao Yang Dong et Kit Po Wong, A Hybrid Approach for Probabilistic Forecasting of Electricity Price,  IEEE Transactions on Smart Grid, 5 (1) (2014) 463- 470.

DOI: 10.1109/tsg.2013.2274465

[13] Chen xi Zhou, S. Viswanathan, Comparison of a new bootstrapping method with parametric approaches for safety stock determination in service parts inventory systems, Int. J. Production Economics, 133 (2011) 481–485.

DOI: 10.1016/j.ijpe.2010.09.021

[14] Aris A. Syntetos, M. Zied Babai, Everette S. Gardner Jr., Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping, Journal of Business Research, 68 (2015) 1746–1752.

DOI: 10.1016/j.jbusres.2015.03.034

[15] Ghorbel N., Gestion des réapprovisionnements périodiques des pièces de rechange sous incertitudes pour le MCO de parc matériels : maitrise de risque de rupture des stocks, PhD Thesis, Vincennes Saint-Denis Paris 8 university, France, (2013).

DOI: 10.53102/1984.3.01-2.39

[16] Naim P., Wuillemin P. H., Leray P., Pourret O., Becker A., Réseaux bayésiens, third edition, eyrolles, (2007).

[17] Ghorbel N., Duta L., Addouche S-A, EL Mhamedi A., L'intégration de l'incertitude dans la gestion des stocks des pièces de rechange, CIGI Saint-Sauveur, Québec, Canada, 12-13-14 october (2011).

[18] Bouzy B., Réseaux Bayésiens, 3 February (2014).

[19] Brown G. W., Lu J. Y., Wolfson Robbert J., Dynamic modeling of inventories subject to obsolescence, Management Science, 11 (1964) 51-63.

DOI: 10.1287/mnsc.11.1.51

[20] Jaarsveld W. V., Dekker R., Estimating obsolescence risk from demand data - a case study, Int. J. Production Economics, 133 (2011) 423-431.

DOI: 10.1016/j.ijpe.2010.06.014

[21] Cobbaert K., Van Oudheusden D., Inventory models for fast moving spare parts subject to sudden death obsolescence, Int. J. Production Economics, 44 (1996) 239-248.

DOI: 10.1016/0925-5273(96)00062-x

[22] Ghorbel N., Addouches A, EL Mhamedi A., Boujelbene Y., Bayesian approach for spare parts replenishment policies under uncertainties, International Journal of Engineering Research and Development, 10 (11) (2014) 28-45.

[23] Bounou O., El Barkany A., El Biyaali A., Parametric Approaches for Spare Parts Demand, International Journal of Supply Chain Management, 7(4) (2018) 432-439.

DOI: 10.1109/logistiqua.2017.7962899