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Spare Part Replenishment Strategy for Electronic Product Based on Model Predictive Control

School of Automation, Beijing Institute of Technology, Beijing 100081, China
Knowdee AI, Beijing 100085, China
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Abstract

After-sale service plays an essential role in the electronics retail industry, where providers must supply the required repair parts to consumers during the product warranty period. The rapid evolution of electronic products prevents part suppliers from maintaining continuous production, making it impossible to supply spare parts consistently during the warranty periods and requiring the providers to purchase all necessary spare parts on Last Time Buy (LTB). The uncertainty of customer demand in spare parts brings out difficulties to maintain optimal spare parts inventory. In this paper, we address the challenge of forecasting spare parts demand and optimizing the purchase volumes of spare parts during the regular monthly replenishment period and LTB. First, the problem is well defined and formulated based on the dynamic economic lotsize model. Second, a transfer function model is constructed between historical demand values and product sales, aiming to identify the length of warranty period and forecast the spare part demands. In addition, the linear Model Predictive Control (MPC) scheme is adopted to optimize the purchase volumes of spare part considering the inaccuracy in the demand forecasts. A real-world case considering different categories of spare parts consumption is studied. The results demonstrate that our proposed algorithm outperforms other algorithms in terms of forecasting accuracy and the inventory cost.

References

[1]

M. G. Pecht, P. A. Sandborn, and R. Solomon, Electronic part life cycle concepts and obsolescence forecasting, IEEE Trans. Comp. Packag. Technol., vol. 23, no. 4, pp. 707–717, 2000.

[2]

E. Ozyoruk, N. K. Erkip, and Ç. Ararat, End-of-life inventory management problem: Results and insights, Int. J. Prod. Econ., vol. 243, p. 108313, 2022.

[3]

S. Zhang, K. Huang, and Y. Yuan, Spare parts inventory management: A literature review, Sustainability, vol. 13, no. 5, p. 2460, 2021.

[4]

R. J. I. Basten and G. J. van Houtum, System-oriented inventory models for spare parts, Surv. Oper. Res. Manag. Sci., vol. 19, no. 1, pp. 34–55, 2014.

[5]

Q. Hu, J. E. Boylan, H. Chen, and A. Labib, OR in spare parts management: A review, Eur. J. Oper. Res., vol. 266, no. 2, pp. 395–414, 2018.

[6]
P. H. Zipkin, Foundations of Inventory Management. Boston, MA, USA: McGraw-Hill, 2000.
[7]
D. Waters, Inventory Control and Management (2nd Edition). New York, NY, USA: John Wiley & Sons, Inc., 2008.
[8]

J. Huber, S. Müller, M. Fleischmann, and H. Stuckenschmidt, A data-driven newsvendor problem: From data to decision, Eur. J. Oper. Res., vol. 278, no. 3, pp. 904–915, 2019.

[9]

Y. Qin, R. Wang, A. J. Vakharia, Y. Chen, and M. M. H. Seref, The newsvendor problem: Review and directions for future research, Eur. J. Oper. Res., vol. 213, no. 2, pp. 361–374, 2011.

[10]

A. L. Beutel and S. Minner, Safety stock planning under causal demand forecasting, Int. J. Prod. Econ., vol. 140, no. 2, pp. 637–645, 2012.

[11]

Y. Zhou and X. Zhao, Optimal policies of an inventory system with multiple demand classes, Tsinghua Science and Technology, vol. 15, no. 5, pp. 498–508, 2010.

[12]
T. de Castro Moraes and X. M. Yuan, Data-driven solutions for the newsvendor problem: A systematic literature review, in Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, A. Dolgui, A. Bernard, D. Lemoine, G. von Cieminski, and D. Romero, eds. Cham, Switzerland: Springer International Publishing, 2021, pp. 149–158.
[13]

G. Y. Ban and C. Rudin, The big data newsvendor: Practical insights from machine learning, Oper. Res., vol. 67, no. 1, pp. 90–108, 2019.

[14]

O. Besbes and O. Mouchtaki, How big should your data really be? Data-driven newsvendor: Learning one sample at a time, Manag. Sci., vol. 69, no. 10, pp. 5848–5865, 2023.

[15]

M. Pourakbar, J. B. G. Frenk, and R. Dekker, End-of-life inventory decisions for consumer electronics service parts, Prod. Oper. Manag., vol. 21, no. 5, pp. 889–906, 2012.

[16]

L. Fortuin, The all-time requirement of spare parts for service after sales: Theoretical analysis and practical results, Int. J. Oper. Prod. Manag., vol. 1, no. 1, pp. 59–70, 1980.

[17]

L. Fortuin, Reduction of the all-time requirement for spare parts, Int. J. Oper. Prod. Manag., vol. 2, no. 1, pp. 29–37, 1981.

[18]

S. Van der Auweraer and R. Boute, Forecasting spare part demand using service maintenance information, Int. J. Prod. Econ., vol. 213, pp. 138–149, 2019.

[19]

S. Van der Auweraer, R. N. Boute, and A. A. Syntetos, Forecasting spare part demand with installed base information: A review, Int. J. Forecast., vol. 35, no. 1, pp. 181–196, 2019.

[20]

J. S. Hong, H. Y. Koo, C. S. Lee, and J. Ahn, Forecasting service parts demand for a discontinued product, IIE Trans., vol. 40, no. 7, pp. 640–649, 2008.

[21]

W. Romeijnders, R. Teunter, and W. van Jaarsveld, A two-step method for forecasting spare parts demand using information on component repairs, Eur. J. Oper. Res., vol. 220, no. 2, pp. 386–393, 2012.

[22]

C. A. González Vargas and M. Elizondo Cortés, Automobile spare-parts forecasting: A comparative study of time series methods, Int. J. Automot. Mech. Eng., vol. 14, no. 1, pp. 3898–3912, 2017.

[23]

T. Y. Kim, R. Dekker, and C. Heij, Spare part demand forecasting for consumer goods using installed base information, Comput. Ind. Eng., vol. 103, pp. 201–215, 2017.

[24]

Z. Shi and S. Liu, Optimal inventory control and design refresh selection in managing part obsolescence, Eur. J. Oper. Res., vol. 287, no. 1, pp. 133–144, 2020.

[25]

S. Van der Auweraer, S. Zhu, and R. N. Boute, The value of installed base information for spare part inventory control, Int. J. Prod. Econ., vol. 239, p. 108186, 2021.

[26]

J. B. G. Frenk, S. Javadi, M. Pourakbar, and S. O. Sezer, An exact static solution approach for the service parts end-of-life inventory problem, Eur. J. Oper. Res., vol. 272, no. 2, pp. 496–504, 2019.

[27]

C. Xu, M. Bai, C. Wu, Q. Wang, and Y. Wang, An optimal pricing and ordering policy with trapezoidal-type demand under partial backlogged shortages, Tsinghua Science and Technology, vol. 29, no. 6, pp. 1709–1727, 2024.

[28]

M. Ortega and L. Lin, Control theory applications to the production–inventory problem: A review, Int. J. Prod. Res., vol. 42, no. 11, pp. 2303–2322, 2004.

[29]

H. Sarimveis, P. Patrinos, C. D. Tarantilis, and C. T. Kiranoudis, Dynamic modeling and control of supply chain systems: A review, Comput. Oper. Res., vol. 35, no. 11, pp. 3530–3561, 2008.

[30]
B. Kouvaritakis and M. Cannon, Model Predictive Control: Classical, Robust and Stochastic. Cham, Switzerland: Springer International Publishing, 2016.
[31]

W. Wang, D. E. Rivera, and K. G. Kempf, Model predictive control strategies for supply chain management in semiconductor manufacturing, Int. J. Prod. Econ., vol. 107, no. 1, pp. 56–77, 2007.

[32]

P. Doganis, E. Aggelogiannaki, and H. Sarimveis, A combined model predictive control and time series forecasting framework for production-inventory systems, Int. J. Prod. Res., vol. 46, no. 24, pp. 6841–6853, 2008.

[33]

E. Aggelogiannaki, P. Doganis, and H. Sarimveis, An adaptive model predictive control configuration for production–inventory systems, Int. J. Prod. Econ., vol. 114, no. 1, pp. 165–178, 2008.

[34]

D. A. Álvarez-Rodríguez, J. E. Normey-Rico, and R. C. C. Flesch, Model predictive control for inventory management in biomass manufacturing supply chains, Int. J. Prod. Res., vol. 55, no. 12, pp. 3596–3608, 2017.

[35]

K. Subramanian, J. B. Rawlings, and C. T. Maravelias, Economic model predictive control for inventory management in supply chains, Comput. Chem. Eng., vol. 64, pp. 71–80, 2014.

[36]
B. Ietto and V. Orsini, Effective inventory control in supply chains with large uncertain decay factor using robust model predictive control, in Proc. 30th Mediterranean Conf. Control and Automation (MED), Vouliagmeni, Greece, 2022, pp. 133–138.
[37]

R. Wu and D. Zhao, Lyapunov-based MPC for nonlinear process with online triggered linearised model, Int. J. Autom. Contr., vol. 17, no. 1, pp. 1–18, 2023.

[38]

K. Matsuyama, The multi-period newsboy problem, Eur. J. Oper. Res., vol. 171, no. 1, pp. 170–188, 2006.

[39]

D. Piga, M. Forgione, S. Formentin, and A. Bemporad, Performance-oriented model learning for data-driven MPC design, IEEE Control Syst. Lett., vol. 3, no. 3, pp. 577–582, 2019.

[40]
S. Baluja, Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning, Tech. Rep., Pittsburgh, PA, USA: Carnegie Mellon University, 1994.
Complex System Modeling and Simulation
Pages 1-15
Cite this article:
Fu X, Wu C-g, Fu B, et al. Spare Part Replenishment Strategy for Electronic Product Based on Model Predictive Control. Complex System Modeling and Simulation, 2025, 5(1): 1-15. https://doi.org/10.23919/CSMS.2024.0027
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