Document Type : Research Paper
Authors
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract
Keywords
Main Subjects
Axsäter, S. (2003). Evaluation of unidirectional lateral transshipments and substitutions in inventory systems. European Journal of Operational Research, 149(2), 438–447.
Babai, M. Z., Ali, M. M., & Nikolopoulos, K. (2012). Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis. Omega, 40(6), 713–721. https://doi.org/10.1016/j.omega.2011.09.004
Babai, M. Z., Boylan, J. E., & Rostami-Tabar, B. (2021). Demand forecasting in supply chains: A review of aggregation and hierarchical approaches. International Journal of Production Research, 1–25.
Babai, M. Z., Dallery, Y., Boubaker, S., & Kalai, R. (2019). A new method to forecast intermittent demand in the presence of inventory obsolescence. International Journal of Production Economics, 209, 30–41.
Basten, R. J., Van der Heijden, M. C., Schutten, J. M., & Kutanoglu, E. (2015). An approximate approach for the joint problem of level of repair analysis and spare parts stocking. Annals of Operations Research, 224(1), Article 1.
Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. In Das summa summarum des management (pp. 265–275). Springer.
Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289–303.
DeLurgio, S. A. (1998). Forecasting principles and applications. McGraw-Hill/Irwin.
Dodin, P., Xiao, J., Adulyasak, Y., Etebari Alamdari, N., Gauthier, L., Grangier, P., Lemaitre, P., & Hamilton, W. (2021). Bombardier Aftermarket Demand Forecast with Machine Learning. Available at SSRN 3957452.
Driessen, M. A. (2018). Integrated capacity planning and inventory control for repairable spare parts.
Fan, L., Liu, X., Mao, W., Yang, K., & Song, Z. (2023). Spare Parts Demand Forecasting Method Based on Intermittent Feature Adaptation. Entropy, 25(5), 764.
Farhadi, M. (2013). Developing a model for maximizing the availability considering the redundancy. https://ganj-old.irandoc.ac.ir/articles/588615
Goltsos, T. E., Syntetos, A. A., Glock, C. H., & Ioannou, G. (2022). Inventory – forecasting: Mind the gap. European Journal of Operational Research, 299(2), 397–419. https://doi.org/10.1016/j.ejor.2021.07.040
Gross, D., & Ince, J. F. (1978). Spares Provisioning for Repairable Items: Cyclic Queues in Light Traffic. A I I E Transactions, 10(3), 307–314. https://doi.org/10.1080/05695557808975219
Jain, S., & Raghavan, N. R. S. (2009). A queuing approach for inventory planning with batch ordering in multi-echelon supply chains. Central European Journal of Operations Research, 17(1), 95–110. https://doi.org/10.1007/s10100-008-0077-8
Keizer, M. C. A. O., Teunter, R. H., & Veldman, J. (2017). Joint condition-based maintenance and inventory optimization for systems with multiple components. European Journal of Operational Research, 257(1), 209–222. https://doi.org/10.1016/j.ejor.2016.07.047
Lapide, L. (1998). New developments in business forecasting. The Journal of Business Forecasting, 17(1), 21.
Li, L., Kang, Y., Petropoulos, F., & Li, F. (2023). Feature-based intermittent demand forecast combinations: Accuracy and inventory implications. International Journal of Production Research, 61(22), 7557–7572. https://doi.org/10.1080/00207543.2022.2153941
Marfuah, U., Panudju, A. T., & Mansyuri, U. (2023). Dynamic Programming Approach in Aggregate Production Planning Model under Uncertainty. International Journal of Advanced Computer Science and Applications, 14(3). https://www.researchgate.net/profile/Umi-Marfuah/publication/369824095_Dynamic_Programming_Approach_in_Aggregate_Production_Planning_Model_under_Uncertainty/links/642e3bd74e83cd0e2f93fda9/Dynamic-Programming-Approach-in-Aggregate-Production-Planning-Model-under-Uncertainty.pdf
Mircetic, D., Rostami-Tabar, B., Nikolicic, S., & Maslaric, M. (2022). Forecasting hierarchical time series in supply chains: An empirical investigation. International Journal of Production Research, 60(8), 2514–2533.
Moini, G., Teimoury, E., Seyedhosseini, S. M., Radfar, R., & Alborzi, M. (2021). A forward-reverse repairable spare part network considering inventory management. International Journal of Industrial Engineering and Production Research, 32(4), 1–23.
Nikolopoulos, K., Syntetos, A. A., Boylan, J. E., Petropoulos, F., & Assimakopoulos, V. (2011). An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: An empirical proposition and analysis. Journal of the Operational Research Society, 62(3), 544–554. https://doi.org/10.1057/jors.2010.32
Oey, E., Wijaya, W. A., & Hansopaheluwakan, S. (2020). Forecasting and aggregate planning application – a case study of a small enterprise in Indonesia. International Journal of Process Management and Benchmarking, 10(1), 1–21. https://doi.org/10.1504/IJPMB.2020.104229
Pinçe, Ç., Turrini, L., & Meissner, J. (2021). Intermittent demand forecasting for spare parts: A Critical review. Omega, 105, 102513. https://doi.org/10.1016/j.omega.2021.102513
Punia, S., & Shankar, S. (2022). Predictive analytics for demand forecasting: A deep learning-based decision support system. Knowledge-Based Systems, 258, 109956.
Sanguri, K., Patra, S., Nikolopoulos, K., & Punia, S. (2023). Intermittent demand, inventory obsolescence, and temporal aggregation forecasts. International Journal of Production Research, 1–23. https://doi.org/10.1080/00207543.2023.2199435
Sarlo, R., Fernandes, C., & Borenstein, D. (2023). Lumpy and intermittent retail demand forecasts with score-driven models. European Journal of Operational Research, 307(3), 1146–1160. https://doi.org/10.1016/j.ejor.2022.10.006
Setiawan, I., Nurdiansyah, N., Tosin, M., Lusia, V., & Wahid, M. (2022). Aggregate Planning Implementation for Planning and Controlling the Materials in the Beverage Packaging Industry. Spektrum Industri, 20(1), 91–100.
Sherbrooke, C. C. (1968). METRIC: A multi-echelon technique for recoverable item control. Operations Research, 16(1), Article 1.
Sousa, M., Tomé, A. M., & Moreira, J. (2022). Long-term forecasting of hourly retail customer flow on intermittent time series with multiple seasonality. Data Science and Management, 5(3), 137–148. https://doi.org/10.1016/j.dsm.2022.07.002
Syntetos, A. A., & Boylan, J. E. (2005). The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303–314.
van Jaarsveld, W., Dollevoet, T., & Dekker, R. (2015). Improving spare parts inventory control at a repair shop. Omega, 57, 217–229.
Walker, J. (1997). Base stock level determination for “insurance type” spares. International Journal of Quality & Reliability Management, 14(6), 569–574.
Willemain, T. R., Smart, C. N., & Schwarz, H. F. (2004). A new approach to forecasting intermittent demand for service parts inventories. International Journal of Forecasting, 20(3), 375–387.