Inventory Control of Products at the End of their Lifecycle Based on Nonparametric Methods

Simple item page
dc.contributor.authorHuskova, Katerina
dc.contributor.authorDyntar, Jakub
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2023-09-27T12:40:56Z
dc.date.available2023-09-27T12:40:56Z
dc.description.abstractIn this article, we verify that the use of the past stock movement simulation with all combination search, where both control variables are fully discretized, compared to traditional parametric methods, which are often used in management of inventory with sporadic demand, brings economic savings in area of holding and ordering costs. We use sporadic demand data coming from a small size e-commerce company to compare the best achieved holding and ordering costs in continuous review fixed order quantity inventory control policy where the reorder point calculation is based on moving average and linear regression. At the same time, we examine how the results are affected by the required fill rate of service level, which we test for four levels in the interval 25 % - 95 %. The results of our experiments show that AC outperforms traditional parametric methods in achieving the best holding and ordering costs. Moreover, as the level of required service level decreases, the success of AC in achieving the best costs increases. Simultaneously, we see that the success of the simulation increases with increasing variability of demand, i.e. in the case when the differences in quantity between individual non-zero demands increase.en
dc.formattext
dc.identifier.doi10.15240/tul/009/lef-2023-07
dc.identifier.isbn978-80-7494-627-1
dc.identifier.urihttps://dspace.tul.cz/handle/15240/172823
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.isbasedonBABAI, M. Z., A. TSADIRAS and C. PAPADOPOULOS (2020). On the Empirical Performance of Some New Neural Network Methods for Forecasting Intermittent Demand. IMA Journal of Management Mathematics, 2020, 31(3), 281-305. https://doi.org/10.1093/imaman/dpaa003
dc.relation.isbasedonBAJEGANI, H. Z. and M. R. GHOLAMIAN (2020). Optimal Inventory Control of Obsolete Products with Price-Dependent Demand. Journal of Engineering Research, 2020, 8(4), 169-184. https://doi.org/10.36909/jer.v8i4.8316
dc.relation.isbasedonCROSTON, J. D. (1972). Forecasting and Stock Control for Intermittent Demands, Operational Research Quarterly, 1972, 23(3), 289-303. https://doi.org/10.2307/3007885
dc.relation.isbasedonDYNTAR, J. and E. KEMROVA (2011). Efficiency of Past Stock Movement Simulation in Intermittent Demand Stock Control. Scientific Papers of the University of Pardubice. Series D., 2011, 21(3), 31-42.
dc.relation.isbasedonELSAYED, K. and H. WAHBA (2016). Reexamining the Relationship Between Inventory Management and Firm Performance: An Organizational Life Cycle Perspective. Future Business Journal, 2016, 2(1), 65-80. https://doi.org/10.1016/j.fbj.2016.05.001
dc.relation.isbasedonHUSKOVA, K. and J. DYNTAR (2022). Increasing Efficiency in Inventory Control of Products with Sporadic Demand Using Simulation. Acta Informatica Pragensia, 2022, 11(2), 254-264. https://doi.org/10.18267/j.aip.184
dc.relation.isbasedonHUSKOVA, K. and J. DYNTAR (2023). Speeding Up Past Stock Movement Simulation in Sporadic Demand Inventory Control. International Journal of Simulation Modelling, 2023, 22(1), 41-51. https://doi.org/10.2507/ijsimm22-1-627
dc.relation.isbasedonJIANG, Y., C. SHI and S. SHEN. (2019). Service Level Constrained Inventory Systems. Production and Operations Management, 2019, 28(9), 2365-2389. https://doi.org/10.2139/ssrn.2818044
dc.relation.isbasedonLEVÉN, E. and A. SEGERSTEDT (2004). Inventory Control with a Modified Croston Procedure and Erlang Distribution. International Journal of Production Economics, 2004, 90(3), 361-367. https://doi.org/10.1016/s0925-5273(03)00053-7
dc.relation.isbasedonMOUSAVI, A., M. MOHAMMADZADEH and H. ZARE (2022). Developing a System Dynamic Model for Product Life Cycle Management of Generic Pharmaceutical Products: Its Relation with Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 2022, 8(1), 1-14. https://doi.org/10.3390/joitmc8010014
dc.relation.isbasedonPINÇE, Ç., L. TURRINI and J. MEISSNER (2021). Intermittent Demand Forecasting for Spare Parts: A critical review. Omega, 2021, 105, 1-30. https://doi.org/10.1016/j.omega.2021.102513
dc.relation.isbasedonPORRAS, E. and R. DEKKER (2008). An Inventory Control System for Spare Parts at a Refinery: An Empirical Comparison of Different Reorder Point Methods. European Journal of Operational Research, 2008, 184(1), 101-132. https://doi.org/10.1016/j.ejor.2006.11.008
dc.relation.isbasedonPOURHEJAZY, P. (2020). Destruction Decisions for Managing Excess Inventory in E-Commerce Logistics. Sustainability, 2020, 12(20), 8365. https://doi.org/10.3390/su12208365
dc.relation.isbasedonRINALDI, M., M. FERA, R. MACCHIAROLI and E. BOTTANI (2023). A New Procedure for Spare Parts Inventory Management in ETO Production: A Case Study. Procedia Computer Science, 2023, 217, 376-385. https://doi.org/10.1016/j.procs.2022.12.233
dc.relation.isbasedonSYNTETOS, A. A. and J. E. BOYLAN (2005). The Accuracy of Intermittent Demand Estimates. International Journal of Forecasting, 2005, 21(2), 303-314. https://doi.org/10.1016/j.ijforecast.2004.10.001
dc.relation.isbasedonSYNTETOS, A. A., J. E. BOYLAN and J. D. CROSTON (2005). On the Categorization of Demand Patterns. Journal of the Operational Research Society, 2005, 56(5), 495-503. https://doi.org/10.1057/palgrave.jors.2601841
dc.relation.isbasedonTEUNTER, R. H. and L. DUNCAN (2009). Forecasting Intermittent Demand: A Comparative Study. Journal of the Operational Research Society, 2009, 60(3), 321-329. https://doi.org/10.1057/palgrave.jors.2602569
dc.relation.isbasedonTEUNTER, R. H., A. A. SYNTETOS and M. Z. BABAI (2011). Intermittent Demand: Linking Forecasting to Inventory Obsolescence. European Journal of Operational Research, 2011, 214(3), 606-615. https://doi.org/10.1016/j.ejor.2011.05.018
dc.relation.isbasedonWILLEMAIN, T. R., C. N. SMART and H. F. SCHWARZ (2004). A New Approach to Forecasting Intermittent Demand for Service Parts Inventories. International Journal of Forecasting, 2004, 20(3), 375-387. https://doi.org/10.1016/s0169-2070(03)00013-x
dc.relation.ispartofLiberecké ekonomické fórum 2023cs
dc.relation.ispartofLiberec Economic Forum 2023en
dc.subjectlogisticsen
dc.subjectinventory controlen
dc.subjectsporadic demanden
dc.subjectnonparametric methodsen
dc.subjectsimulationen
dc.subject.classificationM21
dc.subject.classificationC63
dc.titleInventory Control of Products at the End of their Lifecycle Based on Nonparametric Methodsen
dc.typeproceeding paperen
local.accessopen
local.citation.epage67
local.citation.spage60
local.facultyFaculty of Economics
local.fulltextyes
local.relation.abbreviationLEFcs
local.relation.abbreviationLEFen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
LEF_2023_07.pdf
Size:
500.01 KB
Format:
Adobe Portable Document Format
Description:
conference paper
Download
Collections
LEF 2023