An Integrated Framework for Inventory Management Considering Demand and Supply Uncertainties
DOI:
https://doi.org/10.26713/cma.v15i2.2545Keywords:
Inventory aggregation, Distribution network, Multi-criteria decision modeling, Supply uncertainties, Demand uncertainties, TOPSISAbstract
Due to uncertainties on both the demand and supply sides, managing inventories is the most critical and cost-effective challenge faced by supply chain managers in the supply chain network. While inventory management problems with uncertain demand and supply have been covered in the supply chain management literature for a long time, it is fair to say that no one approach can be applied universally across all scenarios. This paper presents an integrated inventory management framework considering non-linear optimization and multi-criteria decisionmaking tools. First, it presents a non-linear model to minimize the total inventory management cost at a centralized location (e.g., distribution center) by considering multiple inventory aggregation scenarios. A series of sensitivity analyses are conducted to examine the impact of demand and supply uncertainties on various key performance indicators related to inventory operations. Lastly, the best inventory aggregation model is selected by using a multi-criterion-based TOPSIS (Technique for order performance by similarity to ideal solution)) analysis.
Downloads
References
P. Agrawal, Effect of uncertain and turbulent environment on organizational design, Economics & Business Journal: Inquires & Perspectives 5(1) (2014), 11 – 24, URL: https://nebeconandbus.org/journal/EBJIP2014v5n1Agrawal-p11.pdf.
E. Arikan and L. Silbermayr, Risk pooling via unidirectional inventory transshipments in a decentralized supply chain, International Journal of Production Research 56(17) (2017), 5593 – 5610, DOI: 10.1080/00207543.2017.1394586.
J. R. T. Arnold, S. N. Chapman and L. M. Clive, Introduction to Materials Management, 6th edition, Pearson Prentice Hall, 515 pages (2008).
S. M. Belenson and K. C. Kapur, An algorithm for solving multicriterion linear programming problems with examples, Journal of the Operational Research Society 24(1) (1973), 65 – 77, DOI: 10.1057/jors.1973.9.
F. Bernstein, G. A. DeCroix and Y. Wang, The impact of demand aggregation through delayed component allocation in an assemble-to-order system, Management Science 57(6) (2011), 1154 – 1171, URL: https://www.jstor.org/stable/25835765.
B. Brunaud, J. M. Laínez-Aguirre, J. M. Pinto and I. E. Grossmann, Inventory policies and safety stock optimization for supply chain planning, AIChE Journal 65(1) (2018), 99 – 112, DOI: 10.1002/aic.16421.
P.-L. Chang and C.-T. Lin, On the effect of centralization on expected costs in a multi-location newsboy problem, Journal of the Operational Research Society 42(11) (1991), 1025 – 1030, DOI: 10.1057/jors.1991.193.
C. J. Charles and K. Rajaram, A generalization of the inventory pooling effect to nonnormal dependent demand, Manufacturing & Services Operation Management 8(4) (2006), 351 – 358, DOI: 10.1287/msom.1060.0117.
O. Cosma, P. Pop and C. Sabo, Solving the two-stage supply chain network design problem with risk-pooling and lead times by an efficient genetic algorithm, in: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020), Advances in Intelligent Systems and Computing series, Vol. 1268, Á. Herrero, C. Cambra, D. Urda, J. Sedano, H. Quintián and E. Corchado (editors), Springer, Cham., DOI: 10.1007/978-3-030-57802-2_49.
C. Edirisinghe and D. Atkins, Lower bounding inventory allocations for risk pooling in twoechelon supply chains, International Journal of Production Economics 187 (2017), 159 – 167, DOI: 10.1016/j.ijpe.2017.02.015.
G. D. Eppen, Note–Effects of centralization on expected costs in a multi-location newsboy problem, Management Science 25(5) (1979), 413 – 501, DOI: 10.1287/mnsc.25.5.498.
A. Eynan and T. Fouque, Benefiting from the risk-pooling effect: internal (component commonality) vs. external (demand reshape) efforts, International Journal of Services and Operations Management 1(1) (2005), 90 – 99, DOI: 10.1504/IJSOM.2005.006320.
S. Gaur and A. R. Ravindran, A bi-criteria model for the inventory aggregation problem under risk pooling, Computers & Industrial Engineering 51(1) (2006), 482 – 501, DOI: 10.1016/j.cie.2006.08.009.
R. N. Giri, S. K. Kumar and M. Maiti, Joint ordering inventory policy for deteriorating substitute products with price and stock dependent demand, International Journal of Industrial Engineering 27(1) (2020), 58 – 71, DOI: 10.23055/ijietap.2020.27.1.3413.
N. Hu, J.-Y. (Fisher) Ke, L. Liu and Y. Zhang, Risk pooling, supply chain hierarchy, and analysts’ forecasts, Production and Operations Management 28(2) (2019), 276 – 291, DOI: 10.1111/poms.12904.
K. Inderfurth, Safety stock optimization in multi-stage inventory systems, International Journal of Production Economics 24(1-2) (1991), 103 – 113, DOI: 10.1016/0925-5273(91)90157-O.
M. Monenikiyai, S. Ebrahiminejad and B. Vahdani, A bi-objective mathematical model for inventory-distribution-routing problem under risk pooling effect: Robust metaheuristic approach, Economic Computation and Economic Cybernetics Study and Research 52(4) (2018), 257 – 274, DOI: 10.24818/18423264/52.4.18.17.
P. Oeser and P. Romano, Exploring risk pooling in hospitals to reduce demand and lead time uncertainty, Operations Management Research 14 (2021), 78 – 94, DOI: 10.1007/s12063-020-00171-y.
M. S. Puga and J.-S. Tancrez, A heuristic algorithm for solving large location–inventory problems with demand uncertainty, European Journal of Operational Research 259(2) (2017), 413 – 423, DOI: 10.1016/j.ejor.2016.10.037.
D. S. Putman, The scope of risk pooling, in: Selected Presentation at the 2020 Agricultural & Applied Economics Association Annual Meeting (Kanas City, Missouri, July 26-28, 2020), AgEcon Search, 44 pages, DOI: 10.22004/ag.econ.304480.
S. Routroy and R. Kodali, Differential evolution algorithm for supply chain inventory planning, Journal of Manufacturing Technology Management 16(1) (2005), 7 – 17, DOI: 10.1108/17410380510574059.
F. Salimi and B. Vahdani, Designing a bio-fuel network considering links reliability and riskpooling effect in bio-refineries, Reliability Engineering & System Safety 174 (2018), 96 – 107, DOI: 10.1016/j.ress.2018.02.020.
A. J. Schmitt, S. A. Sun, L. V. Synder and Z.-J. Shen, Centralization versus decentralization: Risk pooling, risk diversification, and supply chain disruptions, Omega 52 (2015), 201 – 212, DOI: 10.1016/j.omega.2014.06.002.
D. Singh, H. Singh and K. Singh, Distinct and joint price approaches for multi-layer, multi-channel selling price by manufacturer, Journal of Optimization and Supply Chain Management 1(1) (2024), 50 – 62, DOI: 10.22034/ISS.2024.7892.1010.
D. Singh, A. Jayswal, M. G. Alharbi and A. A. Shaikh, An investigation of a supply chain model for co-ordination of finished products and raw materials in a production system under different situations, Sustainability 13(22) (2021), 12601, DOI: 10.3390/su132212601.
K. Singh, K. C. Sharma and D. Singh, An analysis of the supply chain model for production system under selling price demand rate, controllable deterioration rate with several market demand, Communications in Mathematics and Applications 14(2) (2023), 845 – 860, DOI: 10.26713/cma.v14i2.2216.
K. Singh, K. C. Sharma, D. Singh and Hariom, An integrated two-layer supply chain inventory with declining and price sensitive demand for deteriorating items, International Journal of Research and Analytical Reviews 9(4) (2022), 496 – 506, DOI: 10.1729/Journal.32484.
L. V. Synder, M. Daskin and C.-P. Teo, The stochastic location model with risk pooling, European Journal of Operations Research 179(3) (2007), 1221 – 1238, DOI: 10.1016/j.ejor.2005.03.076.
G. Tagaras, Effects of pooling on the optimization and service levels of two-location inventory systems, IIE Transactions 21(3) (1989), 250 – 257, DOI: 10.1080/07408178908966229.
G. Tagaras, Pooling in multi-location periodic inventory distribution systems, Omega 27(1) (1999), 39 – 59, DOI: 10.1016/S0305-0483(98)00030-9.
E. B. Trikoalaee, J. Mahmoodkhami, M. R. Baurani and R. T. Moghaddam, A selflearning particle swarm optimization for robust multi-echelon capacitated location-allocationinventory problem, Journal of Advanced Manufacturing System 18(4) (2019), 677 – 694, DOI: 10.1142/S0219686719500355.
P. Vats, G. Soni and A. P. S. Rathore, A review of multi-objective inventory control problem, International Journal of Intelligent Enterprises 5(3) (2018), 213 – 230, DOI: 10.1504/IJIE.2018.093396.
P. Vats, G. Soni, A. P. S. Rathore and S. P. Prakash, A demand aggregation approach for inventory control in two echelon supply chain under uncertainty, OPSEARCH 56 (2019), 840 – 868, DOI: 10.1007/s12597-019-00389-w.
B. M. Vishkaei, S. T. A. Niaki, E. Khorram and M. Farhangi, A bi-objective inventory model to minimize cost and stock out time under backorder shortages and screening, International Journal of Industrial Engineering 26(5) (2019), 707 – 718, DOI: 10.23055/ijietap.2019.26.5.3431.
C. A. Weber, J. R. Current and W. C. Benton, Vendor selection criteria and methods, European Journal of Operational Research 50(1) (1991), 2 – 18, DOI: 10.1016/0377-2217(91)90033-R.
Z. K. Weng, Risk-pooling over demand uncertainty in the presence of product modularity, International Journal of Production Economics 62(1-2) (1999), 75 – 85, DOI: 10.1016/S0925-5273(98)00226-6.
K. Xu and P. T. Evers, Managing single echelon inventories through demand aggregation and the feasibility of a correlation matrix, Computers & Operations Research 30(2) (2003), 297 – 308, DOI: 10.1016/S0305-0548(01)00097-1.
K. P. Yoon and C.-H. Hwang, Multiple Attribute Decision Making: An Introduction, Sage Publications, Inc., (1995), DOI: 10.4135/9781412985161.
F. You and I. E. Grossman, Balancing responsiveness and economics in process supply chain design with multi-echelon stochastic inventory, AIChE Journal 57(1) (2009), 178 – 192, DOI: 10.1002/aic.12244.
M. Zeleny, A concept of compromise solutions and the method of the displaced ideal, Computers & Operations Research 1(3-4) (1974), 479 – 496, DOI: 10.1016/0305-0548(74)90064-1.
Y. Zhang, G. Hua, T.C.E. Cheng, J. Zhang and V. Fernandez, Risk pooling through physical probabilistic selling, International Journal of Production Economics 219 (2020), 295 – 311, DOI: 10.1016/j.ijpe.2019.04.014.
C. L. Hwang and K. Yoon, Methods for multiple attribute decision making, in: Multiple Attribute Decision Making, Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer, Berlin — Heidelberg (1981), DOI: 10.1007/978-3-642-48318-9_3.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a CCAL that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.