In modern business today, organizations that hold large numbers of inventory items, do not find it economical to make policies for the management of individual inventory items. Managers, thus, need to classify these items according to their importance and fit each item to a certain asset class. The method of grouping and inventory control available in traditional ABC has several disadvantages. These shortcomings have led to the development of an optimization model in the present study to improve the grouping and inventory control decisions in ABC. Moreover, it simultaneously optimizes the existing business relationships among revenue, investment in inventory and customer satisfaction (through service levels) as well as a company’s budget for inventory costs. In this paper, a mathematical model is presented to classify inventory items, taking into account significant profit and cost reduction indices. The model has an objective function to maximize the net profit of items in stock. Limitations such as budget even inventory shortages are taken into account too. The mathematical model is solved by the Benders decomposition and the Lagrange relaxation algorithms. Then, the results of the two solutions are compared. The TOPSIS technique and statistical tests are used to evaluate and compare the proposed solutions with one another and to choose the best one. Subsequently, several sensitivity analyses are performed on the model, which helps inventory control managers determine the effect of inventory management costs on optimal decision making and item grouping. Finally, according to the results of evaluating the efficiency of the proposed model and the solution method, a real-world case study is conducted on the ceramic tile industry. Based on the proposed approach, several managerial perspectives are gained on optimal inventory grouping and item control strategies.
@article{RO_2021__55_4_2309_0,
author = {Abdolazimi, Omid and Shishebori, Davood and Goodarzian, Fariba and Ghasemi, Peiman and Appolloni, Andrea},
title = {Designing a new mathematical model based on {ABC} analysis for inventory control problem: {A} real case study},
journal = {RAIRO. Operations Research},
pages = {2309--2335},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {4},
doi = {10.1051/ro/2021104},
mrnumber = {4293498},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2021104/}
}
TY - JOUR AU - Abdolazimi, Omid AU - Shishebori, Davood AU - Goodarzian, Fariba AU - Ghasemi, Peiman AU - Appolloni, Andrea TI - Designing a new mathematical model based on ABC analysis for inventory control problem: A real case study JO - RAIRO. Operations Research PY - 2021 SP - 2309 EP - 2335 VL - 55 IS - 4 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2021104/ DO - 10.1051/ro/2021104 LA - en ID - RO_2021__55_4_2309_0 ER -
%0 Journal Article %A Abdolazimi, Omid %A Shishebori, Davood %A Goodarzian, Fariba %A Ghasemi, Peiman %A Appolloni, Andrea %T Designing a new mathematical model based on ABC analysis for inventory control problem: A real case study %J RAIRO. Operations Research %D 2021 %P 2309-2335 %V 55 %N 4 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2021104/ %R 10.1051/ro/2021104 %G en %F RO_2021__55_4_2309_0
Abdolazimi, Omid; Shishebori, Davood; Goodarzian, Fariba; Ghasemi, Peiman; Appolloni, Andrea. Designing a new mathematical model based on ABC analysis for inventory control problem: A real case study. RAIRO. Operations Research, Tome 55 (2021) no. 4, pp. 2309-2335. doi: 10.1051/ro/2021104
[1] and , Designing a multi-objective supply chain model for the oil indus-try in conditions of uncertainty and solving it by meta-heuristic algorithms (2020).
[2] , , and , A comparison of solution methods for the multi-objective closed loop supply chains. Adv. Ind. Eng. 54 (2020) 75–98.
[3] , , and , Robust design of a multi-objective closed-loop supply chain by integrating on-time delivery cost and environmental aspects case study of a Tire Factory. J. Cleaner Prod. 264 (2020) 121566. | DOI
[4] , and , Design of a closed supply chain with regards to the social and environmental impacts under uncertainty (2020).
[5] , and , Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods. Neural Comput. Appl. 33 (2021) 6641–6656. | DOI
[6] and , A location-inventory-pricing model in a supply chain distribution network with price-sensitive demands and inventory-capacity constraints. Transp. Res. Part E: Logistics Transp. Rev. 82 (2015) 238–255. | DOI
[7] , and , Optimal inventory policy. Econ.: J Econ. Soc. 19 (1951) 250–272. | MR | Zbl
[8] and , Decentralized decomposition algorithms for peer-to-peer linear optimization. RAIRO:OR 54 (2020) 1835–1861. | MR | Numdam | DOI
[9] , Partitioning procedures for solving mixed-variables programming problems. Comput. Manage. Sci. 2 (2005) 3–19. | MR | Zbl | DOI
[10] , Multi-item inventory aggregation into groups. J. Oper. Res. Soc. 32 (1981) 19–26. | MR | Zbl | DOI
[11] , , and , A case-based distance model for multiple criteria ABC analysis. Comput. Oper. Res. 35 (2008) 776–796. | Zbl | DOI
[12] , , and , An Introduction to Efficiency and Productivity Analysis. Springer Science & Business Media (2005).
[13] and , Multi-item classification and generic inventory stock contr. Prod. Inventory Manage. J. 29 (1988) 6.
[14] and , A new model for multi-criteria ABC inventory classification: PROAFTN method. In: KES (2016) 550–559.
[15] , and , An improved Lagrangian relaxation-based heuristic for a joint location-inventory problem. Comput. Oper. Res. 61 (2015) 170–178. | MR | DOI
[16] , Note – An early classic misplaced: Ford W. Harris’s economic order quantity model of 1915. Manage. Sci. 35 (1989) 898–900. | DOI
[17] , The Lagrangian relaxation method for solving integer programming problems. Manage. Sci. 50 (2004) 1861–1871. | DOI
[18] and , Implementing multiple criteria ABC analysis. J. Oper. Manage. 7 (1987) 79–85. | DOI
[19] and , Multicriteria inventory classification using a genetic algorithm. Eur. J. Oper. Res. 105 (1998) 29–37. | Zbl | DOI
[20] , An improvement to multiple criteria ABC inventory classification. Eur. J. Oper. Res. 201 (2010) 962–965. | Zbl | DOI
[21] , and , Logic-based benders decomposition algorithm for contamination detection problem in water networks. Comput. Oper. Res. 115 (2020) 104840. | MR | DOI
[22] and , Methods for multiple attribute decision making. In: Multiple Attribute Decision Making. Springer, Berlin Heidelberg (1981) 58–191. | DOI
[23] , , and , Enhanced lagrange decomposition for multi-objective scalable TE in SDN. Comput. Netw. 167 (2020) 106992. | DOI
[24] and , ABC analysis and product quality of manufacturing firms in nigeria. J. Manage. Inf. Decision Sci. 24 (2021) 1–9.
[25] , and , Learning criteria weights with TOPSIS method and continuous VNS for multi-criteria inventory classification. Electron. Notes Discrete Math. 47 (2015) 197–204. | MR | DOI
[26] and , Inventory control in a two-level supply chain with risk pooling effect. Int. J. Prod. Econ. 135 (2012) 116–124. | DOI
[27] , and , Scalable algorithm for the dynamic reconfiguration of the distribution network using the Lagrange relaxation approach. Int. J. Electr. Power Energy Syst. 94 (2018) 188–202. | DOI
[28] and , A Benders decomposition algorithm for the order fulfilment problem of an e-tailer with a self-owned logistics system. Transp. Res. Part E: Logistics Transp. Rev. 122 (2019) 463–480. | DOI
[29] , , and , A parallel Lagrange algorithm for order acceptance and scheduling in cluster supply chains. Knowl.-Based Syst. 143 (2018) 271–283. | DOI
[30] , , and , A classification approach based on the outranking model for multiple criteria ABC analysis. Omega 61 (2016) 19–34. | DOI
[31] , , and , A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem. Expert Syst. Appl. 81 (2017) 12–21. | DOI
[32] , , and , An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem. J. Cleaner Prod. 235 (2019) 1499–1514. | DOI
[33] , The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality. Ann. Probab. 18 (1990) 1269–1283. | MR | Zbl | DOI
[34] , Integrating ABC analysis and rough set theory to control the inventories of distributor in the supply chain of auto spare parts. Comput. Ind. Eng. 139 (2020) 105673. | DOI
[35] , and , Optimizing ABC inventory grouping decisions. Int. J. Prod. Econ. 148 (2014) 71–80. | DOI
[36] and , Optimal network-level traffic signal control: a benders decomposition-based solution algorithm. Transp. Res. Part B: Method. 121 (2019) 252–274. | DOI
[37] , and , A Benders decomposition approach for a real case supply chain network design with capacity acquisition and transporter planning: wheat distribution network. Ann. Oper. Res. 291 (2020) 685–705. | MR | DOI
[38] and , Using the analytic hierarchy process for ABC analysis. Int. J. Oper. Prod. Manage. 13 (1993) 29–44. | DOI
[39] Report: World military spending tops 1T in 2004. USA Today. 7 June 2005. Archived from the original on 7 May 2012. Retrieved 6 April 2008. “World Military Spending Soars’’. CBS news channel. 9 June 2004.
[40] , and , An accelerated Benders decomposition algorithm for reliable facility location problems in multi-echelon networks. Comput. Ind. Eng. 124 (2018) 523–534. | DOI
[41] , and , Inventory Management and Production Planning and Scheduling. Wiley, New York 3 (1998) 30.
[42] , and , Profit based unit commitment for GENCOs using Lagrange Relaxation-Differential Evolution. Eng. Sci. Technol. Int. J. 20 (2017) 738–747.
[43] , and , ABC classification: service levels and inventory costs. Prod. Oper. Manage. 19 (2010) 343–352. | DOI
[44] and , A multiple objective particle swarm optimization approach for inventory classification. Int. J. Prod. Econ. 114 (2008) 656–666. | DOI
[45] U.S. Bureau of Economic Analysis (BEA) – bea.gov Home Page.
[46] , and , Analysis and comparison of two strategies for multi-item inventory systems with joint replenishment costs. Eur. J. Ope. Res. 59 (1992) 405–412. | Zbl | DOI
[47] , and , A Lagrange decomposition-based branch and bound algorithm for the optimal mapping of cloud virtual machines. Eur. J. Oper. Res. 276 (2019) 28–39. | MR | DOI
[48] , and , Green supply chain network design considering chain-to-chain competition on price and carbon emission. Comput. Ind. Eng. 145 (2020) 106503. | DOI
[49] and , Closed-loop supply chain network design integrated with assembly and disassembly line balancing under uncertainty: an enhanced decomposition approach. Int. J. Prod. Res. 59 (2021) 2690–2707. | DOI
[50] , Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Syst. App. 38 (2011) 3416–3421. | DOI
[51] , and , Exact algorithms based on Benders decomposition for multi-commodity uncapacitated fixed-charge network design. Comput. Oper. Res. 111 (2019) 311–324. | MR | Zbl | DOI
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