Optimization of Inventory Inflation Budget Based on Spare-parts and Miscellaneous Costs of a Typical Automobile Industry
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Abstract
Brainstorming has been a common approach in many industries where the result is not always accurate, especially when procuring automobile spare parts. This approach was replaced with a scientific and optimized method that is highly reliable, hence the decision to optimize the inventory inflation budget based on spare parts and miscellaneous costs of the typical automobile industry. Some factors required to achieve this goal were investigated. Through this investigation, spare parts (consumables and non-consumables) were found to be mostly used in Innoson Vehicle Manufacturing (IVM), Nigeria but incorporated miscellaneous costs to augment the cost of spare parts. The inflation rate was considered first due to the market's price increase. Different types of vehicles were used to implement the Non-preemptive goal programming model and to predict the cost of procurement of the spare parts and miscellaneous and the profit for the current year. The result proved that the solution did not fully achieve the goals since the objective function is not equal to zero, but deviations for going below the profit goal and above the cost of procurement goal were significantly minimized.
Article received: 22/08/2022
Article accepted: 18/10/2022
Article published: 01/05/2023
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References
Chakrabarti, S., 2022. Financial aspects of project engineering. Project Engineering Primer for Chemical Engineers. Springer, Singapore. Doi:10.1007/978-981-19-0660-2_5.
Chung, W., 2015. Applying large-scale linear programming in business analytics. IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1860-1864. doi:10.1109/IEEM.2015.7385970
Clemen, R.T. and Reilly, T., 2016. Correlations and copulas for decision and risk analysis”. Management Science, 45, 2016, pp. 208–224. doi:10.1287/mnsc.45.2.208
Coetzee, J., 2004. Maintenance, Trafford Publishing, Canada.
Den Boer, J., Lambrechts, W., and Krikke, H., 2020. Additive manufacturing in military and humanitarian missions: advantages and challenges in the spare parts supply chain. J. Clean. Prod. 257, 120301. doi:10.1016/j.jclepro.2020.120301
Diaz, C.A.B., Fathi, M., Aslam, T., and Amos, H.C., 2021. Optimizing reconfigurable manufacturing systems: a simulation-based multi-objective optimization approach”. 54th CIRP Conference on Manufacturing Systems. Elsevier, Procedia CIRP 104, 1837-1842. doi:10.1016/j.procir.2021.11.310
Eberhard, L., 2022. Operation, maintenance, and turnarounds. Life Cycle of a Process Plant, chapter 8, pp. 157-186, Elsevier. Doi:10.1016/B978-0-12-813598-3.00006-9.
Eze, O., 2020. The budget, policy and the automobile sector. www.//punchng.com/the-budget-policy-and-the-automobile-sector/. 2020.
Frandsen, C. S., Nielsen, M. M., Chaudhuri, A., Jayaram, J., Govindan, K., 2019. In search for classification and selection of spare parts suitable for additive manufacturing: a literature review. Int. J. Prod. Res. 58, 970-996. doi:10.1080/00207543.2019.1605226
Gershon, K., 2015. ATS Budget Manual (ATS Server)”.
Giliyana, S. A., and Kalaiarasan, R., 2015. Maintenance strategy according to the Professional Maintenance methodology as part of World Class Manufacturing, Mälardalen University, Sweden.
Keisler, J., 2004. Value of information in portfolio decision analysis. Decision analysis, 1(3), pp.177-189. . doi:10.1287/deca.1040.0023
Kumar, P.P., Vinodkumar, O. and Yugandhar, T., 2018. An optimization techniques on the managerial decision making. International Journal of Mechanical and Production Engineering Research and Development, 8(6), pp.507-516. doi:10.24247/ijmperddec201854
Maku, A.O. and Adelowokan, O.A., 2013. Dynamics of inflation in Nigeria: An autoregressive approach. European Journal of Humanities and social sciences, 22(1), pp.1175-1184.
Nielsen, H. B., 2006. Uctp problems for unconstrained optimization. Technical Report, Technical University of Denmark.
Ojo, O.O., Farayibi, P.K. and Akinnuli, B.O., 2020. Modified goal programming model for limited available budget a location for equipment procurement under inflation condition. Adv. Res, 21(4), pp.25-35. Doi:10.9734/AIR/2020/v21i430198
Orumie, U.C. and Ebong, D.W., 2011. An alternative method of solving goal programming problem. Nigerian Journal of Operations Research, 2, pp.68-90.
Osueke, C.O., Akinnuli, B.O., and Ojo, O.O., 2015. Modeling equipment procurement strategic decisions competing for limited available budget under redundant accessory cost. Engineering Management Research, 4(2), pp 80.
Özdamar, L. and Yazgaç, T., 2016. A hierarchical planning approach for a production distribution system. International Journal of Production Research, 37, pp. 3759–3772.
Raouf, O.A., and Hezam, I.M., 2017. Sperm motility algorithm: a novel metaheuristic approach for global optimisation. International Journal of Operational Research, 28(2), pp. 143.
Ugwueze, M.I., Ezeibe, C.C., and Onuoha, J.I., 2020. The political economy of automobile development in Nigeria. Review of African Political Economy, 2020, pp. 1-11.
Unekwe, C., Ekechukwu, B., and Nwokoye, H., 2012. Model Development for Auto Spare Parts Inventory Control and Management. West African Journal of Industrial and Academic Research, 5(1), 2012.
Zhang, S., Huang, K., Yuan, Y., 2021. Spare parts inventory management: A literature review. Sustainability, 13, 2460. Doi:10.3390/su13052460.
Zuerl, K., 2022. Cost break down and cost structure analysis. in: effective cost cutting in Asia. Management for Professionals. Springer, Cham. Doi:10.1007/978-3-030-82782-3_6.