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Tytuł artykułu

Raw material management for rubber parts manufacturing supply chain using the LPSC model

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The paper proposes a new raw material cost management concept in the rubber parts supply chain using the Linear Programming Sharing Cost (LPSC). Rubber parts are widely used in the auto parts industry. There are a variety of product models and material types as well as produced by several subcontractors in the 2nd Tier companies. However, the whole chain controls overall cost management as the 1st Tier company. Cost-effective models are more important in the competitive era. Design/methodology/approach: The research methodology combined linear programming (LP) with sharing cost (SC) and was applied to inventory management. The LPSC model is developed to deal with Tier 1 companies linked with Tier 2 companies of the subcontractors. The LPSC is combined with the EOQ inventory management model. Findings: A new approach has been developed to reduce the cost of raw material management in the rubber part supply chain. Research limitations/implications: The limitation of the model development is that it does not yet have real-time control of the supply chain management system. Practical implications: The conceptual idea was introduced to Thailand's automotive rubber part supply chain and accepted for testing with the pilot test of actual orders. Originality/value: The paper presents the new conceptual raw material cost management model in the automotive rubber part supply chain. The LPSC combined with EOQ is effective, increases value to the supply chain, and simultaneously reduces waste and overproduction.
Rocznik
Strony
25--32
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Hospitality, Faculty of Liberal Arts, Rajamangala University of Technology Krungthep, 2 Nanlinji Road Tungmahamek, Sathorn, Bangkok, 10120, Thailand
autor
  • Division of Digital Startup, Faculty of Business Administration, Rajamangala University of Technology Krungthep, 2 Nanlinji Road Tungmahamek, Sathorn, Bangkok, 10120, Thailand
autor
  • Department of Business Administration, Northeastern University, 199/19 Mittraphap Road, Mueang Khon Kaen, Khon Kaen, 40000, Thailand
Bibliografia
  • [1] W. Xu, D.-P. Sone, Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies, Operational Research 22 (2022) 2343-2371. DOI: https://doi.org/10.1007/s12351-020-00609-y
  • [2] A. Ates, P. Garengo, P. Cocca, U. Bititci, The development of SME managerial practice for effective performance management, Journal of Small Business and Enterprise Development 20/1 (2013) 28-54. DOI: https://doi.org/10.1108/14626001311298402
  • [3] J. Korpysa, Entrepreneurial management of SMEs, Procedia Computer Science 176 (2020) 3466-3475. DOI: https://doi.org/10.1016/j.procs.2020.09.050
  • [4] H. Frank, D. Roessl, Problematization and conceptualization of “entrepreneurial SME Management” as a field of research: overcoming the size-based approach, Review of Managerial Science 9/2 (2015) 225-240. DOI: https://doi.org/10.1007/s11846-014-0154-4
  • [5] R. Brooksbank, Defining the small business: a new classification of company size, Entrepreneurship and Regional Development 3/1 (1991) 17-31. DOI: https://doi.org/10.1080/08985629100000002
  • [6] P. Flores, E. Segura, R. Jaramillo, L. Ulcuango, L. Suárez, Micro-enterprise Management Towards Scenario Building for Decision Making, in: M. Botto- Tobar, O.S. Gómez, R. Rosero Miranda, A. Díaz Cadena, W. Luna-Encalada (eds), Trends in Artificial Intelligence and Computer Engineering. ICAETT 2022. Lecture Notes in Networks and Systems, vol. 619, Springer, Cham, 2023, 575-584. DOI: https://doi.org/10.1007/978-3-031-25942-5_45
  • [7] C.K. Riemenschneider, D.A. Harrison, P.P. Mykytyn, Understanding it adoption decisions in small business: integrating current theories, Information and Management 40/4 (2003) 269-285. DOI: https://doi.org/10.1016/S0378-7206(02)00010-1
  • [8] R.F. Ogarca, An Investigation of Decision-Making Styles in SMEs from South-West Oltenia Region (Romania), Procedia Economics and Finance 20 (2015) 443-452. DOI: https://doi.org/10.1016/S2212-5671(15)00095-7
  • [9] A. Hauser, F. Eggers, S. Güldenberg, Strategic decision-making in SMEs: effectuation, causation, and the absence of strategy, Small Business Economics 54/3 (2020) 775-790. DOI: https://doi.org/10.1007/s11187-019-00152-x
  • [10] G. Kuechle, B. Boulu-Reshef, S.D. Carr, Prediction- and Control-Based Strategies in Entrepreneurship: The Role of Information, Strategic Entrepreneurship Journal 10/1 (2016) 43-64. DOI: https://doi.org/10.1002/sej.1211
  • [11] P. Greenbank, Micro‐business start‐ups: challenging normative decision making?, Marketing Intelligence and Planning 18/4 (2000) 206-212. DOI: https://doi.org/10.1108/02634500010333415
  • [12] A. Stachowiak, P. Niewiadomski, N. Pawlak, Quantitative analysis of raw material used in manufacturing process of parts and subassemblies of agricultural machinery in the aspect of leaning the organization, Research in Logistics and Production 6/2 (2016) 129-139. DOI: https://doi.org/10.21008/J.2083-4950.2016.6.2.3
  • [13] M.A.R. Al-Shboul, K.D. Barber, J.A. Garza-Reyes, V. Kumar, M.R. Abdi, The effect of supply chain management practices on supply chain and manufacturing firms' performance, Journal of Manufacturing Technology Management 28/5 (2017) 577-609. DOI: https://doi.org/10.1108/JMTM-11- 2016-0154
  • [14] V.P. Kaliani Sundram, V. Chandran, M. Awais Bhatti, Supply chain practices and performance: the indirect effects of supply chain integration, Benchmarking: An International Journal 23/6 (2016) 1445-1471. DOI: https://doi.org/10.1108/BIJ-03-2015-0023
  • [15] A. Khanuja, R.K. Jain, The conceptual framework on integrated flexibility: an evolution to data-driven supply chain management, The TQM Journal 35/1 (2023) 131-152. DOI: https://doi.org/10.1108/TQM- 03-2020-0045
  • [16] S. Butdee, K. Tangchaidee, Neuro fuzzy based for prediction quality of a rubber curing process on a compression machine under uncertainty circumstances, Materials Today Proceedings 26/2 (2020) 2953-2960. DOI: https://doi.org/10.1016/j.matpr.2020.02.610
  • [17] G. Krzesniak, Effective data usage for the proper and beneficial automotive production cost improvement, Journal of Achievements in Materials and Manufacturing Engineering 119/1 (2023) 27-34. DOI: https://doi.org/10.5604/01.3001.0053.8696
  • [18] E. Jonda, T. Karkoszka, K. Jonda, Recycling materials database as a Green Kaizen for sustainable development in the automotive industry, Journal of Achievements in Materials and Manufacturing Engineering 120/1 (2023) 33-41. DOI: https://doi.org/10.5604/01.3001.0053.9645
  • [19] N. Abramczyk, D. Żuk, A. Czech, A.Charchalis, Using statistical analysis to assess the impact of the addition of rubber recyclate on the strength properties of the epoxy-glass composite, Journal of Achievements in Materials and Manufacturing Engineering 121/1 (2023) 77-92. DOI: https://doi.org/10.5604/01.3001.0054.3208
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cc553cfa-b4f0-4472-b80a-e70f78a9a242
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