Home / Current Issue / Paper 1709461
Optimization Technique to Minimize In-Process Inventory Costs in Multi-Stage Electric Cable Production Environment
Subject area: Science,Engineering and Technology · Area of research: Manufacturing
Abstract
Manufacturing firms are involved in an organized formal effort to manage manufacturing resources as well as uncertain associated costs prevalent in their inventory control policies. This uncertainty is faced by multi-product manufacturing organizations in their in-process inventory cost decision making. Realizing the significance of this inherent uncertainty, the in-process inventory costs ( Ip) of a cable and wire industry located in the southern part of Nigeria where three products (Coaxial cable, Twin-axial cable and Core cable) are produced in multi-stage were investigated for a period of six months. The company?s analytical method which it has been adopting to reduce in-process inventory costs was first utilized. The obtained results indicate that the average in-process cost (Ip) and the total process cost (Tc) within the time under review were twenty-six million, seven hundred and two thousand naira (?26.72m) and thirty-five million, four hundred and two million naira (?35.42m) respectively. To optimize the in- process inventory cost and control policies of the manufacturing company, a mathematical model is derived in this research and solved using Lingo software (15.0 version). The optimal solution result indicated a cost reduction from twenty-six million, seven hundred and two thousand naira (?26.72m) to thirty-five million, four hundred and two million naira( ?23.85m) which accounts for 8% cost effectiveness for the firm. This has demonstrated the capability of the proposed model in handling in-process inventory cost optimization for similar companies.
Keywords
Multi-Product, In-Process Cost, Inventory Management, Control Policy, Cost Optimization
References
[1] Davis R. P and Kennedy W. J. (2013). Markovian modeling of manufacturing system. International journal of production Research 23: 327-336.
[2] Funk J. L. (2019). A comparison of inventory cost reduction strategies in a JIT manufacturing system. International Journal of production Research 27 (7):1965-1980.
[3] Groover J. (2007). A two echelon Inventory model with lost sales. International Journal of Production Economics 69: 307-315.
[4] Hackman S. T and Reachman R. C. ( 2017). A general frame work for modeling production management science 35:478-495.
[5] Kim B., Xung F., Pung K.H and Kiy S. (2010) Extended model for a Hybrid production planning approach. International Journal of production Economics 13:165-173.
[6] Kiy U. S . and Kim B. (2011) Capacity loading and Release planning with work-in-progress (WIP) and lead times. Journal of manufacturing and operations management 2:105-123.
[7] Lang R J. ( 2010). Principles of inventory and material management, 4 th Edition.
[8] Liu C., Yao D. and Kim R. (2016). Analysis and optimization of a multistage inventory-queue system. Management science 30:365-380.
[9] Portues E.I., Sipper D and Shapira R. (2002). Just in-time and work in progress. A trade-off analysis-international Journal of production Research 27 (6) 903-914.
[10] Sarker B. R. (2012). Optimal manufacturing and delivery schedules in a supply chain system of deteriorating items. International Journal of production research. 40:260-274.
[11] Swamidas A. E. ()2015) Modeling and analysis of multi-product, multi-stage production concepts. A review working paper school of Business studies, university of Vaasa, Vaasa.
[12] Uzorh A.C. Nnanna I. and Ezeaku I.I. (2016). In-process inventory versus Echelon stock policies for multi-level inventory control. Research Report, linkoping Institute of Technology, linkoping.
[13] Zhang. X and Gerchack V. (2017). Joint lot sizing and inspection policy in an E and Q model with random yield. 11 E 22:41-47.
How to cite this paper
@article{1709461,
author = {Ezeaku I. I, Nwadinobi C. P, Nwachukwu U. C, Nwakwuruibe V. C},
title = {Optimization Technique to Minimize In-Process Inventory Costs in Multi-Stage Electric Cable Production Environment},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {1},
pages = {82-90},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709461.pdf},
abstract = {Manufacturing firms are involved in an organized formal effort to manage manufacturing resources as well as uncertain associated costs prevalent in their inventory control policies. This uncertainty is faced by multi-product manufacturing organizations in their in-process inventory cost decision making. Realizing the significance of this inherent uncertainty, the in-process inventory costs ( Ip) of a cable and wire industry located in the southern part of Nigeria where three products (Coaxial cable, Twin-axial cable and Core cable) are produced in multi-stage were investigated for a period of six months. The company?s analytical method which it has been adopting to reduce in-process inventory costs was first utilized. The obtained results indicate that the average in-process cost (Ip) and the total process cost (Tc) within the time under review were twenty-six million, seven hundred and two thousand naira (?26.72m) and thirty-five million, four hundred and two million naira (?35.42m) respectively. To optimize the in- process inventory cost and control policies of the manufacturing company, a mathematical model is derived in this research and solved using Lingo software (15.0 version). The optimal solution result indicated a cost reduction from twenty-six million, seven hundred and two thousand naira (?26.72m) to thirty-five million, four hundred and two million naira( ?23.85m) which accounts for 8% cost effectiveness for the firm. This has demonstrated the capability of the proposed model in handling in-process inventory cost optimization for similar companies.},
keywords = {Multi-Product, In-Process Cost, Inventory Management, Control Policy, Cost Optimization },
month = {July},
}