Journals / Journal of Turkish Operations Management / 2022 / Cilt: 6 - Sayı: 2

An Inventory Optimization Model for a Textile Manufacturing Company

Pages
1252–1262
Publication Date
2022-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results shows that the company in concern can improve its total production and inventory costs by 3.8 percent.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Inventory management is a crucial issue in most businesses from factories in industry to small and large organizations in the production or service sector.
📊
Key Findings & Data:A company should determine an optimum inventory level between excessive inventory that takes up physical space, costs much, and lack of inventory which disrupts the supply chain causing unavailability of the product that makes customers change their idea and buy from another supplier.
💡
Conclusion & Impact:The results shows that the company in concern can improve its total production and inventory costs by 3.8 percent.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Inventory Management · Optimization · Mix Integer Programming

Abstract

Inventory management is a crucial issue in most businesses from factories in industry to small and large organizations in the production or service sector. A company should determine an optimum inventory level between excessive inventory that takes up physical space, costs much, and lack of inventory which disrupts the supply chain causing unavailability of the product that makes customers change their idea and buy from another supplier. In this study, we first classify the items that the company manufactures by using ABC analysis and develop a mathematical model to minimize total cost to enable a better inventory management. We use ABC analysis method to evaluate products in a textile company in terms of importance and to track these products according to their priorities. Accordingly, we propose a mixed integer programming model to determine production quantities and inventory levels with minimum cost. The results shows that the company in concern can improve its total production and inventory costs by 3.8 percent.

🔖

Cite This Article

PINAR, A., UTKU, D. H., KASIMOĞLU, F. (2022). An Inventory Optimization Model for a Textile Manufacturing Company. Journal of Turkish Operations Management, Cilt: 6 - Sayı: 2, 1252–1262. https://ajindex.com/articles/869434

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Addy-Tayie, N. (2012). Improving Warehouse and Inventory Management: Operational Efficiency and Transport Safety Alawneh, F., & Zhang, G. (2018). Dual-channel warehouse and inventory management with stochastic demand. Transportation Research Part E: Logistics and Transportation Review, 112, 84-106.
  2. Ancarani, A., Di Mauro, C., & D’Urso, D. (2016). Measuring overconfidence in inventory management decisions. Journal of Purchasing and Supply Management, 22(3), 171-180.
  3. Armour, G. C., & Buffa, E. S. (1963). A heuristic algorithm and simulation approach to relative location of facilities. Management science, 9(2), 294-309.
  4. Balcik, B., Bozkir, C. D. C., & Kundakcioglu, O. E. (2016). A literature review on inventory management in humanitarian supply chains. Surveys in Operations Research and Management Science, 21(2), 101-116.
  5. Chandra, S., Christiansen, M., & Fagerholt, K. (2016). Combined fleet deployment and inventory management in roll-on/roll-off shipping. Transportation Research Part E: Logistics and Transportation Review, 92, 43-55.
  6. Corporation, G. D. (2010). General Algebraic Modeling System (GAMS). In (Version 23.5.1) Dabiri, N., Tarokh, M. J., & Alinaghian, M. (2017). New mathematical model for the bi-objective inventory routing problem with a step cost function: A multi-objective particle swarm optimization solution approach. Applied Mathematical Modelling, 49, 302-318.
  7. Dai, Z., Aqlan, F., & Gao, K. (2017). Optimizing multi-echelon inventory with three types of demand in supply chain. Transportation Research Part E: Logistics and Transportation Review, 107, 141-177.
  8. Ehrhardt, R. (1979). The power approximation for computing (s, S) inventory policies. Management science, 25(8), 777-786.
  9. Eissa, M., & Rashed, E. (2020). Application of statistical process optimization tools in inventory management of goods quality: suppliers evaluation in healthcare facility. Journal of Turkish Operations Management, 4(1), 388-408.
  10. Fiestras-Janeiro, M. G., García-Jurado, I., Meca, A., & Mosquera, M. A. (2011). Cooperative game theory and inventory management. European Journal of Operational Research, 210(3), 459-466. García-Alvarado, M., Paquet, M., Chaabane, A., & Amodeo, L. (2017). Inventory management under joint product recovery and cap-and-trade constraints. Journal of cleaner Production, 167, 1499-1517.
  11. Herron, D. (1976). Industrial engineering applications of ABC curves. AIIE Transactions, 8(2), 210-218.
  12. Janssen, F., Heuts, R., & de Kok, T. (1999). The impact of data