Optimization of Warehouse Location and Inventory Management for an Industrial Textile Manufacturer Company in Türkiye

Year 2024, Volume: 13 Issue: 4, 1260 - 1270, 31.12.2024

Tutku Tutkun , İrem Nur Nergiz , Rukiye Kaya , Uğur Satıç

https://doi.org/10.17798/bitlisfen.1549483

Abstract

In this study, we consider the demand forecasting, facility location, and inventory management problems of an industrial textile manufacturer company in Türkiye. First, we begin with the demand forecasting problem for thirty-two different products and employ ABC analysis to categorise the products. Then we test multiple forecasting methods and find out that Exponential Smoothing and Croston's TSB methods perform better in our categories. Using the demand forecast results in the facility location problem, we search for a location in Europe for a warehouse. For the facility location problem, we use a mixed-integer nonlinear mathematical model to minimise the transportation cost, and warehouse rental cost. We solve the model by using GAMS Solver. Then, we handle the inventory management problem and determine the quantity of the products that are sent from the factory and the warehouse to the customer. We propose a genetic algorithm approach that generates reorder quantities and reorder points for both the factory and the warehouse to minimise the total logistics costs, including holding, ordering and stockout costs. We use simulation models to calculate the logistics costs then we use these costs as fitness values to choose the best reorder quantities and reorder points. The proposed approach offers improvement in demand forecasting, inventory management, and facility location problems and brings up a 26% reduction in total logistic costs.

Keywords

Facility Location, Demand Forecasting, Inventory Management, ABC Analysis, Genetic Algorithms

Ethical Statement

The study is complied with research and publication ethics.

References

• [1] Y. Tadayonrad and A. B. Ndiaye, “A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality,” Supply Chain Analytics, vol. 3, Sep. 2023, doi: 10.1016/j.sca.2023.100026.
• [2] M. Abolghasemi, E. Beh, G. Tarr, and R. Gerlach, “Demand forecasting in the supply chain: The impact of demand volatility in the presence of promotion,” Comput Ind Eng, vol. 142, Apr. 2020, doi: 10.1016/j.cie.2020.106380.
• [3] Y. Y. Song and Y. Lu, “Decision tree methods: applications for classification and prediction,” Shanghai Arch Psychiatry, vol. 27, no. 2, pp. 130–135, Apr. 2015, doi: 10.11919/j.issn.1002-0829.215044.
• [4] M. Moayedi and R. Sadeghian, “A multi-objective stochastic programming approach with untrusted suppliers for green supply chain design by uncertain demand, shortage, and transportation costs,” Journal of Cleaner Production, vol. 408, Jul. 2023, doi: 10.1016/j.jclepro.2023.137007.
• [5] B. Santosa and I. G. N. A. Kresna, “Simulated Annealing to Solve Single Stage Capacitated Warehouse Location Problem,” Procedia Manufacturing, Elsevier B.V., 2015, pp. 62–70. doi: 10.1016/j.promfg.2015.11.015.
• [6] E. Szczepański, R. Jachimowski, M. Izdebski, and I. Jacyna-Gołda, “Warehouse location problem in supply chain designing: A simulation analysis,” Archives of Transport, vol. 50, no. 2, pp. 101–110, 2019, doi: 10.5604/01.3001.0013.5752.
• [7] B. Basciftci, S. Ahmed, and S. Shen, “Distributionally robust facility location problem under decision-dependent stochastic demand,” European Journal of Operational Research, vol. 292, no. 2, pp. 548–561, Jul. 2021, doi: 10.1016/j.ejor.2020.11.002.
• [8] M. Kchaou Boujelben, C. Gicquel, and M. Minoux, “A MILP model and heuristic approach for facility location under multiple operational constraints,” Computers & Industrial Engineering, vol. 98, pp. 446–461, Aug. 2016, doi: 10.1016/j.cie.2016.06.022.
• [9] R. Aboolian, O. Berman, and D. Krass, “Optimizing facility location and design,” European Journal of Operational Research, vol. 289, no. 1, pp. 31–43, Feb. 2021, doi: 10.1016/j.ejor.2020.06.044.
• [10] C. Y. Lo, “Advance of Dynamic Production-Inventory Strategy for Multiple Policies Using Genetic Algorithm” Information Technology Journal, vol. 7, pp. 647-653, 2008, doi: 10.3923/itj.2008.647.653
• [11] M. Z. Babai, A. Syntetos, and R. Teunter, “Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence,” International Journal of Production Economics, Elsevier B.V., 2014, pp. 212–219. doi: 10.1016/j.ijpe.2014.08.019.
• [12] W. Hernandez and G. A. Suer, "Genetic algorithms in lot sizing decisions" Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, DC, USA, 1999, vol. 3, pp. 2280-2286 doi: 10.1109/CEC.1999.785558.
• [13] S. H. R. Pasandideh, S. T. A. Niaki, and A.R. Nia, “A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model”, Expert Systems with Applications, vol. 38, no. 3, pp. 2708-2716, 2011, doi: 10.1016/j.eswa.2010.08.060.
• [14] M. Mahjoob, S. S. Fazeli, S. Milanlouei, L. S. Tavassoli, and M. Mirmozaffari, “A modified adaptive genetic algorithm for multi-product multi-period inventory routing problem,” Sustainable Operations and Computers, vol. 3, pp. 1–9, Jan. 2022, doi: 10.1016/j.susoc.2021.08.002.
• [15] G. H. de Paula Vidal, R. G. G. Caiado, L. F. Scavarda, P. Ivson, and J. A. Garza-Reyes, “Decision support framework for inventory management combining fuzzy multicriteria methods, genetic algorithm, and artificial neural network,” Computers & Industrial Engineering, vol. 174, Dec. 2022, doi: 10.1016/j.cie.2022.108777.
• [16] Alfawaer Zeyad M., Proceedings, 2020 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA): Epoka University, Albania: partially held online as a live interactive virtual conference, 9th-10th December, 2020. IEEE, 2020.
• [17] S. Dutta, H. Shah, and P. A. Dasari, K. Singal, N. Y. Harikeerthi, Y. R. Talakola, “Optimizing Inventory Through Abc Classification and Demand Forecasting.” Proceedings of the International Annual Conference of the American Society for Engineering Management. American Society for Engineering Management (ASEM), 2017.
• [18] J. Fattah, L. Ezzine, Z. Aman, H. El Moussami, and A. Lachhab, “Forecasting of demand using ARIMA model,” International Journal of Engineering Business Management, vol. 10, Oct. 2018, doi: 10.1177/1847979018808673.
• [19] N. N. Chau, “Intermittent Demand Forecasting for Inventory Control: The Impact of Temporal and Cross-sectional Aggregation,” May 2020.
• [20] J. M. Rožanec, B. Fortuna, and D. Mladenić, “Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand,” Sustainability (Switzerland), vol. 14, no. 15, Aug. 2022, doi: 10.3390/su14159295.
• [21] K. C. So and X. Zheng, “Impact of supplier’s lead time and forecast demand updating on retailer’s order quantity variability in a two-level supply chain,” Int J Prod Econ, vol. 86, no. 2, pp. 169–179, Nov. 2003, doi: 10.1016/S0925-5273(03)00050-1.
• [22] I. Samuel, T. Ojewola, A. Awelewa, and P. Amaize, “Short-Term Load Forecasting Using The Time Series And Artificial Neural Network Methods,” IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), vol. 11, no. 1, pp. 72–81, doi: 10.9790/1676-11137281.
• [23] D. SAATÇIOĞLU, “Yapay Sinir Ağlari Yöntemi ile Aralikli Talep Tahmini,” Beykoz Akademi Dergisi, vol. 4, no. 1, pp. 1–32, Apr. 2016, doi: 10.14514/byk.m.21478082.2016.4/1.1-32.
• [24] J. E. H. Branco, D. H. Branco, E. M. de Aguiar, J. V. Caixeta Filho, and L. Rodrigues, “Study of optimal locations for new sugarcane mills in Brazil: Application of a MINLP network equilibrium model,” Biomass Bioenergy, vol. 127, Aug. 2019, doi: 10.1016/j.biombioe.2019.05.018.
• [25] A. C. Türkmen, T. Januschowski, Y. Wang, and A. T. Cemgil, “Intermittent Demand Forecasting with Renewal Processes” ArXiv, 2020. Accessed November 10, 2024. https://arxiv.org/abs/2010.01550.
• [26] J. Feizabadi. “Machine learning demand forecasting and supply chain performance” International Journal of Logistics Research and Applications, vol. 25, no. 2, pp. 119–142, April 2020, doi: 10.1080/13675567.2020.1803246