Design of Economic Order Quantity on Polyester Yarn Raw Material Based on Artificial Neural Network Forecasting
Abstract
Polyester yarn is a key raw material in textile manufacturing due to its durability and affordability. PT ABC relies on external suppliers for polyester yarn, making inventory management crucial for production efficiency. However, the company's current ordering approach has led to occasional stock shortages, impacting operations. This study develops an inventory control model using the Economic Order Quantity (EOQ) method, incorporating safety stock and reorder point calculations to minimize stockouts and reduce inventory costs. Additionally, Artificial Neural Networks (ANN) are used to forecast demand for 2022, improving estimation accuracy. Based on historical demand data from 2019 to 2021, the EOQ method lowers inventory costs compared to the company’s approach, achieving efficiency gains of 19%, 12%, and 29%, saving IDR45,745,000, IDR23,735,000, and IDR98,020,000, for each respective year. The ANN model utilizing the TrainLM training function achieves the lowest Mean Squared Error (MSE) of 0.063528 and forecasts a total raw material requirement of 2,510,628 kg for 2022. The EOQ value for 2022 is set at 44,817 kg, with safety stock and reorder point levels of 8,438 kg and 29,360 kg, respectively.
Keywords – Artificial Neural Network, Economic Order Quantity, Inventory, Reorder Point, Safety Stock.
Full Text:
PDFReferences
V. Dahiwale and P. B. Sangode, “a Comparative Study of the Inventory Management Tools of Textile Manufacturing Firms,” Impact, vol. 7, no. 4, pp. 335–344, 2019.
K. P. Kumari, S. Jagadeeswaran, and K. Prabha Kumari, “A Study on Inventory Control Techniques in Apparel Industry-tea College of Knitwear Fashion, Tirupur,” Int. J. Res. Rev., vol. 5, no. 2, p. 61, 2018.
H. K. Smith, “Inventory Management for Entrepreneurs : Balancing Costs , Efficiency , and Customer Satisfaction,” 2024.
L. Sanchez-Ruiz, B. Blanco, and A. Kyguolienė, “A Theoretical Overview of the Stockout Problem in Retail: from Causes to Consequences,” Manag. Organ. Syst. Res., vol. 79, no. 1, pp. 103–116, 2018.
A. K. Banikya, P. Ranade, and I. K. Verma, “Managing Deadstock in the Fashion and Apparel Industry: An Exploration of Causes, Solutions and Technological Interventions,” p. 9, 2024.
M. Bertolini, G. Ferretti, G. Vignali, and A. Volpi, “Reducing Out of Stock, Shrinkage and Overstock through RFID in The Fresh Food Supply Chain: Evidence from an Italian Retail Pilot,” Int. J. RF Technol. Res. Appl., vol. 4, no. 2, pp. 107–125, 2013.
G. Mweshi, “Effects of Overstocking and Stockouts on the Manufacturing Sector,” Int. J. Adv. Eng. Manag., vol. 4, no. September, p. 1054, 2022.
S. Murairwa, “Perishable Economic Order Quantity with A Short Receipt Lag Framework for Perishable Economic Order Quantity with A Short Receipt Lag Framework for MSMES In Zimbabwe,” J. Bus. Econom. Stud., vol. 2, no. 1, p. 10, 2025.
K. P. Mubiru and M. N. Ssempijja, “A Stochastic Goal-Based Economic Order Quantity Model for Dairy Products,” J. Appl. Res. Technol. Eng., vol. 5, no. 2, pp. 45–52, 2024.
R. Hidayat and F. D. Sitania, “Soybean Inventory Management at Gesit Tahu Factory Using the Economic Order Quantity ( EOQ ) Method,” Indones. J. Ind. Eng. Manag., vol. 5, no. 2, pp. 477–486, 2024.
W. Olabiyi, “Integrated Solutions for Repair and Waste Disposal in Economic Order Quantity Models : A Systems Approach Author : Winner Olabiyi Date : 26 th , Sep 2024,” 2024.
N. Nurhasanah et al., “Analysis of Demand Forecasting in Upstream and Midstream Supply Chain Network of Sunflower Agro-industry Using Artificial Neural Networks,” in ICAISD 2024 - International Conference on Advanced Information Scientific Development: AI for Investing the Sustainability Development of Human Living Digitally, Proceedings, 2024, no. 1, pp. 41–46.
F. Dalponte Voigt, R. Antonio, and F. Machado, Development of an Articial Neural Network to Maximize The Reproducibility of Dyeing Polyamide Fabrics. Santa Catarina, Brazil: Universidade Federal de Santa Catarina, 2024.
S. Swaroop, D. K. Chaturvedi, and S. Mani, “Forecasting Model of Export of Indian ‘Textile Upper Footwear’ Commodities using Deep Learning Neural network,” Int. J. Footwear Sci. Eng., vol. 3, no. 1, pp. 1–18, 2025.
M. P. Sikka, A. Sarkar, and S. Garg, “Artificial intelligence (AI) in textile industry operational modernization,” Res. J. Text. Appar., vol. 28, no. 1, pp. 67–83, 2024.
K. Yılmaz, İ. Ö. Aksu, M. Göçken, and T. Demirdelen, “Sustainable Textile Manufacturing with Revolutionizing Textile Dyeing: Deep Learning-Based, for Energy Efficiency and Environmental-Impact Reduction, Pioneering Green Practices for a Sustainable Future,” Sustain., vol. 16, no. 18, p. 23, 2024.
V. A. Chepurko and A. N. Chernyaev, “Method of Estimating the Size of an SPTA with a Safety Stock,” Dependability, vol. 21, no. 3, pp. 13–19, 2021.
F. Yiğit, “A Novel Type-2 Hexagonal Fuzzy Logic Approach for Predictive Safety Stock Management for a Distribution Business,” Nature Publishing Group UK, 2023.
A. F. Ridwan and Muhammad Ahsan, “Penentuan Reorder Point dan Safety Stock pada Consumable Material Berdasarkan Peramalan Menggunakan Artificial Neural Network,” J. Rekayasa Ind., vol. 6, no. 1, pp. 51–61, 2024.
S. H. Belnov, M. Arifin, and A. Martina, “Perencanaan Pengadaan Spare Packing Dan Seal Pada Perusahaan XYZ Menggunakan Metode Reorder Point,” J. Mhs. Dirgant., vol. 3, no. 1, pp. 18–24, 2024.
A. Allmon, L. Willman, K. Butler, and S. Khorasani, “Inventory Control and Optimization of Reorder Point: a Case Study,” in Proceedings of the American Society for Engineering Management 2023 International Annual Conference and 44th Annual Meeting: Climbing Higher with Engineering Management, 2023, no. March, pp. 494–499.
R. J. Tersine, Principles of inventory and materials management, Fourth edi. New Jersey: Prentice-Hall International, 1994.
D. Swargam, M. Natte, D. A. Javvadi, V. K. C. Aray, V. R. S. Rachuri, and S. R. Veguru, “Power Demand Forecasting Using ANN and Prophet Models for the Load Despatch Center in Andhra Pradesh, India,” Int. J. Soft Comput. Eng., vol. 14, no. 1, pp. 1–8, 2024.
S. Tjen, L. Gozali, H. J. Kristina, A. Gunadi, and A. P. Irawan, “Demand Forecasting Using Time Series and ANN with Inventory Control to Reduce Bullwhip Effect on Home Appliances Electronics Distributors,” Lect. Notes Data Eng. Commun. Technol., vol. 166, no. May, pp. 285–300, 2023.
S. Taniguchi et al., “Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan,” Rice, vol. 18, no. 1, p. 16, 2025.
M. Menssen and J. Rathjens, “Prediction Intervals for Overdispersed Binomial Endpoints and their Application to Historical Control Data,” 2024.
R. Isaev and G. Gimaletdinova, “Stock Market Prediction and Forecasting Using Historical Data,” no. January, 2025.
N. Rahayu and H. Mustafidah, “Perbandingan Ketepatan Pola Data pada Jaringan Backpropagation Berdasarkan Metode Pembobotan Random dan Nguyen Widrow,” Sainteks, vol. 19, no. 1, p. 27, 2022.
R. Ramadana, A. P. Windarto, and D. Suhendro, “Comparative Analysis of Gradient Descent Learning Algorithms in Artificial Neural Networks for Forecasting Indonesian Rice Prices,” J. Resti, vol. 5, no. 2, pp. 466–478, 2024.
N. Nurhasanah, C. A. Nurpraja, S. I. P. S. Himawan, and A. Ramadanti, Metode Peramalan Deret Waktu: Teori dan Aplikasinya. KBM Indonesia, 2022.
H. Boche, V. Pohl, H. V. Poor, and L. Fellow, “Characterization of the Complexity of Computing the Minimum Mean Square Error of Causal Prediction,” in IEEE Transaction on Information Theory, 2024, pp. 1–13.
DOI: http://dx.doi.org/10.36722/sst.v10i2.4105
Refbacks
- There are currently no refbacks.
LP2M (Lembaga Penelitian dan Pengembangan Masyarakat)
Universitas AL-AZHAR INDONESIA, Lt.2 Ruang 207
Kompleks Masjid Agung Al Azhar
Jl. Sisingamangaraja, Kebayoran Baru
Jakarta Selatan 12110
This work is licensed under CC BY 4.0
