Forecasting Rebar Product Sales Using a Linear Regression Model: A Case Study of the Bar Rolling Mill at the Libyan Iron and Steel Company (LISCO)
DOI:
https://doi.org/10.65405/.v10i37.703Keywords:
Linear Regression, Sales Forecasting, Rebar Products, Libyan Iron and Steel Company.Abstract
This the study investigates the use of linear regression to forecast rebar sales for the Libyan Iron and Steel Company from November 2023 to January 2025. Employing a descriptive analytical and case study approach, 12 months of sales data were analyzed using Excel and statistical methods. Results indicated the linear model was statistically significant (F=4.9253, p=0.05074) but with only a medium correlation (R=0.5745). The model explained 33% of variance, with a high standard error of 7666.63, suggesting low predictive accuracy. Recommendations include: adding more independent variables, exploring alternative models (multiple regression, time series), expanding data samples, and conducting market studies to improve forecasting and optimize sales strategies. These efforts are to improve sales forecasting.
Downloads
References
[1] M. A. Shahout and A. Q. Shtewan, “Forecasting Sales Volume Using the Multiple Linear Regression Method,” Technical Research Journal, vol. 1, no. 1, 2023.
[2] M. Sitompul, “Forecasting Simcard Demand Using Linear Regression Method,” IT Journal Research and Development (ITJRD), pp. 48-60, 2023. E-ISSN: 2528-4053, P-ISSN: 2528-4061.
[3] A. Awainia, “Forecasting Sales Volume Using Autoregressive Models, A Case Study of FERPHOS Tebessa - in the Period 2002-2022,” Master's thesis, University of Muhammad Al-Sharif Al-Musaidia, 2022.
[4] M. Al-Hallak, “Demand forecasting for sales and raw materials using linear and non-linear time series, a case study of Terco Company for food manufacturing,” Master's thesis, Higher Institute of Business Administration, 2021.
[5] F. Farizal, Y. Qaradhawi, C. I. Cornelis, and M. Dachyar, “Fast Moving Product Demand Forecasting Model with Multi Linear Regression,” AIP Conference Proceedings, vol. 2227, pp. 040028-1 to 040028-6, 2020. https://doi.org/10.1063/5.0001031.
[6] O. O. Ade-Ikuesan, “Linear Regression Long-Term Energy Demand Forecast Modeling in Ogun State Nigeria,” J. Appl. Sci. Environ. Manage, vol. 23, no. 4, pp. 753-757, 2019.
[7] S. Sengupta, “Comparative Study of Demand Forecast Accuracy for Healthcare Products Using Linear and Non Linear Regression,” Int. J. Business Manag. Invention, vol. 3, no. 5, pp. 1-10, May 2014.
[8] J. M. Al-Gamal, “A study of the causes of increased consumption of rolling tools at the rod rolling mill at the Libyan Iron and Steel Company,” M.S. thesis, Eng. Project Manag., Libyan Academy, Misurata, Libya, 2014.
[9] A. Badar, “The Comparison between Time Series Model and Simple Regression Model in Forecasting Sales in an Economic Institution: A Case Study of the Al-Hadna Mills in M'sila,” M.S. thesis, Bus. Admin., Muhammad Boudiaf Univ., 2006.
[10] A. K. Mohsen and S. M. Al-Najjar, “Production and Operations Management”, 3rd ed. Baghdad: Al-Dhakira Library, 2009.
[11] A. Y. Al-Barbar, “Market demand forecasting,” M.S. thesis, Dept. Bus. Admin., Faculty of Commerce, Damietta Univ., 2020.
[12] S. Abidat and M. A. Salem, “Production Operations Management”. Cairo: Arab United Co. for Marketing and Supplies, 2013.
[13] Local Marketing Department, Libyan Iron and Steel Company, “Sales Reports for the products of the rod rolling mill,” 2023-2024.
[14] Libyan Iron and Steel Company. Available: http://libyansteel.com/lisco
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
