IJBTT

Improving Demand Analysis and Supply Chain Management for Hair Products During the COVID-19 Pandemic: A Machine Learning Approach

Improving Demand Analysis and Supply Chain Management for Hair Products During the COVID-19 Pandemic: A Machine Learning Approach
	© 2023 by IJETT Journal
	Volume-71 Issue-9
	Year of Publication : 2023
	Author : Midori Kato-Yoshida, Ivone Mosquera-Mendoza, Yvan Jesus Garcia-Lopez, Juan Carlos Quiroz-Flores
	DOI : 10.14445/22315381/IJETT-V71I9P234

How to Cite?

Midori Kato-Yoshida, Ivone Mosquera-Mendoza, Yvan Jesus Garcia-Lopez, Juan Carlos Quiroz-Flores, "Improving Demand Analysis and Supply Chain Management for Hair Products During the COVID-19 Pandemic: A Machine Learning Approach," International Journal of Engineering Trends and Technology, vol. 71, no. 9, pp. 385-396, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I9P234

Abstract
This research analyzes the demand for hair care products during the COVID-19 pandemic. Two forecasting models, Arima and Sarima, based on Machine Learning technology, were proposed to improve data analysis and supply chain management. The results showed that the SARIMA model had higher mean absolute error levels than the Arima model. The study also analyzed the demand for four hair dyes using statistical models, finding that three had seasonal demand. The SARIMA model accurately predicted demand for most hair dyes except one. Errors in the predictions were measured using different indicators, and the SARIMA model had lower error levels than the Arima model. The study's results were validated and compared with previous research, showing that the SARIMA model predicted the demand for hair dyes. Overall, this study highlights the usefulness of Machine Learning models in demand analysis and supply chain management of hair care products during the COVID-19 pandemic. These findings provide a reference framework for manufacturing industries with similar characteristics that wish to optimize demand management using Machine Learning techniques.

Keywords
Business intelligence, Machine learning, Business analytics, Time series, Demand forecast.

References
[1] CEPAL, Employment Situation in Latin America and the Caribbean. Work in Times of Pandemic: The Challenges of the Coronavirus Disease (COVID-19), 2020. [Online]. Available: https://www.cepal.org/en/publications/45582-employment-situation-latin-america-and-caribbean-work-times-pandemic-challenges
[2] Chia-Nan Wang, Thanh-Tuan Dang, and Ngoc-Ai-Thy Nguyen, "A Computational Model for Determining Levels of Factors in Inventory Management Using Response Surface Methodology," Mathematics, vol. 8, no. 8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Adnan Aktepe, Emre Yanık, and Süleyman Ersöz, "Demand Forecasting Application with Regression and Artificial Intelligence Methods in a Construction Machinery Company," Journal of Intelligent Manufacturing, vol. 32, no. 2, pp. 1587-1604, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Celsus Murenjekha Shilehwa, Sibilike Khamala Makhanu, Alex Wabwoba Khaemba, "Application of Artificial Neural Network Model In Forecasting Water Demand: Case of Kimilili Water Supply Scheme, Kenya," SSRG International Journal of Civil Engineering, vol. 6, no. 9, pp. 6-11, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Ankit Kumar Nigam, and D.C Rahi, "Analysis of Water Demand and Forecasting Water Demand for Year 2048 Jabalpur City," SSRG International Journal of Civil Engineering, vol. 3, no. 7, pp. 37-42, 2016.
[CrossRef] [Publisher Link]
[6] Tran Thi Bich Chau Vo et al., "Demand Forecasting and Inventory Prediction for Apparel Product using the ARIMA and Fuzzy EPQ Model," Journal of Engineering Science and Technology Review, vol. 14, no. 2, pp. 80-89, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Dwivedi, Yogesh K, "Impact of COVID-19 Pandemic on Information Management Research and Practice: Transforming Education, Work, and Life," International Journal of Information Management, vol. 55, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Ming Lei, Shalang Li, and Shasha Yu, "Demand Forecasting Approaches Based on Associated Relationships for Multiple Products," Entropy, vol. 21, no. 10, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Nguyen Thi Kim Huyen, "Financial Management of Supply Chains In Vietnam: A Case Study of Companies In The Steel Industry," SSRG International Journal of Economics and Management Studies, vol. 7, no. 12, pp. 56-61, 2020.
[CrossRef] [Publisher Link]
[10] Manrique Nugent et al., "Supply Chain Management: A Theoretical Perspective," Venezuelan Journal of Management, vol. 24, no. 88, pp. 1136-1146, 2019.
[Google Scholar] [Publisher Link]
[11] Elcio Tarallo et al., “Machine Learning in Predicting Demand for Fast-Moving Consumer Goods: Exploratory Research,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 737-742, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Montemayor Gallegos, and Jose Enrique, “Forecasting Methods for Business,” Editorial Digital Del Tecnológico De Monterrey, 2014.
[Publisher Link]
[13] Tsukasa Demizu, Yusuke Fukazawa, and Hiroshi Morita, "Inventory Management of New Products in Retailers Using Model-Based Deep Reinforcement Learning," Expert Systems with Applications, vol. 229, 2023.
[CrossRef] [Google Scholar] [Publisher Link].
[14] D. Sobya, and Parthasarathi Chakraborty, "Study on Effective Inventory Management by Determining the Appropriate Safety Stock in an Automobile Manufacturing Industry," SSRG International Journal of Mechanical Engineering, vol. 7, no. 7, pp. 10-17, 2020.
[CrossRef] [Publisher Link]
[15] Hossein Hassani, and Emmanuel Sirimal Silva “A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts,” Econometrics, vol. 3, no. 3, pp. 590-609, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Kamal Pandey, and Bhaskar Basu, "Mathematical Modeling for Short Term Indoor Room Temperature Forecasting using Box-Jenkins models: An Indian Evidence," Journal of Modelling in Management, vol. 15, no. 3, pp. 1105–1136, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Esperanza Manrique Rojas, "Machine Learning: Analysis of Programming Languages and Development Tools," Iberica Journal of Information Systems and Technologies, vol. 4, no. 28, pp. 586-599, 2020.
[Publisher Link]
[18] Gonzalo RÌos Time series, University of Chile, 2008. [Online], Available: https://gc.scalahed.com/recursos/files/r161r/w24113w/Semana%2011/Series_de_Tiempo.pdf
[19] Sima Siami-Namini, Neda Tavakoli, and Akbar Siami Namin, "A Comparison of ARIMA and LSTM in Forecasting Time Series," Conference Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, pp. 1394-1401, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] F. Morales, Learn About 3 Types of Research: Descriptive, Exploratory and Explanatory, 2012. [Online]. Available: https://gc.scalahed.com/recursos/files/r161r/w23919w/Conozca%203%20tipos%20de%20investigaci_%B3n.pdf
[21] Daniel Angel Cordova Sotomayor, "Application of the Autoregressive Integrated Moving Average for the Analysis of Covid-19 Case Series in Peru," Journal of the Faculty of Human Medicine, vol. 21, no. 1, pp. 65-74, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] C.Premila Rosy, and R. Ponnusamy, "Intelligent System to Support Judgmental Business Forecasting: The Case of Unconstraint Hotel Room Demand," SSRG International Journal of Computer Science and Engineering, vol. 1, no. 8, pp. 1-5, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Cabrera Feijoo Gianella, and Germana Valverde Jimena, "An Explainable Machine Learning Model for Optimizing Demand Forecasting in Enterprise DEOS," Proceedings of the 2nd Indian International on Industrial Engineering and Operations Management: Sociedad IEOM, pp. 1839-1850, 2022.
[Publisher Link]
[24] Alisson Lozano, Jean Mora and José Antonio Taquía Gutiérrez, "Lean Logistic and Demand Planning Model," Proceedings of the 3rd South American International Industrial Engineering and Operations Management, 2022.
[CrossRef] [Publisher Link]