Leveraging Economic Factors for Volume Forecasting in Manufacturing Industries

Leveraging Economic Factors for Volume Forecasting in Manufacturing Industries
  IJETT-book-cover           
  
© 2025 by IJETT Journal
Volume-73 Issue-9
Year of Publication : 2025
Author : Karan Salunkhe, Sudhanshu Gonge, Gaurang Maheshwari, Kalyani Kadam
DOI : 10.14445/22315381/IJETT-V73I9P101

How to Cite?
Karan Salunkhe, Sudhanshu Gonge, Gaurang Maheshwari, Kalyani Kadam,"Leveraging Economic Factors for Volume Forecasting in Manufacturing Industries", International Journal of Engineering Trends and Technology, vol. 73, no. 9, pp.1-9, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I9P101

Abstract
In this study, we investigate the output of the sector. The success of a manufacturer is contingent on the management team’s capacity to forecast future sales volumes precisely. Businesses can learn about customer demand, manufacturing capability, and resource allocation based on economic variables through sales volume forecasting. This study examines the impact of production volume and economic forecasts on business decision-making. As an illustration, we use the DataRobot tool to create a regression model based on sample data to forecast vehicle sales in a particular industry in the United States. Total Vehicle Sales (TOTALSA) are a key economic indicator for predicting future automobile demand. Using this information to make decisions regarding manufacturing capacity, marketing strategies, and supply chain management will better enable the industry to compete in the fiercely competitive auto industry. Incorporating economic factors into predictive models can yield substantial benefits for businesses.

Keywords
DataRobot, Manufacturing, Prediction, Regression, Volume.

References
[1] U.S. Bureau of Economic Analysis, Total Vehicle Sales, 2023. [Online]. Available: https://fred.stlouisfed.org/series/TOTALSA?utm_source#
[2] Bastian Grühn et al., Winning Tomorrow’s Car Buyers using Artificial Intelligence in Marketing and Sales, McKinsey & Company, 2019. [Online]. Available: https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/winning-tomorrows-car-buyers-using-artificial-intelligence-in-marketing-and-sales
[3] Sakir Bingol, and Safaa Sadik, “Gold Price Prediction in Times of Financial and Geopolitical Uncertainty: A Machine Learning Approach,” 2020.
[Google Scholar]
[4] History, Ford’s Assembly Line Starts Rolling, 2025. [Online]. Available: https://www.history.com/this-day-in-history/december-1/fords-assembly-line-starts-rolling
[5] Fong-Ching Yuan, and Chao-Hui Lee, “Intelligent Sales Volume Forecasting using Google Search Engine Data,” Soft Computing, vol. 24, no. 3, pp. 2033-2047, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Serge Guefano et al., “Forecast for the Cameroon's Residential Electricity Demand Based on the Multilinear Regression Model,” Energy and Power Engineering, vol. 12, no. 5, pp. 182-192, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Kate Carroll de Gutes, IBM, 2022. [Online]. Available: https://www.ibm.com/think/author/kate-carroll-degutesibm-com?mhsrc=ibmsearch_a&mhq=kate%20carroll%20de%20gutes%26comma%3B%20author
[8] Siddhesh Shinde, How the Use of Data and AI Is Transforming the Automotive Industry, EMERITUS, 2021. [Online]. Available: https://emeritus.org/blog/big-data-ai-in-the-automotive-industry/
[9] T. Ibn-Mohammed et al., “A Critical Analysis of the Impacts of COVID-19 on the Global Economy and Ecosystems and Opportunities for Circular Economy Strategies,” Resources, Conservation and Recycling, vol. 164, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Haichen Jiang, Jiatong Ruan, Jianmin Sun, “Application of Machine Learning Model and Hybrid Model in Retail Sales Forecast,” 2021 IEEE 6th International Conference on Big Data Analytics, Xiamen, China, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Eunju Lee, Dohee Kim, and Hyerim Bae, “Container Volume Prediction using Time-Series Decomposition with a Long Short-Term Memory Models,” Applied Sciences, vol. 11, no. 19, pp. 1-16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Patrick Lemay, Machine Learning in Manufacturing: An Introduction to Industrial AI, Tulip, 2022. [Online]. Available: https://tulip.co/blog/machine-learning-in-manufacturing-an-introduction-to-industrial-ai/
[13] Aci Mehmet, and Dogansoy Gamze Ayyildiz, “Demand Forecasting for E-Retail Sector using Machine Learning and Deep Learning Methods,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 37, no. 3, pp. 1325-1339, 2022.
[Google Scholar]
[14] Fernanda M. De Almeida et al., “Retail Sales Forecasting for a Brazilian Supermarket Chain: An Empirical Assessment,” 2022 IEEE 24th Conference on Business Informatics, Amsterdam, Netherlands, pp. 60-69, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Flori Needle, I Created This Step-By-Step Guide to Using Regression Analysis to Forecast Sales, HubSpot, 2020. [Online]. Available: https://blog.hubspot.com/sales/regression-analysis-to-forecast-sales
[16] Doruk Sen, M. Erdem Günay, and K.M. Murat Tunç, “Forecasting Annual Natural Gas Consumption using Socio-Economic Indicators for Making Future Policies,” Energy, vol. 173, pp. 1106-1118, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Vasilios Plakandaras, Theophilos Papadimitriou, and Periklis Gogas, “Forecasting Transportation Demand for the U.S. Market,” Transportation Research Part A: Policy and Practice, vol. 126, pp. 195-214, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Velappan Shalini, Sridharan Krishnamurthy, and Srinivasan Narasimhan, “Predictive Analytics in Automobile Industry: A Comparison between Artificial Intelligence and Econometrics,” SAE Technical Paper, 2017.
[Google Scholar] [Publisher Link]
[19] Nimrod Zalk, “What is the Role of Manufacturing in Boosting Economic Growth and Employment in South Africa?,” Econ3x3, pp. 1-7, 2014.
[Google Scholar]