Effective Cement Demand Forecasting using Deep Learning Technology: A Data-Driven Approach for Optimal Demand Forecasting

Authors : Sarita Nandal; Samyak Sabannawar; Akhil Pawan; Purvaja Fursule; Bharani Kumar Depuru

Volume/Issue : Volume 8 - 2023, Issue 9 - September

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/mwjs2mt4

DOI : https://doi.org/10.5281/zenodo.8413806

This research addresses the critical challenge of forecasting cement demand tailored to specific markets while concurrently optimizing distribution strategies to minimize transportation costs, reduce delivery times, and enhance inventory management. Leveraging a rich dataset comprising monthly sales data spanning from January 2018 to April 2023, we employ advanced data analysis techniques and machine learning algorithms. Our holistic approach considers a multitude of factors, including GDP growth, transportation distance, delivery timeframes, pricing dynamics, and cement types, to construct a robust and precise demand forecasting model. We deploy an array of time series analysis methods, including ARIMA, SARIMA, SARIMAX, and Artificial Neural Networks (ANN), including the ANN-DWT variant, to project future cement demand. To rigorously assess and compare the forecasting models' accuracy, we employ established metrics such as the Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Mean Square Error (MSE). Our results demonstrate substantial cost savings, heightened customer satisfaction due to improved delivery timelines, and the implementation of highly efficient inventory management practices. This research contributes significantly to the cement manufacturing industry by reshaping distribution paradigms and fostering operational excellence. The projected cost savings of over $1 million underscore the economic impact of our endeavor, signifying a pivotal milestone for the cement industry and providing a blueprint for similar industries striving for operational optimization. By employing data-driven insights, cutting-edge forecasting models, and meticulous evaluation, this study paves the way for a new era of heightened operational efficiency, enhanced customer experiences, and sustainable growth in the cement manufacturing landscape.

Keywords : Time series analysis, Demand forecasting, Cement industry, Inventory management, Deep learning, Exploratory Data Analysis.


Paper Submission Last Date
31 - December - 2023

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