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.