Authors :
Mohd Amer Hussain; Akhil Rasamsetti; Vaanishree Kamthane; Deba Chandan Mohanty; Bharani Kumar Depuru
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/yz379tk9
Scribd :
https://tinyurl.com/my7y6r44
DOI :
https://doi.org/10.5281/zenodo.10319844
Abstract :
Effective forecasting of sales and production
in the field of TMT (Thermo-Mechanically Treated) steel
is crucial for businesses to optimise their operations,
manage inventory, and meet customer demand. This
abstract presents a forecasting model that utilises
historical sales and production data, as well as relevant
market indicators, to predict future sales and production
volumes for TMT steel products. The model employs
advanced statistical techniques and machine learning
algorithms to analyse the complex relationships between
various factors influencing sales and production. By
incorporating factors such as economic indicators,
market trends, and customer behaviour patterns, the
model aims to provide accurate and reliable forecasts.
The proposed forecasting model offers a valuable tool
for TMT steel manufacturers and distributors to
enhance decision-making, resource allocation, and
strategic planning, leading to improved operational
efficiency and increased profitability in the dynamic and
competitive steel industry.
Additionally, this study highlights the utilisation of
advanced deep learning models for forecasting sales and
production in the field of TMT steel. The forecasting
model integrates deep learning algorithms, such as
recurrent neural networks (RNNs) and long short-term
memory (LSTM) networks, to capture temporal
dependencies and patterns within the data. These deep
learning models excel at handling complex sequential
data and can effectively capture nonlinear relationships,
allowing for more accurate and robust predictions. By
incorporating advanced deep learning techniques into
the forecasting model, it aims to improve the accuracy
and reliability of sales and production forecasts for TMTsteel. The combination of traditional statistical methods
and advanced deep learning models offers a
comprehensive approach to forecasting, enabling
businesses in the TMT steel industry to make informed
decisions, optimise operations, and adapt to market
dynamics with increased precision and confidence.
Keywords :
Sales Forecasting, TMT Steel, Predictive Analytics, Machine Learning Models (LSTM, RNN, GRU), Production Optimization, Inventory Management.
Effective forecasting of sales and production
in the field of TMT (Thermo-Mechanically Treated) steel
is crucial for businesses to optimise their operations,
manage inventory, and meet customer demand. This
abstract presents a forecasting model that utilises
historical sales and production data, as well as relevant
market indicators, to predict future sales and production
volumes for TMT steel products. The model employs
advanced statistical techniques and machine learning
algorithms to analyse the complex relationships between
various factors influencing sales and production. By
incorporating factors such as economic indicators,
market trends, and customer behaviour patterns, the
model aims to provide accurate and reliable forecasts.
The proposed forecasting model offers a valuable tool
for TMT steel manufacturers and distributors to
enhance decision-making, resource allocation, and
strategic planning, leading to improved operational
efficiency and increased profitability in the dynamic and
competitive steel industry.
Additionally, this study highlights the utilisation of
advanced deep learning models for forecasting sales and
production in the field of TMT steel. The forecasting
model integrates deep learning algorithms, such as
recurrent neural networks (RNNs) and long short-term
memory (LSTM) networks, to capture temporal
dependencies and patterns within the data. These deep
learning models excel at handling complex sequential
data and can effectively capture nonlinear relationships,
allowing for more accurate and robust predictions. By
incorporating advanced deep learning techniques into
the forecasting model, it aims to improve the accuracy
and reliability of sales and production forecasts for TMTsteel. The combination of traditional statistical methods
and advanced deep learning models offers a
comprehensive approach to forecasting, enabling
businesses in the TMT steel industry to make informed
decisions, optimise operations, and adapt to market
dynamics with increased precision and confidence.
Keywords :
Sales Forecasting, TMT Steel, Predictive Analytics, Machine Learning Models (LSTM, RNN, GRU), Production Optimization, Inventory Management.
