Review on Forecasting Demand for Products Using Machine Learning

Abstract

For a smooth management of the inventory and plan operations in a retail company, accurate product demand forecasting is important. The objective of the project is demand forecasting for the new products, using historical sales data as well as characteristics and store and external to predict future demand to minimize overstock and understock of the products to enhance cost and customer satisfaction. Advanced data processing techniques, EDA, and feature engineering are applied for the identification of most prominent factors that affect demand. Using regression models and advanced timeseries analysis techniques, robust predicting machine learning algorithms are built in order to evaluate performance against accuracy using metrics like Root Mean Square Error and Mean Absolute Error for bias and variance. The important novelties in this work are managing sparsity for new products, capturing external effects like holidays and promotions, and the methods for managing seasonality and trend in the demand pattern. It provides a solution that is scalable and adaptive enough for the use cases in the real-world retail domain with diverse applications like supply chain optimization and the sales strategy development. This work fits into a developing area of data-driven decision-making in the retail environment in showing what machine learning can do for operational efficiency and strategic planning.

Introduction

Accurate demand forecasting is critical for retail business success, helping optimize inventory, reduce costs, and improve customer satisfaction. Traditional forecasting methods struggle with modern challenges like new products, dynamic market trends, promotions, and seasonality. This project leverages machine learning (ML) and data analytics to build a robust demand forecasting system that integrates multiple data sources—historical sales, store features, promotions, holidays, and external factors—to improve accuracy.

Methodology

• Algorithms Used:

  • SARIMA for capturing seasonal trends in time-series data.

  • XGBoost for handling nonlinear relationships using product attributes.

  • LSTM networks to model long-term sequential dependencies in sales data.

• Data Processing Steps:

  • Collection of relevant data including sales, market trends, weather, promotions.

  • Cleaning and preprocessing (handling missing values, outliers, normalization, feature engineering).

  • Data splitting into training and testing sets.

  • Model building with hyperparameter tuning to avoid overfitting.

  • Evaluation using metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

  • Final demand forecasts used for inventory, production, and logistics planning.

Key Features and Benefits

• Identifies seasonality (e.g., holidays) and trends (upward or downward demand shifts).

• Enables inventory optimization to balance stock levels, reduce holding costs, and prevent stockouts.

• Provides future sales predictions (e.g., next three months), assisting in budgeting and strategic decision-making.

• Adaptable and scalable to different retail environments and changing market conditions.

Impact

The project demonstrates how a data-driven, machine learning approach significantly enhances forecasting accuracy, especially for new products and complex retail scenarios, thereby supporting profitability and operational efficiency in competitive markets.

Conclusion

The Product Demand Forecasting project exemplifies the transformative power of machine learning in retail analytics. Through the utilization of historical data and external influences, the project was able to design a robust predictive system capable of tackling even the most challenging demand forecasting scenarios. The model\'s flexibility and scalability allow it to be used in numerous retail contexts, thereby enabling companies to maximize their inventory, optimize operations, and increase profitability. These kinds of findings focus on the need for companies to make strategic business benefits of investing in advanced analytics for effective traversal over competitive and dynamic retail environments by making decisions on data.

References

[1] S.Anitha and R. Neelakandan (2024) .Demand forecasting model leveraging Machine Learning to decode customer prefence for new fashion products. [2] Elcio Tarallo , Getúlio K. Akabane ,Camilo I. Shimabukuro , Jose Mello , Douglas Amancio (2019) . Machine Learning in predicting demand for fast moving consumer goods : An Exploratory Reasearch. [3] Trang T. M. Ho1, Ly V. Tran1, Hieu M. Tran2,Son V.T. Dao (2022). Machine Learning in Demand Forecasting [4] Resul Tugay and S¸ule Gu¨ndu¨z O¨ g¸u¨du¨cu (2017). Demand Prediction using Machine Learning Methods and Stacked Generalization. [5] P S Smirnov1 and V A Sudakov (2020) Forecasting new product demand using machine learning. [6] Shanshan Huang , Andrew E.B. Lim (2018) Newproduct Demand Forecasting for Longlived Products.

Copyright

Copyright © 2025 Prof. Sushma Ghose, Bhavna Mohite, Manthan Tilekar, Harsh Gupta, Vaishnao Vaidya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.