International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

SMART TEA: Churn, Trend, Inventory and Sales Prediction System Using Machine Learning

J.H.P VithanageFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaSalwathura S.RFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaDe Silva D.K.T.J.SFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaWickramasinghe D.K.G.T.IFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaSuriya KumariFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaUthpala SamarakoonFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Vol 7 No 11 (2023): Volume 7, Issue 11, November 2023 | Pages: 453-460

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 17-11-2023

doi Logo doi.org/10.47001/IRJIET/2023.711061

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Abstract

Managing operations at a tea factory requires consistency and planning. This paper presents a complete platform that uses advanced machine learning methods specifically designed for the tea sector. Sales prediction, churn prediction, trend prediction, and smart inventory management are the four essential features of our solution. While using Neural Networks for Churn Prediction offers exact insights into customer churn, utilizing Gradient Boosting for Sales Prediction guarantees accurate revenue estimates. Linear regression models were used for trend prediction and smart inventory management to enable efficient utilization of resources and trend identification. With the help of this integrated system, tea companies can now operate more profitably and sustainably in a market that is always changing. This research acts as a beacon, demonstrating the revolutionary potential of data-driven management as operations in the tea industry evolve.

Keywords

Sri Lankan Tea Factories, Neural Networks, Gradient Boosting Regressor, Ensemble Methods, Linear regression, Sales Price prediction, Churn prediction, Trend prediction, Inventory management


Citation of this Article

J.H.P Vithanage, Salwathura S.R, De Silva D.K.T.J.S, Wickramasinghe D.K.G.T.I, Suriya Kumari, Uthpala Samarakoon, “SMART TEA: Churn, Trend, Inventory and Sales Prediction System Using Machine Learning” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 11, pp 453-460, November 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.711061

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