International Journal on Science and Technology

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Accelerating EV Adoption with Integrated ERP and Machine Learning for Enhanced Direct Sales and Operations

Author(s) Sudheer Panyaram
Country United States
Abstract Electric vehicle adoption is increasing globally yet faces obstacles like fractured supply chains and ineffective sales techniques which prevent broader market penetration. This research investigates how the combination of Enterprise Resource Planning (ERP)-enabled direct sales platforms and machine learning (ML) algorithms can speed up the adoption of electric vehicles (EVs) by streamlining business operations and improving customer interaction. Through ERP system integration the proposed approach connects core business operations such as inventory management, production control, distribution logistics, and customer relationship handling which results in immediate access to data throughout the EV sales landscape. The Weighted Support Vector Machine (WSVM) model predicts consumer behavior and market trends which leads to smarter business decisions and tailored marketing tactics. Through the use of ML technologies including predictive analytics and recommendation systems the platform achieves better forecasting accuracy and more efficient inventory management while also improving customer targeting. The integrated ERP-ML platform demonstrated notable outcomes: The integrated system showed heightened sales conversion rates alongside reduced inventory costs and better demand prediction accuracy. The system improved customer satisfaction ratings while decreasing average delivery times by 15.9%. The combination of ERP systems with machine learning in direct sales platforms enhances both operational efficiency and customer engagement while providing a scalable solution to break adoption barriers and propel sustainable growth in the EV industry.
Field Engineering
Published In Volume 13, Issue 4, October-December 2022
Published On 2022-10-05
DOI https://doi.org/10.71097/IJSAT.v13.i4.6022
Short DOI https://doi.org/g9m7p4
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