Authors : Venkata Saiteja Kalluri

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://shorturl.at/g5QUj

Scribd : https://shorturl.at/B9eJ0

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP864

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The boiler manufacturing industry faces unique challenges in supply chain management due to complex product specifications, stringent regulatory requirements, and fluctuating demand patterns. This paper presents an innovative approach to optimizing supply chain management in boiler manufacturing through the implementation of artificial intelligence (AI) enhanced Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) integration. Our study employs a multi-faceted methodology, combining machine learning algorithms, predictive analytics, and natural language processing to create an intelligent system that seamlessly connects customer- facing CRM data with backend ERP processes. This AI- driven approach enables real-time decision making, predictive demand forecasting, and adaptive inventory management specifically tailored to the boiler manufacturing sector. The research demonstrates significant improvements in key performance indicators across the boiler manufacturing supply chain, including reduced lead times for custom boiler orders, optimized inventory levels for critical components, enhanced supplier relationship management for specialized parts, and increased customer satisfaction through improved order tracking and delivery precision. A case study of a mid- sized boiler manufacturer that implemented this AI- enhanced integration is presented, showcasing a 20% reduction in operational costs and a 18% increase in on- time deliveries over a 12-month period. Furthermore, we address industry-specific challenges such as regulatory compliance tracking, energy efficiency optimization, and integration with Industrial Internet of Things (IIoT) sensors for predictive maintenance. The findings of this study have significant implications for boiler manufacturing enterprises seeking to leverage AI and data integration to gain a competitive edge in supply chain management and meet the evolving demands of the energy sector.

Keywords : Supply Chain Management, Boiler Manufacturing, Artificial Intelligence (AI), Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Machine Learning, Predictive Analytics, Natural Language Processing, Demand Forecasting, Inventory Management, Lead Time Reduction, Supplier Relationship Management, Industrial Internet of Things (IIoT), Regulatory Compliance, Energy Efficiency, Predictive Maintenance, Custom Boiler Orders, Operational Efficiency, Performance Indicators, Data Integration.

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