Citation :

Shreesha Hegde Kukkuhalli, 2023. "Global Parts Management through Data and AI Leveraging Structured and Unstructured Data" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 1, Issue 1: 115-118.

Abstract :

In today's complex global manufacturing and consumer service landscape, effective parts management is crucial for maintaining operational efficiency and competitive advantage. This paper presents an innovative approach to parts management that integrates both structured and unstructured data sources. I propose a hybrid system that combines traditional database management with advanced natural language processing, generative AI and machine learning techniques to extract valuable insights from diverse data types. My research demonstrates how this integrated approach can significantly improve field parts search, inventory management, and supply chain optimization in a global manufacturing context. I present a case study of a multinational escalator and elevator manufacturer that implemented this system for field and factory operating units, resulting in a 25% reduction in overall parts spend and 20% improvement in parts search by field mechanics. The findings highlight the potential of leveraging both structured and unstructured data for enhanced decision-making and operational efficiency in manufacturing enterprises focused on both factory and field operations.

References :

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Keywords :

Parts Management, Global Manufacturing, Structured Data, Unstructured Data, Data Engineering, Machine Learning, Document and Image Processing.