Transforming Supply Chains: Case Studies of Generative AI Implementation

EasyChair Preprint 12933

Abstract

This abstract delves into real-world case studies where organizations have successfully implemented generative artificial intelligence (AI) techniques to revolutionize their supply chains. By analyzing these examples, we aim to uncover the challenges faced, lessons learned, and the measurable benefits achieved through innovative approaches powered by generative AI.

Case Study 1: Demand Forecasting Optimization

Organization: RetailTech Inc.

Description: RetailTech Inc. implemented generative AI techniques to optimize demand forecasting for their diverse product portfolio. By leveraging generative AI algorithms, RetailTech Inc. was able to synthesize diverse scenarios and overcome data scarcity challenges, resulting in more accurate demand forecasts. The organization also employed advanced analytics to identify demand patterns and customer preferences, enabling proactive inventory management and enhancing customer satisfaction.

Challenges: Initial challenges included data integration issues and algorithm refinement. However, with iterative model training and collaboration between data scientists and supply chain experts, RetailTech Inc. successfully addressed these challenges and achieved significant improvements in demand forecasting accuracy.

Lessons Learned: Collaboration between data science and supply chain teams is crucial for successful implementation. Additionally, continuous model refinement and validation are essential for maintaining accuracy in dynamic market environments.

Benefits: RetailTech Inc. experienced a reduction in stockouts, improved inventory turnover, and increased revenue due to better alignment between supply and demand.

Keyphrases: Challenges, Generative AI, Success Stories, case studies, implementation, lessons learned, supply chains

@booklet{EasyChair:12933,
  author    = {Dylan Stilinski and Lucas Doris and Louis Frank},
  title     = {Transforming Supply Chains: Case Studies of Generative AI Implementation},
  howpublished = {EasyChair Preprint 12933},
  year      = {EasyChair, 2024}}