Generative AI for Supply Chain Decision Making
Generative Artificial Intelligence (AI) can be used for supply chain decision-making in many ways such as demand forecasting, warehouse optimization and others. As an example Amazon is using Generative AI in the following ways:
- Demand forecasting: By analyzing data such as previous sales, customer behavior, and product reviews, Amazon can predict which products will be in high demand and adjust their inventory levels accordingly. This helps to minimize inventory costs and reduce waste.
- Route optimization: By analyzing real-time traffic data, weather conditions, and other factors, Amazon can determine the most efficient routes for its delivery vehicles.
- Warehouse optimization: By analyzing data on product demand, storage capacity, and other factors, Amazon can determine the most efficient layout for its warehouses and ensure that products are stored in the optimal location.
- Quality control: By analyzing data on product quality, manufacturing processes, and other factors, Amazon can identify potential issues before they become a problem.
And much more.
[This article is written based on open source data]
About the Author:
Dr. Ray Islam (Mohammad R Islam) is a Data Scientist (AI and ML) and Advisory Specialist Leader at Deloitte, USA. He holds a PhD in Engineering from the University of Maryland, College Park, MD, USA and has worked with major companies like Lockheed Martin and Raytheon, serving clients such as NASA and the US Airforce. Ray also has a MASc in Engineering from Canada, a MSc in International Marketing, and an MBA from, UK. He is also the Editor-in-Chief of the upcoming peer-reviewed International Research Journal of Ethics for AI (INTJEAI), and his research interests include generative AI, augmented reality, XAI, and ethics in AI.
