AI for Supply Chain Optimisation

Supply Chain Optimisation: AI-Driven Demand Forecasting

Challenge:
A global logistics company was facing critical issues with inventory management and demand forecasting. Inaccurate predictions led to frequent stockouts and excess inventory, resulting in operational inefficiencies, increased costs, and lost revenue opportunities. The company needed a solution to optimise its supply chain and improve product availability while minimising costs.

Solution:
We developed and implemented an advanced AI-based demand forecasting model. By leveraging historical sales data, market trends, and external factors, the model provided accurate, data-driven insights into future demand. Integrated seamlessly with the company’s existing inventory management system, it enabled automated stock replenishment and dynamic inventory adjustments in real-time. The system continuously learned from evolving trends to enhance forecasting accuracy.

Results:
- Reduced Stockouts by 20%: Improved forecasting accuracy ensured products were consistently available, leading to increased customer satisfaction.
- Optimised Inventory Levels: The AI model minimised overstocking, reducing inventory holding costs and freeing up valuable resources.
- Operational Savings: The solution resulted in millions of dollars saved in operational costs by improving inventory turnover and reducing waste.

This project not only improved the company's supply chain efficiency but also gave it a competitive edge by enabling data-driven decision-making and smarter inventory management.