Set to Make Your Ambitions a Reality?
The Challenge
In March 2024, FXI faced a major supply chain issue when the cost of raw materials unexpectedly spiked due to global supply chain disruptions.
- High raw material costs: Last-minute purchases due to poor forecasting led to overpaying for materials by $200k.
- Inventory shortages: Out-of-stock fabric dyes led to delays in fulfilling high-priority customer orders.
- Unoptimized production: The scheduling system lacked the ability to dynamically adjust to changes in real-time data, resulting in production delays.
The Solution
Technical Approach: A data-driven AI system combining machine learning and real-time analytics to optimize supply chain operations:
Key components:
- Demand forecasting (ML): Predictive models forecast raw material needs based on historical demand and seasonal trends, improving order timing.
- Dynamic production scheduler: AI adjusts production schedules based on real-time inventory levels, customer demand, and external supply disruptions.
- Supply chain visibility: IoT sensors track material deliveries and stock levels in real-time, providing immediate insights into shortages or surpluses.
Implementation Roadmap
1 - POC & Discovery
Draft a tailored AI solution roadmap, align it with business goals, and secure stakeholder commitment at the strategic level in 10 weeks.
2 - Design & Model building
Deploy AI on a focused area such as raw material procurement and scheduling by using real production data to build and train predictive models in 12 weeks.
3 - Controlled Roll-out
Split test performance between two groups: Control Group: Manual demand forecasting + Test Group: AI-driven forecasting and scheduling.
4 - Evaluation & Scaling
Analyze pilot impact on KPIs (efficiency, accuracy, cost), refine the model, and plan for enterprise-wide deployment based on validated outcomes.
Results
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