How Syren Helped a Medical Device Giant Optimize 6B Units with AI-Driven Scheduling
Problem Statement
In high-volume manufacturing environments, production planning is a complex and resource-heavy task, especially when managing thousands of SKUs across hundreds of 24/7 production lines. For a global leader in medical devices manufacturing, manual scheduling methods such as relying on spreadsheets and legacy tools, etc., could not keep pace with the scale, variability, and speed required. Weekly schedules were an intricate web of constraints including machine availability, changeover costs, inventory positions, and forecasted demand. Even minor inefficiencies in this process resulted in material waste, underutilized capacity, and missed service targets.
With over 6 billion units produced annually, this site’s reliance on human-intensive planning exposed it to massive cost and service implications. Leadership questioned the scientific validity of schedules and demanded a shift toward more data-driven, strategic planning to ensure trust, responsiveness, and performance across the manufacturing network.
ChallengesÂ
- Frequent changeovers increased downtime and drove up material waste, labor costs, and overhead.
- Lack of standardization made it hard to validate or compare the quality of schedules.
- Misalignment between production and demand caused either overstocking or order delays.
- Limited transparency and trust inhibited the organization’s ability to respond to demand shifts and scale operations efficiently.
Our Solution
- Syren delivered a custom-built Production Scheduling Optimization Tool, tailored to the site’s complex operational requirements. This advanced decision support system automatically generates weekly production schedules that are constraint-aware and scientifically validated. At the core of the solution is a mathematical optimization engine that evaluates millions of scheduling permutations in real-time, dynamically assigning Production Line Orders (PLOs) based on business priorities such as demand urgency, inventory levels, machine availability, and raw material constraints.
- The model integrates seamlessly with ERP systems using data lake for real-time data ingestion, while less volatile inputs are handled via a custom scheduling template. The algorithm prioritizes SKUs using weighted logic based on inventory gaps, average weekly demand, and forecast urgency, ensuring production is aligned to service-level targets with minimal changeovers. The solution includes logic to handle partial scheduling, backorders, and iterative re-ranking of PLOs, enhancing both agility and transparency.
- The outcome is a scalable, repeatable, and data-driven planning process that reduces reliance on manual input, optimizes machine utilization, and drives cost-effective, customer-focused manufacturing.
Key Value Delivered
- Transformed scheduling into a proactive, data-driven capability using advanced optimization.
- Improved trust across stakeholders with transparent, justifiable scheduling logic.
- Significantly reduced planning effort, freeing up planners for strategic work.
- Enabled real-time responsiveness to demand and supply fluctuations.
- Created a repeatable and scalable framework for future multi-site rollout.
- Reduced changeovers, maximizing throughput and minimizing production waste.
Impact
| Impact Area | Potential Metrics |
|---|---|
| Changeover Reduction | 10–35% decrease in changeover time |
| Planning Efficiency | Process overhead reduced from a few hours to 30 minutes |
| Capacity Utilization | 10–15% improvement in line throughput |
| Cost Efficiency | $10M+ savings |
Conclusion​
By transforming an intricate, error-prone manual planning process into a scientifically optimized, data-driven system, Syren empowered the manufacturer to gain full control over production scheduling. The result: faster, smarter decisions; fewer disruptions; and a more agile, cost-efficient supply chain.