How Syren's Lean AI Adoption Drove 85% Warehouse Cost Savings
Problem Statement
The traditional systems like SAP EWM used by the global pharma company were effective at tracking material movements, managing storage locations, and preparing shipments. But when it came to optimizing warehouse capacity in a fast-changing environment, these systems lacked the dynamic intelligence and flexibility the business needed.
Despite exploring AI-driven solutions—and involving data scientists, engineers, business teams, and warehouse operations—the process remained slow, expensive, and difficult to scale. Iterative prototyping and agile development were difficult to execute effectively with large, siloed teams. The more people involved, the harder it became to maintain speed, clarity, and cost-efficiency.
What the business needed wasn’t more AI. It needed better AI.
A solution that empowered users to interact with data in real time, run simulations, test ideas early, and make smarter decisions without relying on massive support teams or prolonged development cycles.
Challenges
- Dependence on IT Teams: Running scenario-based simulations or adjusting workflows required constant involvement from data engineers, leading to bottlenecks and delayed decisions.
- High CapEx and OpEx: Developing, maintaining, and scaling AI-powered systems demanded large cross-functional teams and long development cycles — making innovation expensive and hard to sustain.
- Complex Architecture: The end-to-end lifecycle - from building to debugging - required niche skills and highly proficient professionals. Governing, securing, and maintaining the system added further complexity.
Solution
Power Platform Integration
To overcome these challenges, Syren implemented AI-driven simulation and forecasting models by integrating with Microsoft Power Platform tools.
By leveraging low-code/no-code capabilities and pre-built connectors, the solution was deployed rapidly and economically using a lean delivery model — without the need for large development teams or long lead times. We integrated machine learning models directly into Power Platform, enabling accurate warehouse capacity forecasts and simulation of potential disruptions like stock surges, power outages, or facility damage. This approach enabled faster experimentation, real-time scenario planning, and smarter decision-making at scale.
Key Technologies Used
- Power BI: Enabled advanced analytics and real-time visualization of warehouse metrics.
- Power Apps: Allowed users to input and modify data dynamically, enabling real-time decision-making.
- Power Automate: Automated data refresh workflows, ensuring continuous updates without manual intervention.
Prototyping & Simulation
We developed and integrated a machine learning model within Power Platform to:
- Forecast Warehouse Capacity: Predict storage needs over multiple years based on demand trends.
- Simulate Disruptions: Model real-world scenarios such as sudden surges in material inflow, power outages, natural disasters, or warehouse damage.
User Interaction
Syren integrated Power Apps with Power BI dashboards to enable seamless interaction between users and simulation tools.
With this AI-enhanced solution, managers could visualize current warehouse conditions, test hypothetical scenarios, and receive actionable insights—all within a single interactive platform.
Impact & Benefits
1. Cost Efficiency
- A lean Power Platform-based delivery model minimized reliance on large-scale data science and engineering teams.
- Early-stage prototyping and rapid iteration with business users reduced rework and minimized wasted effort.
2. Democratized Access & Agility
- Self-service simulations, scenario exploration, and assumption adjustments became accessible to business teams — no dependency on data engineers.
- Decision-making gained speed and flexibility with tools designed for frontline users.
3. Seamless Integration & Easy Deployment
- Low-code tools and prebuilt connectors enabled rapid deployment with minimal infrastructure overhead.
- Reporting, simulation, and user inputs were consolidated within a single Power Platform environment, eliminating the complexity of multi-service integration.
4. Unified Security & Governance
- A centralized governance model replaced fragmented access controls across Azure ML, ADF, and storage services.
- Region-based access, role-based permissions, and pipeline visibility managed entirely within the Power Platform.
- A reduced security footprint through consolidation of data, models, and logic within a single platform, limiting exposure to cross-service vulnerabilities.
Conclusion
Syren’s approach redefined what AI-powered warehouse optimization can look like — practical, and user-friendly. Instead of bulky, expensive systems, the focus was on lean, high-impact delivery, for rapid deployment, intuitive user interfaces, and minimal engineering overhead.
By leveraging Microsoft Power Platform, we delivered a user-friendly, future-ready system that empowered business teams to run simulations, forecast capacity, and respond to real-world disruptions with agility and confidence.
With deep expertise in data science and Power Platform, we helped the pharma giant unlock real ROI from AI, not by doing more, but by doing it better.