Development
AI-Enabled Predictive Inventory Management
A large retail chain struggled with manual, historic-average–based inventory forecasting, resulting in frequent stockouts, overstocking, and high costs. Seasonal demand, promotions, and local events made planning even more complex.
To address this, a predictive analytics platform was developed using machine learning to integrate data from sales history, promotions, weather, local events, and supply chain lead times. The system featured dashboards, alerts, auto-order triggers, and was built on scalable, cloud-based microservices with monitoring for model accuracy.
The solution cut stockouts by 50% and overstock by 30%, reduced holding costs, and empowered planners with data-driven insights. Its scalable design supports growth, while self-improving models and cloud infrastructure ensure long-term sustainability and lower support costs.
Challenges
- A retail chain had many stores and warehouses; inventory forecasting was manual or via simple historic-averages, leading to overstock in some places, stockouts in others.
- Seasonal demand, promotions, and local events caused unpredictable swings.
- The lack of automation meant delayed actions, high holding costs, lost sales.
Solution
- Developed a predictive analytics platform with Machine Learning models that ingest multiple data sources (past sales, promotions calendar, local event data, supply chain lead times).
- Built dashboards and alerts to inform purchasing and replenishment decisions.
- Implemented auto-order triggers when predicted inventory falls below thresholds.
- Designed using decoupled microservices so forecasting, alerting, order-generation modules scale independently.
- Hosted on cloud for scalability and disaster recovery; added monitoring and logging for model drift, accuracy tracking.
Benefits
-
Higher availability:
Reduced stockouts by 50%, ensuring customer satisfaction. -
Lower holding cost:
Reduced overstock by 30%, freeing up capital. -
Data-driven decisions:
Planners can rely on predictive insights rather than intuition.
-
Scalable system:
As the chain adds stores, the system handles more data and more SKUs without performance issues. -
Sustainability:
The models self-improve, infrastructure maintains robustness, support costs drop over time.
Find out what
Periscope can do for you.
Our Partner
Ecosytem
Industry Expertise
Our Clients
