Inventory Prediction & Optimization

Overview: The system ingests historical demand, consumption data and real-time order transactions from multiple sources (ERP databases, Excel planning sheets, sales order APIs), processes them using Azure Machine Learning models, and outputs actionable replenishment signals.

The architecture processes data from multiple sources and delivers predictive analytics & insights to client for material inventory, using the following workflow: • Source: Data originates from SQL Databases and Excel Sheets. • Ingest: > Azure Data Factory is used to extract and load data from source systems. > Data is stored in Azure Data Lake Storage for further processing. • Process: > Azure Machine Learning service trains and manages ML models using the ingested data. > The ML model is deployed as an Azure ML Endpoint for consumption. • Deployment: > Azure Functions are used to trigger and manage ML endpoints. > Azure Services host the deployed models and services for integration. • Consumers: Power BI connects to the deployed endpoints to create BI dashboards and analytics for end users. • Azure MLOps: The solution uses Azure MLOps to monitor, manage, and automate ML workflows including model retraining, deployment, and cost management.

Scenario Details: Architecture shows a predictive inventory management system designed to: • Predict Safety Stock: Calculate the minimum stock level (SS - Safety Stock) required for each material code to prevent stockouts while minimizing carrying costs. The logic for SS calculation is defined by the client and can be customized/configurable as per the client requirement. • Determine Reorder Point (ROP): Predict the optimal point at which a replenishment order should be placed, factoring in supplier lead times, demand variability, and service level targets. The logic for ROP calculation is defined by the client and can be customized/configurable as per the client requirement • Real-Time Demand Tracking: Continuously monitor demand for each material code and adjust predictions dynamically as new data arrives. The system ingests historical demand data and real-time order transactions from multiple sources (ERP databases, Excel planning sheets, sales order APIs), processes them using Azure Machine Learning models, and outputs actionable replenishment signals.

Potential Use Cases:

Retail: Monitor store-level stock in real time, predict stock-outs, and plan for seasonal demand spikes. Manufacturing: Forecast raw material needs, manage work-in-progress inventory, and prevent production delays. E-commerce & Distribution: Automate replenishment for fast-moving SKUs and balance stock across multiple warehouses. Pharmaceuticals & Healthcare: Predict reorder points for medicines and medical supplies while meeting compliance requirements. Automotive & Spare Parts: Anticipate spare part needs to minimize downtime and optimize dealer stock levels. Food & Beverage: Plan replenishment for perishables to minimize waste and adapt to supply chain disruptions.