A manufacturing and distribution company used a monolithic SCM system. It ran orders, inventory, procurement, shipping, billing, and reporting in one codebase with one database. Over time, this monolith became a burden. Release cycles slowed, performance dropped under peak loads, and scaling one function meant scaling everything.
The client wanted to break free from these limits. Their goal was to move from monolithic to microservices and enable faster changes, better scale, and modern features like AI forecasting.
The monolithic system created multiple bottlenecks. Any change in one module forced redeployment of the entire application, which increased risk. A single point of failure meant downtime stopped every supply chain function.
The database became a major choke point, causing contention and delays. Scaling order processing also scaled billing and reporting, even when not needed. Most importantly, business users lacked forecasting features, leaving them blind to demand trends. These issues pushed the client to consider a monolithic to microservices migration.
We recommended the Strangler Fig pattern to make the transition smooth. This approach allowed us to move features step by step into microservices while the monolith kept running. It reduced risk and avoided disruption for end users.
The monolith and microservices worked together during migration. New services took over one function at a time. Over months, the old system shrank as more services became independent. This incremental path proved safer than a big-bang replacement.
We built services with clear responsibilities. The Order Service managed order placement and lifecycle. Inventory Service tracked stock levels and reservations. Procurement Service created purchase orders. Warehouse Service handled picking and packing. Shipping Service managed carriers and tracking. Billing Service took care of invoices. Catalog Service held product data. Notification Service triggered alerts. Auth Service handled identity and access.
An API Gateway acted as the single entry point. It routed requests, applied authentication, and directed traffic to the right service. Each service owned its database and exposed lightweight APIs. Communication was managed through synchronous gRPC calls and asynchronous events with Azure Service Bus.
We applied proven design patterns to ensure stability and scale. Domain-Driven Design set clear service boundaries. The Saga Pattern managed long-running workflows, like order placement moving through stock reservation, shipping, and billing.
CQRS separated reads and writes in high-volume areas like inventory. Retry, timeout, and circuit breaker rules improved reliability. An Anti-Corruption Layer kept the monolith and new services in sync during migration. For visibility, we used Serilog and Application Insights with correlation IDs, which gave end-to-end traceability.
The order process highlighted the value of microservices. A customer placed an order through the API Gateway. Order Service saved it and triggered a Saga orchestrator.
The orchestrator asked Inventory Service to reserve stock. If stock was available, the flow continued to Warehouse, Shipping, and Billing. If stock failed, compensating actions reversed the order, canceled invoices, and released stock. Notifications went out through events. This design gave consistency without needing heavy distributed transactions.
Once the new services were stable, the client asked for demand forecasting. After refactoring a monolith to microservices, it was easy to add. We created a Forecasting Service as its own microservice.
The service used ML.NET with historical data to predict demand for the next 7–30 days. Inventory Service updated forecast tables by calling the Forecasting Service. Procurement Service subscribed to forecast events and raised vendor purchase orders if demand exceeded stock.
This setup gave the client predictive intelligence. They could avoid stockouts, reduce excess inventory, and make vendor orders automatically. Because the forecasting tool was a separate microservice, it integrated smoothly without disrupting existing flows.
The migration was supported by a modern stack. We used .NET microservices built with ASP.NET Core Minimal APIs. MassTransit with Azure Service Bus managed messaging. Each service used its own SQL Server database.
The API Gateway was built with Ocelot and JWT authentication. Logging and monitoring were handled with Serilog, Application Insights, and OpenTelemetry. Forecasting used ML.NET and ONNX models. All services were containerized with Docker and deployed on Azure Kubernetes Service. Azure DevOps pipelines enabled CI/CD with automated testing and blue-green deployments.
The monolithic to microservices migration delivered major improvements. Release cycles became 50 percent faster as teams deployed services independently. Order processing scaled three times during seasonal peaks without affecting billing or reporting.
Failures no longer took down the entire system. Issues stayed limited to single services. Centralized logs improved traceability and made debugging faster. Forecasting added predictive power, helping procurement make smarter decisions while lowering working capital needs.
The client gained a platform that supported growth and could adapt to new features quickly. The shift from monolithic and microservices during migration proved effective, balancing stability and innovation.
This project showed the value of refactoring a monolith to microservices. The old SCM system had become slow and hard to scale. By moving step by step to microservices, the client built a modern system that supported speed, flexibility, and intelligence.
The new platform delivered faster releases, stronger scaling, higher reliability, and AI-powered forecasting. It proved that businesses stuck with monoliths can modernize without high risk.