Automated deployment of supply planning models increases demand forecast accuracy and reduces manual intervention

amandaengineer
Sat, 01 Feb 2025 03:09:31 +0000

We implemented automated deployment pipelines for our demand planning models in D365 Supply Chain Management, and the results have been transformative for our forecasting operations.

Previously, deploying updated planning models required extensive manual coordination between our supply chain analysts and IT team. Each model update meant manual configuration changes, parameter adjustments, and careful validation across multiple planning scenarios. This process typically took 2-3 days per deployment and introduced human error risks.

Using Azure DevOps, we built a CI/CD pipeline that automatically deploys planning model updates, including forecast algorithms, safety stock parameters, and demand pattern configurations. The automation handles environment-specific settings, runs validation tests against historical data, and deploys to production only after accuracy thresholds are met.

The impact on forecast accuracy has been substantial. Our automated deployment approach enabled us to iterate on planning models weekly instead of monthly, allowing rapid response to changing demand patterns. MAPE (Mean Absolute Percentage Error) improved from 23% to 14% within six months. Manual intervention in the planning process decreased by 65%, freeing analysts to focus on exception management rather than routine deployments.

Happy to share implementation details and lessons learned from our journey.