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Welcome to Part 3 of our Demand Planning series in Dynamics 365 Supply Chain Management! In this session, we dive deep into how to transform raw planning data into structured time series — the foundation for creating accurate and intelligent demand forecasts.
📊 What You'll Learn:
Creating Transformation Profiles: Understand how to configure transformation profiles that map your imported data into time series format, setting the stage for advanced forecasting.
Configuring Data Elements: Learn to define timestamp columns (e.g., order date), measure columns (e.g., quantity ordered), and dimension columns (e.g., product category, warehouse) for better planning granularity.
Using Calculation Profiles: Discover how calculation profiles let you derive new calculated measures (like revenue = quantity × unit price) during the transformation process — enabling richer, customized forecasting datasets.
Aggregating Time Series Data: See how to roll up daily data into weekly, monthly, or custom periods that match your organization's planning cycles.
Applying Filters for Targeted Forecasting: Explore how "Filter in Transformation" helps you selectively focus on particular subsets of data, making your forecasts sharper and more scenario-specific.
By mastering data transformation, including both transformation profiles and calculation profiles, you will be able to generate clean, structured datasets that fuel more precise and actionable forecasts in Dynamics 365 SCM.
🔔 Stay Tuned: Subscribe and follow along as we continue to explore advanced forecasting models (ARIMA, ETS, Prophet) and show how to integrate custom models via Azure Machine Learning!