Summary of "Part 1 Intro to Demand Forecasting in Dynamics 365 Supply Chain Management - TechTalk"

Summary (TechTalk Part 1: Intro to Demand Forecasting in Dynamics 365 Supply Chain Management)

What demand forecasting is (and why it’s useful)

Demand forecasting predicts future demand for products/services to support:

Revenue estimation
Strategic and operational planning

Key benefits discussed:

Reduce buffer inventory and cash tied up in on-hand inventory
Avoid costs from unexpected demand (e.g., expediting procurement/production)
Support make-to-order by reducing sales-order-to-delivery time (ordering/making earlier for long-lead items)
Improve global capacity planning by analyzing demand over larger time windows
Prevent stock-outs (especially important in retail to avoid lost sales)

High-level system architecture

Forecasting in Dynamics 365 Supply Chain Management uses:

Dynamics 365
Azure Machine Learning
Azure Data Lake

Conceptually:

The “Dynamics” part triggers modeling
The “Azure” part performs statistical/time-series forecasting

Core time-series forecasting concept used

Dynamics/Azure Machine Learning uses time series forecasting based on historical demand patterns.

Forecast components:

Trend (up/down direction)
Seasonality (repeating spikes/patterns; may be quarterly/annual, etc.)
Error/Noise (random variation not perfectly explained)

How Dynamics evaluates and compares forecasting models

When generating a forecast:

Dynamics builds the model using a subset of historical demand
It keeps remaining points to evaluate accuracy

Accuracy metric:

MAPE (Mean Absolute Percentage Error) compares actual vs forecast values
Guidance:
- MAPE ~20 or less is considered “pretty good”
- Lower is better (e.g., 10 is “even better”)

Notes:

Dynamics does not expose the raw underlying equation
It provides forecast outputs, confidence intervals, and traceability

Forecast “granularity” and dimensions

Forecast results are generated per granularity attribute—a unique combination of enabled forecast dimensions.

Mandatory dimensions:

Company
Site
Allocation key (item allocation key)

Optional additional dimensions may include:

country/region, state
customer group/account
warehouse inventory status
item number
product dimensions (size/color/style/configuration)

Key impact explained:

Running forecasting at a detailed level and aggregating may produce different results than forecasting directly at a less detailed level.

Major forecasting algorithm parameters in Dynamics (used by Azure/R)

Parameters highlighted include:

Time series model choice:
- ARIMA, ETS, STL
- combinations such as ETS+ARIMA, ETS+STL
- All: tests all options and selects the lowest MAPE (can take longer)
Minimum / Maximum forecasted value:
- clamps predictions within allowed bounds (e.g., avoid values above a max)
Missing value substitution:
- handles gaps in history (e.g., interpolation vs 0; recommended default often 0)
- scope can be global vs specific granularity attribute/date range (recommended global)
Confidence level (1–99):
- controls confidence interval band size in the outputs
- affects how the forecast is presented more than the mean forecast quantity
Seasonality hint:
- interval length for repeating seasonality
- example: quarterly pattern → monthly buckets → set hint accordingly
Trend vs seasonality relationship:
- additive: seasonality magnitude independent of trend
- multiplicative: seasonality magnitude scales with trend
- none/auto options (auto recommended if unsure)
Test set size percentage:
- portion of history reserved for accuracy testing (example: 20%)
- tradeoff: enough data for reliable MAPE vs performance

Implementation guidance / best practices highlighted

Test and retest: tweak parameters and compare outputs/MAPE
Include a data scientist (or statistical background) to reduce issues like flat forecasts
Dynamics UI parameters aren’t the full set of R parameters:
- if defaults aren’t sufficient, review R package parameters (noted as part of future tech talks)
Data requirements:
- historical horizon should be at least 2× the forecast horizon
- include at least two cycles of seasonality if applicable

Dynamics 365 setup required (before the Azure modeling)

Key setup items include:

Forecasting unit:
- requires unit conversions to generate forecasts
Forecast strategy for transaction history:
- options include manual/copy historical vs Azure machine learning (focus of the session)
- older “Azure machine learning classic” was mentioned as deprecated
Forecast dimensions tab:
- configure granularity via company/site/allocation key plus additional dimensions
Item allocation keys:
- group items to improve forecast generation performance (parallel-style processing)
- allow different algorithm settings per allocation key
Forecast models:
- track multiple forecast sets (system-generated vs customer/sales/team-provided)
Outlier removal queries:
- remove anomalous demand events (e.g., one-time huge order)
Intercompany planning groups:
- define forecast sequence (downstream first, upstream later)
Master plan tie-in:
- choose how forecast is used to satisfy demand during planning
Forecast reduction method:
- define how to reduce forecast when real orders/transactions arrive (periodic/real-time/fixed percentage, etc.)

End-to-end forecast generation process (demo walkthrough)

Ensure historical demand exists (use data migration if needed via historical external demand).
Generate forecast: “Generate statistical baseline forecast”
- set:
  - historical horizon (e.g., ≥ 2 years for a 1-year forecast)
  - forecast horizon (e.g., 12 months)
  - bucket size (day/week/month)
  - optional freeze time fence
Review outputs: “Adjusted demand forecast”
- forecast lines shown per granularity
- confidence interval displayed based on confidence level
- traceability:
  - historical values used
  - system forecast vs manually edited values (edited row bolded)
- example noted:
  - when little/no history exists, forecasts may default to minimum (e.g., value of 1)
Authorize/publish forecast to system for planning use.

Q&A highlights from the session

Grouping items to reduce granularity
- remove item number from dimensions and forecast at allocation key level (already part of required setup)
SKU retirement / supersession
- no native item supersession
- workaround: export old item history, transform to align with the new SKU, then re-import
Seasonality on a weekly basis
- depends on data volume/gaps; deep granularity may lack sufficient data
Where does country/region come from?
- needs follow-up (speaker wasn’t sure from memory)
Use sales forecast vs statistical generation
- manual forecast lines can be entered (e.g., UI/Excel) and differentiated by forecast model; can later be edited
License/cost questions for Azure ML component
- Azure costs are on the customer’s Azure subscription
- no extra D365 licensing required specifically for forecasting generation
- compute cluster running the R script is a major cost driver (Azure pricing estimator can help)
Can forecasts be updated/re-generated?
- yes; regenerate at any time (e.g., quarterly or after management changes) and override published forecasts
Can Dynamics use external factors beyond historical demand?
- not natively; forecasting here is time-series based on historical demand only
- extensibility exists but requires partner/customization to become multivariate
Add more forecast dimensions in code?
- described as “mission impossible” / not straightforward—would require UI/code/enumeration changes

Main speakers / sources

Ann Krupke — Senior Fast Track Solution Architect (Microsoft)
Nikhil Paldicar — Fast Track group (Microsoft)
Toby James — Fast Track group (Microsoft)
Andre Garmash — Senior Software Engineer, Product team (Demand forecasting)
David — Moderator (introduced session; also TechTalk host)
Microsoft Azure Machine Learning + R “forecast” package — referenced as the forecasting engine/tooling behind the scenes