If you've ever tried to understand how your forecasts are calculated in Dynamics 365 Supply Chain Management, you'll love the new "Explainability" tab in Demand Planning. It's like a behind-the-scenes look at what makes your forecasts tick. Let's just jump into it.
What is the "Explainability" Tab?
The Explainability tab is a feature that allows users to dive deeper into how the forecast models are built and why certain results are generated. For those managing demand planning, this tab provides the algorithmic insights—things like the type of model used (e.g., ARIMA, best-fit), and, more importantly, shows key performance indicators like Mean
Absolute Percentage Error (MAPE).
This isn't just for the data scientists in the room—this tab makes complex forecasting logic understandable for anyone running a supply chain.
Why You Should Care About MAPE
MAPE stands for Mean Absolute Percentage Error. It’s a statistical measure that tells you how accurate your forecasts are by comparing the forecasted values to the actual results. A lower MAPE percentage means your forecast is spot on, while a higher percentage indicates there’s a bigger gap between what was predicted and what actually happened.
Think of it like this: if your MAPE is 10%, it means your forecast was off by 10% on average. This is incredibly useful because it gives you a quantitative way to assess the reliability of your forecasts and adjust your planning strategies accordingly.
Key Concepts in Demand Planning
Forecast Models: The "Explainability" tab shows which model was used to generate the forecast, whether it’s an ARIMA model (AutoRegressive Integrated Moving Average) or a best-fit algorithm. ARIMA is commonly used for time-series forecasting because it factors in trends and seasonal variations.
Time Fences: These are boundaries that prevent last-minute forecast changes from disrupting your demand plans. For example, if you set a time fence of two weeks, any new demand forecasts won’t affect your plans within that two-week period. This keeps your operations more stable.
Best Fit: In cases where multiple forecasting models can be applied, the system picks the best fit based on past performance. The Explainability tab allows you to see why a particular model was chosen for a specific demand forecast.
How to Use the Explainability Tab
Algorithm Transparency: View the exact algorithms used for forecasting and understand why certain predictions were made.
Accuracy Measurement: Use MAPE and other metrics to determine how well your forecast performed. You’ll know exactly where the deviations happened, which helps you fine-tune your planning.
Scenario Analysis: The tab lets you assess different forecast scenarios, giving you the power to make informed decisions based on data-backed insights.
Dad Tip
Here’s my “dad” advice: If you’re not already using the Explainability tab, you’re flying blind. It’s like getting the answers to the test before you even take it. Understanding your forecast accuracy through tools like MAPE will give you the confidence to make decisions that won’t leave you in the dark. It’s also a great way to justify your demand planning decisions to stakeholders—after all, numbers don’t lie!
To wrap it up (without a conclusion, because conclusions aren't my style), if you're serious about demand planning, the Explainability tab is your new best friend. Dive into it and start using MAPE and forecast models to fine-tune your strategy. I basically wrote a wrap up that was a conclusion in hindsight. I'll be better.
And as always, don’t forget: “I’d tell you a forecast joke, but it’s just too unpredictable.”
Craving chicken pot pie,
DynamicsDad
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