Power BI owns the visuals in supply chain.
This is not about learning how to code.
It’s not about building the fanciest dashboard.
You are not competing with the Mona Lisa.
The goal is clear:
faster, better decisions with less manual work.
A dashboard that drives action.
If you’re entering the Power BI world in supply chain, I’ve got you covered.
Here’s how to start the right way and what most guides don’t explain.
.
1. Pick ONE Decision (This Is the Hardest Step)
The document starts with a non-negotiable rule:
Don’t start with a dashboard. Start with one decision.
Examples given in the guide:
- Which SKUs will stock out next?
- Which POs are delayed?
- Where is demand changing fastest?
Everyone agrees with this. Very few actually do it.
Why? Because choosing one decision forces trade-offs:
- What you will not show
- What you will ignore for now
- Who the dashboard is not for
That discomfort is a good sign.
Start Today:
Use one of the document’s example decisions and rewrite it in plain language:
“Which SKUs require action this week?”
If you can’t explain it without jargon, it’s not ready.
Tomorrow’s Win:
Your dashboard has a clear purpose and doesn’t turn into a reporting monster.
2. Clean Data in Power Query (But Design for Change)
The guide is explicit here:
data cleanup belongs in Power Query, not Excel.
From the document:
- Import raw sales, inventory, forecast, and PO files
- Remove duplicates
- Fix date fields
- Fill missing weeks
- Standardize SKU codes
Here’s a big, frequent miss:
Power Query is not just cleanup. It’s designing for change.
Column names will change.
File formats will change.
Someone will break the template.
Start Today:
Follow the guide’s cleanup steps, but also rename each Power Query step clearly (not “Changed Type 3”).
Tomorrow’s Win:
Next month, you click Refresh instead of rebuilding Excel files.
3. Build Simple Relationships (Your Data Model)
The document calls this out:
connect tables using keys like:
- SKU
- Date
- Warehouse
- Customer
- Product hierarchy
This replaces endless VLOOKUPs and manual joins.
What the guide implies but doesn’t spell out:
Your model should reflect how demand and supply flow, not how teams report.
Dates, SKUs, and locations matter more than org charts.
Start Today:
Confirm that every fact table connects to the same Date table.
Tomorrow’s Win:
Time-based analysis finally works (no mismatched weeks or broken trends).
4. Create the 5 Essential DAX Measures (Before Any Visuals)
This is where many readers get lost and this is core.
The guide explicitly recommends starting with these five planner-critical measures:
- Inventory Days
Inventory / Average Daily Demand - Forecast Accuracy
1 – ABS(Forecast – Actual) / Actual - Bias
(Forecast – Actual) / Actual - OTIF %
Delivered On Time / Total Orders - Backorder Value
SUM(Backorder Qty × Price)
These are not “starter examples.”
They are the foundation.
Here’s the key insight:
If these measures are wrong, great visuals make things worse.
Start Today:
Build one measure only and reconcile it against your Excel calculation.
Tomorrow’s Win:
Your KPIs update automatically as new data arrives. No rework.
5. Add Visuals That Drive Action (Using the Guide’s Examples)
The document is very intentional about visuals. It recommends:
- Line chart → Demand vs Forecast
- Table → Stockouts, excess, backorders
- Bar chart → Accuracy by category
- Cards → Total excess, total stockouts
- Heatmap → Service issues by region
The insight behind this:
Dashboards should surface exceptions, not averages.
If everything is green, the dashboard isn’t useful.
Start Today:
Take one table from the guide and sort it to show the worst-performing SKUs first.
Tomorrow’s Win:
Problems are visible in seconds, not buried in reports.
6. Add Slicers to Drill Down (Exactly as in the Guide)
The guide suggests slicers for:
- SKU
- Category
- Warehouse
- Customer
- Month
- Planner
This is what turns a report into a decision tool.
But slicers are not for exploration. They’re for ownership.
Start Today:
Limit yourself to 3 slicers tied directly to responsibility.
Tomorrow’s Win:
Users stop exporting to Excel and trust the dashboard.
7. Build Alerts for Exceptions & Publish
The final step in the document is about alerts:
- Stockouts
- Low OTIF
- High bias
- Demand spikes
- Delayed POs
This is what makes the dashboard proactive.
But alerts only work if ownership is clear.
Start Today:
Pick one alert, define the threshold, and assign one owner.
Tomorrow’s Win:
The dashboard highlights what needs action today.
Final Insight (Not in the Guide, But Critical)
Your first Power BI dashboard is not about scale.
It’s about trust.
Trust that:
- Numbers are consistent
- Refresh works
- Decisions improve
Once trust is built, expansion is easy.
Without it, Power BI becomes “that tool we tried once.”
Start small.
Deliver value fast.
Then grow.
