Toyota Dashboard in Power BI: Star Schema, 40 DAX Measures, Forecast and What-if (5,500 Rows of Example Data)

By Ravindra Bagale · Last updated: 27 October 2026

About this data. Simulated example data for practice, not real Toyota figures. Only public facts (HQ: Toyota City, Aichi, Japan; industry: automobiles) are real. Money is in Japanese yen (¥).

No .pbix file. A .pbix can only be saved by Power BI Desktop on Windows, which is not used to make this page. So you get the data, the DAX and pictures of the layout, and you build the report yourself in about 30–45 minutes. The images below are pictures of the layout, not screenshots of Power BI.

Downloads: Sales.csv · Product.csv · Region.csv · Date.csv · toyota_powerbi_data.xlsx · measures.dax

1. The report you will build

2. Rebuild it from a blank report

Step 1. Get the data

Home › Get Data › Text/CSV. Load Sales.csv, Product.csv, Region.csv and Date.csv one by one (or Excel workbook › the _powerbi_data.xlsx and tick the 4 tables). In Power Query check the types: Date = Date, money columns = Decimal number, Qty = Whole number. Close & Apply.

Step 2. Build the star schema

Model view. Drag Sales[Date] to Date[Date], Sales[Product] to Product[Product], Sales[Region] to Region[Region]. Each is one-to-many, single direction, from the dimension to Sales.

Step 3. Mark the date table

Select the Date table › Table tools › Mark as date table › Date column. Time-intelligence functions such as SAMEPERIODLASTYEAR need this. Sort Date[Month] by Date[MonthNo].

Step 4. Create the measures

Home › New measure, then paste each measure from measures.dax (40 measures). Put them in a Measures table (Enter data › empty table) to keep the model tidy. Format: money measures as currency ¥, % measures as percentage with 1 decimal.

Step 5. Overview page: slicers and KPI cards

Top band: 4 slicers (Year, Region, Product, Channel) as drop-downs. Under it 6 card visuals (Revenue, Profit, Margin %, YoY %, Units, Discount %). Use the new Card visual with reference labels for ΔPY %.

Picture: see Figure 1 above.

Step 6. Overview page: charts

Line chart: Date[Month] on X, [Revenue] and [Revenue MA3]. Bar chart: Product vs [Revenue], sorted. Donut: Channel (few parts only). Gauge: [Margin % 2026] with target value. Write the finding in each title (Format › Title › fx and a text measure).

Picture: see Figure 1 above.

Step 7. Analytics page: diagnostic

Waterfall: Category = Product, Y = [ΔPY]. Decomposition tree: Analyse = [ΔPY], Explain by = Region, Product, Channel. Click the + to drill down and find the biggest drag.

Picture: see Figure 2 above.

Step 8. Analytics page: predictive

Line chart of [Revenue] by month › Analytics pane › Forecast › length 12 months, confidence 95%. Compare with the 'Revenue Trend' measure (LINESTX straight line).

Picture: see Figure 2 above.

Step 9. Analytics page: prescriptive

Modeling › New parameter › Numeric range: Price change from −0.10 to 0.10, step 0.01 (repeat for Discount, Cost and Volume). Use the parameter values in [WhatIf Revenue] and [WhatIf Profit] instead of the fixed constants. Add a table with Product and [Recommendation].

Picture: see Figure 2 above.

Step 10. Theme

View › Themes › Browse for themes and load the JSON below (save it as theme.json). Background #1F1F1F, panels #2B2B2B, cards #333333, accent yellow #F2C811, text white #FFFFFF / grey #BDBDBD, good #4CAF50, bad #E57373.

Step 11. Check and publish

Change every slicer and confirm every visual changes. Compare the card values with the table in section 4. Then File › Publish to share it in the Power BI service.

Theme JSON and hex codes

{
 "name": "RB Yellow Charcoal",
 "dataColors": [
  "#F2C811",
  "#FFE066",
  "#BDBDBD",
  "#8C8C8C",
  "#4CAF50",
  "#E57373"
 ],
 "background": "#1F1F1F",
 "foreground": "#FFFFFF",
 "tableAccent": "#F2C811",
 "good": "#4CAF50",
 "bad": "#E57373",
 "neutral": "#BDBDBD",
 "visualStyles": {
  "*": {
   "*": {
    "background": [
     {
      "color": {
       "solid": {
        "color": "#333333"
       }
      },
      "transparency": 0
     }
    ],
    "title": [
     {
      "fontColor": {
       "solid": {
        "color": "#FFFFFF"
       }
      }
     }
    ]
   }
  },
  "page": {
   "*": {
    "background": [
     {
      "color": {
       "solid": {
        "color": "#1F1F1F"
       }
      },
      "transparency": 0
     }
    ]
   }
  }
 }
}

3. All DAX measures

The four types of analytics in DAX: descriptive (Revenue, Profit, Margin %), diagnostic (ΔPY, ΔPY %, Contribution %), predictive (Revenue MA3, Revenue Trend) and prescriptive (WhatIf Revenue, WhatIf Profit, Price Change Needed, Recommendation).

// Toyota · Power BI measures (example practice data). Paste each into New measure.
// Tables: Sales (fact), Date, Product, Region.

Revenue = SUM(Sales[Revenue])

Profit = SUM(Sales[Profit])

Units sold = SUM(Sales[Qty])

Margin % = DIVIDE([Profit], [Revenue])

Revenue 2026 = CALCULATE([Revenue], 'Date'[Year] = 2026)

Revenue 2025 = CALCULATE([Revenue], 'Date'[Year] = 2025)

YoY % = DIVIDE([Revenue 2026] - [Revenue 2025], [Revenue 2025])

Discount % = DIVIDE(SUM(Sales[Discount]), SUM(Sales[GrossSales]))

Revenue per vehicle = DIVIDE([Revenue], [Units sold])

Electrified % = DIVIDE(CALCULATE([Units sold], Sales[Powertrain] IN {"Hybrid","EV"}), [Units sold])

EV % = DIVIDE(CALCULATE([Units sold], Sales[Powertrain] = "EV"), [Units sold])

Sell-through % = DIVIDE([Units sold], SUM(Sales[Produced]))

Units per dealer-month = DIVIDE([Units sold], COUNTROWS(Sales))

Fleet % = DIVIDE(CALCULATE([Units sold], Sales[Channel] = "Fleet"), [Units sold])

Gross Sales = SUM(Sales[GrossSales])

Discount Amt = SUM(Sales[Discount])

Cost = SUM(Sales[Cost])

Revenue PY = CALCULATE([Revenue], SAMEPERIODLASTYEAR('Date'[Date]))

ΔPY = [Revenue 2026] - [Revenue 2025]

ΔPY % = DIVIDE([ΔPY], [Revenue 2025])

Contribution % = DIVIDE([ΔPY], CALCULATE([ΔPY], ALL(Sales[Product])))

Margin % 2026 = DIVIDE(CALCULATE([Profit], 'Date'[Year] = 2026), [Revenue 2026])

Revenue MA3 = DIVIDE(CALCULATE([Revenue], DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -3, MONTH)), 3)

Price Change = 0.03

Discount Change = -0.10

Cost Change = 0.02

Volume Change = -0.01

WhatIf Revenue = (CALCULATE([Gross Sales], 'Date'[Year] = 2026) * (1 + [Price Change]) - CALCULATE([Discount Amt], 'Date'[Year] = 2026) * (1 + [Discount Change])) * (1 + [Volume Change])

WhatIf Profit = [WhatIf Revenue] - CALCULATE([Cost], 'Date'[Year] = 2026) * (1 + [Cost Change]) * (1 + [Volume Change])

Target Margin = 0.2

Price Change Needed = DIVIDE(CALCULATE([Cost], 'Date'[Year] = 2026) * (1 + [Cost Change]) / (1 - [Target Margin]) + CALCULATE([Discount Amt], 'Date'[Year] = 2026) * (1 + [Discount Change]), CALCULATE([Gross Sales], 'Date'[Year] = 2026)) - 1

Recommendation = IF([Margin % 2026] < 0.1, "Fix price / cut discount", IF([ΔPY %] < 0, "Find the cause, push sales", IF([ΔPY %] >= 0.1, "Invest to grow", "Keep steady")))

Revenue Trend =
VAR pts = ADDCOLUMNS(SUMMARIZE(ALL('Date'), 'Date'[MonthIndex]), "@rev", [Revenue])
VAR fit = LINESTX(pts, [@rev], 'Date'[MonthIndex])
VAR slope = SELECTCOLUMNS(fit, [Slope1])
VAR icpt = SELECTCOLUMNS(fit, [Intercept])
RETURN icpt + slope * MAX('Date'[MonthIndex])

Correl Discount vs Qty =
VAR n = COUNTROWS(Sales)
VAR mx = AVERAGE(Sales[Discount]) VAR my = AVERAGE(Sales[Qty])
VAR cov = SUMX(Sales, (Sales[Discount] - mx) * (Sales[Qty] - my))
VAR sx = SQRT(SUMX(Sales, (Sales[Discount] - mx) ^ 2)) VAR sy = SQRT(SUMX(Sales, (Sales[Qty] - my) ^ 2))
RETURN DIVIDE(cov, sx * sy)

4. How the DAX was checked

Each measure was run in a DAX test engine on the same data and compared with pandas: 22 of 22 checks matched. Revenue Trend (LINESTX) and the CORREL measure were checked in pandas only. Use these values to check your report with no slicer selected:

Next: the same dashboard in Excel · all 50 Power BI company dashboards.

5. FAQ

Can I download a .pbix?

No. A .pbix needs Power BI Desktop on Windows. You get the data, DAX and layout pictures and build it yourself.

Is this real Toyota data?

No, it is simulated example data for practice.

Why a separate Date table?

Time intelligence (SAMEPERIODLASTYEAR, DATESINPERIOD) needs a full, marked date table.

6. Quick quiz

Five questions on this page. Sign in to save your score.

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