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A Power BI logistics dashboard can turn inconsistent sales and delivery records into a clearer view of revenue, margins, returns, and delivery costs—but its conclusions are only as dependable as its data cleaning and assumptions. In a JCars project, Gabriel Wainaina reports building a six-page dashboard from a 32-column dataset, with reported revenue of Ksh 1.46 billion and gross margin of 1.80%. Those are project outputs, not audited company figures.
What the JCars dashboard contains
In a DEV Community article published September 30, 2026, project author Gabriel Wainaina describes using a dataset called Jcars_data, with 32 columns covering customers, sales, deliveries, and pricing. The records had accumulated inconsistent spellings, formats, and placeholder values over time, without validation rules.
Wainaina reports creating fact and dimension tables, adding DAX calculations, and building a six-page Power BI report. Its coverage includes an executive view, products, geography and sales team, customers and payments, and logistics and delivery. The published description groups some page topics together, so it does not establish a precise page-by-page inventory.
The article’s metrics and rankings below are the author’s reported dashboard outputs. The underlying data and calculations have not been independently validated, and the article does not provide enough model detail to reconstruct relationship cardinality or audit individual measures. Read the project account on DEV Community.
#1 Best Overall
How the raw data was prepared
Normalize categories without silently dropping values
The project mapped variants such as corp, CORPORATE, govt, and n.g.o to canonical customer-type labels. Values not covered by the mapping were converted to Proper Case rather than discarded. That keeps unfamiliar categories visible, but they may still need review: capitalization alone cannot determine whether two differently named values mean the same thing.
Convert currency using explicit assumptions
The author says five money fields—unit selling price, unit cost, delivery fee, logistics cost, and recorded revenue—were standardized to Kenyan shillings (KES). The project used fixed rates of KES 1.00 per KES, KES 129.20 per US dollar, KES 147.67 per euro, and KES 7.84 per South African rand. The article gives no rate date or rate provider; it also says values without a currency marker were assumed to be KES and one rate was applied across all transaction dates. These are project assumptions, not independently checked exchange rates. For a business report, confirm source currency and transaction-date policy before comparing converted amounts.
Rank #2
Parse dates consistently
Order and delivery dates included Excel serial numbers and multiple text formats. The project treated ambiguous slash-separated dates as day/month/year, all-digit strings as Excel serials, and two-digit years as 20xx; unparseable placeholders became nulls. These choices affect time-series reporting and delivery intervals. If a source includes mixed date conventions, validate representative records against the original system before relying on trends.
Clean placeholders and model the data
The author promoted the first row to column headers, changed blanks and placeholders such as N/A and unknown to nulls, and used Power Query cleaning code to address spelling, spacing, and capitalization variants. The cleaned data was then split into fact and dimension tables and connected for dashboard analysis. The article does not publish the full schema or measures, so its model cannot be reproduced from the description alone.
Rank #3
What the dashboard reported
The following figures are attributed to Wainaina’s JCars dashboard article, published in 2026; they are not audited company accounts or independently verified results.
| Measure or comparison | Reported dashboard output |
|---|---|
| Units sold | 458 |
| Total revenue | Ksh 1.46 billion |
| Gross profit and gross margin | Ksh 26.43 million; 1.80% |
| SUV revenue and margin | Ksh 844.86 million, described as 57.7% of revenue; 9.14% margin |
| Returns and cancellations | 149 returned units; 37 cancelled transactions |
| Logistics costs and delivery fees collected | Ksh 27.78 million; Ksh 24.91 million |
The dashboard reportedly ranked Thika highest and Nairobi lowest for branch revenue. Toyota led revenue by make, while the Harrier led unit sales by model. Rift Valley was reported as the largest region by revenue, at Ksh 377.47 million. Revenue rank is not a profitability ranking: the reported regional margins differ substantially.
Where reported margins were weak—and what that does not prove
Vehicle categories
The project article reports negative margins for sedans (-16.24%), trucks (-65.57%), vans (-27.67%), and crossovers (-9.84%), alongside a 9.14% margin for SUVs. These differences identify categories to investigate, not established causes. The author suggests checking purchase cost, discounts, and delivery cost; the dashboard does not prove that any one factor caused the losses.
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Reported regional margins were Central -4.13%, Nyanza -8.90%, Nairobi -16.77%, and Rift Valley 4.58%. The source does not establish whether vehicle mix, discounting, or costs explain the gap. Before comparing regions, check that periods and transaction volumes are comparable; a revenue leader need not be a margin leader.
Best Value
Lead sources
The article reports positive margins for Instagram (17.20%), WhatsApp (12.51%), and Facebook (10.09%), and negative margins for referrals (-22.21%) and corporate tenders (-18.02%). These figures do not show that a channel caused the margin difference: channels may attract different customer or vehicle mixes. Compare like-for-like sales and volumes before treating a lead-source margin as evidence of channel effectiveness.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reconcile returns, cancellations, and delivery economics
The article says car dealers, government, NGOs, and corporates together represented about 83% of revenue. It also flags uncertainty around the headline totals: one cancelled order reportedly still carried Ksh 4.38 million in revenue, but the article does not establish whether returned or cancelled sales were included in total revenue. That ambiguity matters when reading the 458-unit and Ksh 1.46-billion figures.
The reported Ksh 27.78 million logistics cost and Ksh 24.91 million delivery fees collected are not interchangeable measures. A difference between them alone does not explain profitability: the article does not specify the full allocation, timing, or treatment of delivery amounts and returns. Reconcile cancelled and returned transactions to revenue, units, and costs, then document whether delivery fees are treated as revenue, a cost recovery, or another accounting item.
How to use the dashboard for follow-up decisions
- Verify the foundations. Review category mappings, null handling, date parsing, currency assumptions, and the fact/dimension relationships before using totals for decisions.
- Trace negative-margin sales. For the loss-making vehicle categories, compare each transaction’s purchase cost, selling price, discounts, and delivery or logistics cost. This can identify where the margin calculation is formed, without assuming a cause in advance.
- Reconcile transaction status. Check returned and cancelled records against recognized revenue, units sold, and associated costs, including the reported cancelled order carrying Ksh 4.38 million in revenue.
- Compare groups fairly. For regional and lead-source comparisons, use comparable periods and review unit volume, vehicle mix, and customer mix. Small groups can produce volatile margins, particularly when makes or models have few sales.
- Track margin over time. A period-by-period view can distinguish a persistent issue from a short-lived fluctuation, provided date parsing and transaction-status rules are consistent.
As Wainaina puts it, “The next step is to test the causes behind the losses, starting with vehicle level pricing and cost, so management can act on evidence rather than assumption.”
Quick Recap
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