Dashboard examples
This is what the solutions we build look like
Three dashboards with sample data, inspired by real challenges. Hover over the charts or open “View data” to see the numbers.
Distributor performance dashboard
A single version of the numbers for the whole network: revenue, volume and target attainment for each distributor, month by month.
- Period: Jan–Dec 2025
- Region: All
- Channel: All
- Net revenue
- $48.6M ▲ 9.2% vs. 2024
- Volume sold
- 1,284 t ▲ 4.1% vs. 2024
- Average order size
- 412 kg ▼ 2.3% vs. 2024
- Active customers
- 6,940 ▲ 3.8% vs. 2024
Monthly revenue vs. target
$ millions
- Actual
- Target
View data
| Month | Actual | Target |
|---|---|---|
| Jan | $3.6M | $3.8M |
| Feb | $3.4M | $3.6M |
| Mar | $3.9M | $3.9M |
| Apr | $3.8M | $3.9M |
| May | $4.0M | $4.0M |
| Jun | $4.1M | $4.1M |
| Jul | $4.3M | $4.2M |
| Aug | $4.2M | $4.2M |
| Sep | $4.0M | $4.2M |
| Oct | $4.3M | $4.3M |
| Nov | $4.4M | $4.4M |
| Dec | $4.6M | $4.5M |
Revenue by channel
$ millions
View data
| Channel | Value |
|---|---|
| Supermarkets | $17.4M |
| Wholesale | $12.9M |
| Self-service stores | $9.8M |
| Convenience stores | $6.1M |
| E-commerce | $2.4M |
Distributor ranking
| Distributor | Revenue | vs. target | Status |
|---|---|---|---|
| Central Distributor | $12.8M | 104% | On target |
| North Distributor | $9.6M | 98% | Watch |
| Litoral Distributor | $8.7M | 101% | On target |
| Cuyo Distributor | $6.9M | 92% | Below |
| South Distributor | $5.8M | 96% | Watch |
| Patagonia Distributor | $4.8M | 89% | Below |
The usual challenge
Each distributor reports sales in its own spreadsheets, with different criteria. Consolidating them takes days and nobody fully trusts the final number.
How we solve it
We integrate the sources into a single model, align the criteria and publish a self-updating dashboard with filters by region, channel, category and product.
What your team gains
Leadership and the sales team see the same number and spot early which distributor or channel is drifting away from target.
Coverage and cross-selling dashboard
Which product lines each point of sale buys and where the cross-selling opportunities are, by distributor and by sales rep.
- Cycle: June 2025
- Distributor: All
- Sales rep: All
- Points of sale in portfolio
- 3,215 ▲ 2.6% vs. previous cycle
- Classic Line coverage
- 91.4% ▲ 1.2 pp
- Premium Line coverage
- 63.8% ▲ 4.5 pp
- Buy both lines
- 58.9% ▲ 3.9 pp
What each point of sale buys
% of points of sale in portfolio
- Classic Line only 32.5%
- Both lines 58.9%
- Premium Line only 4.9%
- No purchase this cycle 3.7%
Coverage by distributor
% of points of sale buying each line
- Classic Line
- Premium Line
View data
| Distributor | Classic Line | Premium Line |
|---|---|---|
| Central | 95% | 72% |
| Litoral | 93% | 69% |
| North | 92% | 61% |
| South | 90% | 60% |
| Cuyo | 88% | 55% |
| Patagonia | 86% | 52% |
Cross-selling opportunities
Points of sale that buy the Classic Line but not the Premium Line
| Region | Sales rep | Points of sale | Monthly potential |
|---|---|---|---|
| North | Sales rep 07 | 148 | $1.9M |
| Cuyo | Sales rep 12 | 131 | $1.6M |
| South | Sales rep 03 | 117 | $1.4M |
| Patagonia | Sales rep 09 | 96 | $1.1M |
| Central | Sales rep 15 | 84 | $1.0M |
The usual challenge
The sales team knows how much it sells, but not which points of sale are missing each product line or which rep has the most opportunities.
How we solve it
We cross-reference each point of sale’s purchases by product line and build a coverage dashboard with a distributor ranking and lists by sales rep.
What your team gains
Every sales rep heads out with a concrete list of customers to offer the line they don’t buy yet.
Forecasting and replenishment dashboard
A machine learning model that anticipates demand for the coming weeks and recommends how much of each product to restock.
- Horizon: 8 weeks
- Category: All
- Warehouse: Central
- Forecast accuracy
- 92.4% ▲ 6.1 pp vs. previous method
- Forecast demand (8 wks)
- 231,600 units ▲ 12.5% vs. last 8 wks
- Products at risk of stockout
- 37 ▼ 12 vs. last week
- Estimated overstock
- $2.3M ▼ 18% vs. last quarter
Weekly sales and forecast
Thousands of units
- Actual
- Forecast
- Likely range
View data
| Week | Actual (thousands) | Forecast (thousands) | Likely range |
|---|---|---|---|
| Week 1 | 21.2 | — | — |
| Week 2 | 22.0 | — | — |
| Week 3 | 21.6 | — | — |
| Week 4 | 23.1 | — | — |
| Week 5 | 22.8 | — | — |
| Week 6 | 24.0 | — | — |
| Week 7 | 23.5 | — | — |
| Week 8 | 22.9 | — | — |
| Week 9 | 24.6 | — | — |
| Week 10 | 25.1 | — | — |
| Week 11 | 24.3 | — | — |
| Week 12 | 25.8 | — | — |
| Week 13 | 26.2 | — | — |
| Week 14 | 25.5 | — | — |
| Week 15 | 26.9 | — | — |
| Week 16 | 27.4 | — | — |
| Week 17 | — | 27.9 | 27.1 to 28.7 |
| Week 18 | — | 28.3 | 27.2 to 29.4 |
| Week 19 | — | 27.6 | 26.3 to 28.9 |
| Week 20 | — | 29.0 | 27.4 to 30.6 |
| Week 21 | — | 29.4 | 27.6 to 31.2 |
| Week 22 | — | 28.8 | 26.8 to 30.8 |
| Week 23 | — | 30.1 | 27.9 to 32.3 |
| Week 24 | — | 30.5 | 28.1 to 32.9 |
Stockout risk by category
Number of products
View data
| Category | Value |
|---|---|
| Beverages | 12 |
| Snacks | 9 |
| Dairy | 7 |
| Cleaning | 5 |
| Frozen | 4 |
Suggested replenishment
Next 2 weeks, in units
| Product | Stock | Forecast demand | Reorder | Status |
|---|---|---|---|---|
| Cola 2.25 L | 1,840 | 3,200 | 1,400 | Restock now |
| Potato chips 150 g | 960 | 1,450 | 500 | Restock now |
| Drinkable yogurt 1 L | 1,120 | 1,300 | 200 | Watch |
| Ice cream 1 kg | 380 | 520 | 200 | Watch |
| Dish soap 750 ml | 2,400 | 1,900 | — | Covered |
The usual challenge
Purchasing is planned with historical averages: some products run out of stock at peak times while others pile up in the warehouse.
How we solve it
We train a model that combines sales history with seasonality and promotions, and integrate it into a weekly replenishment dashboard.
What your team gains
The purchasing team knows what to restock, and how much, before it runs out, and frees up capital tied up in overstock.
Trusted by
Consumer goods · Healthcare · Technology · Distribution · Retail
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