LuDoMaTiQuE
Services / Software / Steering

Predictive dashboard

Charts, yes.

But above all a sentence in plain language: "this week, you will run out of stock on SKU X".

The problem

A good dashboard should tell a story in ten seconds. Most enterprise dashboards look like an aircraft cockpit: 40 charts, 200 KPIs, nothing readable at a glance.

Result: the leader stops consulting it. Decisions are made on gut feeling, or too late, when a problem becomes visible.

A smart dashboard adds a layer of plain-language reading, detects anomalies and predicts the coming weeks. The sentence that matters replaces the chart you scrutinise.

How it works
Step 01

Sources

ERP, CRM, accounting, field files, IoT, manual feedback.

Step 02

Model

Business rules + statistical baseline + predictive models calibrated to your cycle.

Step 03

Interface

A page readable on mobile. A header sentence, 3 anomalies, the forecasts.

Step 04

Alerts

Email, Slack, SMS by severity. Nothing goes out if nothing is abnormal.

Three variants

Same service, three profiles, three stacks

Restaurant group · 3 sites

Daily covers, margins per site, stocks. Management travels between the 3 venues.

MetabasePythonProphetClaude 4.7
Result

Supplier orders adjusted to real forecast. 12% savings on food waste.

Textile SME · 45 staff

Strong seasonality, expensive stocks, unsold to anticipate.

SupabasePythonXGBoostVue 3
Result

Alert 8 weeks ahead on overstock SKUs. Unsold reduced by 23%.

Local authority

Attendance at sports, cultural, library facilities. Annual budget arbitration.

Next.jsPostgresProphetMistral Large
Result

Budget decisions backed by clear trends. Less gut-feel arbitration in committee.

What it changes
  • A sentence per dashboard rather than 10 charts: leaders actually consult it.
  • Real-time alerts, calm digest for the rest.
  • Reliable predictions at 2 to 8 weeks depending on the sector.
  • Accessible to an SME: no data team needed to keep the tool alive.