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Weather, holidays and demographics next to sales
Sales move with the heat, with long weekends and with who lives in a province, but none of that is in the ERP. In Muvia weather, calendar and demographics sit next to sales: you measure what one degree more is worth and where to move stock before a long weekend.
Illustrative scenario: it describes a typical case, not a customer project.
Heat sells. How much, nobody knows.
Everyone knows that more soft drinks sell in summer and that cities empty out on long weekends. But when it is time to reorder, weather and calendar remain a hunch: sales live in the ERP, the weather in an outside service, holidays in a calendar, demographics in a public file.
Without putting them in the same table you cannot say what one degree more is worth, which category reacts and which does not, which province fills up on a long weekend and which one empties.
- Who it's for
- Demand planning and supply chain, category managers, regional managers.
In Muvia, step by step
Real product screens, recorded on a project with sample data.
The video · Sales move with the heat, with long weekends and with who lives in a province, but none of that is in the ERP. In Muvia weather, calendar and demographics sit next to sales: you measure what one degree more is worth and where to move stock before a long weekend.
What you get
- A degree has a number
- The effect of temperature on each category is measured, not guessed, and it updates with new data.
- Long weekends prepared in advance
- You know which provinces empty out and which fill up on long weekends, and stock moves in time.
- Outside data like inside data
- Weather, calendar and demographics are sources like any other, each with its own table and query.
- The reasons written once
- The notebook explains what drives sales to whoever decides reorders and stock, always on the latest copy of the data.
For the technical team
How it is built in Muvia
- 1
Add the outside data as sources
Weather comes from a REST API, the holiday calendar from Excel, demographics from a CSV file: each one becomes a table and a query next to sales, with no schema to design.
- 2
Join them in a query
In the SQL editor, put sales, temperature, type of day and population side by side for every day and province. The query becomes a dataset that analyses, dashboards and notebooks read the same way.
- 3
Measure the effect in Analysis
The XY chart plots temperature against each category's sales; the Layers view overlays a province's long weekends to see whether they repeat; notes mark Easter, mid-August and the bridge days.
- 4
Put long weekends on the dashboard
A dashboard shows, for every province, how sales change on long weekends and in each temperature band: which ones empty out and which ones fill up.
- 5
Write down what to do
A notebook with text and figures that stay current gathers the drivers and the actions: reorders that take the weather into account, stock moved ahead of long weekends.
- The data it needs
- Daily sales by province and category, from the till files
- Temperature and rain day by day per province, from a weather service read through a REST API
- Holidays, bridge days and long weekends, from a calendar in Excel
- Population and age bands per province, from a public data file
- Parts of Muvia used
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