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/ Case Study · Food & Beverage
A Digital Financial Solution for the Food Industry. Finance and Utilities, on One Dashboard.
/ The engagement
A Power BI Dashboard Program for a Food Company.
With more than 85 years of history and a team bigger than many cities in Brazil, the client believes a better future demands quality food built on sustainable management. Brainvire built Power BI finance and utilities dashboards, with icon-led navigation, tooltips, and sensor visualizations for temperature, pressure, and energy consumption across equipment.
/ About the client
With more than 85 years of history and a team bigger than many cities in Brazil, the client believes a better future will demand quality food. It has a long, complex chain that requires sustainable management for the development of its employees, partners, clients, and consumers alike.
Brainvire's team created thumbnails under specific headings and subheadings using icons in the finance dashboard, and provided tooltips for details such as workspace name or profile. Icons were linked to embedded URLs to reach each report. In the utilities dashboard, the team built visualizations for the temperature, pressure, and energy consumption of different equipment and sensors, with side navigation and a date-range slicer.
/ The approach
Design, architecture, and features, shipped as one program.
A column per sensor
Different sensors shared one value column, so the team created duplicate value columns and ran the calculations each sensor required.
Clean on the axis
Graphs needed the right time intervals, so the date column moved to date format and the time column to hh:mm, so the data displays distinctly and clearly.
One slicer for all
A single date-range slicer had to filter sensors across tables, so a calendar dimension table joins the minimum and maximum dates and relates to every sensor table.
Dynamic by selection
Legends had to follow the sensor slicer, so a DAX query concatenates the sensor name with predefined text to display dynamic legends on the visuals.
/ What stood in the way
Four problems, solved without a maintenance window.
Select a challenge to see how it shaped the build.
A single value column
Different sensors have different data types, but there was only one value column for all of them. The development team created duplicate value columns and performed calculations as per the requirement.
Data setting on graphs
Setting up the data on graphs across different time intervals was a challenge. The team converted the date column from date and time to date format and changed the time column from hh:mm:ss to hh:mm, so the data displays distinctly and clearly.
Data filtering
A single date-range slicer had to filter data for sensors present in different tables. The team created a calendar dimension table, with the minimum date as the start and the maximum date as the end, and related it to all the other sensor tables.
Dynamic legends
Dynamic legends had to display on the visuals as per the sensor slicer selection. The team created a DAX query using a concatenate function to join the sensor name with predefined legend text to meet the client's requirement.
/ Product screens
The delivered experience.
The interface people actually use.
/01Overview
/02Finance
/03Utilities
/04Sensors
/05Reports/ The results
What changed, in numbers.
/ Next step
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