Case Study

Turning Sensor Data into Monthly Reports in Minutes

Innovacloud built a Python and Azure reporting workflow for 16 sensor feeds, cutting monthly report preparation from four hours to ten minutes and projecting 46 staff hours saved annually.

01 / Business context

Connected sensors were producing temperature readings, but the business still needed to turn that information into reports its team could use.

This project extended the refrigeration-monitoring work into a custom software solution. Innovacloud connected data collection with processing, visual presentation and scheduled report generation.

02 / The challenge

Readings from multiple sensors needed to be organised by equipment and date, checked for gaps and presented consistently. Repeating those tasks manually each month took time and made the report dependent on individual preparation.

The challenge was to build a dependable reporting workflow around the business's data, with outputs that were straightforward for staff to review.

03 / The approach

Innovacloud developed a Python application to process readings received through the LoRaWAN gateway. The software organised the data, generated temperature graphs and assembled the monthly report.

Supporting Azure services provided cloud resources for the workflow, while scheduled execution made report production repeatable. The output brought the sensor history into a format staff could review without working through raw measurements.

Python used pandas to clean and organise measurements and Matplotlib to generate equipment-level graphs. A scheduled Azure Function ran the reporting process, Azure Blob Storage held report outputs, and Power Automate sent each report to the team for review. The workflow consumed the readings already collected through the LoRaWAN system.

We connected the stages into one process, from incoming readings to a completed report ready for review.

04 / Results & business impact

Monthly report preparation fell from four hours of manual work to approximately ten minutes of review, while processing all 16 sensor feeds. Across twelve monthly reporting cycles, that difference equates to a projected 46 staff hours saved per year.

The business gained a consistent reporting output and recovered time previously spent assembling it. Staff could focus on reviewing temperature trends and deciding what required attention.