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Smart Factory Analytics

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Case Study

Driving Better Decisions Through Data

This case study highlights how Amesium Analytics transforms raw data into meaningful insights that support smarter business decisions. By analysing challenges, implementing tailored data solutions, and delivering clear outcomes, we help organisations improve efficiency, strengthen operations, and achieve measurable growth.

Smart Factory Analytics

Enabling real-time production visibility and improved equipment efficiency with Azure IoT and analytics.

Client

  • UK Manufacturing Company

Duration

  • 14 Weeks

Team Package

  • Standard Package (3-Person Team)

INTRODUCTION

A manufacturing client implemented IoT sensors across production lines but lacked a centralised analytics platform to monitor equipment performance, detect issues, and optimise efficiency. We delivered an Azure-based solution to provide real-time analytics and actionable insights.

THE CHALLENGE

The factory generated large volumes of sensor data, but faced key issues:

  • No real-time visibility of machine performance
  • Manual monitoring processes
  • Inconsistent maintenance scheduling
  • Difficulty analysing downtime and production flow
  • Lack of predictive analytics

The organisation needed a scalable cloud solution to turn IoT data into operational intelligence.

THE SOLUTION

We built a real-time Smart Factory analytics platform powered by Azure IoT and Synapse.

  • Azure IoT Hub to capture sensor and equipment data
  • Stream Analytics for near real-time processing
  • Azure Synapse Analytics for modelling and reporting
  • Power BI dashboards for plant managers
  • Predictive maintenance indicators using ML models

This provided end-to-end production visibility across the factory floor.

OUR APPROACH (4 Steps)

1. Sensor Data Assessment

Mapped sensor points, equipment events, and production metrics to identify analytic requirements.

2. Azure IoT Architecture Design

Designed a secure and scalable IoT ingestion pipeline using IoT Hub, Stream Analytics, and Data Lake.

3. Real-Time Analytics Development

Built dashboards for equipment performance, downtime analysis, and production KPIs.

4. Predictive Analytics & Handover

Implemented ML-based anomaly detection and provided full training to the operations team.

OUT RESULTS

The factory achieved measurable operational improvements:

  • 25% reduction in unexpected downtime

  • Real-time dashboards across all production lines

  • Faster maintenance planning and issue detection

  • Improved utilisation of machines

  • Enhanced visibility for operations and engineering teams

TECHNOLOGIES USED

    • Azure IoT Hub

    • Azure Stream Analytics

    • Azure Synapse Analytics

    • Azure Data Lake

    • Power BI

    • Azure Machine Learning

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