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Building The Factory of The Future

Building The Factory of The Future

Remote Monitoring & Predictive Maintenance

Remote Monitoring

The first step many companies take to build more agile factories is to connect their equipment and factory to gain rapid visibility. Remote monitoring enables companies to continuously monitor the health and performance of individual assets and entire factories to predict potential problems and manage changing customer demand. As these organizations seek to drive greater value through more reliable connecting environments, implementing remote monitoring is a key first step to shift from reactive to proactive and predictive service models.

Monitor key parameters of equipment to discover anomalies before they become critical issues and use this data to develop predictive maintenance programs. Connect your systems and products with sensors that gather data and relay this information to the cloud at regular intervals. Avoid premature and expensive equipment maintenance costs, extend the lifespan of your machinery, and avert critical downtime with the Azure IoT solution accelerator for remote monitoring.

Top Use Cases

  • Factory visibility (environment, machines, systems)
  • Early identification of equipment failure
  • Real-time safety monitoring

Data Flow

  • Collect and analyze data from any device or sensor
  • Ingest and store real-time analytics to drive data-driven decisions
  • Visualize operational data in real time
  • Deliver analytics that will improve factory performance & effectiveness


  • Improve productivity – Enable immediate remote responses to mechanical or physical equipment issues and sending automatic notifications to the right personnel so that problems can be resolved quickly, and downtime minimized
  • Optimize production – Provide detailed insights on machine performance to keep equipment running smoothly, extend the operational lifespan and increase overall production uptime
  • Enable real-time decisions – Provide real-time analytics and alerts about the condition of factory equipment & environment to empower data-driven decisions that can increase equipment effectiveness, and optimize performance & productivity
remote monitoring in manufacturing

Predictive Maintenance

Manufacturers have evolved from isolated plants to integrated digital factories, growing vertically and horizontally. With technologies available from Microsoft, you can apply technology to your existing operations and greatly improve efficiency.

We can help connect devices on the plant floor – different models from different vendors. By connecting these devices, the manufacturer gains enhanced visibility and granular control over what’s going on in the plant. They are able to proactively maintain equipment, streamline operations and reduce waste. By connecting LOB applications, third-party logistics, and other areas of their business, they can optimize processes and improve their overall production performance.

Microsoft technologies enable efficient, effective, connected operations, utilizing a variety of flexible solutions. With Microsoft’s IoT solution, you can connect heterogeneous devices, sensors, and systems to connect and improve production using your existing machines, OS’s, and connection protocols. Dynamics 365 empowers your management capabilities, while advanced analytics turns data gathered from your connected plant into actionable insights. Together, Microsoft has a holistic, flexible manufacturing solution, with IoT, advanced analytics, and Dynamics 365 enabling you to truly integrate and enhance production.

Predictive Maintenance Examples

  • Remotely monitor production flow in near-real time with smart connected machines to get ahead of production issues. You can monitor whether components are arriving at the plant floor as expected, and slow production if needed to reduce or eliminate excess work-in-process inventory with smart, connected machines.
  • Securely connect factories to share information across regions and departments, such as enabling both AI and human experts to provide guidance across the business regardless of location leveraging security and compliance at scale helping ensure the confidentiality, integrity, and availability of data stored in the cloud.
  • Analyze plant data to gain production insights and share best practices across both your owned/ operated sites and subcontracted manufacturing sites to gain insights on production and workforce performance, respond quickly to changes in the market place with digital intelligence that allows you to be agile and adjust to higher or lower demand, as well as adopting new models of existing products, and provide cross- channel visibility into inventories to optimize the supply chain.
  • Share best practices across plant sites and across plant departments to ensure quality, maximize efficiently, and improve workforce performance through knowledge sharing and connected communications.
  • Aggregate data, identify and correct quality assurance issues – with IoT and the ability to perform Big Data analytics, manufacturers can increase the number of quality checks that are performed, collect more quality inspection data and analyze more data than ever before. This enables them to spot defect patterns, and correct them more quickly. This also enables manufacturers to create predictive algorithms so that times/places where quality issues may occur are identified ahead of time.
  • Establish predictive maintenance schedules – with planned maintenance, cost of operations can be reduced and throughput can be increased. Implement predefined rules for equipment use and plant management (e.g. shut down production or equipment based on demand or environmental data), to optimize productivity and profitability.

Predictive maintenance boosts equipment reliability and helps manufacturing organizations stay ahead of unexpected issues that can easily derail production. This architecture shows how to analyze metrics and data related to the lifecycle maintenance of IoT-enabled equipment through predictive maintenance (PdM) to predict both timelines for probable maintenance events and upcoming capital expenditure requirements, allowing them to streamline their maintenance costs and avoid critical downtime.

Top Use Cases

  • Anomaly detection and resolution
  • Predictive asset maintenance preventing downtime and accidents

Data Flow

  • Continuously collect machine data in real-time from industrial devices – some with built-in OPC UA I/F
  • Analyze the data in real time to rapidly detect failures & root cause patterns
  • Build predictive models/learning algorithms (e.g., RUL, failure path)
  • Send alerts/notifications to take action


  • Azure IoT Hub is a high-scale service enabling secure bidirectional communication from a variety of devices
  • Event Hub ingests raw assembly-line data and passes it on to Stream Analytics
  • Azure Stream Analytics: Stream Analytics provides near real-time analytics on the input stream from the Azure Event Hub. Input data is filtered and passed to a Machine Learning endpoint, finally sending the results to the Power BI dashboard
  • Azure Functions facilitates executing actions based on insights garnered from device telemetry data during stream processing
  • Machine Learning: Machine Learning predicts potential failures based on real-time assembly-line data from Stream Analytics
  • Azure Data Lake: is a distributed data store that can persist large amounts of relational and nonrelational data without transformation or schema definition and is used to store assembly-line data along with failure predictions
  • Azure Data Bricks is an Apache Spark-based analytics platform optimized for Azure and provides streamlined workflows, and an interactive collaborative workspace
  • Power BI visualizes real-time assembly-line data from Stream Analytics and the predicted failures and alerts from Data Bricks


  • Reduce costs – Utilize historical and performance data from multiple sources to make accurate predictions about asset health, utilization, and the possibility of failure, enabling customers to take action based on this data. A McKinsey study found that AI-enhanced predictive maintenance of industrial equipment can generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and a 25% reduction in inspection costs.
  • Increase efficiencies – Minimize the frequency of unscheduled downtimes resulting from equipment failure, improve the overall availability of equipment, and maximize the useful life of equipment. Improve the overall equipment effectiveness (OEE) of assets by performing predictive maintenance based on real-time performance data.
  • Improve safety – By consolidating and analyzing data over long periods of time, customers can identify potentially hazardous conditions, estimate its impact on working conditions, and quickly take appropriate action to mitigate safety risks.
predictive maintenance in manufacturing

Interested in learning more on how the Oakwood Team can assist your organization in becoming more efficient through remote monitoring and predictive maintenance, please leave us a note below.

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