IoT in process manufacturing

Increase equipment efficiency, drive production quality and tap intelligent supply chains for oil and gas, agriculture and process manufacturers with the Internet of Things (IoT).

Azure IoT for process manufacturing

Achieve operational excellence across your processes, maximise production yield while reducing waste and monitor asset integrity to avoid critical and costly downtime.

Reduce operational costs and power the growth of your business with IoT

Explore these common uses for IoT in process manufacturing and imagine how a new Azure IoT solution could help your business.

Operational excellence

Get a complete view of your operations and better meet your customers' needs by collecting data from equipment and factories, then consolidating and analysing it. Improve operational performance and decision-making, anticipate disruptions and help employees make better, faster decisions by giving them the right data at the right time. Achieve operational excellence with Azure IoT solution accelerator for connected factory.

Connected logistics

Decrease supply-chain risk and ensure the quality and authenticity of in-transit products with a full survey of your inbound and outbound logistics. Improve security and increase efficiency by tracking the location of materials and monitoring resource consumption with IoT sensors connected throughout your supply chain. Achieve connected logistics with Azure Sphere, Azure Maps and Azure Blockchain.

Precision farming

Ensure the safety and quality of goods from harvest to shelf by investing in smart farming systems. Track agriculture yields throughout the supply chain and collaborate with other manufacturers and food and beverage providers using shared geolocation and sensor data. Achieve precision farming with Azure Maps, Azure IoT Edge and Azure IoT solution accelerator for remote monitoring.

Prescriptive maintenance

Mitigate production and service disruptions by connecting your equipment and applying advanced analytics and machine learning to anticipate outages. Ensure production uptime with rich insights and automatic alerts triggered by manufacturing data. Make these advancements with Azure IoT solution accelerator for prescriptive maintenance.

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Increase equipment reliability with prescriptive maintenance

Identify potential issues before they happen with a prescriptive maintenance solution built entirely from Azure IoT products. In this demo, see how to analyse streaming data from sensors and devices, collect data over time and apply machine learning to predict and prevent equipment failures and costly downtime.

Dashboard

KPI summary

Alerts and warnings

Asset details

Alert resolution

Step 1 of 3

Real-time remote monitoring

Operators can see the real-time location and health status of the entire infrastructure on the dashboard.

Step 2 of 3

View asset location and status

Understanding asset health is critical as any lost-time incident can be extremely costly to production volumes and supply contracts.

Step 3 of 3

Keep tabs on remote locations

Where a site might have been visited once every six months for routine maintenance, its status can now be tracked in real time.

Step 1 of 2

Track business metrics in real time

Critical site and production data rolls up to summarise important business KPIs, tracking them against targets and thresholds throughout the day or production period.

Get real-time results from assets that traditionally, may not be monitored for days, weeks or months

Step 2 of 2

Integrate with existing systems

Real-time sensor data can be combined with information from other external sources, or even other enterprise systems such as CRM, or ERP services.

Step 1 of 3

Real-time alerts and warnings

Alerts are escalated in real time for attention by the operator. Breakdowns can be responded to quickly through the custom portal or machinery shut-down through commands sent from the dashboard.

Step 2 of 3

Predict failures before they happen

Importantly, some alerts are predicted. Maintenance can be carried out in advance of a failure when data points to a situation, or trend, that the prediction model recognises as problematic.

Step 3 of 3

Resolve issues before they escalate

The operator can select the highest priority critical error that isn’t already being resolved or monitored.

Step 1 of 3

Analyse real-time data feeds

The dashboard ingests data in real time. At this level, the actual production performance and health status of an individual asset can be monitored.

Step 2 of 3

Empower decision-makers to take action

This data allows decision-makers to plan for scheduled (or unscheduled) work, organise maintenance windows or predict production outputs from assets that might be in remote locations.

Step 3 of 3

Conduct maintenance before an asset fails

In this example, a fan has a critical predicted warning. It will fail within days and cause the asset to be shut down. It is also well within the standard service life of this part. The operator can select the specific part to take action.

Step 1 of 5

Take action and resolve

The dashboard alert details provide the operator with specific information about the part and the recognised problem. This includes serial number, part number and even inventory and location of replacement items.

Step 2 of 5

Analyse business impact

The predicted failure notes that the air filter fan will fail before the unit is scheduled to have routine maintenance. This will result in a lost-time shut down of the asset.

Step 3 of 5

Analyse data in real time

Real-time data is ingested from devices in the field and displayed in the portal. The operator can monitor the real-time actual data feed to verify that the alerts and information provided are all correct. The threshold for the alert is also displayed so that the user can easily see the tracking in relation to normal operation.

Step 4 of 5

Create service tickets

The operator can create a ticket to have maintenance staff replace the part and keep the asset operational. They also have information and data to carry out business analytics and make any changes to operations based on the outcome.

Step 5 of 5

Create service tickets

The operator can create a ticket to have maintenance staff replace the part and keep the asset operational. They also have information and data to carry out business analytics and make any changes to operations based on the outcome.

Process manufacturers are doing great things with IoT

Bühler uses IoT and machine learning to reduce energy consumption and food waste while making foods safer to eat.

“We set this target to reduce energy consumption and waste by thirty percent in our customers’ value chains and digitalization is an enabler of that.”

Stuart Bashford, Digital Officer, Bühler Group

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Buhler

Syngenta increases yield by deriving connected farming insights from plant data.

“We are embarking on a subscription-based software-as-a-service model for the agriculture industry and industrial agriculture customers.”

Prabal Acharyya, Worldwide Director of IOT Analytics, OSIsoft

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Syngenta

Ecolab solves global water challenges with cloud technologies.

“We can capture any data, anywhere, and transmit that information around the world very rapidly. We can now harness the power of this platform to serve many more customers, measuring many more flows at many more plants than we could even conceive of in the past.”

Christophe Beck, President, Nalco Water, an Ecolab company

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Ecolab

Tetra Pak keeps food and drink flowing safely from farm to table with precision farming systems.

“When you have plants around the world, the service knowledge we gain from one plant comes to benefit another.”

Johan Nilsson, Vice President, Tetra Pak Services

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Tetra Pak

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