Internet of Things

Fill your Missing Links

Things meet Artificial Intelligence

As an industrial manufacturer, you know that instrumenting your production lines and products with sensors is a unique business opportunity to reinvent and transform yourself.

Your value chain can be transformed from product-centric to customer-centric. Data-enabled offerings and valuable feedback into engineering and product design are within reach.

Today's IoT platforms are great in monitoring and visualizing huge amounts of data that are constantly created by connected assets. Real competitive differentiation, however, is gained when these data can be understood, acted upon and monetized.

Does your IoT Platform look like this?

Two ways to empower your platform

Traditional Approach

1

Establish AI project team

2

Discover use case

3

Perform feasibility study

4

Fine-tune project planning

5

Develop data model

6

Implement prototype

7

Make production ready

8

Advise business users

9

Generate actionable insights

Blueprint Approach

1

Search market place and find matching problem description

2

Select recommended blueprints and load into orchestrator

3

Orchestrate blueprints to match project needs

4

Upload blueprints to data fabric and fine-tune before execution

5

Execute blueprints and generate actionable insights

Use Case: Predictive Maintenance

Modern machinery comes with embedded sensors and computer chips that send constant readings from these machines. Preventing asset failure by analyzing these data to identify patterns and predict issues before they happen, is a promising use case for manufacturers and asset managers.

Suppose, you run an IoT platform that constantly collects production asset data and monitors pressure, temperature, vibrations and more to visualize asset health in real-time.

But for predicting production issues before they happen, you need more: Business data need to be added to provide context to the machine data. This is the basis for predictive analytics to predict future failures.

Empowering your favorite IoT platform is easy: Visit the blueprint market place, select profiler for your IoT platform, ERP and MES system, optionally choose a rule engine to add logic, pick a predictor engine and determine the maintenance graph (or any other data store) as your data destination. Arrange these components into a data workflow.

Feed Predictive Works' big data fabric with this blueprint and execute it. Then, you are ready to perform root cause analysis and customize your maintenance activities for each machine or even for each component on a machine.

View other Use Cases

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Publishing

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