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WEBINAR EXECUTIVE SUMMARY

IIoT in Manufacturing:

The Next Big Digital Disruption

KEY TAKEAWAYS

  • Manufacturing is in the midst of a digital transformation.
  • Four key digital technologies are driving change in manufacturing.
  • IIoT brings together data from disparate sources, enabling holistic analysis and insight.
  • Equipment suppliers also see increased business value with IIoT.
  • Siemens offers developer-friendly IoT solutions for rapid and robust implementations.

Digital technologies are dramatically changing manufacturing. Customers and the market are expecting Internet-like flexibility and time-to-market. Manufacturers are grabbing hold of new technologies—like the cloud, edge computing, machine learning, and application programming interfaces (APIs) —to give them a competitive advantage.

Manufacturers are also looking to the Industrial Internet of Things (IIoT), a manufacturing-centric implementation of IoT, to collect, sift through, and analyze data coming from their plants. IIoT is poised to be the next big digital disruption, providing manufacturers everything they need to decrease costs, increase uptime, and improve products to meet customer and market demands.

In our recent webinar, Jagannath Rao discussed major changes manufacturers are facing and introduced technologies that manufacturers can use to succeed in the changing marketplace. Watch the video or read an executive summary below.

Presenter: Jagannath Rao
Senior Vice President IoT and Go-To-Market
Strategy, Siemens Industry, Inc.

WATCH THE WEBINAR NOW

Industrial loT in Manufacturing:
The Next Big Digital Disruption

We are still reacting; we are not proactive. We need to be doing much more with this data in harnessing the richness that enables us to be predictive.

– Jagannath Rao

Manufacturing is in the midst
of a digital transformation.

 

Manufacturing is in the midst of a digital transformation.

Changing market and customer demands are driving the digital transformation of manufacturing, creating new business models and ecosystems where manufacturers will need to become proactive and flexible to remain competitive.

As businesses transform, data analysis takes on an even more critical role. Today, less than 5% of all data generated in manufacturing plants is analyzed for insights, even though that analysis can be used to increase reliability and predict potential failures. Analytics can predict problems that lead to common issues in the factory—warranty issues, rejects, rework, poor quality, and waste—and improve reliability for the parallel processes in what Mr. Rao refers to as the “Hidden Factory.” The Hidden Factory includes aspects of factory operations where visibility is often lacking.

Four key digital technologies are driving change in manufacturing.

Cloud Computing

These four digital technologies are cloud computing and analytics, edge computing and analytics, machine learning and deep learning, and APIs.

Cloud computing moves data and processes from the personal computer, which has limited storage and processing power, to the Internet.

What is the Cloud?

  • Hosts servers, networks, virtual machines (VMs), applications, and services over the Internet
  • Offers unlimited scalable compute power
  • Provides unlimited scalable storage capacity
  • Is a secure solution with robust performance
  • Enables complex application development with advanced analytics

Edge Computing

Edge computing optimizes cloud services by performing data processing and analytics near the source of the data, pre-processing data to avoid sending large volumes to the cloud. Edge computing is ideal for mission-critical applications, where problems needs to be identified and resolved quickly, before insight comes back from the cloud, as well as for sensitive data. Remote facilities with low bandwidth—including oil rigs in the middle of the ocean that only receive a 2G cellular signal—find edge computing beneficial so they are not reliant on the cloud. This technology also reduces latency, meaning data can be worked with in near real time.

Machine Learning and
Deep Learning

Machine learning uses algorithms to parse past data, learn from it, and make a prediction or infer something about the world. These algorithms can learn from experience and build models without explicit programming. Deep learning is a subset of machine learning, in which artificial intelligence (AI) has networks that are capable of learning—unsupervised—from data that is unstructured or unlabeled. Image classification, object detection, and facial recognition are all driven by deep learning.

APIs

APIs enable developers to design products powered by a service, like those available in the cloud. Modern APIs adhere to standards, typically hypertext transfer protocol (HTTP) and representational state transfer (REST), that are developer-friendly, easily accessible, and broadly understood. While many APIs are free to use, some have become so valuable that they comprise a large part of revenue for many businesses.

IIoT brings together data from disparate sources, enabling holistic analysis and insight.

Individual components, machines, and systems in manufacturing plants generate large amounts of data. But because the information comes from disparate sources and typically remains separate, it is impossible to gain a holistic view. Using cloud and edge computing, machine and deep learning, and APIs, IIoT brings together this data, offering manufacturers the ability to gain holistic insight through analysis.

Mr. Rao shared an example of how IIoT was used in a bottling and packaging plant to decrease costs associated with unplanned downtime when one machine failed.

Use Case: IIoT in a bottling and packaging plant

Pre-IIoT: Plant challenges

  • Plant has multiple conveyer lines, each with 250 or more fractional horsepower (FHP) motors
  • Motors were allowed to run to failure; the cost of monitoring each motor is more expensive than the motor itself
  • Unplanned downtime impacts productivity, leading to waste, rework, excess inventories, and other hidden factory issues

Post- IIoT: Benefits

  • Using data from inexpensive sensors ($10 each) placed on each motor, the manufacturer was able to predict motor failure, so failing motors could be replaced during a planned plant shutdown

IIoT is a very cost-effective solution, and at the same time, very meaningful in the context of the manufacturing plant.

– Jagannath Rao

Not only does IIoT increase productivity and lower overall costs, but it is quickly becoming table stakes. As IIoT use rises—50 billion devices are expected to be connected by 2020, and by 2025, 85% of manufacturers will be using IIoT—businesses that do not adapt will be disrupted and will not remain competitive.

Equipment suppliers also see increased business value with IIoT.

Manufacturers operating IIoT-connected equipment aren’t the only ones seeing benefits; equipment suppliers are also experiencing value from IIoT.

By capturing and analyzing equipment data, suppliers can increase service efficiency and reduce warranty costs while offering customers additional and differentiating services, such as availability and performance guarantees. Suppliers can also use data in a feedback loop with R&D, allowing improvements based on real-world metrics.

Siemens offers developer-friendly IoT solutions for rapid and robust implementations.

Developer-friendly IoT and IIoT solutions allow businesses to put robust solutions in place quickly. MindSphere offers easy access, provides rapid solution implementation, and delivers robust support

MindSphere: Robust and Smart Developer Services

Easy Access

  • Dedicated IoT tenant
  • Open platform-as-a-service (PaaS)
  • Scalable and cost effective

Rapidly Implement Solutions

  • Open APIs
  • Reusable modules
  • Native cloud accessibility

Robust Support

  • Developer community
  • Flexible training
  • MindSphere store

Charlotte Energy Hub: We Power the World

MindSphere delivers wide range of device and enterprise system connectivity protocol options, industry applications, advanced analytics and an innovative development environment that utilizes both Siemens’ open Platform-as-a-Service (PaaS) capabilities along with access to AWS cloud services.  Through these capabilities, MindSphere connects real things to the digital world and provides powerful industry applications and digital services to help drive business success.

MindSphere’s open PaaS capabilities enable a rich partner ecosystem to develop and deliver industry applications. Profit from the experience and insights of our partners. No development required on your part  to advance your IoT strategy.  Siemens provides business-focused solutions to help drive closed-loop innovation through digital twins for products, production, and performance.

Additional Resources

For more information about Siemens Mindsphere