The Importance of Digital Twins in Asset Performance Management

Transforming Manufacturing with Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML)

In a world governed by artificial intelligence, automation, data, it’s hard to leave the ‘Internet of Things’ (IoT) out of a list of innovative and game changing technologies. In fact, IoT is a primary input and factor in the success of numerous other technologies like machine learning and artificial intelligence.

As the market scenarios evolve over time, it is critical for businesses to see how technologies are changing. Some of the most successful businesses are the ones who visualize creatively about evolving technologies. Coming up with ideas for innovative ways to use and combine these technologies together would not have been possible without keeping an eye on these trends.

IoT technology has a great deal of potential to advance the manufacturing sector. With a wide array of IoT sensors on plant and factory floors, the manufacturing domain has become more automated than ever before. One of the most important results of the expansion of IoT sensors in manufacturing is that these networks are powering advanced artificial intelligence solutions. AI cannot pave the way for predictive maintenance, defect detection, digital twins, and generative design without critical data produced by sensors.

Why IoT Analytics is a Must for Manufacturing Companies

The manufacturing industry is characterized by continuous digital disruption, globally-dispersed value chains, service-centric invention, and a vital need to be at the frontier of the digital revolution. IoT analytics is a very important tool for driving growth in this complex business terrain, and should be on every organization’s radar.

Combining data from sensors, the enterprise, and the internet, IoT analytics helps manufacturing companies deliver visibility, intelligence, and predictive insights. This leads to growth opportunities by embedding a more measured approach into enterprise processes.

Taking IoT/ AI operations up to plant scale poses three big challenges for companies − there are numerous tools to stitch seamlessly together; the hundred or thousands of product cases in production make scaling up extremely complex; and the AI ecosystem is continuously changing.

The need for manufacturers to have access to a cohesive, open architecture enterprise ecosystem − which connects data from the edge to augmented analytics and end-user operations − is real.

Asset performance management and therefore the digital twin can help operations and Types of IoT Analytics

Descriptive analytics on IoT data

Descriptive analytics is usually shown as an interactive dashboard that show current trends and historical sensor data, key performance indicators (KPIs), statistics and alerts.

Typical questions addressed include:

• What are the anomalies that are happening or occurring?

• What’s the application and output of this machine?

• Where do my assets reside?

• How many components are we making with this machine?

• How much energy is this machine using?

Diagnostic analytics on IoT data

Diagnostic analytics probes further into questions, such as “why is this happening? It analyzes IoT data to identify core problems and to fix or enhance a service, product or process.

Typical questions addressed include:

  • Why is this tool producing more defective parts than other machines or tools?
  • Why is this machine consuming excessive energy?
  • Why aren’t we producing enough parts or numbers with this machine?

Predictive analytics on IoT data

This type of analytics addresses questions that are more forward looking (e.g. what will happen in future?). It ascertains the possibility that something will happen within a defined time period, according to historical information or data. The aim is to proactively take corrective measures or action before an undesired outcome occurs, to mitigate risk, or to isolate opportunities.

Typical questions addressed include:

• What are the chances of this machine failing in the coming 24 hours?

• What’s the anticipated useful life of this tool?

• When should I service this machine?

• What will be the demand for this service or product?

Prescriptive analyticson IoT data

Poses the question: what action should I take? Suggests steps and actions based on the result of a prediction or diagnosis, or provides some visibility to the reason behind a prediction or diagnostic. It gives recommendations on how to optimize or fix something.

Addresses questions for-example

  • This machine is 80 percent likely to fail in the coming 12 hours. How should I help this?
  • The overall equipment effectiveness (OEE) of this machinery device is below performance level. How can I maximize it?
  • This machine is creating numerous defective parts. How can I avoid this?
  • This design is performing in numerous manufacturing issues. How can I correct it?

Business Benefits Await

Only when such an ecosystem of data persist can manufacturers effectively collaborate, generate and share data-driven insights, develop AI-augmented analytics, and produce scalable, secure IoT/analytics operations rapidly and with lower cost. It also helps cater to the development of innovative IoT-powered Digital Twins that rely heavily on real-time data fed to IoT platforms.

Outlook: A way to start on a digital transformation in manufacturing industry

A comprehensive road to digital manufacturing — one that considers all of a company goals and overarching business objectives and technology – can help manufacturers overcome the hurdles that stand between pilot success and company-wide rollouts. Significant value can be realized through digital transformation, as has been demonstrated by several real-world cases, if approached correctly.

At Visionaize, we make these visions a reality. As an AI, ML and IoT development and consulting company, we both equipped and excited to propel digital transformations across Oil & Gas, Industrial Manufacturing, Utilities and other industrial sectors.

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