Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

In the past decade, multiple technologies have gradually contributed to the growth of smart manufacturing. The product lifecycle management that we utilize and implement today is far from the traditional model that used manual time-intensive tasks. It is believed that three main technologies and advancements contributed to the growth of smart manufacturing: the digital twin, the Industrial Internet of Things, and connected smart assets.

Digital twin, in particular, promotes a simple concept of virtually engineering a physical model, object, or product. Through programming simulations, we create a virtual model of a physical product or object to understand desired performance and achieve our goals.

Why is this driving smart manufacturing today?

Nirav Goradia, Director – Client Success, India, Searce

With the help of a digital twin of production systems, smart industry manufacturers can reduce the cost and time spent installing, assembling, and validating systems used for production. Further, using the same for asset management allows manufacturers to maintain field equipment without challenges.

Ideally, we need digital twins in smart manufacturing to optimize performance.

You may wonder that this model sounds conceptual, and it is, to some extent. However, operational data acquired in real-time helps create digital twins of real physical models and events. This means that we need quality assets and operational data to understand the behavior and state of digital twins, which will then stimulate process improvement and optimization.

How is smart manufacturing enabling a digital twin model and what is its impact on product lifecycle management?

The original purpose of digital twins is to align complicated processes and assets such that these assets gain the ability to interact with other environments. If we were to create a digital twin of the factory floor, it would give us valuable insights in real time. With sensors on the actual machine, we capture data in real-time, which will help us collect data on behavioral characteristics of the machine and processes, such as cooler qualities, thickness, torque, etc. This data is continuously aggregated as received from the digital twin, which creates a data stream.

As a result, we can know the actual performance of our system and the entire manufacturing process.

When we combine this advancement with product lifecycle management, we can create a digital twin for every product, and this model consistently updates using the data collected throughout the cycle.

What approaches to data management can enhance smart manufacturing efficiency?

The central idea of the digital twin is driven by data, isn’t it? Hence, we need to have a better data management strategy for improved efficiency.

Here’s what we know so far:

  • Data Collection is the first step where we identify entities that will contribute to a digital twin. For identification, we use sensors, internal networks, and end-user inputs to collect and store data in a database.
  • Since data is extremely important for the correctness of our digital twin, Data Validation is the next important stage. We need to appropriately deal with noise, missing values, and data modalities (or types).
  • The next essential stage is Knowledge Extraction, which helps us find relevant information from the above step. We may use event detection, labeling, and process discovery for this.
  • Now, we need to create a Data Model. The processes and events identified through the above steps will help us simulate this data model. This model will then allow us to create a link between our data streams of the smart factory and our model.
  • The final step is to Validate the Model continuously. We will continuously collect data in real-time, using which the model will be validated.

How is the digital twin being implemented today?

Across the industry, one of the major focus areas is asset lifecycle management. In this field, managing assets has always been a problem for manufacturers. It is time-consuming and extremely costly but critical to maintain uptime. Hence, maintenance teams have started using advancements like AR (augmented reality) with virtual models to overlay virtual models over the actual equipment. This helps improve the accuracy of maintenance, which enhances performance.

Virtual simulation complements the digital twin technology, as it can validate product design functionality without challenges.

What advancements have the fourth industrial revolution and other technological developments brought to smart manufacturing?

We always use Industry 4.0 as a synonym for smart manufacturing because most technological breakthroughs in the Fourth Industrial Revolution are linked to smart manufacturing processes.

  • Big Data is one of the earliest and best developments in smart manufacturing. It allows for collecting meaningful data regularly.
  • 3D Technology is another development that is being openly welcomed to simplify prototyping and other processes.
  • Internet of Things for connected experience through different devices and touchpoints.
  • Data Visualisation to graphically understand and analyze data in the form of maps, infographics, and charts.
  • Cloud Computing, although not directly related, helps store data in the cloud and reduce device dependency.
  • Smart Sensors allow us to collect, process, and use data to improve process efficiency.

Do you expect everything from the supply chain to manufacturing to get reimagined eventually?

Indeed!

Digital twins are moving in the direction where we can expect the entire supply chain and manufacturing unit to work in a new way – driven by data.

Here’s how digital twin will help reimagine manufacturing:

  • Optimized supply chain, where businesses identify and understand patterns because we can use outcome prediction to find transformation and continuity risk.
  • It is possible to continuously monitor the entire supply chain and find risks and vulnerabilities. The digital twin model can help decide the best action for such unfortunate events.
  • Since digital twin enables a comprehensive view of processes, we can identify bottlenecks before they start impacting our processes, like sourcing, shipment, etc.
  • During transportation, the digital twin can help understand how demand and supply impact the physical locations and our support system in the supply chain. Leveraging this data, we can better plan our transportation.
  • When it is about inventory, the digital twin helps forecast future demands, which allows us to keep stock and optimize associated costs intelligently.
  • Lastly, digital twins can simulate the performance of our packaging. We can test different shapes and materials to find an optimum fit.

Authored by

Nirav Goradia, Director – Client Success, India,  Searce 

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members