ARTICLE

What is a Digital Twin in manufacturing?

In manufacturing, Digital Twins can make a big difference in helping you replicate your Golden Batch.

Comparing products and their manufacturing conditions can help you identify why some batches have more variability, defects, or inefficiencies. Ultimately, these insights can help you repeat your best runs or the “golden batch.”

The challenge is that the production data manufacturers generate are in various systems and formats. Some companies collect too much raw data but don’t have the resources to do something meaningful with it. This may include having employees that don’t know what to do with data. Other companies lack complete data connectivity, making it hard to piece together a full picture of production. There are also organizations that simply have problems getting the right information: maybe they have unusable data or immensely complex processes with high data volumes. 

Whether it is one of these reasons or a handful of others, the overriding issue is that, even if raw data exists, many companies get stuck with poor information. Many manufacturers try to optimize their materials, processes, or machines. Despite their best efforts, though, they can’t find some of the less obvious sources of variation. 

This is where Digital Twins can make a big difference in helping your manufacturing company break through data fatigue and turn data into your competitive advantage. But what exactly are manufacturing Digital Twins and how are they used?

What is a Digital Twin in manufacturing?

A Digital Twin is a digital replica of any process, system, or physical asset that will enhance applications serving business objectives. In manufacturing, Digital Twins can be built for assets, specific production lines, by end product, or for any other “real world” scenario within a production process. 

Using a Digital Twin enables you to merge the physical and digital worlds. Digital Twins are used for simulation and operational phases of a product or process lifecycle. Regardless of how you build a Digital Twin, the overall outcome is having a digital representation that you can use to gain more knowledge and deeper visibility into your production. 

digital-twin-analysis-by-engineers

Every product produced and every process executed is unique. There are hundreds, if not thousands, of key input variables to describe products, assets, entire lines, and processes. In the case of manufacturing operations, a Digital Twin is a dynamic replica that is designed to capture, map, and structure process variables into a continuously updated database. This database can be accessed and used across the organization. By making this data more readily available in a digital environment, teams can use data in other applications, models, or third-party programs to make meaningful discoveries.

Why use a Digital Twin in Manufacturing?

Once you have a digital replica of your process with all the data from relevant sources linked to the right end product (called a process map model), you can start solving your biggest challenges. Let’s look at how a Braincube Digital Twin—built by process, asset, or thread—makes this possible. 

For example, process Digital Twins can serve as a single source of truth throughout your organization. They encapsulate the exact manufacturing conditions of every product, batch, or serial code that you produce. As a result, teams can leverage their production data through our Smart IIoT Platform, which has over 25 business apps to solve day-to-day challenges and increase visibility across facilities and departments.  

Engineers and data scientists save valuable time using process Digital Twins. Gone are the days of manual data pulls and complex manual analyses. A Digital Twin automates data pulling, cleaning, structuring, and transforming. This puts the most important information directly into the hands of your skilled engineers. Your teams can focus on solving problems that move your company forward instead of manual data manipulation. 

With Digital Twins, teams can focus on solving problems that move your company forward instead of manual data manipulation.

Operational teams can use a Digital Twin to determine which input variables make the biggest impact on specific outputs. Identifying these key input parameters is a crucial step in making the necessary process changes to drive your goals forward. 

Data accessibility and Digital Twins

Since it’s possible to build a Digital Twins of nearly anything (a machine, a process, a system, etc.), it can feel overwhelming trying to figure out what kind of Digital Twin you need at your organization. While choosing the right Digital Twin type is important, it is only one component of your digitalization strategy. 

The way a Digital Twin is built—in other words, how the data is collected, modified, mapped, and made usable—can have huge implications on your overall ROI and digital effectiveness. In many cases, the way a Digital Twin is built is directly correlated with your objectives. 

All Digital Twins are designed to capture and contextualize data in some capacity: maybe it is just the data coming from one machine, or maybe it is data from a specific part of your production process. Braincube’s advanced Digital Twin is unique compared to most manufacturing Digital Twins on the market. To start, our process Digital Twin not only continuously collects data, but it contextualizes it to include real-time production conditions based on continuously calculated lag times (i.e. following the flow of materials). It also includes calculated variables for lag times or other flows when no sensors exist. 

Basing Braincube-powered Digital Twins on your process map allows our middleware to cleanse data (removing outliers, merging formats, ETL) as well as produce up-to-date product data. This transforms your production data into interconnected information, making it available for further visualization, analysis, decision making, and innovation. 

Once built, data is continuously ingested through Braincube middleware to ensure teams always have access to updated, accurate production information. This unique differentiation means teams don’t need to manually pull or prepare data to understand what happened during production. The Digital Twin data continuously updates for each product in real-time as it comes off the line. 

Data collection is immensely valuable as it creates a single source of data truth at your organization. However, data is only valuable when it can be put to use. For example, even if a Digital Twin prepares your data, data scientists are likely the only employees who can identify patterns and build predictive models. 

While data scientists greatly benefit from having prepared information to use in their predictions, manufacturing tools today also need to empower citizen data scientists. By equipping process experts (aka those with day-to-day operational knowledge), companies take one step closer to organization-wide transformation. 

While data scientists greatly benefit from having prepared information, manufacturing tools today also need to empower citizen data scientists.

For example, maybe a data scientist discovers a difference in asset performance and brings it to the operations teams as a possible place to look at improving throughput. The process engineer points out that a given machine was off during that time, so, yes, it makes sense that production was compromised. Both teams go back to the drawing board, working in silos on the same overall goals. 

While this is an exaggeration, the takeaway is that by limiting data use to a few teams means that unexpected causes of variation may be missed. The data isn’t truly democratized for everyone at your organization. If advanced technical skills are necessary to derive value from the data, then a large portion of your employees still are unable to make discoveries and drive progress forward. 

This is where other Industry 4.0 technologies, such as IIoT Platforms and advanced Applications, come into play. They enable your Digital Twin data to be utilized by both technical and non-technical teams, making it possible for any at your company to uncover optimizations. 

How do IIoT Platforms and Digital Twins interact with each other?

IIoT Platforms act as the gateway to your Digital Twin data. An Industrial IoT platform (IIoT Platform) aggregates real-time data from hardware, software systems, sensors, and other data points into a centralized environment, which can usually be accessed by a wide group of users. It bridges the gap between systems, people, and machines by pulling that data into a centralized system, usually in a cloud, but sometimes also on-prem or on edge. 

As we’ve discussed, though, improved data access doesn’t mean improved data usability. In most cases, an employee must still have the technical know-how in order to make valuable discoveries. 

Here at Braincube, we believe the future of manufacturing lies in the Smart IIoT Platform: an IIoT Platform enhanced with advanced business applications. These apps make it easy for anyone to get real-time visibility into production conditions or dive deeper into Digital Twin data using proprietary AI to optimize your processes and quality.

Braincube’s Smart IIoT Platform was designed to democratize data and empower Citizen Data Scientists. Our comprehensive product suite leverages business intelligence applications, connectors, and self-service AI, making it possible for everyone at your organization to draw value from your Digital Twins

The Smart IIoT platform stores your Digital Twins into a centralized environment accessible to a wide group of users. A Braincube-built Digital Twin serves as the single source of truth for your organization. Teams can rely on Digital Twins to solve quality concerns, identify process optimizations, or even build predictive models either in Braincube’s suite of apps or other systems. Process improvement discoveries are discovered, tested, and implemented faster using a Digital Twin in manufacturing.

When building out the way our Digital Twins worked in conjunction with other 4.0 technologies, our goal was to build a multi-factor technological solution that starts with a customer’s challenge or objective in mind and with the right App suite to leverage it. In this sense, our Digital Twin works in conjunction with what you want to achieve

Actionable insights from using a Digital Twin

With a process Digital Twin in place, you can leverage the replicas in a Smart IIot Platform to: 

  • Test operating conditions and receive the expected results digitally
  • Optimize production
  • Find new revenue streams
  • Reduce cost, waste, and energy consumption
  • Personalize products
  • Perform predictive maintenance 
  • Improve quality and customer satisfaction
  • Easily trace each product from start to finish
  • And more

Summary

Digital Twins are on the rise in manufacturing, with the Digital Twin market expected to reach $48.2 billion USD by 2026. With so much buzz around Digital Twins and their value, it’s tempting to jump right into the first solution that seems like a good fit.

Before selecting your Digital Twin vendor, consider both your short and long-term goals. Is the type of Digital Twin you’re building designed for growth as your factory changes? Is the data model built to encompass future initiatives, even if you don’t know what they are yet? Will the data be readily available and usable to more teams, or still siloed to just your most technical teams? Will the lack of certain data be compensated by the right calculated variable?

As a leader in assisting manufacturers to transform their operations, Braincube’s advanced process Digital Twins, Smart IIoT Platform, and Business Intelligence apps can help all types of manufacturers uncover opportunities within their own processes. Reach out to learn how our unique expertise and tools designed for manufacturing can help you reach your goals—both now and in the future.

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