As manufacturers, comparing the products we manufacture helps us identify why some batches have more variability, defects, or inefficiencies. But the problem is, the data we generate doesn’t describe individual products. We generate data describing the live conditions of the plant.
Because of this, many manufacturing companies get stuck. The complexity of their processes, lack of connectivity, or unusable data makes it difficult to maximize production processes. Many manufacturers try to make tweaks to their materials, processes, or machines, but despite their best efforts, they can’t find the sweet spot.
In manufacturing, a Digital Twin makes a big difference.
What is a Digital Twin in manufacturing?
A Digital Twin in the manufacturing process is an exact digital replica of your entire manufacturing process. This means you have a digital model of your production process, end-to-end, accounting for every process variable. Digital Twins are used in manufacturing by calculating the exact manufacturing condition of every product.
Every product produced and every process executed is unique. With thousands of key input variables to describe both products and processes, a Digital Twin maps and captures these variables. Digital Twins can enable you to drill down as granular as to see the key inputs and outputs for an individual product or serial batch. This allows manufacturers to capture an exact replica of every production run.
Not all Manufacturing Digital Twins are created equal
Braincube’s Digital Twin is unique compared to most manufacturing Digital Twin software on the market. To start, our Digital Twin is a continuous and automatic digital flow of data collected from your machines and processes. This unique differentiation means teams don’t need to manually pull data or reports for a specific batch—the Digital Twin automatically updates with structured data made into a usable format.
Other Digital Twins typically represent your processes as they exist in a single moment in time. This is great for when you are first looking to make changes to your processes or make initial assessments. However, after making production changes, your teams will need to manually incorporate any new process data into future analyses. In other words, once it is built, your manufacturing Digital Twin is a finite, inflexible tool; it doesn’t adapt as your teams work towards continuous improvement efforts.
Through a proprietary algorithm, Braincube’s Digital Twin contextualizes your production data by accounting for time. This includes how long materials sit in a specific vessel when the materials pass through a change of state process, or even how long (and the conditions) raw materials were stored before use. This gives you the cleanest data set possible and a full picture of your production process without skipping or streamlining steps.
From manufacturing data to valuable information
Braincube’s Digital Twin is not our only differentiating benefit. Once your data is mapped within the data transformation model, we structure your data through Braincube middleware into your Digital Twin. This transforms your production data into interconnected information, making it available for further analysis, decision making, and innovation. As a result, teams can leverage their production data through our IIoT Platform to solve day-to-day challenges and increase visibility across facilities and departments.
Another point of differentiation is we build Digital Twin by starting with what our customers want to learn about their processes. Other Digital Twin software starts at the machine level. They collect and store data in case someone might look to do something with it sometime in the future. Our goal was to build a multi-factor technology that starts with a customer’s challenge or objective in mind. In this sense, our Digital Twin works in conjunction with what you want to achieve.
Why manufacturers should use a Digital Twin
Once you have a contextualized replica of your process or product, you can start solving your biggest challenges. Tackling these challenges means that teams need to identify the inputs that generate their ideal outputs. These outputs might be related to improved quality, product consistency, reduced waste, or countless other objectives.
Braincube’s Digital Twin makes it possible for operational teams 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.
Using Digital Twins in manufacturing saves your engineers and data scientists valuable time. Gone are the days of manual data pulling 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.
Making these changes in a digital environment saves resources before putting process changes into production. Teams can test, validate, and confirm process changes by using a Digital Twin before the changes go live. Engineers and data scientists can pair their intrinsic knowledge with Braincube’s CrossRank AI discoveries, resulting in process changes that impact your specific products and goals. Additionally, operators are reassured that a process change will have a positive impact. This is far more effective (and encouraging) than relying on trial-and-error methods for continuous improvement.
As manufacturing enters the Age of the Citizen Data Scientist, more company employees can leverage the power of data. Paired with a Smart IIoT Platform, Digital Twins make it possible to present everyone at your organization with the same cleansed data. An IIoT platform aggregates the clean, structured, real-time data from your Digital Twin into a centralized environment that is usually accessible to a wide group of users. Process improvement discoveries are no longer just for your data scientists and engineers.
Learn more about
Digital Twins in Manufacturing
Dive into a Digital Twin Model
Engage in an interactive model or listen to the AI in Manufacturing podcast to learn more about the power of Digital Twins in manufacturing.
Digital Twin Case Study
See how Cargill leveraged their Digital Twin to improve their weekly recipe output, decrease energy consumption, and motivate teams to leverage data to implement best practices in their factories.
Request a Demo
See Braincube’s IIoT Platform in action and learn how our Digital Twin helps industrials improve operations.