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Top 6 Digital Twins Use Cases for Manufacturing in 2024

Manufacturing industry generates a high volume of data and is one of the top 5 industries benefiting from analytics, making it a great candidate to implement digital twins. However, it is not the only reason why digital twins are broadly implemented in manufacturing. Digital twins technology enables manufacturers to optimize processes and design of the products, improve customer experience, identify new business opportunities, reduce costs, and save time and resources.

How are digital twins used in manufacturing?

Digital twins are utilized to run scenario simulations with upcoming business products to help understand potential usage, reliability and efficiency. The data used to generate process mining in manufacturing consists of both structured and unstructured and is collected manually or by using software machines and humans during every stage of production. To maintain top quality and avoid reworks, manufacturing analytics monitor and respond to the data obtained from these sources:

  • Machines: robotics, sensors, actuators, IoT devices, etc. 
  • Operators: ERP, sales, logistics, etc.

What are the use cases of digital twins in manufacturing?

Some of the areas of manufacturing where digital twins are applied include:

Product design

During the design of a product, digital twins serve as the virtual prototypes to test different simulations and adjust or re-design accordingly before the manufacturers produce the solid prototype. That would save time and reduce the cost that would occur due to number of iterations in the production phase of the prototype.

Process optimization

Digital twin technology allows manufacturers to identify new ways to optimize production and reduce variances. Additionally, combined with manufacturing analytics, a digital twin can help businesses identify the root-cause of issues by analyzing processes and important performance indicators.

Quality management

In addition to process modeling, digital twins can be used to model materials used in production to identify and optimize the areas variances appear.

Predictive Maintenance

Digital twins are modeled based on historical data of product/machine material and operation, therefore, a digital twin of a product/machine enables manufacturers to predict preventative repairs or maintenance needs before a serious problem occurs.

Predictive analytics

Digital twins of a manufacturing organization can be used to predict:

  • Workloads (i.e. distribution of work across departments).
  • Tool calibration requirements (i.e. the process of evaluating the accuracy and consistency for measuring tools by comparing against reference calibrating equipment).
  • Cycle times (i.e. the total time spent to for production and shipping the goods).

Customer experience analysis

Digital twin models can integrate customer experience data (e.g. online reviews and comments, customer tickets (i.e. issue reports), features they are interested in having). As a result, a digital twin can be used to modify the product according to customer requirements or issues to avoid previous errors, and in turn enhance customer experience.

To collect customer data, businesses can leverage web scrapers to pull online reviews and comments which can be input to the digital twin model.

What are the challenges?

Some of the challenges that face digital twin adoption in manufacturing include:

Lack of skills

Digital twins for predictive maintenance tasks might be more challenging and open to improvement for many manufacturers. For predictive maintenance, it is required to generate machine-specific virtual replicas. Yet, these replicas demand expensive data science talent to build and maintain them.

Data quality

To generate accurate digital twin models, the data must be cleaned by removing duplicates or non up-to-date data and managing the gaps in the streams. To tackle this problems, manufacturers can leverage RPA bots and workload automation tools to manage data warehouses and avoid data errors in data transfers and cleaning.

Further reading

To explore digital twins and their applications in detail:

If you believe your business will benefit from digital twins, feel free to check our data-driven list of digital twin software.

And let us help you choose the right tool for your business:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Hazal Şimşek
Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.

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