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Top 5 Ways Digital Twins Transform Supply Chains in 2024

Top 5 Ways Digital Twins Transform Supply Chains in 2024Top 5 Ways Digital Twins Transform Supply Chains in 2024

The global industry 4.0 market is expected to reach $210B by 2026 as more businesses embrace digitalization. Digital twin technology is one of the top components of the digital transformation in the manufacturing industry as it increases productivity and revenue, and improves customer experience. Supply chain industry analysts can also leverage digital twins technology to achieve digital transformation and hyperautomation of their business.

In this article, we explain five use cases of digital twins technology in supply chain industry.

What is a digital twin in supply chain?

A supply chain digital twin is a virtual simulation model of a real supply chain used to analyze supply chain dynamics and predict process success. DT models benefit from real-time data and snapshots of planned and released work orders, sales orders, pending approvals, demand and supply. The data is gathered from sources like:

  • IoT devices (e.g. sensors)
  • Logistics and transportation databases
  • Operations databases
  • Vendor information (e.g. CRM data, bills, invoices)
  • User experiences (e.g. online reviews, customer service tickets)

To collect customer experience data, supply chain managers can rely on web scrapers to pull real-time data from online resources such as business or competitor websites, online listings, and analyst reviews.

The data digital twin technology leverages is mostly historical data. However, there are cases where digital twins can work with real-time data as well. Real-time data refers to the information that is delivered immediately after collection.

Transform supply chains with digital twin technology

Digital twins help analysts understand a supply chain’s behavior, predict unusual situations, and provide an action plan to reduce costs and improve efficiency of processes.

1. Optimize overall supply chain processes

A digital twin can help businesses understand patterns, and model the outcome of modifications in different processes for:

  1. Improving design tests of supply chain process: DT models in supply chain moderate business continuity and transformation risks before they occur by outcome prediction. The models enable calculating benefits, savings, and potential ROI before the transformation of the process occurs. For example, a business generates DT models to redefine the global operations by simulating various scenarios that include data related to manufacturing, inventory and product distribution.
  2. Monitoring risk and testing probabilities: DT allows supply chain companies to test and discover the best course of action for emergencies, and try different scenarios in a virtual environment, significantly improving organizational stability.

2. Identify bottlenecks

DT provides a perpetual, end-to-end view of processes and bottlenecks across the supply chain, facilitating more agile problem resolution with minor human intervention. By collecting data, digital twins help to identify potential weaknesses in all aspects of delivery. For example, a shipment digital twin will rely on data gathered from sensors which transmit updated data during shipment, and can be analyzed to spot performance and bottlenecks during transportations and delivery trips.

3. Plan transportation and facilities

A digital twin can assess how changes in demand and supply affect the supply chain’s physical locations and supporting system while delivering products and services to end customers. By leveraging real-time data, digital twins enable supply chain management to better plan transportation resources.

4. Optimize inventory

A supply chain digital twin can input data from demand forecasting processes to avoid stock-outs and minimize overall costs of production and warehousing. As a result, it addresses the “single-echelon” challenge (optimizing inventory in a single warehouse) and the “multi-echelon” challenge (optimizing inventory across the entire network).

5. Predict the performance of packaging materials

When applied to packaging, digital twins can simulate package shapes and packaging material in order to test for defects before deploying them, which does not only decrease the financial but also environmental cost.

Overcome the challenges of supply chain digital twins

Improve data quality

To generate digital twins of supply chain systems, the data that is extracted from various sources should be cleaned and wrangled to fix duplications and missing data.

Though data quality is crucial part and it seems challenging, companies can leverage data transformation tools and data mining tools that use AI and machine learning (ML) to enhance data quality. Process mining is helpful to extract and analyze process data before generating the digital twin of the supply chain processes.

Increase technology adoption

There are variability in business practices with regards to the level of technological embracement. In some sectors, like agriculture, product-technology is complex and the infrastructure is not always clear. However, more businesses on the way to transform by adopting new technologies, promising that the issue might be tackled down soon.

Further Reading

If you are interested in digital twins, other use cases and integrations with different technologies, you can check our in-depth articles:

If you believe your business will benefit from a digital twin, 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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