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Various approaches to supply chain twins offer enormous benefits in reducing supply chain bottlenecks, increasing efficiency, and achieving sustainability goals.
Digital twins can be used to create digital copies of product lines, manufacturing systems, warehouse inventory, and other processes that are then identified, thus enabling supply chain managers to extract data, predict supply and demand, and streamline operations, said Kevin Beasley, CEO of a company that provides integrated enterprise resource planning (ERP) solutions for databases.
Digital copies may mirror supply chain touchpoints, thus assisting businesses to streamline business operations by identifying the exact processes being implemented. By implementing digital twin technology to align with ongoing supply chain touchpoints and operations, companies can gain better insights into how to pivot and manage hiccups.
Businesses are faced with a variety of challenges in transforming raw supply chain data into living and breathing digital twins.
IoT technology and predictive analytics tools to capture and process data and drive business insights have become increasingly important to the success of digital twins, according to Beasley.
Digital twins are becoming more widespread in the future, as supply chain segments were more separated and data was compromised. Now, digital twins are capable of forecasting trends, managing warehouse inventory, managing quality issues, and integrating one seamless flow of data.
Beasley anticipates that digital twins will be used alongside (AI)-enabled modeling and IoT technology. Unlike other IoT devices and sensors throughout the supply chain, however, this system is being more powerful because to the use of AI.
Manufacturers will be able to use data insights and create digital twin technology that will be able to revolutionize their operations, predict inventory, and reduce waste.
Seven methods to transform raw data into actionable supply chain twins:
Jason Kasper, the director of product marketing at a product development software manufacturer, believes that putting the digital thread together when planning out a digital twin is vital. These two must work in tandem for practical analysis and decision-making within the supply chain.
In the context of a supply chain, he considers a digital twin as a representation of all assets, including warehouses, manufacturing and supplier facilities, trucks, ships, and planes. It also connects to digital thread information such as inventory, location status, and condition of assets.
Organizations can combine deep connections, connections, decisions, and who made them by developing their core values.
Creating this complete view enables a comprehensive understanding of a specific supply chain's status and actions to keep it operationally efficient, Kasper said.
According to Richard Henderson, the president of TigerGraph, most enterprise applications capture data and place them into tables. Only the relationships or links between objects represented by the data are revealed when you execute a query and join the data.
As a query grows in scope and complexity, this overhead makes queries across a reasonably large digital twin too slow to be useful in the operation context, requiring hours or even days. Businesses, such as luxury car manufacturer, have discovered they can solve this problem by building their digital twin using a graph database.
Testing revealed that it would take three weeks to run a query to see their supply chain for one model of a car over six months. When they created the model in TigerGraph, the same query took 45 minutes, and this is now down to seconds.
In a safe, sandbox environment, individuals were able to view business relationships that previously existed in silos, identifying key paths, tracing components, and processes in more detail than ever before.
Data drift is another major challenge for digital twins, according to Greg Price, a cloud-based TMS solution provider. Teams must ensure the data collected for the digital twin accurately and consistently represents the true value of the digital twin. This is slowly improving as teams progress towards streaming analytics, but the practice is not yet prevalent within the industry.
It is not only the ability to have data, but the ability to understand it. It's possible that the interpretations may be off-base, which could lead to poor decision-making. Companies must build competency to understand how data drift can occur across the supply chain and then develop countermeasures to minimize its impact across each aspect of the supply chain, such as pricing and route management.
Because data is not standardized and that digital systems used to manage the supply chain, such as ERP systems or warehouse management systems, were not created to be connected or sharing information.
"The biggest challenge in exchanging data is that it's extremely unstable across the supply chain," said Sam Lurye, the CEO and CEO of, a supply chain logistics and data solutions company.
New companies are emerging to solve this problem, and they do so in one of two ways: aggregating existing data or generating a new data source.
is an example of a business that aggregates data from antiquated systems and makes it operational. Companies like Samsara develop their own unique data sources, which provide a source of truth by using real-time, accurate data. The better the digital twin.
Even if supply chain twins are focused on modeling the relationships between suppliers and distributors, they can benefit from better 3D representations of products, processes, and facilities.
When new items are introduced in a supply chain, as they often are in such a dynamic environment, there's the challenge of ensuring that all components are continuously updated, as the representation must work hand-in-hand with the data to ensure the exactness of this solution, said Ravi Kiran, the CEO and CEO of the AI engineering business.
Efforts in are attempting to address this issue by automation, but the technology must evolve before it can be used in complex situations.
To ensure a robust digital twin is configured, it takes a concerted effort to integrate with appropriate systems.
"The challenge to making this work well is by resuming the required subject-matter experts from the daily management of the supply chain and its processes to assist the digital twin's configuration," said Owen Keates, the industry head.
These experts understand how real-world interactions integrate into the flow between ERP, supplier, and third-party logistics systems, from point-of-sale systems.
"Such investment from supply chain specialists will ensure that non only the digital twin is a real representation of the real world, but it also gets the company deeply invested in the digital twin and expedite the adoption of the digital twin process," he said.
Cloud providers are starting to provide a ground for consolidating supply chain data across business apps and even across partners. For example, Google Supply Chain Twin brings together data from multiple sources while requiring less partner integration time than traditional API-based integration.
Customers have seen a 95% reduction in analytics processing time, putting some companies down from two and a half hours to eight minutes, according to Google Cloud's managing director.
Large enterprises only exchanged data based on legacy technologies like EDI until recently. A cloud-based approach can not only improve data sharing among partners, but it can also reduce the risk of using contextual data for their operations to gain deeper insight.
Our objective for the supply chain is to revolutionize the world by leveraging intelligence to create a transparent and sustainable supply chain for everybody. Building an ecosystem with partners on data, applications, and implementation services is a high priority to enable this vision, Thalbauer said.
Supply chain leaders are starting to profit from Microsoft's digital twin integrations.
Microsoft Azure might be a game changer for many industries that depend on internal and extraneous data sources for their planning and scheduling, said Yogesh Amraotkar, the managing director of NTT Data's supply chain transformation.
Azure also has tools that allow you to combine real-time sensory data using the IoT Hub with the visualization of supply chain elements with IoT Central.
Blue Yonder's supply chain's software-as-a-service solutions are built on the Microsoft Azure Cloud, which is rapidly expanding across the globe.
"Supply chain planning in the cloud, in the form of SaaS solutions, has already become the norm in the supply chain software industry," said Puneet Saxena, the corporate vice president of global manufacturing high-tech at Blue Yonder, a supply chain management company.
While the creation of a network of data providers requires time and effort, these automated connections will continue to be effective without excessive human effort, and trends in this field of technology are unlikely to change.
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