7 ways to improve data for supply chain digital twins

We’re excited to bring Transform 2022 back to life on July 19th and virtually July 20-28. Join AI and data leaders for sensible conversations and exciting networking opportunities. Register today!

Starting to create digital twins from different aspects of their supply chain for enterprise simulation purposes. The various approaches to supply chain twins demonstrate tremendous value in overcoming supply chain barriers, improving 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 analyzed – allowing supply chain managers to extract data, predict supply and demand and streamline operations.” Beasley said at the CIO. Vormittag Associates Inc., a company that offers Integrated Enterprise Resource Planning (ERP) solutions for databases.

Digital copies can reflect supply chain touchpoints, which help streamline business operations by pointing out specific processes taking place. By implementing digital twin technology to align current supply chain with touchpoints and operations, companies can gain a better understanding of how to manage pivots and hiccups.

But the enterprise faces numerous challenges in transforming raw supply chain data into a living, digital twins breath.

“As supply chains continue to generate more data than ever before, the adoption of IoT technology and predictive analytics tools to capture and process this data and drive business insights has become increasingly critical to the success of digital twins,” Basley said.

Things are starting to improve. In the past, implementing the use of digital twins was more challenging as supply chain segments were more diverse and data was slid. Now, with the rise of cloud-based systems and automated supply chain management tools, digital twins are becoming increasingly useful for forecasting trends, managing warehouse inventory, minimizing quality defects, and integrating a seamless flow of data.

Going forward, Beasley expects to see the use of digital twins with Artificial Intelligence (AI) enabled modeling and IoT technology. For example, while IoT devices and sensors located across the supply chain have accelerated the use of data to run predictions on supply chain trends, the use of AI will make the system more powerful.

As the AI-enabled model moves forward, manufacturers will be able to use data insights and create digital twin technology that will transform their ability to streamline operations, predict inventory and reduce waste.

Here are seven ways to convert raw data into efficient supply chain twins:

Start with digital threads

Jason Casper, Product Development Software Provider, Director of Product Marketing at Aras Corporation, explains that digital threads must be included when planning digital twins. Work should be done in concert for practical analysis and decision making within this supply chain.

In terms of the supply chain, they see the digital twin as a representation of all asset configurations, including warehouses, manufacturing and supplier facilities, trucks, ships and aircraft. It also links to digital thread data such as inventory, location status and asset status.

By developing the backbone for digital threads, organizations can weave together meaningful relationships, connections, decisions, and who created them.

“Creating this holistic view enables a complete understanding of the state of a particular supply chain and the actions to keep it efficient,” Casper said.

Move from tables to graphs

Most enterprise applications capture data and place it in tables, and the relationships or links between the objects represented by the data are revealed only when you run a query and connect to the data – and connecting is comparatively expensive, said Richard Hands, director of presses at EMEA. According to Tigergraph.

As the query increases in scope and complexity, this overhead makes queries in any reasonably sized digital twin too slow to be useful in an operational context, even taking hours or even days. Businesses such as luxury vehicle maker Jaguar Land Rover have discovered that they can tackle this problem by creating their own digital twin using a graph database.

When Jaguar Land Rover tried to build a model of its manufacturing supply chain using SQL, testing found that it would take three weeks to run a query to see their supply chain for a model of a car for more than six months. When they modeled in Tigergraph, it took 45 minutes for the same query and with further refinement, this is being brought down in seconds.

The graph database approach allowed them to visualize the relationships between business areas that previously existed in Silos, identifying important paths, identifying components and processes in more detail than ever before, and exploring business scenarios in a secure, sandbox environment.

Keep pace with data drift

Another major challenge for digital twins is data drift, said Greg Price, CEO and co-founder of Shipwell, a cloud-based TMS solution provider. Teams need to ensure that the data collected for the digital twin accurately and consistently represent the true conditions of the physical twins. In addition, having the best quality data is the key to getting the perfect value out of a digital twin. This is slowly getting better as teams move toward streaming analytics, but the practice is not yet prevalent in the industry.

Not only sound education but his alertness and dedication too are most required. Without an understanding of good behavior, interpretations run the risk of straying from the base, which can lead to poor judgment. Companies need to develop competency to understand how data drift can occur in the supply chain and then develop countermeasures to minimize its impact on every aspect of the supply chain such as cost and route management.

Bridge Data Silos

Because the data is not standardized and the digital systems used to manage the supply chain, such as ERP systems or warehouse management systems (WMS), are not designed to connect or share information.

Sam Lury, CEO and founder of Supply Chain Logistics and Data Solutions Platform, Cargo, explained, “The biggest challenge in exchanging data is that it is extremely quiet throughout the supply chain.”

New companies are emerging to solve this problem and they do so in either way: collecting existing data or generating a new data source.

Project44 is an example of a company that collects data from ancient systems and makes it work. Companies like Samsara and Cargo create their own unique data sources that are a source of truth with real time, accurate data. The more real-time data you have, the better the digital twin.

Improving 3D capture

While the supply chain twins focus on modeling the relationship between suppliers and distributors, they can also benefit from better 3D models representing products, processes and features.

“When new items are introduced in the supply chain, as it is often in such a dynamic environment, it is challenging to ensure that all components are constantly updated, as the presentation must work hand in hand with the data to maintain accuracy. About this solution, ”said Ravi Kiran, CEO and founder of AI engineering company SmartCow.

Photographic efforts are trying to solve the problem through automation, but the technology must be developed before it can be used in complex supply chain applications.

Involve subject matter experts

A solid effort is required to integrate with appropriate systems to ensure a robust digital twin is configured.

“The challenge for doing this well is to withdraw from its processes to support the day-to-day operation of the supply chain and the configuration of the digital twin by the required subject matter experts,” said Owen Keats, industry executive of Hitachi Vantara’s manufacturing practice.

These experts understand how real-world processes integrate into flows between ERP, supplier and third-party logistics systems, and point-of-sale systems.

“This kind of timely investment by supply chain experts will ensure that not only is the digital twin a true representation of the real world, but that the team invests deeply in the digital twin and speeds up the process of adopting the digital twin process.” Added.

Take advantage of the cloud

Cloud providers are starting to provide staging grounds for integrating supply chain data into business applications and even partners. For example. The Google Supply Chain Twin brings together data from a variety of sources when less partner integration time is required than traditional API-based integration.

“Consumers have seen a 95% reduction in analytics processing time since Google Cloud launched the supply chain twin, with some companies dropping from two and a half hours to eight minutes,” said Hans Thalbaur, managing director of Google Cloud’s global supply chain and logistics. Transportation.

Until recently, large companies only exchanged data based on legacy technology like EDI. The cloud-based approach can not only improve data sharing between partners, but it can also reduce the bar for weaving in contextual data about weather, risk and customer sentiment so that they gain a deeper understanding of their operations.

“Our vision for the supply chain is to change the world by using intelligence to create a transparent and sustainable supply chain for everyone. Creating an ecosystem with partners on data, applications and implementation services is a top priority to enable this vision, ”Thalbour said.

Supply chain leaders are also beginning to take advantage of Microsoft’s digital twin integration.

“Microsoft Azure can be a game-changer for many businesses that rely on internal and external data sources for their planning and scheduling,” said Yogesh Amravatkar, Managing Director, NTT Data Supply Chain Transformation.

Azure also provides tools that make it easy to combine real-time sensitive data using an IoT hub with visualization of supply chain elements with IoT Central.

Blue Yonder’s software-as-a-service solutions for the supply chain are built on Microsoft Azure Cloud, which is growing rapidly worldwide.

“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, Corporate Vice President of Global Manufacturing Hi-Tech at supply chain management provider Blue Yonder. .

Linking data providers’ ecosystems still requires time and implementation efforts, but once established, these automated connections can operate successfully without much human effort, and trends in this vein of technology are likely to continue.

Venturebeat’s mission Digital Town Square is set to become a place for technical decision makers to gain knowledge about the changing enterprise technology and practices. Learn more about membership.

Similar Posts

Leave a Reply

Your email address will not be published.