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Enterprises are starting to create digital twins of different aspects of their supply chains for simulation purposes. Different approaches to twin chains in the supply chain show tremendous value in solving supply chain bottlenecks, 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 – enabling supply chain managers to extract data, forecast supply and demand, and streamline operations,” said Kevin Beasley, CIO at Vormittag Associates Inc., a company that provides integrated enterprise resource planning (ERP) solutions for databases.
Digital copies can mirror supply chain touchpoints, streamlining operations by locating the exact processes taking place. By implementing digital twin technology to align with ongoing supply chain touchpoints and operations, businesses can gain a better understanding of how to reverse and manage hiccups.
But enterprises face numerous challenges in transforming raw supply chain data into living, breathing digital twins.
“As supply chains collect 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 important to the success of digital twins,” said Beasley.
Things are starting to improve. In the past, using digital twins has been more challenging to implement as supply chain segments were more segregated and data was stored in silos. 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 errors, and integrating one seamless data stream.
Going forward, Beasley expects to see the use of digital twins evolve alongside artificial intelligence (AI)-based modeling and IoT technology. While IoT devices and sensors across the supply chain have accelerated the use of data to make predictions about supply chain trends, the use of AI would make this system even more powerful.
As AI-enabled models advance, manufacturers will be able to leverage data insights and create digital twin technology that can transform their ability to streamline operations, forecast inventory and reduce waste.
Here are seven ways to turn raw data into actionable supply chain twins:
Start with digital threads
Jason Kasper, director of product marketing at Aras Corporation, a supplier of product development software, explains that it is essential to include the digital thread when planning a digital twin. These should work together for practical analysis and decision making within the supply chain.
In the context of a supply chain, he sees a digital twin as a representation of the configuration of all assets, including warehouses, manufacturing and supplier facilities, trucks, ships and aircraft. It also links to digital thread data such as inventory, location status, and asset condition.
By developing the backbone for a digital thread, organizations can weave together meaningful relationships, connections, decisions and who made them.
“By creating this complete overview, you gain a complete understanding of the status of a specific supply chain and the actions to keep it operationally efficient,” said Kasper.
Go from tables to charts
Most business applications capture and tabulate data, and the relationships or associations between objects represented by the data are only revealed when you query and merge the data — and merges are computationally expensive, according to Richard Henderson, director of presales EMEA at TijgerGrafiek.
As a query grows in scope and complexity, this overhead makes queries in any moderately sized digital twin too slow to be useful in the operational context, taking hours or even days. Companies such as luxury vehicle manufacturer Jaguar Land Rover have found they can get around this problem by building their digital twin brother using a graphics database.
When Jaguar Land Rover attempted to build a model of its production supply chain using SQL, tests showed that it would take three weeks to conduct one query to map their supply chain for one model of a car over a six-month period. to watch. When they built the model in TigerGraph, the same search took 45 minutes, and with further refinements, it’s reduced to seconds.
A graphical database approach enabled them to visualize relationships between business areas that previously existed in silos to identify critical paths, track components and processes in greater detail than ever before, and explore business scenarios in a secure, sandboxed environment.
Keep up with data drift
Another big challenge for digital twins is data drift, says Greg Price, CEO and co-founder of Shipwell, a cloud-based provider of TMS solutions. Teams must ensure that the data collected for the digital twin accurately and consistently reflects the real-life conditions of the physical twin. Plus, having the best quality data is key to extracting full value from a digital twin. This is slowly improving as teams move to streaming analytics, but the practice is not yet mainstream in the industry.
It’s also not just the ability to have the data, but the ability to understand it. Without a good understanding of behavior, the interpretations run the risk of being out of line, which can lead to poor decision-making. Companies must build competence 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 pricing and route management.
Bridging data silos
Because 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 made to be connected or share information.
Sam Lurye, CEO and founder of Kargo, a supply chain logistics and data solutions platform, explains, “The biggest challenge with exchanging data is that it is extremely isolated in the supply chain.”
New companies are emerging to solve this problem and they do it in two ways: aggregating existing data or generating a new data source.
Project44 is an example of a company that collects data from outdated systems and makes it operational. Companies like Samsara and Kargo build their own unique data sources that create a source of truth with real-time, accurate data. The more real-time data you have, the better the digital twin.
Improve 3D recording
Even when supply chain twins focus on modeling the relationships between suppliers and distributors, they can benefit from better 3D models that represent products, processes and facilities.
“When new items are introduced into a supply chain, as is often the case in such a dynamic environment, the challenge is to ensure that all components are continuously updated as the representation has to work hand-in-hand with the data to maintain accuracy . of this solution,” said Ravi Kiran, CEO and founder of SmartCow, an AI engineering company.
Efforts in photogrammetry attempt to address the problem through automation, but the technology must evolve before it can be used in complex supply chain applications.
Involve subject matter experts
It takes a concerted effort to integrate with appropriate systems to ensure a robust digital twin is configured.
“The challenge of making this work well is taking the required subject matter experts away from the day-to-day management of the supply chain and associated processes to support the configuration of the digital twin,” said Owen Keates, industry manager for manufacturing practice at Hitachi Vantara . †
These experts understand how to integrate real-world processes into the flow between ERP, suppliers and third-party logistics systems, right through to POS systems.
“Such time investment from supply chain specialists will ensure that the digital twin is not only a faithful representation of the real world, but also invests the team deeply in the digital twin and accelerates the adoption of the digital twin process,” he says. added.
Take advantage of the cloud
Cloud providers are beginning to provide a stage for consolidating supply chain data across business apps and even between partners. For example. Google Supply Chain Twin brings together data from different sources and requires less time for partner integration than traditional API-based integration.
“Since Google Cloud launched Supply Chain Twin, customers have reduced analytics processing time by 95%, with some businesses dropping from two and a half hours to eight minutes,” said Hans Thalbauer, Managing Director of Global Supply Chain at Google Cloud, logistics and transportation.
Until recently, large companies only exchanged data based on legacy technologies such as EDI. A cloud-based approach can not only improve data sharing between partners, but can also lower the bar for weaving contextual data about weather, risk and customer sentiment to gain a deeper understanding of their operations.
“Our supply chain vision is to change the world by leveraging intelligence to create a transparent and sustainable supply chain for all. Building an ecosystem with partners in data, applications and implementation services is a top priority to enable this vision,” said Thalbauer.
Supply chain leaders are also starting to benefit from Microsoft’s digital twin integrations.
“Microsoft Azure could be a game-changer for many industries that rely on internal and external data sources for their scheduling and scheduling,” said Yogesh Amraotkar, managing director of NTT Data’s supply chain transformation.
Azure also provides tools that make it easier to combine real-time sensory data with IoT Hub with the visualization of the supply chain elements with IoT Central.
Blue Yonder’s software-as-a-service supply chain solutions are built on the 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 high-tech at Blue Yonder, a supplier of supply chain management. †
Linking an ecosystem of data providers still requires time and implementation efforts, but once established, these automated links can continue to work successfully without too much human effort and trends in this technology trend are likely to continue.
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This post 7 Ways to Improve Data for Digital Twins in the Supply Chain
was original published at “https://venturebeat.com/2022/04/15/7-ways-to-improve-data-for-supply-chain-digital-twins/”