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Creating Supply Chain Digital Twin – Terms, Conditions, and Use Cases

Learn about supply chain digital twins, their applications in logistics, and how they relate to other technologies

Updated on December 09, 2021

9 mins read

“Houston, we have a problem.” Odd as it may sound, the Apollo 13 rescue mission was the forerunner of a technology we now call digital twins. Back in 1970, NASA physically recreated the entire Apollo 13 spacecraft system on Earth and tested a potential recovery plan before sending the crew to the severely damaged craft looped around the Moon.

The advent of smart transportation solutions like logistics IoT sensors, cloud computing, GIS mapping, and artificial intelligence has led to the gradual introduction of digital twin technology in supply chains. But despite widespread adoption in manufacturing, energy, healthcare, and many other industries, supply chain digital twins are still a subject of intense research.

Creating Supply Chain Digital Twin – Terms, Conditions, and Use Cases


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What you will learn by reading this article:

  • The difference between digital twins and other modeling techniques
  • What technologies give digital twins a competitive edge in logistics
  • What challenges logistics companies should overcome to benefit from digital twins
  • The structure of digital twins and the benefits of building one for a supply chain
  • Use cases of digital twins in logistics and supply chains
  • Why supply chain digital twins are not just hype

The difference between digital twins and other modeling techniques

Digital twins have proven useful over the years. Originating from pencil drawings and comprehensive blueprints before transitioning to AutoCAD models and interactive simulations, this technology has gone through countless metamorphoses to appear in its present state. Creating a digital twin involves various applications and stakeholders who attach digital labels to real-life objects and systems, building relationships between 3D models of objects, recognizing and simulating object behaviors, and tracking assets in real time. All of this adds to the complexity of introducing digital twins in new areas.

Distinguishing features of a digital twin:
Creating Supply Chain Digital Twin – Terms, Conditions, and Use Cases

Potential use cases may add specific context to defining features of digital twins. For example, a digital twin can be developed prior to a real object, serving as a virtual blueprint. Or it may be scanned from a real object or continuously updated during construction. A single object can have several twins for different use cases or to simulate behavior under different conditions. Also, each piece of a bigger system may have its own digital twin, while the system as a whole may have a twin that reflects its overall functionality.

What technologies reinforce the competitive edge provided by digital twins in logistics

In recent years, advances in transportation technology have made digital twins in logistics much more realistic. Logistics companies apply IoT in transportation to collect sensor data on trucks and drivers’ performance, track shipments and valuable assets using GPS, and calculate arrival times based on machine learning algorithms. They also use cloud infrastructure to unite large chunks of data and run analytics for insight-driven decisions. All these smart transportation solutions create a solid foundation for supply chain digital twin.

Tech enablers of digital twins in the supply chain

  • Logistics IoT — The most critical condition for digital twins is the ability to be continuously aligned with a real object. For this, IoT sensors are definitely a key.
  • GIS data — When dealing with the movements and locations of objects, a supply chain digital twin needs access to GIS data and accurate maps.
  • Cloud computing — Digital twins consume a lot of resources — from storage to processing power — to enable real-time synchronization of all attributes related to enormous objects such as entire cities.
  • Open APIs — While developers continue to shift from proprietary solutions with strict formats, digital twins are winning from borderless platforms with available tools and accessible data.
  • Artificial intelligence — Analytics and automated decisions on critical changes in real objects increases the importance of artificial intelligence solutions in digital twins for transportation.

This list could contain more tech approaches, but IoT technology advancement, artificial intelligence, and cloud computing provide the most critical sensing and processing power within reliable infrastructure for digital twins to thrive across industries including logistics.

Learn how to build an AWS-based SaaS platform for digital twins that can be applied to many industries

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What challenges should logistics companies overcome to benefit from digital twins?

Acquiring the necessary computing and financial resources are among the first challenges when it comes to matching objects with their twins and updating the states of those objects in real time.

Bringing the relevant technologies together into a full digital twin implementation is a complex and challenging task. Close collaboration between all partners along the value chain is therefore essential to fully capture the potential.

Markus Kückelhaus, VP of Innovation and Trend Research at DHL

There’s always room for improvement when it comes to data quality, as digital twins deal with thousands of sensors that send new (and not always necessary) data. Broken or unusable files should be filtered to clean data streams.

Costs are not endless, and their flows have a tendency to change when companies prioritize on more important assets. Operation and maintenance are probably the most resource-demanding stages of developing digital twins, and companies can burn through funding really fast.

Accurately representing an object is hard. Engineers are pushed to simplify digital twins compared to real objects and rely on rough assumptions when predicting behavior in different conditions in the event that multiple twins are not available.

Moreover, the importance of cybersecurity grows alongside digitalization, especially when a digital twin covers objects where people are working. Not to mention that a twin becomes another potential vector for malicious actors to disrupt business operations.

A twin doesn’t always belong to the owner of the real object, which can also cause issues with intellectual property rights. Additionally, a twin depicts all design and functionality of an object, meaning the object it represents could be illegally reproduced if the twin were stolen.

Developing any technology requires development of the relevant skills and knowledge to operate it. Educating experts to maintain digital twins requires soft and hard skills that are rare on the market.

Integration and unification of solutions that accelerate the development of digital twins and the connection of their virtual parts can be challenging. A twin has to show what happens when a malfunction occurs in one part and how that influences other parts, making interoperability a decisive factor.

Structure and benefits of building a digital twin for a supply chain

Each digital twin reflects a single object, assisting with decisions to resolve specific problems with that object. Developing a digital twin is tightly related to the product development life cycle: creating, producing, operating, and servicing the real-world object as well as terminating the object when it’s no longer in use.

The architecture of a digital twin extensively relies on databases and sensors that fill them with data. Also, a twin has to operate in a flexible infrastructure to acquire and withstand a large amount of logistics IoT data. The typical architecture of a digital twin unites four basic layers: data acquisition, processing, and visualization as well as a semantic layer to determine specific KPIs and input additional data for specific use cases.

Architecture of a digital twin for logistics
Creating Supply Chain Digital Twin – Terms, Conditions, and Use Cases
Source: Digital Twin for Real-Time Data Processing in Logistics

A digital twin can bring additional value to the product itself, related processes, usage of the product, and the organization that produced it.

Visual reflection — The value of visually representing supply chain assets is critical when it comes to remote objects that are moving. Digital twins close the gap between remote objects and make data accessible for remote analytics.

Analytical power — Digital twins offer extensive simulation capabilities to build bold predictions for how objects will respond to different circumstances. They also make it possible to directly monitor changes an object goes through.

Remote diagnostics — A digital twin is a tool for remotely assessing the state of an object to identify causes of malfunctions, drops in production, and to predict performance based on historical data and AI algorithms.

Learn how businesses can start implementing digital twin technology in today’s environment

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Use cases of digital twins in logistics and supply chains

Combining technologies like AI, GIS, and the cloud to build precise and functional supply chain digital twins is worth every penny. Still, few logistics companies are tapping into the benefits of this smart transportation solution. Shedding some light on potential use cases for digital twins in logistics and supply chains can help investors weigh the pros and cons.

Packaging and shipping containers

Almost every product is packaged before shipment. Creating a digital twin of a product makes it easy to design, monitor, and manage packaging. Producers and senders can wisely use materials for packaging by knowing a product’s size, weight, and durability. This also contributes to sustainable logistics operations.

Digital twins can assist in planning the capacity of a container before loading it. When it comes to entire truck fleets or cargo ships, optimizing the loads of containers can save lots of money. In addition, the problem of damaged cargo can be addressed by scanning containers and monitoring their state with connected sensors during delivery to find causes of damage and calculate potential losses.

Warehouses and distribution centers

Digital twins can create 3D models of warehouses or distribution centers along with all static and moving objects inside. This can help with planning inventory locations, determining the quantity of products to keep in stock, and optimizing routes for loaders. Digital twins could be especially useful for building new facilities according to product and operational requirements.

Comprehensive data united within a digital twin can play a key role in optimizing employees’ work. Such data can be constantly updated from IoT sensors, drones, and workstations that scan venues to connect digital twins to real objects and communicate with those objects in real time.

Global network of logistics operations

Creating Supply Chain Digital Twin – Terms, Conditions, and Use Cases

The most intriguing and beneficial application of digital twin technology is for reproducing entire supply chain networks. The ultimate digital twin would include a digital representation of roads, facilities, modes of transportation, and all delivery assets. In addition, it would be linked with digital maps of oceans, trains, and airspace.

An example can be found in Singapore, where the local ministry of state transport has started development of a center of excellence for modeling and simulating next-generation ports. Eventually, the project will introduce a digital twin of the Tuas Terminal mega port with all assets represented by digital copies.

Why supply chain digital twins are not just hype

In 2018, Gartner released a report titled “Hype Cycle for Emerging Technologies,” calling digital twins one of the most demanded technologies with the greatest potential in the years to come. But a strange thing happened when in the next year’s report they did not include digital twins at all, giving the top spot and all the praise to 5G.

At the same time, the total digital twins market is predicted to grow from $3.8 billion in 2019 to $35.8 billion by 2025 at a CAGR of 37.8%. So why are digital twins still missing their place in the daily operations of logistics service providers? The answer is simple: there are not enough investors to cover the cost of implementing them.

What differentiates digital twins from other technologies like the blockchain, which has not stood up to the hype, is the fact that digital twins is a technology that’s already implemented in demanding and secure industries like aerospace. The long history of digital twins and the many improvements thanks to advanced technologies promise a bright future. The logistics industry is among the potential adopters to join the race.

For potential adopters of logistics IoT solutions and digital twins technology, it’s important to find a reliable and experienced software development partner. Contact us to tap into the technology expertise and industry knowledge we’ve been delivering to clients since 2002.

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