Digital Twins in Supply Chains ~ how do we use them and unlock their potential

Digital Twins in Supply Chains ~ how do we use them and unlock their potential

A brief guide for companies to optimize their supply chain functions by unlocking the potential of digital twins

Prof Archie D’Souza

(Source: Google)

Since the start of this millennium myriad industries have started adopting digital twin technology. It’s a revolutionary technology that has become more accessible and affordable. Still, it remains underutilized in supply chains, way below the potential it has. Why does this happen? The main reason has been attributed to the complex nature of supply chains. However, according to me, the reason is the reluctance of logistics players to adopt it. Misunderstandings about the technology’s applications, capabilities, and potential value has been a common excuse. This may be partially true. What prevents the players from getting experts and starting its adoption on a pilot basis? I feel that immense benefits across a wide range of supply chains can be delivered with a proper adoption and implementation strategy.

Let’s, to begin with, look at what digital twins are. A digital twin is a digital representation of a physical object, process, service, or environment that behaves and looks like its counterpart in the real-world. [source: https://www.twi-global.com/technical-knowledge/faqs/what-is-digital-twin], e.g., a digital replica of an object in the physical world, such as a missile, jet engine, or computer chip. Items could be tiny or exceptionally large, and could include buildings, manufacturing plants, townships, or even entire cities. Digital twin technology can also be used to replicate processes in order to collect data to predict how they will perform. So, in essence, a digital twin is a computer programme that uses real world data to create simulations that can predict how a product or process will perform. The beauty about these programmes is their ability to integrate IoT, artificial intelligence and software analytics to enhance the output.

These virtual models have turned out to be a staple in modern engineering to drive innovation and improve performance. For this we need to thank the advancement of machine learning and factors such as big data. Creating such a model can allow the enhancement of strategic technology trends, besides preventing costly failures in physical objects; additionally, by using advanced analytical, monitoring, and predictive capabilities, test processes and services. The prospects are numerous.

Digital Twins – Distinguishing Characteristics

Digital twins, as we’ve seen, are virtual replicas of physical entities and their interactions. They consist of a combination of enabling technologies and analytics capabilities. However, very often the technology is misunderstood. It is assumed by many that digital twins are themselves sensors, 3D models, simulators, or applications of AI technology. This assumption is erroneous. Also, some mistakenly consider digital twins to be largely theoretical and hence, not relevant for supply chain management. It is also often assumed that a digital twin can be built only after the physical twin has been created. None of these assumptions is true.

As stated, digital twins are a combination of multiple enabling technologies, e.g., sensors, cloud computing, AI & advanced analytics, simulation, visualization, and augmented & virtual reality, to name some of them. A customised mix of technologies is used depending on the individual user’s needs and expectations. One of the most distinguishing features of digital twins is their ability to emulate human capabilities, support critical decision-making, and even make decisions on behalf of humans, a feature that makes them so powerful. Digital twins observe their physical environment through a network of sensors that dynamically gather real-time data. By learning from this information, they evolve, and it isn’t just the information but also the contexts. They keep interacting with humans, devices, and other networked digital twins. These capabilities make them active social tools, it enables them to continuously communicate and collaborate with their associated physical and digital objects and also with humans. The technology supports end-to-end visibility and traceability, so essential in supply chains. Thus, supply chain practitioners can spot patterns of overly complex and dynamic behaviour.

Another feature of digital twins is their ability to build nonlinear supply chain models by overseeing many internal and external moving parts in end-to-end supply chains. They can thus compute thousands of what-if scenarios. The technology learns from these decisions and gains in maturity over time. Managers thus can make faster, more accurate, and better-informed decisions. This obviously has a long-term impact with the added bonus of considerably lower costs.

Digital Twins – their Working  

Digital twin are virtual models designed to accurately reflect physical objects. To cite an example, a jet engine is various sensors related to vital areas of functionality are fitted to it. The fitted sensors produce data about different aspects of performance of the physical object, such as energy output, temperature, weather conditions and more. This data is then relayed to a processing system and applied to the digital copy. Once the data is obtained, simulations can be run using the virtual model. Not just that, it’s even possible to study performance issues, and come up with improvements. Once these valuable insights are obtained, they can be applied back to the original physical object.

Differences between digital twins & simulations: Are simulations and digital twins the same? Or are they different? Let’s examine. Both utilize digital models to replicate a system’s processes. A digital twin being a virtual environment, is far richer for study. So, what’s the difference? It is largely a matter of scale. Simulations typically study a particular process. A digital twin, on the other hand, can itself run several useful simulations to study multiple processes. That’s not all. Does one see simulations benefiting from having real-time data? No! The beauty of digital twins is that they are designed around a two-way flow of information. A flow that first occurs when object sensors provide relevant data to the system processor. It happens again when insights created by the processor are shared back with the original source object. Digital twins can study more issues from far more vantage points than standard simulations can perform many tasks. They do this by having better and constantly updated data related to a wide range of areas. This, combined with the added computing power that accompanies a virtual environment, gives is greater ultimate potential to improve products and processes.

Types of digital twins: Digital twins are of different types depending on the level of product magnification. The area of application is the biggest difference between these twins. It isn’t uncommon to see different types of digital twins co-existing within a system or process. Let’s look at some of these types. This will enable us to learn the differences and how they are applied.

1.      Component twins may be looked at as the basic unit of digital twin. They are the smallest example of a functioning component.

2.      Parts twins pertain to components of slightly less importance. But for that they are the same as component twins.

3.      Asset twins: Two or more components work together form what is termed as an asset. Asset twins let a person study the interaction of those components. In the process, they create a wealth of performance data that one can process and then turn into actionable insights.

4.      System or Unit twins are the next level of magnification. They enable one to see how different assets come together to form an entire functioning system. These twins provide visibility regarding the interaction of assets. Therefore, they may suggest performance enhancements.

5.      Process twins, the macro level of magnification, reveal how systems work together to create an entire production facility. They enable a person to judge whether those systems all synchronized to operate at peak efficiency, or whether delays in one system will affect others. With process twins one can help arrive at the precise timing schemes that ultimately influence overall effectiveness.

Digital Twin Technology – a brief history

American computer scientist David Gelernter was the first person to come up with the idea of digital twin technology. He did so in 1991, in a book he authored Mirror Worlds. However, the person who first applied it in manufacturing was Dr Michael Grieves, a faculty member at the University of Michigan.  He did so in 2002, formally announcing the digital twin software concept. Eventually, NASA’s John Vickers introduced a new term digital twin in 2010.

The term may have been conceived as late as in 2010, however, the core idea of using a digital twin as a means of studying a physical object dates back several decades before that. One can rightfully credit NASA with pioneering the use of digital twin technology during its space exploration missions of the 1960s. At that time, each voyaging spacecraft was exactly replicated in an earthbound version that was used for study and simulation purposes by NASA personnel serving on flight crews.

Digital Twins - advantages & benefits

Nancy White, a content marketing strategist for the Corporate Brand team at PTC has written a very interesting article entitled Top 8 Digital Twin Benefits.                         [see: https://www.ptc.com/en/blogs/corporate/digital-twin-benefits ]. Some of the points she makes are listed out below.

Better R&D: It’s use enables more effective research, especially if it involves designing new products. This is because an abundant amount of data is created, about likely performance outcomes. The information created leads to insights that help companies make needed product refinements even before starting production.

Greater efficiency: Digital twins can help mirror and monitor production systems even after a new product has gone into production. It’s done with an eye to achieving and maintaining peak efficiency throughout the entire manufacturing process.

Product end-of-life: Manufacturers can decide what to do with products that reach the end of their product lifecycle and need to receive final processing, with the help of digital twins. This is done through recycling and/or other measures. Digital twins help in determining which product materials can be harvested.

Digital Twins in Supply Chains

Before we look at the working of digital twins in supply chains, let’s look at how they work in manufacturing. Digital twins do offer a great deal. Some of their features are prized. However, their use may not be warranted for every manufacturer or every product created. We’ll see why. Not all objects are so complex as to require the intense and regular flow of sensor data that are a must for digital twins. Also, it may not always be financially feasible. A great deal of resources is required to create them. As a digital twin is an exact replica of a physical object,  its creation is expensive.

According to anyLogistiz.com a Supply Chain Digital Twin is a detailed simulation model of an actual supply chain which uses real-time data and snapshots to forecast supply chain dynamics. From this, analysts can understand a supply chain’s behaviour, predict abnormal situations, and work out an action plan. [see: https://www.anylogistix.com/features/supply-chain-digital-twins/ ]

A supply chain digital twin can be used for:

·    Understanding supply chain dynamics and behaviour

·    Bottleneck discovery

·    Testing supply chain design changes and development

·    Monitoring risk and testing contingencies

·    Transportation planning

·    Inventory optimization

·    Cash to serve and cost to serve analysis

·    Forecasting and testing operations over the coming days and weeks

By clicking on the link above you will get access to a white paper which investigates how digital twins and control towers are resolving challenges in the supply chain. Read the white paper and estimate the value digital supply chain twins can offer your business!

https://www.mckinsey.com/capabilities/quantumblack/our-insights/digital-twins-the-key-to-unlocking-end-to-end-supply-chain-growth  

[To be concluded]

[The subject is wide and cannot be covered in such a short space. Please read Part Two (to be published soon) for more]