Digital twin: Conecting the digital and real world

The term “digital twin” might sound futuristic, but it’s quickly becoming a practical tool reshaping how operations and supply chains work today.

In simple terms, a digital twin is a virtual replica of a real-world object, system, or process; anything from a single machine to an entire manufacturing network.

Imagine your factory, warehouse, or supply chain recreated in a digital space. This virtual version mirrors what’s happening in real time, thanks to data flowing in from sensors, IoT devices, and business systems. Every movement, transaction, and temperature change can be captured and reflected in the digital twin.

So, what’s the point? The value lies in simulation and insight. A digital twin allows you to experiment and test scenarios without affecting actual operations. You can explore what might happen if a supplier goes offline, if demand spikes unexpectedly, or if a new route could shorten delivery times. Instead of reacting to problems after they occur, you can anticipate and plan for them.

This technology helps organizations make smarter, faster decisions. Maintenance can become predictive instead of reactive. Inventory planning can adjust automatically to real-time demand. Logistics teams can visualize the entire flow of goods and identify inefficiencies before they cause delays.

Ultimately, a digital twin acts as your operation’s virtual brain, continuously learning, adapting, and optimizing. It bridges the gap between the physical and digital worlds, giving supply chain leaders greater visibility, control, and confidence in every decision.

The future of operations isn’t just physical anymore. It’s mirrored, modeled, and improved through digital twins.

 

What is AI?

Artificial intelligence has been a "buzz word" over the last few years and a lot has been said about how this new technology will have thousands of applications in fields as diverse as medicine, Supply Chain or engineering and will revolutionise the way we work.

However, it´s never been overly clear what is AI, but also, what is not.

The below video, explains all you need to know about AI, some of it´s applications and what we can expect from this technology. 

Amazon, the business that made Supply Chain a real competitive advantage

It´s no secret that Amazon is one of the most relevant companies ever created in the last century and it´s also no surprise that Supply Chain is at the core of what they do.

Amazon is by definition, the company that has scaled operations to levels we never saw before, and its Supply Chain have reached the status of almost being unbeatable.

The one project that has put Amazon´s Supply Chain capabilities on the spot once more is their latest endeavour to start using drones to manage their deliveries in the very near future.

These automated machines that are not limited by traffic or other mundane constraints that traditional delivery methods face, can once more demonstrate that Supply Chain can be a competitive advantage for those companies willing to invest the time and resources on developing these capabilities.

Artificial Intelligence, Machine Learning and Deep Learning, what is what?

In this post, I am hoping we will shed some light on these topical concepts, that although are usually used interchangeably, they do not quite refer to the same things.

The main idea behind these three concepts, lie in the image below:

As you can see, Deep Learning (DL) is a subset of Machine Learning (ML), which is also a subset of Artificial Intelligence (AI).

Let’s dig deeper so that we can understand better what each of them three concepts encompass.

Artificial Intelligence:

As the name suggests, artificial intelligence can be interpreted as incorporating human intelligence to machines.

Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), that behaviour is what is called artificial intelligence.

We classify AI-powered machines into two groups; general and narrow.

The general artificial intelligence machines can intelligently solve problems, for example, moving or manipulating objects, recognizing whether someone has raised the hands, or solving a mathematic problem.

The narrow intelligence AI machines can perform specific tasks very well, sometimes better than humans can; however, they are limited in scope. The technology used for classifying images on Pinterest is an example of this.

Machine Learning:

As we already saw, ML is a subset of AI (in fact it is just a technique for realizing AI) and can be loosely described as ability of the computer systems to learn. 

The intention of ML is to train algorithms to enable machines to learn by themselves how to make decisions.

Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information.

For example, below is a table that identifies the type of fruit based on certain characteristics:

As you can see on the table above, the fruits are differentiated based on their weight and texture.

However, the last row gives only the weight and texture, without the type of fruit. A machine-learning algorithm can be developed to try to identify whether the fruit is an orange or an apple.

After the algorithm is fed with the training data, it will learn the differing characteristics between an orange and an apple and predict accurately the type of fruit with those characteristics in the future.

Deep Learning:

As earlier mentioned, deep learning is a subset of ML; in fact, it’s simply a technique for realizing machine learning; DL is the next evolution of machine learning.

DL algorithms are inspired by the information processing patterns found in the human brain. Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines.

The brain usually tries to decipher the information it receives. It achieves this through labelling and assigning the items into various categories.

Whenever we receive a new information, the brain tries to compare it to a known item before making sense of it which is the same concept deep learning algorithms employ.

Comparing deep learning vs machine learning can help to understand their differences. While DL can automatically discover the features to be used for classification, ML requires these features to be provided manually.

Hopefully you are now slightly more clear about what each of these concepts mean and how they are interlinked!

The Internet of Things

A lot has been written already about the Internet of Things (IoT) and how it will affect nearly every business and industry; In my opinion, one of the most exciting areas of impact and disruption is the global Supply Chain.

One great example to illustrate this is this short clip from the TV show Portlandia, in this episode, two friends are dinning out and before ordering they insist on knowing as much as possible about the chicken they will be eating. They find out his name, what he was fed, his social habits etc. The process of assessing the chicken before agreeing to eat it might be a bit too bizarre, but with the IoT, this will become the norm. We will be able to experiment that type of transparency, and eventually it will be demanded by suppliers, customers and end consumers.

Among many other, some of the benefits that the IoT will bring are:

- Operational efficiency: The real-time visibility derived from the IoT enables information to be shared at every level allowing deficiencies to be identified quickly so that problems can be immediately rectified, or possibly even prevented altogether. Companies can see delays, slowdowns or trends that will affect the bottom line and inefficient processes that are costing them money can be identified and corrected

- Customer services: The IoT will dramatically reduce the amount of time from click to fulfillment. With customers demanding more and more information the IoT will fulfill up to the minute details on where their item is in transit and accurate alerts notifying them of delivery dates and times.

- Inventory management: The IoT will allow organizations to automatically know when products must be restocked or reordered, eliminating delays or inventory issues that would send customers to the competition.

Linked to this, loss management will greatly improve: with sensors tracking every movement, it will be almost impossible for merchandise to simply “disappear”, and if it does, it will be possible to know exactly where the incident happened and what factors may have contributed to merchandise loss.

- Asset Tracking and in transit visibility: New RFID (we talked about RFID technology here) and GPS sensors can track products “from floor to store” and even beyond. At any point in time, manufacturers can use these sensors to gain granular data like the temperature at which an item was stored, how long it spent in cargo, and even how long it took to fly off the shelf.

With some many possibilities, challenges will also need to be considered:

- Need of many different technical elements to deploy the end-to-end IoT solutions: Network infrastructure, devices, applications, platforms, security solutions, and integration services.

- Security: All the information must be prevented from falling into the wrong hands, or hacked. Sensors should only send specific information, which must be held in a secure, private cloud environment. Here is where Blockchain technology (see more about Blockchain here) will play a definitive role.

Overall, with everything becoming much more internet-driven, IoT in the supply chain is still only in its infancy, but sure to take off, exciting times lie ahead!