How is AI shaping tomorrow’s supply chain?

We live in a world where technology is a fundamental part of our day-to-day lives.

As these new & intelligent technologies advance at an exponential rate, businesses have been fast to jump on board. Developments like Machine Learning & Artificial Intelligence (AI) have gone from buzzword to board room agenda topics in no time at all.

The end goal is that these machines not only make decisions but that these decisions are ‘good’ and do not need to be validated by a human.

But how are businesses applying AI, Machine Learning & other clever technologies today? And how can your business benefit?

Can machines make good decisions?

Machine Learning & AI are being applied to support all kinds of day to day activities.

Examples can be seen every time we perform the most basic actions:

Machine Learning Mini Table


Many of these algorithms utilise each piece of data they accumulate to learn & get better.

A famous example is IBM’s Deep Blue Chess (DBC) system. In 1997, the machine managed to defeat the world chess champion, Garri Kasparov, using algorithms that are perfected through Machine Learning. However, these advances are not without consequence.

It is almost as though our world could start to resemble a chapter of Black Mirror. Or rather, we have all become a person of interest that is constantly being watched.

And yet, we are all fully convinced that these innovative technologies make our life easier, better & faster.

If we extrapolate this to the complexity of our supply chain, the application of intelligent technology to the company is something that has been going on for a long time.

However, the focus has mainly been on the automation of processes in factories. The icing on the cake is in developing machines to learn how to solve problems by making the best decisions autonomously.

What are we talking about?

First of all, let’s explain some concepts that many of us have probably heard of but perhaps we do not know exactly what they refer to.

1 Artificial Intelligence (AI)

The concept of trying to get machines to match human behaviour. In recent times, this has gained importance due, in part, to the sheer volume and variety of data that companies are now able to collect and the speed at which they can process it.

2 Machine Learning

This is a discipline within AI that is dedicated to the study of algorithms to perform a task through the use of data. More importantly, the machine uses this data to automatically learns and improves without human intervention.

In essence, Machine Learning combines applied statistics and computer science with the speed and precision to predict future behaviour.

To do this, they need to collect and store a large amount of data (Big Data), which can often be a handicap for many businesses.

3 Analysis

This large amount of data can be used to make a descriptive analysis (which is based on explaining what has happened through statistics, graphs and tables).

However, where Machine Learning techniques really add value is by performing two more sophisticated types of analysis: predictive analytics (make predictions based on past situations for future use) & prescriptive analysis (simulating different scenarios and evaluate what actions will attain the best results in the future).

Let’s see an example…

Imagine that you work for a gym and you want to identify customers who are thinking about cancelling their membership in the next few weeks.


Machine Learning Chart


Supply chain applications

The integration of Machine Learning in supply chain management helps businesses to automate tasks of little value allowing more time to focus on strategic and higher impact business activities.

However, Machine Learning opens up a universe of more ambitious possibilities.

Here are some examples of how Machine Learning has benefitted supply chain teams:

Machine Learning Resized

Machine Learning Ai Resized

Let’s explore this further

There are lots of ways AI & Machine Learning is being applied to support supply chain processes.

But lets take a deeper look at how this clever tech is helping businesses to make better supply chain decisions.

Machine learning forecasting models

We need a vast amount of data to build robust demand forecasts. But not all data is created equally.

Some types of data are more relevant for determining future demand than others. Equally some data is more reliable while some we need to ignore completely.  Thankfully, this is where Machine Learning comes in!

In the past, supply chain teams depended on historic sales data to anticipate future demand. With Machine Learning, planning teams can enrich the demand forecasts with a whole raft of external data insights. By taking into account social factors, customer behaviour & even the weather, planning teams gain a far richer view of future demand. But more importantly, AI can then help businesses identify errors & anomalies as well as to detect when an item is at risk of going obsolete.

The result: businesses become far better positioned to take the ‘right’ commercial actions to exploit an opportunity or mitigate an imminent risk.

Artificial Intelligence in supply chain planning

The chain is only as strong as the weakest link. The problem is: do you have the right insights to determine who your weakest supply chain partner actually is?

When it comes to supply planning, Artificial Intelligence in supply chain management plays an increasingly important role. From optimising replenishment routes to providing real-time visibility of where every pallet sits within the supply chain, Artificial Intelligence is changing the way businesses engage with their supply chain partners.

By bringing together data from a multitude of sources, AI allows planning teams to monitor, track & optimise the upstream supply chain. Taking into account constraints such as storage costs, delivery time, warehouse capacity as well shipping delays and stock shortages, businesses can anticipate supply-side risks far sooner.

As a result, planning teams can work with supply partners to safeguard availability and reduce supply chain costs.

Artificial Intelligence In Supply Chain Management Machine Learning

Machine Learning supply chain optimisation

Supply chains are made up of thousands of moving pieces. From forecasting demand, placing orders, optimising logistics routes and even fulfilling customer demand, the supply chain team often have to do lots of heavy lifting.

By Appling Artificial Intelligence & Machine Learning in supply chain management, businesses can automate tasks of little value allowing more time to focus on strategic and higher impact business activities.

The challenge however is in successfully implementing this technology throughout the supply chain. After all, to succeed, the technology requires information sharing between different areas of the company. Furthermore, AI & Machine Learning supply chain optimisation required new processes, new ways of working & a new skill set within the workforce.

For companies that hope to work more efficiently, this clever offers significant competitive advantages. And the exciting thing is that this technology is available today.

Implementing Machine Learning in your business

As we can see, adding science to our Supply Chain will allow us to manage it in a better way.

However, we must bear in mind that it is an important cultural change in the company.

After all, to succeed, the technology requires information sharing between different areas of the company. Furthermore, it is vital
not to lose sight of the main objective of implement a Machine Learning strategy within the company.

Another aspect to take into account, as in any data-based system, is the quality and reliability of the data.

Valid conclusions cannot be drawn if the original data is incorrect. Even so, for companies that hope to work more
efficiently, Machine Learning offers significant competitive advantages.

Machine Learning Cartoon With Checklist





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