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Artificial intelligence in supply chain management: its development and implications explained

01 Aug 2017
Artificial Intelligence (AI) has been subject to massive developments: enhanced algorithms cannot only gather tonnes of data, but they can also analyse and interpret this information. The ‘machine learning’ capabilities of AI has several consequences for the field of supply chain management that can lead to improved business results.

Artificial Intelligence (AI) has been subject to massive developments: enhanced algorithms cannot only gather tonnes of data, but they can also analyse and interpret this information. The ‘machine learning’ capabilities of AI has several consequences for the field of supply chain management that can lead to improved business results.


Before we dive into the practical implications of AI, let’s first take a look at its history and development.


The concept of Artificial Intelligence dates back to the ‘50s. Its ‘intelligence’ was based on if this then that statements, all inserted in the algorithm by programmers. The primary goal was to simulate the actions and movements of workers so computers could be a substitute for humans. Although named artificial intelligence, it had nothing to do with intelligence.

Collecting and interpreting data

Nowadays, artificial intelligence has improved. AI now entails collecting, organising, finding patterns and classifying data, and – more importantly – interpreting this data into practical information.


Coding if this then that statements is no longer required – algorithms can ‘learn’. Machine learning is essential because the current stream of data is enormous. The amount of data these AI algorithms have to process cannot be interpreted without the capability of learning and making connections. Human programmers only need to insert a framework of conditions into the equation for the algorithms to start learning.

Extensive possibilities of AI

As you can imagine, the potential of AI is invaluable as it surpasses the capabilities of humans by far. Moreover, this is particularly the case for supply chain management.


AI is especially helpful when there are numerous streams of structured and unstructured data involved – data that cannot be analysed by humans. Supply chain management is the classic example of this. AI can, therefore, assist in the decision-making process involved in the supply chain.


For example, algorithms can:
  • determine optimal and real time safety stock requirements for your inventory;
  • track products and components in real time and forecast arrival times;
  • design your network for logistics and optimal transportation.

Practical implications of machine learning

These examples are only the tip of the iceberg as there are many more situations where AI positively influences the efficiency and productivity of supply chains. Let’s get into more detail to see how advanced AI can benefit supply chain management and ultimately increase business results.


Analysing big data streams

As mentioned, data streams are increasing in volume and speed. Algorithms need to keep up with this development so they can collect, analyse and interpret over a trillion actions per day. The data is gathered from multiple sources, such as satellites, radars, sensors, and smartphones. The outcome of examining this data has a significant impact on logistic applications. For example, algorithms can calculate and estimate the time of arrival of a shipment by taking into account weather conditions and bottlenecks on the navigational route.


Tracking the supply chain logistics

Linked to the above implication of AI on supply chain management, is the possibility to follow the whereabouts of products and components in the logistical chain. However, it entails more than solely tracking the location; by collecting and analysing the data from various sources, even from social media and news feeds, AI can predict potential problems and disruptions. By feeding this info to logistical personnel, interventions can be made on time.


Elevated inventory levels and product releases

Another example where businesses benefit from the enhanced effect of AI on supply chains is the option of tracking demand signals early on. With this data, algorithms can predict where demand will increase. The product development and sales department can then develop and launch new products specifically targeted to this rise in demand. Of course, inventory levels and refill plans can also be optimised with this data.

The future of supply chains

As you have come to understand, the development of AI has a major effect on supply chain management. Algorithms now have the ability to collect, analyse and interpret big data, therefore, assisting managers and employees in real-time for decision-making processes. AI is not solely here to replace human workers; it is mainly here to increase the efficiency of (logistical) operations and enhance the employee’s productivity.


We think that AI will play a significant – if not crucial – role in the future of supply chain management.

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