Artificial Intelligence (AI) and Logistics
Article outline:
- What is Artificial Intelligence (AI)?
- AI in Africa
- What can we do with AI today in the field of logistics?
- The potential of AI in logistics
- 3 concrete applications
In this article, we will explore the concept of artificial intelligence (AI) applied to logistics and examine how to effectively leverage AI in logistics operations to optimize processes and improve performance.
What is Artificial Intelligence (AI)?
A concept born in 1956, AI refers to any process automated by a computer whose structure mimics the functioning of the human brain. These are algorithms capable of performing tasks that humans accomplish using their intelligence.
AI has grown in popularity in recent years for two main reasons:
- The enormous amount of data generated – This data is crucial and allows AI algorithms to learn and improve.
- The exponential increase in computing power – Today, we can perform calculations in record time that would have taken years in the past.
AI in Africa
One might think that Africa is slightly behind other continents in terms of AI, especially regarding digital infrastructure, education, and data availability. However, this is changing. Every year, AI-driven startups are emerging across the continent.
In terms of infrastructure, research centers are opening, such as Google’s AI research center in Ghana. Major players like Microsoft and Amazon have opened multiple data centers, notably in South Africa. Finally, there is an explosion of data in Africa, driven by the mobile phone revolution, representing an extremely valuable source of information.
AI holds enormous potential to improve development efforts in Africa. The continent faces many challenges in sectors where AI could be beneficial, such as agriculture, finance, and healthcare.
What can we do with AI today in the field of logistics?
AI is already present in our daily lives. Here are some simple applications possible thanks to AI:
- Prediction: Algorithms can make predictions based on various parameters. For example, GPS applications predict arrival times based on time, distance, and traffic conditions.
- Natural Language Processing (NLP): This AI field allows algorithms to understand and interpret human language. For instance, automatic translation applications read a page’s text, interpret its meaning, and translate it into another language.
- Image Recognition: In this AI domain, algorithms can process images to classify or recognize elements. Image recognition performance has skyrocketed with the advent of deep learning.
The potential of AI in logistics
Logistics generates a lot of data, which is the key fuel for AI algorithms. AI can process enormous volumes of data to learn and address critical challenges. By automating repetitive human tasks, AI reduces errors, saves time, and therefore saves money.
By predicting outcomes based on massive datasets, AI can optimize decision-making, better manage risks, and anticipate problems, thus reducing costs. These reasons explain why AI is increasingly involved in the digital transformation of the supply chain.
Let’s now look at three concrete and recent examples of how AI benefits logistics.
3 Concrete Applications
1. Automatically Updating Contact Information
In logistics, keeping customer contact information up-to-date is crucial for smooth deliveries. According to a Deloitte study, 25% of contact information is outdated.
The American startup CircleBack developed an algorithm to automatically update contact information. Their machine learning algorithm uses millions of data points to determine which contacts need updating. This avoids duplicates when adding contacts and saves significant time in verification.
2. Predicting Potential Delays in Air Freight Transit
Another example where AI improves logistics performance is a machine learning algorithm developed by DHL to predict potential delays in air freight transit. By training the algorithm on a large dataset, it can predict, based on 58 parameters, a week in advance whether average transit times will increase or decrease. This solution also identifies the factors most influencing delays, such as departure day or airline. By anticipating potential delays, companies can act proactively.
3. Image Recognition for Transport Wagons
The third AI application is image recognition used by IBM for preventive maintenance on transport equipment. The goal is to detect damage as early as possible and address it. Transport wagons are photographed during transit.
These photos are processed by an algorithm trained to recognize key parts of a wagon and identify damage. Fractures, oil stains, missing parts—the algorithm can detect issues sometimes missed by humans. Once damage is detected, the maintenance team is alerted to determine corrective actions.
AI thus helps identify damage early, preventing accidents, avoiding costly repairs, and reducing maintenance expenses.
Artificial intelligence is part of our daily lives and continues to prove its value in many fields. Today, its development in logistics is rapidly growing. Its applications are numerous and offer real potential for the coming years.