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The Future of Machine Learning and Road Safety



Millions of deaths occur from traffic accidents every year with many efforts trying to reduce the frequency and severity of these traffic accidents.  The most effective way to tackle this problem is by means of an extensive program of road safety management, in which road safety modelling plays an essential part.

The modelling process attempts to adjust a model to the crash data, the geometric and operational characteristics of the road, and the environmental conditions, incorporating the most important factors. Numerous modelling techniques have been developed to improve the representation of reality, allowing for the employment of techniques that are more appropriate to the problems.

There have been limitations to these approaches, allowing for new opportunities, such as machine learning that SANRAL is currently exploring.  This allows to improve road safety, reduce congestion and information infrastructure development.

Machine learning can be used to detect and segment objects within the camera frame.  These objects can then be classified based on pre-trained image classification.  Which ultimately allows for the detection and classification of different types of vehicles, pedestrians, different types of animals, cyclists, etc.  The possibilities are infinite, based on the data available. 

Currently, there is ample data on the above-mentioned classification types.  While this is still in the exploratory phase within South Africa, it does come with significant risk and efforts are being made to understand how to effectively use this technology, while maintaining strict compliance with legislation as it relates to the privacy of the road users.

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What Is The Future of Artificial Intelligence In Logistics?



Human hand holding digital brain with artificial intelligence text

The future of artificial Intelligence is both incredibly exciting and incredibly promising. Much like the logistics world, the world of AI is fast-paced and ever-changing.

In this article, we will discuss today’s applications and the future potential of artificial intelligence in the logistics world and which areas AI is expected to make the most significant contributions to.

Transport and autonomous vehicles

Today’s example of AI in transport and autonomous vehicles

AI already plays a crucial role in the implementation of autonomous vehicles and drones. Primarily to direct these automated vehicles also known as AGVs or AMRS on which paths to take to reach their destination most quickly and efficiently. AI is also used today on a much smaller scale on large vehicles such as driverless trucks.

In the future: a wider variety of transport applications & cost reductions.

In the future, we could see this being applied to larger vehicles such as trucks in a more widespread way reducing costs and cutting down delivery timeframes and increasing overall supply chain efficiency.

This can be especially important to warehousing, to continue the flow of stock to and from destinations with the least delays and at a reduced cost as these vehicles would not require drivers. This would allow warehousing solutions using AI to have a competitive cost and time advantage over their competition.

Risk management and prediction

Today’s example of AI in risk management

AI can analyse historical data, weather conditions, traffic patterns, and other relevant factors to identify potential risks and mitigate them proactively. It can help logistics companies optimise routes, avoid disruptions, and respond swiftly to unexpected events. AI can also create predictions for possible future risk situations such as stockouts.

We see this today in the form of demand forecasts where an AI will analyse the level of stock in a warehouse and the historical sales data to make demand predictions and suggestions on what to stock up on to mitigate the risk of a stockout situation.

In the future: more accurate, and quicker decision-making.

In the future, we can expect to see AI become more accurate with the predictions it makes. As AI gets better at using the data at its disposal the quality of its suggestions may improve dramatically.

Data analysis and pattern recognition are already one of AI’s strongest areas and as such we can expect its ability to learn from that data and make higher-quality decisions and suggestions to improve.

AI chatbots & GPT 3 and the effects on customer satisfaction.

Today’s example of AI in chatbots

Companies are already using AI technology such as Chat GPT 3 for services such as customer support. How this process works is Chat GPT is used as an intermediary between, the time a customer contacts support, to the time a human can address the customer’s concern. In this post by TechTarget, we can see a deeper insight into exactly how this works.

In the future: Improved chatbot customer support

AI such as chatbots may advance to the point where they can address a wider variety of customer service needs. This would reduce the wait time for the customer to resolve their initial issue and save companies time and money investing in a large customer support network.

This is especially useful for larger companies as they may have hundreds of thousands of users or even millions of users. When you consider that 90% of customers in the US use customer support as a metric to decide whether or not to keep doing business with a company, customer support cannot be overlooked and neither can the quality of that support from a business perspective.

Data Analytics and Insights

Today’s example of AI on data analytics

AI algorithms can process the vast amounts of data which is generated by logistics operations and provide actionable insights for decision-making. By analysing data from various sources, including IoT (Internet of Things) devices, sensors, and customer feedback, AI can optimise operations, improve efficiency, and identify areas for improvement.

In the future: AI that can read for context & create data models.

In the future, we can expect to see AI improve the capacity and speed with which AI can analyse data. The main problem today is that AI struggles to read into the data for deeper context. This means that tasks such as cleaning, validating, and creating data models from this data are primarily done by humans. In future, we can expect to see AI branching out into these areas typically done by humans.


In the complex and fast-paced world of logistics, it is critical to adapt, use new technology, and capitalise on AI’s increased efficiency. This strategy ultimately provides logistics companies with a competitive advantage

While AI offers immense potential, there are challenges to address, such as data privacy, ethics, and the need for human oversight. However, with continued advancements, AI is expected to play a pivotal role in shaping the future of logistics, making it more efficient, reliable, and customer centric.

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Artificial intelligence (AI) in warehousing and logistics



Artificial intelligence in blue

In today’s digital age, few technologies have been more talked about in a good and a bad light than artificial intelligence. There are many sectors that can benefit significantly from the use of AI in one form or another, one such is the warehousing sector. In this article, we will examine how artificial intelligence is used in warehousing, and what the benefits of AI are.

What defines artificial intelligence?

Artificial intelligence is, in essence, computer-simulated human-like intelligence which allows for complex problem-solving abilities i.e., the ability to take in information, analyse any given piece of information and make intelligent rational decisions with a focused end goal in mind.

The true strength of AI

The greatest strength of AI lies in its ability to take vast amounts of data, recognise patterns, make informed decisions, and adapt and improve its performance based on experience. AI systems are designed to understand, learn from, and respond to their environment, enabling them to perform complex tasks, automate processes, and provide intelligent solutions.

How is artificial intelligence (AI) used in warehousing?

AI is currently used in warehousing in three primary ways these can be broken down into the following.

1. To create predicted demand forecasts

AI is used to forecast demand for stock within the warehouse. This is achieved by the AI using historical data and algorithms to analyse large volumes of data for trends, and correlations. This allows the AI to make accurate demand forecasts for items and can be taken one step further by analysing seasonal trends, and consumer buying behaviour for deeper connections to determine future demand.

2. To improve inventory management

Expanding on forecasting demand, AI can be used to manage inventory levels. For example, AI can make suggestions on exactly which items to stock up on, and at which dates to stock up based on the initial forecast demand and live data. This helps reduce out-of-stock events and allows the warehouse to optimise its inventory for events such as seasonal buying changes. Essentially there is less uncertainty involved when restocking the warehouse.

3. To optimise transport logistics

AI algorithms optimise logistics operations by analysing various parameters such as order volume, delivery locations, traffic conditions, and transportation constraints. By considering these factors, AI-powered systems can generate optimal delivery routes, minimise transportation costs, and improve delivery timeframes.

This streamlines logistics operations and enhances customer satisfaction.
Transport optimisation normally takes place on routes to and from the warehouse, however, if your warehouse takes advantage of automated guided vehicles or AGVs, AI can also be used to optimise the paths these robots take within the warehouse, to ensure the fastest route is taken.


The use of AI in warehousing and logistics has become increasingly popular and beneficial. We can forecast demand for stock, improve inventory management, and optimise transport logistics. These applications help reduce out-of-stock events, optimise inventory, generate optimal delivery routes, minimise transportation costs, and improve delivery timeframes. As technology continues to advance, more AI applications will likely be implemented in the warehousing sector, leading to even greater efficiency and customer satisfaction.

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Materials Handling

Unlocking the power of automated guided vehicles (AGVs) in the South African mining sector.



Mining vehicles working on site

AGVs have the potential to take South Africa’s mining and materials handling sectors to new heights by providing many benefits to safety as well as lowering operating costs which we will discuss below. Automated guided vehicles or AGVs are autonomous vehicles that can be programmed or directed to perform specific tasks. These vehicles can range from small to large depending on their intended use. In this article, we will focus on the main benefits of these vehicles in South Africa’s mining and materials handling sector.

The wide variety of AGVs

Numerous types of AGVs specialise in specific jobs for example towing AGVs are designed to carry smaller unpowered vehicles behind them such as carts containing raw materials which cannot move on their own. In contrast heavy load AGVs are designed to carry large amounts of items or a few very heavy items.

Another useful AGV is the unit load AGV which primarily specialises in unloading and loading materials as well as transporting these materials from point A to point B. They do not require human intervention and excel at performing repetitive unloading and reloading tasks that would otherwise require a human operator.

Benefits to the mining sector

The mining sector has the potential to benefit from the implementation of AGVs. We can already see AGVs specifically designed to assist in the mining sector such as in the mine navigation section of the industry.

Mapping mines before entry.

In this example, we see that an AGV with lasers are used to map various underground routes before humans enter the area. The AGV can then be used to define areas that are safe to move through, as well as areas that may be blocked or hazardous.

AGVs can also operate in potentially dangerous areas of mines such as abandoned mines which are revisited for new potential mining opportunities. These mines may not have been inspected for many years and as such having an AGV inspect and/or map the area for potential dangers would be a huge advantage to the safety of miners entering the area at a later date.

Transportation from mines

The actual transportation of raw materials from within mines could also be done via AGVs, as there is great potential for a particular type of AGV known as a heavy burden AGV or heavy load AGV to do this job effectively as their maximum load capacity is around 125 tons depending on the AGV. This would make them suitable to transport enormous quantities of raw materials from mines automatically.

Lower operational costs

This benefit comes in the form of lower operational costs for example, the transportation of goods from the mine to the surface may no longer require as many trained drivers as it usually would to manually transport raw material from the source mine. Instead, AGVs can take over this task as they thrive in repetitive tasks in the form of moving materials from one place to another. The main benefit from a cost perspective here is the fact that this would require fewer human transportation drivers to perform these tasks and thus fewer people who need to be paid to perform these jobs manually.

Safety advantages

In the previous section, we briefly talked about the advantages AGVs have, specifically the fact that you can send them into potentially hazardous environments with minimal risk to human life, however, AGVs are also useful in the transportation of goods which pose a potential threat to the health and safety of workers.

As it so happens South Africa is the world’s 11th largest producer of uranium ore which is known to be dangerous to humans depending on the purity and concentration of the ore. Mining and transporting such items using AGVs allows us to ensure minimal long-term risks to both the actual drivers and the miners themselves as we effectively lesson the contact time between these elements and the workers.

Supply chain efficiency

Not only do AGVs provide benefits in both safety and transportation when handling raw materials, but they also have the advantage of being able to operate practically 24/7. This is important because they can continue to work hours after that an employee cannot without rest, effectively making sure materials can get to their destinations reliably with greater consistency and less room for error, however, the main limiting factor is the battery life of the AGV in question.

Final thoughts.

If implemented correctly, AGVs can improve many areas of the mining and materials handling sector, including safety, transportation, operational costs, and efficiency. While there are concerns about how this would affect jobs in South Africa if AGVs are used more so as a tool to assist workers rather than an active replacement they could contribute to a quality-of-life improvement for workers that will no longer have to do repetitive tasks and a business improvement for employers wanting to lower operational costs and increase on-site safety.

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