Revolutionising cargo load planning with AI | #techUKDigitalTrade
What is one of the biggest costs hiding in today's complex supply chain? Air.
Partially empty containers and inefficiently loaded containers have a domino effect on costs for logistics operators and manufacturers alike. For example, nearly a quarter of every container shipped by sea is empty — 24% on average, according to a survey by Forbes Insight and DS Smith. This is the equivalent of 61 million empty shipping containers each year, or 122 million tonnes of carbon dioxide annually. Solving this could save companies an estimated $46 billion a year globally.
As we all journey together on the Route to Net Zero, every gain in overall transportation efficiency counts. By optimising load planning in real-time, AI helps companies reduce the number of vehicles required to transport goods, which in turn reduces fuel consumption and greenhouse gas emissions. Therefore, companies not only save money on fuel costs but they also contribute to a cleaner environment. Consider the benefit to our environment if this is done at scale.
Many load plan productivity gains, facilitated by the AI, happen even before any vehicle starts moving. Traditionally, load planning has been a time-consuming and manual process. It involves analysing a complex matrix of data such as cargo weight, dimensions, and fragility, as well as vehicle capacity, size, and stability, to determine the most efficient way to load goods. However, with decision-making AI, load planning can be done much faster and more accurately than ever before.
Decision-making AI uses advanced algorithms and machine learning to analyse large amounts of data quickly, and make decisions based on that analysis. The reinforcement learning-based approach – similar to that used to beat human champions at Go and Chess – enables easy-to-use applications such as InstaDeep’s DeepPack™ (https://deeppack.ai/) to continuously learn and improve with experience to adapt to each customer’s unique needs, and achieve better results than previously thought possible.
Decision-making AI factors in multiple variables and constraints simultaneously, which is almost impossible for even a highly-trained human to do manually. Producing the AI-powered load plan in real-time, significantly reduces the time it takes to plan cargo loading, minimises the risk of damage to goods, and improves overall transportation efficiency.
To summarise, AI is a game-changer for load planning. Companies that adopt decision-making AI for load planning can save time and money very quickly, reduce the risk of damage to goods, and significantly improve their overall efficiency and customer service.
