5 Reasons why machine learning applied to the supply chain will save the planet



As we already know, the supply chain plays a very important role in today's economy. Without it, companies would not be what they are today and the globalized market wouldn’t exist.

But not everything is as perfect as they say, most supply chains are not very sustainable or environmentally friendly, often increasing greenhouse gases, further accelerating global warming. But don't worry, we’ll show you some of the reasons why supply chains can be environmentally friendly, without losing efficiency and high production standards thanks to machine learning, big data, and software.


Let's see some specific reasons for using machine learning in the supply chain to save the world



Machine learning for shipping


Shipping our products to our customers is one of the most important processes in our supply chain, so we can make use of collaborative shipping, which is nothing more than sharing shipping methods with other companies. Using AI and machine learning, algorithms and GPS can be implemented to identify opportunities for shared shipping between two or more companies, including coordinating product loading and delivery points, current status, stock, forwarding methods, and costs.

This reduces fuel costs, saving the company money and reducing emissions to the environment, whether they come from land, air, or sea.


Machine learning for delivery decisions


By taking advantage of machine learning, a system of synchronization can be developed for each of the shipments, since many times customers schedule their deliveries for a long term, or others, immediately, and even sometimes their status is changed after being shipped. So using this synchronization system plus algorithms we can determine the best shipping method to decrease the impact on the environment in real-time by continuously switching to the most efficient and sustainable delivery method possible.

Deep learning for the decision-making process


Deep learning by reinforcement is a specific element of machine learning that consists of training an algorithm to make the best possible decisions. This is done through a process of trial and error, in which the robot is guided towards the correct decision by positive feedback on its actions.

This helps with the decision-making process, on the means of shipment and even figuring out the best routes to reduce environmental impact, as well as the exact number of products to be shipped, when to ship them, and which mode of forwarding is the most appropriate.


Machine learning for reducing waste


Machine learning helps us control the amount of waste generated during supply chain operations. Much of the waste that occurs in supply chains is the result of inadequate demand forecasting. When we get these forecasts wrong, there is overproduction and overstocking. With machine learning, we can improve these demand forecasts, including improving production and delivery times.

Machine learning for supply chain balance


In the same way, machine learning can help us in accurately estimating the raw materials we will need to carry out our production. Depending on the industry, news, weather, holidays, etc., machine algorithms can predict how much production you will have.

New technologies such as **machine learning**, big data analytics, and AI can help companies make a positive change, ensuring their supply chains run as efficiently and sustainably as possible. The use of these can have a dramatic effect on supply chains, helping organizations to benefit from faster, cheaper, more sustainable routes for shipping, maintaining or even improving the productivity of the company, thus contributing to the environment and making our processes more sustainable and friendly.

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