Jack Landsmanas: Artificial Intelligence is Changing the Future of Food Logistics ‍

Jack Landsmanas: Artificial Intelligence is Changing the Future of Food Logistics ‍

Jack Landsmanas experto en alimentos

Jack Landsmanas experto en alimentos

Jack Landsmanas experto en alimentos

Jack Landsmanas experto en alimentos

Jack Landsmanas experto en alimentos

Jack Landsmanas experto en alimentos

Jack Landsmanas experto en alimentos

Jack Landsmanas experto en alimentos

Jack Landsmanas experto en alimentos

Jack Landsmanas experto en alimentos

Jack Landsmanas: Artificial Intelligence is Changing the Future of Food Logistics ‍

This will mean that a much smaller number of workers are needed to run a production line, with the cost benefits of automation also being realized”

— Jack Landsmanas

MIAMI, FLORIDA, ESTADOS UNIDOS, January 17, 2023 /EINPresswire.com/ — Food logistics is expected to grow faster than the overall economy through 2021, according to a report by research firm IDC (https://www.idc.com/getdoc.jsp?containerId=US49675222) . This has prompted food giants such as Nestle and Wal-Mart to invest in artificial intelligence (AI) technology so that they can lower the cost of their supply chains and optimize them for higher efficiency. AI will change how we think about data, with more and more information becoming digitized every day. Smart devices are also getting smarter, which means that AI is needed to interpret all the data these devices collect. It is this convergence of new technologies and old business practices that will transform food logistics over the next few years as suppliers, integrators, manufacturers and consumers all adopt these new ideas. Let’s take a look at some of the ways AI will change food logistics in the future…

Smart Transportation and Warehousing

AI will eventually be used to manage the entire transport process, from the routing of trucks and trains, to the scheduling of delivery appointments. This will be aided by the fact that more and more goods are being transported using autonomous vehicles. The further transport of goods is automated, the more fuel they will use, but AI could also be used to predict demand and optimize the amount of stock that needs to be held. AI can also be used to improve how goods are stored in warehouses. This could include predicting which goods are likely to be damaged in transit or damaged by high humidity, for example, and engaging robotic equipment that can take action to optimize the condition of goods.

Data Mining and Machine Learning

Machine learning and data mining are crucial for the AI process, yielding information that is then used to train models with the goal of making specific decisions. This is where smart transportation and the use of sensors comes into play. As goods move through the supply chain, sensors can collect data about them. This could include the temperature of cargo or the humidity of goods, for example, which can be used to train models that will predict the likelihood of damage in transit.

Robotics and Automation

Robotics are another key area of AI in the supply chain, especially when it comes to the use of automation. AI is changing the way companies think about automation, with the goal being to both automate as much as possible and also to allow humans to focus on strategic tasks. One key area is accelerating the rate at which goods are produced and delivered. This can include 3D printing in production, which allows production lines to be shortened and goods to be produced faster, even in small batches. Automation in distribution can also be used to help minimize human error. This could mean that, for example, a robot is sent to collect goods from a distribution center and drive them to a nearby restaurant. In other cases, AI can be used to optimize the route drivers take, helping to ensure that fewer stops are needed, especially if sensors are used to collect data about customer locations.

Augmented and Virtual Reality

Augmented reality is another growing area for AI in the food supply chain. This can be used to improve customer service, with restaurateurs being able to see the status of orders from their kitchen and chefs being able to see ingredients and track stocks. In terms of food production, AI can be used to trace the origins of produce, with more and more food being traced back to farms using blockchain technology. This allows suppliers to track produce from the farm to the customer, including the use of sensors to trace produce back to the field and the weather conditions at the time of harvest.

The Future: AI-Driven Process Automation

The final area that will see significant change is the use of automation in the entire production process. Currently, AI is used to improve the flow from the production of ingredients to the production of finished goods. However, the long-term goal is to integrate AI into all production processes. Even the use of robots will become more automated, with more robots being connected to sensors and using AI to optimize production. “This will mean that a much smaller number of workers are needed to run a production line, with the cost benefits of automation also being realized” says the expert Jack Landsmanas

AI in Supply Networks

As AI becomes a more common tool in the supply chain, it will be used to optimize the entire network. This includes engineers at the manufacturing and transportation hubs, who will use AI to optimize the flow of data and optimize the use of sensors. For example, they will analyze the data they collect to predict the likely locations of breakdowns and equip vehicles with sensors that can detect when they are about to break down, reducing the time it will take for drivers to be dispatched and for repairs to be carried out, with less fuel and emissions being used in the process.

Bottom line

This is just a glimpse of what is coming in the future of AI in the food supply chain. The increased use of sensors and the automation of production will allow for higher throughput and much shorter lead times. This will help to bring down costs for manufacturers, while also helping to optimize the network. As data is used to make these decisions, the efficiency of the network will increase, leading to significant cost savings.

Mia Atkinson
Media Captains
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Jack Landsmanas experto en alimentos

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