The transportation and logistics industry is responsible for the procurement, storage and distribution of products effectively. The current internet age is continuously evolving, and technology has always played an important role for transportation and logistics industry to adapt to these changes. Where previously embedded chips and RFID tags were used, today Artificial Intelligence is redefining the processes in this sector with human like cognitive abilities.
Let’s have a look at how various technologies are powering innovation in the various segments of transportation and logistics industry.
Innovations in the Transportation and Logistics Industry
Businesses rely on the transportation and logistics sector for their material supply. If it is managed inefficiently, tons of businesses stand to lose a lot. For this reason, the logistics industry has remained an epicentre of innovations, the following being a few of the prominent ones:
Robotic transportation and Delivery solutions
Continental expressed their idea of delivery robots at the CES 2019. The company has been working to combine driverless vehicles and deployable robot dogs to successfully deliver packages and goods. The prototype showed a pod-like vehicle that was designed to decrease idle time and improve capacity. Continental’s Urban Mobility Experience (CUbE) platform can carry one or more of these robot dogs and deploy them near to the destination.
Artificial Intelligence in Vehicles
Transportation solution will strengthen from AI powered vehicles. Such vehicles will be able to route and schedule deliveries better than existing solutions. Nvidia and Mercedes-Benz have started working on a partnership aimed at developing AI in cars. They are creating a system that will have a smart cockpit, self-driving capabilities, and many other features.
This is a much better way than the current system of having multiple small processors called Electronic Control Units (ECUs) with each ECU being responsible for one section of the car. In the new AI-based car, there will be a centralized, single system model.
Torc Robotics and Transdev are working together to create I-Cristal, a fully autonomous shuttle platform. This shuttle would be using RC’s Level 4 autonomous self-driving software while depending on Transdev’s autonomous transport system. The latter will provide it with its supervision system, client application, and connected infrastructure. I-Cristal would have self-driving abilities and will function without steering or pedals. Autonomous vehicles can work with existing solutions to form a hybrid workforce for transport and logistics industry. Some of the other more popular, recent innovations include cellular technology that can navigate roads, drones shooting 4K video, and transportation hubs.
Mckinsey Global Institute predicts that the Transport and Logistics industry will benefit from AI with a potential incremental value of 89 percent as compared to the other existing analytical technologies. Here’s a concise look at all the stats outlining other industries as well.
Challenges of Implementing AI in Transportation & Logistics
Although AI solutions will bring various benefits into the logistics sector, there are a few challenges that come with it. As AI in Transportation and Logistics becomes more common, we will be seeing more of these kinds of challenges.
- Cost of Integration: The cost of integrating AI in existing systems within organizations is very high. To work effectively, independent AI-based systems must be integrated together so that the entire ecosystem can work as expected.
- Operational Costs: An AI system is built of independent components where each of them needs to be replaced regularly. This is done to maintain operational integrity. The challenge here is that the components don’t come cheap. Besides this, AI systems also need to be updated constantly and have their batteries replaced, thus increasing the operational cost.
- Development of AI: Artificial Intelligence is a relatively new and rapidly growing technology. There is a lot more we can do with AI, especially if we can combine it with other technologies. For this, we need to have ongoing research and development. This means that the users would have to update to better tech to keep up to the latest technology. This might not be possible for smaller businesses.
AI Adoption Trends in the sector
Despite all these challenges, plenty of organizations in the transportation and logistics sector have already started using AI. Let’s take a look at the transportation and logistics companies who are actively adopting AI.
- Ocado, an online grocery store in Great Britain has managed to successfully develop an automated warehouse using computer vision. The system employs a “hive-grid-machine” robot is designed to do the same tasks as the workers, but faster than them. It has been programmed to help with sorting, moving, and lifting items. The orders are shipped a lot quicker than before now which has helped them cut down on transportation and delivery lags.
- Rolls-Royce partnered with Intel to create autonomous Ships. These ships are designed to be intelligent enough to choose the optimal routes, recognize objects in the water, and monitor engine condition. As a result, the delivery speed is faster than ever and the ship operators have the power to automate navigation and operation.
- DHL Parcel and Amazon are collaborating to improve customer experience. Amazon’s voice assistant, Alexa, can now answer questions about shipments, tracking details, and more details. All the user needs to do is engage verbally with Alexa and receive all the updates immediately. This tech can be used by the logistics industry where they can simply keep track of parcels and goods verbally.
AI Use Cases in Transportation and Logistics
Let’s take a quick look at the most popular use cases of AI in this sector.
AI could change the direction of warehousing operations, allowing companies to increase their profit. AI in warehouses can prove to be extremely useful for predicting demand. Based on these predictions, orders can be placed allowing better planning and execution while lowering the transportation costs at the same time. Companies like Ikea, Walmart, and Nike use automated warehouses.
Robots can be highly useful in this sector. They can not only help with physical tasks but also with back-end administration. Repetitive but important tasks, like creating invoices, adding shipping details, etc., can be done efficiently with robotic process automation. Amazon is already using warehouse robots for tasks like sorting and lifting packages within a facility.
Predictive maintenance in logistics can help save company billions of dollars by increasing asset lifespan. The systems in use could be made to perform better, longer, and with the same initial efficiency. Predictive systems can give you a detailed analysis of potential failures, scheduled repairs, and more. Bosch, Hitachi, and GE all understand the power of predictive maintenance and are using it.
A study by Mckinsey in 2017 found that early adopters of AI in this industry are already enjoying profits margins greater than 5 percent. Here’s a look at some of the core practices that these companies have adopted to successfully incorporate AI into their organizations.
If this adoption rates increase among organizations, both the companies and their clients will be able to experience better operations. If more organizations start using AI-based high-tech driving assistance, the chances of manual errors will decrease drastically. It will also help to mitigate risks, improve forecast, and reduce redundancy. AI can also assist by offering users faster deliveries, better customer service, and more. AI in the logistics industry is currently only used by the top players. If smaller companies also get in the game, it could change the way the industry currently runs. While the businesses will have to bear the high initial costs, it is likely to pay off in the long term.
AI is a relatively new technology in transportation and logistics sector. Despite this, there are plenty of companies that have already started using AI-based systems. Its efficient approach helps with asset performance, predictive maintenance, and working on repetitive tasks. AI in logistics has unlimited use cases. With ongoing development in the field, we are likely to see some unbelievable things AI subsystems can help us with.
This article was taken from Towards AI