The benefits of AI, Machine Learning and Computer Vision in Logistics
The pandemic has hastened the demand for ecommerce with customers becoming more dependent and demanding faster solutions. But logistics companies have had to make drastic changes in the way they operate in a very short amount of time, leading many of them not being able to keep pace. In this blog, we will discuss why the logistics industry has to adapt to the new needs of ecommerce shoppers and how AI, Machine Learning and Computer Vision technologies can be leveraged on the way towards digitization.
- What are the current issues with logistics?
- What technologies are there to help?
- Where can those technologies be of use?
- How can it be successfully implemented?
- How can Anyline be integrated?
1. What are the current issues with logistics?
As a recent Forbes article found, the logistics industry needs to create a digital transformation to keep up with increased demand. To do that, it needs to focus on four areas, including process optimization and automation, people management, network strategy, and supplier management and spend optimization.
Some companies have already made the move to incorporate more technology to improve logistics efforts. They are working to advance digitization in the supply chain. Yet, these changes are fragmented due to the lack of all sectors in the industry coming together, including integrators, forwarders, and logistic firms.
2. What technologies are there to help?
For companies that are ready to make the move to digitize, there are three key technologies that can help achieve this goal: machine learning, computer vision and OCR.
What is machine learning and computer vision?
Machine learning (ML) is a type of artificial intelligence that lets applications predict the outcome of some situations without being programmed to specifically do so.
Computer vision is a type of artificial intelligence that helps train computer systems to interpret and understand the visual world around them. It uses digital images to identify and classify objects. It is then able to react to the information at hand.
Optical character recognition (OCR)
Logistics industries need to have access to data throughout the supply chain. That data comes in from various sources, and workers need to collect and verify it quickly and accurately. For example, when a container is in transit, it can be identified by the container number, while the seal number also holds vital information and can be inspected to insure the cargo has not been tampered with.
In the last mile of delivery, there are also multiple touchpoints where data may need to be collected. When leaving a depot, the delivery vehicle information can also be a useful source of data. License plate scanning can be used to ensure security coming in and out of secure access points. When delivering packages, delivery drivers can also make use of this technology to verify the recipient’s identification using ID scanning for passports driver’s licenses, and other IDs.
OCR technology like the solutions provided by Anyline makes it possible for logistics staff to work faster by creating a secure, fast, and accurate data collection process for electronic proof of delivery software.
3. Where can those technologies be of use?
How do AI, machine learning, and computer vision fit into the logistics industry? These technologies can actually be put to use at many steps of the supply chain and on the journey of shipments.
Machine learning for supply chain planning
In order to streamline supply chain planning, companies need the right tools. With machine learning, companies can enhance decision-making processes and optimize them. This allows them to analyze significant amounts of data and apply intelligent algorithms to that information to create a more balanced demand and supply. AI algorithms can do most of the work for the company, minimizing the risk of mistakes.
Supplier selection and relationship management
A good relationship with a supplier is a must in this industry. Applying machine learning to data sets can be based on supplier relationship management actions. This leads to highly reliable estimates and improves interactions with suppliers. It helps you avoid plenty of risks and mistakes, too.
AI in logistics centers
AI and machine learning can help monitor and predict traffic during peak hours in a logistics center. This can help to predict shipping time more accurately, which allows better planning, leading to less time wasted and happier customers.
5. How can Anyline be integrated?
Anyline works on any smartphone that runs Android, iOS or UWP. The scanning in these processes is done using a standard mobile device camera that most mobile workers already know how to use.
In order to get Anyline’s OCR technology as well as Anyline’s barcode scanning technology, you can simply integrate Anyline’s software development kit (SDK) into your already existing software/ system. It also provides support for common integration frameworks, including Xamarin, React Native, and Cordova. Then, integrate it into any app.