- Increasingly high demands from shippers have dramatically reshaped the logistics industry, pushing logistics providers to explore artificial intelligence (AI).
- Self-learning AI is already transforming logistics, changing processes from manual to automatic and services from standardized to personalized.
- AI is capable of analyzing huge data volumes, thereby enhancing the accuracy of demand forecasting.
In decades past, deliveries of packages to our doorstep were rare. But after the rise of online shopping platforms, prompt shipping to our home or workplace has become a normal thing. The boom of online shopping portals has also boosted the logistics industry significantly. And similar to various other rising industries, AI has changed this sector as well. One of the most common challenges in the logistics industry is finding the shortest routes. The "traveling salesman problem" is to figure out the shortest path connecting a bunch of cities to ensure optimal use of time and resources. Previously, tree search algorithms or heuristics were used to address this problem.
But the shortcomings of these outdated approaches was that a given set of inputs generated a particular output. There was no self-learning while the process was ongoing. This aspect only came into play when deep learning neural networks merged with the existing tree search algorithms. Intelligent route optimization using AI today provides real-time routing algorithms for fleet operators and drivers. That’s how self-learning AI led to breakthroughs in logistics. AI enables logistics companies to optimize supply chain networks to heights that is not achievable with human intelligence alone. AI can help to redefine today’s industry practices and create an AI-enhanced customer experience, in the long run. This way, AI improves production planning and factory scheduling at the logistics warehouses through greater contextual intelligence.
AI is reshaping logistics
Four ways the logistics industry can leverage artificial intelligence to succeed.
1. Anticipatory logistics through predictive analysis
Predictive analysis is the process of making predictions using data analytics based on given input data. This process creates a predictive model for a particular analysis. In this model, apart from analyzing data, statistics and machine learning techniques are also used to forecast future events. Anticipatory logistics is all about these predictive algorithms running on big data. The estimation of demands, even before the customer places an order, allows the logistics team to improve efficiency and quality. Customers’ lack of patience at the delayed deliveries is one of the foremost factors influencing anticipatory logistics today. Customers want their delivery at breakneck speed — it’s what makes their online shopping experience exceptional. Thus, anticipating orders permits businesses to fasten up their resources before the demand shoots up. Anticipatory logistics benefits everyone involved in the supply chain process. For example, AI predicts consumer demand for the latest mobile device model is going to rise. Accordingly, the manufacturer would then increase the production of that particular model. This is how predictive analysis benefits the logistics industry.
2. Real-time decision making
Are the customers ready to order? The sales team surely wants to know this for its predictive analysis. But for logistics, supply chain and transportation planning are equally important. At various phases of the supply chain and the transportation process, the logistics team needs to make decisions according to the situations that occur on a real-time basis. A bulk consignment delivered to the customer may have manufacturing defects or the vehicle may face maintenance issues during transportation. And for such real-time problems, the team has to provide real-time solutions. Also, to deliver real-time solutions, real-time decision-making with the help of AI is critical. For instance, telematics can help determine when a vehicle might need preventative maintenance, thus avoiding breakdowns. This, in turn, reduces the risk of failure to meet customer needs and expectations. This is where real-time decision-making becomes useful in the logistics industry. The real-time decisions act as inputs for the self-learning systems to elevate to higher levels at the warehouses.
3. Self-learning systems
Where, when and how? These are the questions both the logistics team and the customers want answers for. On one hand, leaders in this discipline have to gather and combine information to make the best decisions regarding the deployment of inventories and transportation assets. They need to find out: Where are the drivers? Where are the vehicles? What are the commitments? Where are the customers? All these questions should have real-time answers. While on the other hand, customers have questions, such as, Where is my order right now? When will it be delivered to me? And again, even the updates given to the customers should be in real time. Variables related to such real-time scenarios can be fed to the machine-learning engines that can extract the required data and then drill down a range of situations to optimize this process. With the help of self-learning systems, logistics specialists are able to make better, current decisions as well as informed longer-term decisions. This allows them to make strategic choices, such as warehouse locations, fleet size, fleet specifications, etc. One such tool preferred by logistics companies is ThinkOwl. This help desk software is fully powered by AI and self-learns every time one performs an action on the help desk. For example, ThinkOwl gives expert suggestions to the agents when similar cases reoccur or do the topic analysis of different resembling keywords on the help desk. This self-learning over time eventually helps to generate real-time updates for both the logistics professionals as well as the customers.
4. Warehouse management
AI has greatly reshaped the physical demands of working in logistics. According to a report by DHL and IBM, there are many aspects in the logistics warehouse management and supply-chain management. The use of AI-enabled robotics, computer vision systems and autonomous vehicles in logistics operations has elevated the capabilities of both workforce and warehouses.
Some of these include AI-powered visual inspection and intelligent robotic sorting. Visual inspection captures pictures of cargo using special cameras. This allows to identify damage and an appropriate corrective measure can then be taken to mitigate that damage. Robotic sorting sorts or bundles letters, parcels and palletized shipments more effectively. Intelligent robotic sorting and AI-powered visual inspection reduce the operations costs and optimize inventory at the warehouse. Every day, millions of shipments are sorted with a stream of conveyors, scanning infrastructure and manual handling equipment. The AI engines extract real-time data through different cameras and sensor types. These are capable of identifying a wide range of items, such as food and beverage cartons, by recognizing the 3D forms, labels and logos. It’s easy to sort parcels and letter-sized shipments through such sorting methods to reduce human effort and error rates.
This way, AI improves production planning and factory scheduling at the logistics warehouses through greater contextual intelligence.
The future of logistics with artificial intelligence
As supply-chain leaders continue to make headway in the digital transformation journey, AI will become a bigger and inherent part of day-to-day business. In industries like logistics, AI that learns from experience is a better approach to solving critical problems. AI is playing a major role in accelerating the path towards a predictive, proactive and personalized future for logistics. AI will place human intuition, interaction and connection as an add-on by allowing people to contribute to more meaningful work. Finally, traveling salespeople will no longer have to figure out routes by themselves, as they'll have it at their fingertips. That's the way AI will revolutionize logistics, supply chain management and transportation.