Therefore, transportation organizations have to leverage their human capital, technology, and equipment in new ways. The advance of AI in logistics motivates organizations to digitize their physical operations in search of more efficiency and agility.
AI TRANSFORMS LOGISTICS INDUSTRY
How AI is used in logistics – potential use cases
Complexity: 2 (The solutions tend to rely on the most popular AI technologies. Because of this, they are available for companies of various sizes).
Data needed: Usually, it is data on how the company performs the process in question and the criteria for success. It may also be a set of characterizing information of what AI’s everyday actions are. For a task like a company’s reports automation, AI should understand how to find the needed information
Influence on business: Increases overall efficiency by removing most of the routine back-office tasks that are often a source of errors. Every action would be accounted for, making the entire decision process streamlined.
- Significantly improves all processes that require any data entry. Data should only be entered once and may be reused later in any department.
- With automated report generation, routine data gathering is no longer needed. The probability of getting wrong or conflicting data will also decrease, since with AI automation, each action is coordinated. This will both remove one of the most critical points in operations and make them transparent.
- AI automation of email processing will shift a great deal of work from the back-office departments. It also can be used with similar first-line communication steps. This will allow the department to focus more on the most pressing or valuable tasks.
- AI allows companies to reach the full end-to-end automation of back-office processes. This is why it dramatically improves Robotic Process Automation (RPA) performance or even substitutes it. RPA can only work with set logic and commands, while AI can learn them by itself, continuing to improve in the process. AI allows for much more flexible automation.
Price: This area focuses on the automation of routine processes. Therefore, the main costs are appropriate items’ marking or integration of AI solutions into current processes.
Complexity: 3 (The solution methods rely on established technology. But the main challenge is connecting large amounts of disparate data types in a meaningful way).
Data needed: Historical tracking data on the company’s fleet. In some systems, audio or visual control is used in parallel. Technical information about the vehicle can also be used, such as the engine parameters.
Influence on business: Optimizes the entire fleet operation, allowing for more effective actions on the go. It also makes the whole process more transparent, preventing possible cases of fraud or accidents.
- The gathered data can help find the most efficient way to perform deliveries, correcting them in progress if required. It plays a critical role in real-time route planning, using the updated driving conditions or incoming orders. These tools can also help make the most efficient decision on the road.
- Keeping track of everything that is happening with the fleet helps to assign the tasks efficiently. Companies can choose the most fitting vehicle or driver for the job depending on the current situation.
- Often, the benefits of gathering additional data can reveal themselves in not the most evident improvements. Even such things as optimized fuel consumption or improved driving style can reduce costs.
- AI can detect delivery mistakes or misuse of vehicles. This can be an important factor for eliminating possible human factor problems.
- Some systems can detect anomalies in the driving style. Then they either will signal about a possible accident or prevent the driver from falling asleep.
Price: Includes installing the tracking software on every vehicle and providing it with devices to keep it online as much as possible. The GPS trackers are widely available, but the hardware reliability and the need for constant connection create additional costs.
Examples: There are many such solutions, which all differ based on their goal. Some of them focus on ensuring fleet security, while others analyze the driving and introduce AI-based tracking. Some solutions explicitly focus on improving the driving decisions, while others use the gathered telematics data to create predictive maintenance for fleets.
Complexity: 3 (As a part of behavioral analytics, the conclusions must rely on analyzing complex patterns and combining them with vehicle data. But many of the methods used for solutions are widely known).
Data needed: Initially, the data on driver behavior, with situations of different types labeled as such. To work with each driver, the system needs some calibrating on the person-specific data. Internal vehicle parameters are also required to analyze some of the on-road situations.
Influence on business: Companies understand how each driver reacts to different road situations or follows the legal requirements. This can help assign the required ratings and possibly conduct some training. As a result, travel safety is improved by reducing the number of dangerous situations. Consequently, financial or reputational losses attributed to them will be reduced.
- Allows the drivers themselves to reevaluate their actions and perfect their driving. This increases both the effectiveness and satisfaction from the process of driving.
- The system can determine how smooth the driving is and how much the driver anticipates the road conditions. Such situations can later be recorded to improve the driving process individually.
- Identifying each person by driving patterns can help determine if the right person is indeed driving on the right route. This can increase fleet safety and accountability. A change in the driving pattern can also indicate that the driver has some issues, such as health problems or mobile phone usage.
- In the event of an accident, it is possible to react quickly and adequately review all the surrounding conditions.
Price: Since such systems need to trackmany driving factors, the most important aspect is procuring the sensors. Some solutions may include cameras, which increases the cost. The total price is comparable to real-time route planning and can be reduced if such devices are already on the market.
Examples: There are several solutions developed for different purposes. Most of them focus on determining patterns to discover driving anomalies, assessing the situation, and possibly preventing accidents. Some of them focus on driving efficiency and related analytics, while others analyze security both for drivers and fleets in general.
Complexity: 4 (Is usually lower for the smaller networks or more straightforward sale plans. But for large companies, accounting for every factor is a challenge).
Data needed: Historical data on the item consumption, sales data, storing and stock parameters, price information, product reviews, and anything else that is connected to the item’s demand. Macroeconomic parameters, online search trends, or any other data available are commonly added.
Influence on business: Helps effectively plan the company’s supply orders based on historical data. It is possible to predict their distribution across future periods. It also reduces the number of misused resources for nonoptimal storage.
- Costs spent on storing the items can be reduced. With AI, the company can precisely predict how much of any item it will need for a given period. Therefore, the company can save both costs and storage space.
- AI shows exceptional performance when there is a lot of input information, which is often the case for large logistical networks. It can combine any number of data sources, picking up such trends that otherwise would go unnoticed.
- AI helps reduce the number of stockouts, which is one of the most frustrating issues for any logistics-related business and its clients.
Price: Since this field operates only on the gathered data, any hardware costs are not required. However, the availability of extensive data on demand is a must. Data gathering accounts for most of the costs across all the processes.
Examples: Because of the major benefits, demand prediction with AI has already found its way into many logistics networks worldwide. Some companies develop the solutions themselves, while others use numerous ready software solutions or startups in the related demand prediction fields.
Complexity: 4 (This is often another case of a traveling salesman problem, and the solutions are unique for different items or deliveries).
Data needed: Data about all of the route loading/delivery properties. It usually includes information about items to be loaded or unloaded, as well as the vehicle navigational data. Historical travel data and traffic information are also widely used. Data gathered about the level of fuel or driving patterns can also be helpful.
Influence on business: Effective route planning is one of the biggest tasks of the logistic industry. More effective routes mean much lower costs and much faster delivery time. AI-based solutions allow companies to optimize these values by taking into account as many factors as possible.
- Reduces the time spent on each delivery or route, so the number of performed deliveries rises accordingly.
- Sometimes, the route planning solutions used by companies do not make the most effective choices at the exact moment. And companies themselves might choose routes by the rule of custom or because they are unaware of the faster options. The traffic situation may differ hour by hour. Therefore, AI can help determine routes with such efficiency that is not available even to the navigators.
- AI can inform drivers of the optimal refueling or eating locations. This will help them to be confident in their travel even on new routes.
- Such solutions can help reduce traffic on the roads in general. More effective routes will mean that the vehicle concentration in difficult places can be reduced.
Price: Since it often involves gathering data from vehicles, as for the telematics, a stable or regular connection is required. Together with the need for real-time traffic data, it slightly increases the costs. But they are usually bearable, considering the efficiency of a good route-planning solution.
Examples: Route planning, often in real-time, is one of the biggest AI tasks in logistics and therefore has many implementations. Because of immense popularity, some platforms provide route-planning solutions as a service. There are also some specific applications, such as nurse route optimization.
Complexity: 4 (Depends on partial or complete automation and what types of items are in the operations. Parts of tasks are already widely solved, but individual solutions usually require specification for the needs of every warehouse).
Data needed: Data on the items in question, photos, or product encodings. Usually, the information on where and how the items are stored is needed, with some process requirements. Historical order data is often helpful in predicting the possible chains of actions for each product.
Influence on business: Allows to streamline all warehouse operations. The speed of automated actions is usually several times faster than manual ones. Also, it allows removing some of the most routine logistical tasks while making them fully accountable.
- Any warehouse activity data that goes through the automated path is digitalized. Because of this, all actions in the warehouse are easily tracked. This allows for full transparency of operations and accountability of every step performed.
- The percent of mistakes and wrongly arranged products falls dramatically. As a result, the percentage of wrong deliveries decreases as well. Damage to the stored items is usually also reduced.
- Items’ location around the warehouse can be arranged for minimal operational time. The most requested items will be located in places with fast access, while more seldom will be located according to their popularity.
- Faster processes create possibilities of increasing operational speed through the entire supply line. Even minutes in delivery speed can be crucial for some areas or products, such as restaurant orders.
- Warehouse AI automation allows creating a more accessible, and safer working environment, which can be a hiring advantage in the face of labor shortages.
Price: Demand prediction and AI-driven inventory control are the least costly in the warehouse automation area, offering significant benefits. Other automation steps need hardware and maintenance investment. But it still attracts large companies due to speed and price benefits in the long run. One of the most available hardware systems is automated item sorting.
Examples: Warehouses powered by automated decisions or actions constantly increase in number. Some cases of nearly fully automated facilities, such as grocery fulfillment centers, use completely bot-automated sorting and order forming operations. Some applications start to appear in the areas where delivery speed and reliability are crucial. Several startups exist in the field, and each of them usually has a unique specialty. According to different research, the warehouse automation market is expected to grow at an annual rate of 14% by 2026. More than 60% of companies are predicted to use it by this date.
Complexity: 5 (The main challenge is interacting with the environment. In closed spaces, solutions are more accessible, while in the real obstacle-filled world the task becomes much more complex).
Data needed: Data on the orders, routes to the location in question, and navigational data on the respective area. Data on possible situations along the way is needed for safe delivery. Also, information about the special conditions for the delivered item is necessary, for example, in food delivery where everything needs to stay fresh.
Influence on business: Automated delivery reduces the industry’s reliance on human factors. This improves the delivery quality and average speed. Additionally, it can reduce human interaction with items before the customer.
- Allows to optimally choose all delivery parameters, such as distribution of items per each vehicle. This results in a lower time of delivery, as the fleet spends less time on each order.
- With automated delivery, companies can be more confident in the timing when the package arrives, or that it arrives at the right door. Overall quality of the delivery can also rise, increasing customer satisfaction.
- As much as it sounds futuristic, some unorthodox delivery methods such as drones can help deliver many products even now. The advantage of using aerial transportation is the ability to reach even remote locations quickly.
Price: Largely depends on the distance automated deliveries should cover and the chosen way to do it. If the supposed vehicle should interact with many outside factors like other vehicles or pedestrians, it should also be more foolproof. Therefore, for deliveries inside some controlled spaces, the price is usually much more bearable.
Examples: This field is one of the most complicated uses of AI in general.Therefore, instances of complete real-world delivery automation are usually in the trial phases. The most advanced usages are in drone deliveries. Trials are already underway for good deliveries or even vaccine shipping. The goals of such projects often can be reaching remote locations or fully automating the fleet in the future.
Founder and CEO, Elafris Inc
CEO and Founder, Reply
Michael Korkin, Ph.D.
CTO at Entropix, Inc.
How to Get Started With AI in Logistics?
The influence of AI in logistics becomes increasingly noticeable. However, the full scope of the future technological revolution still lays ahead. Companies using artificial intelligence in logistics are able to redefine their operations, getting the upper hand over their competitors. Your business should embrace the new technology now to be ahead of the pack in the future.
Executives, who want to benefit from AI in logistics and supply chain, should keep in mind the following three principles:
- Data comes first. Implementing AI in logistics starts with studying the available data. You should understand its type, quality, quantity, and to realize what can be achieved.
Still, not enough data isn’t the end of your aspirations. You can still follow through with Artificial Intelligence in logistics. Explore public datasets or check if your business partners may provide useful data. Think about your own strategy to get some input for AI in supply chain and logistics. For instance, you may try to digitize some analog processes.
- An easy start is what you need. Trying to squeeze the most from AI in logistics by designing the most elaborate use cases may not be as rewarding as you think. It’s better to follow through with a simple project and get it done quickly. Artificial Intelligence in logistics is scalable so that you can build on your first solution over time. Your business will receive increasing value, as the benefits of AI for logistics will manifest themselves.
- Don’t shy away from failures. Implementing AI in logistics and supply chain is a complicated process. You’ll need a lot of time and resources, and not all your experiments will be successful.
However, failures are part of organizational learning and are crucial for progress on the way to employing AI in supply chain and logistics successfully. Failing fast and early will help minimize the investments of time and costs.
AI in Logistics: Summary
Supply chains all over the world are under the strain of rising demand. A delivery system needs an overhaul. The modern supply chain needs to cope with shipping medicines, groceries, and other goods, which are indispensable for our daily lives.
AI and logistics need to come together if the industry is going to cope with those difficulties. For instance, the use of AI in warehouse management will result in smart warehouses with improved operations via automation, data consolidation, and analysis.
Try Googling for ‘Artificial intelligence in logistics pdf,’ and you will see the evidence of automation in all stages of the movement of the goods value chain. Various segments of the supply chain are clearly investing more in different types of robotics and autonomous transportation.
How is AI used in the supply chain?
Artificial Intelligence enables advanced supply chain automation. For instance, virtual assistants can be used both internally, within a company, and between parts of the supply chain.
How does AI work in logistics?
The use of AI in transportation and logistics helps in route optimization and demand forecasting. AI in warehouse management suggests replenishment of almost out-of-stock items, improved inventory positioning, and shorter walking routes.
How will AI affect logistics?
The spread of AI in logistics and transportation allows companies to move people and goods in safer, more reliable, and efficient ways.