The future of AI in logistics

Artificial intelligence in logistics

Artificial intelligence is revolutionizing the logistics industry. Recent studies have shown that logistics and supply chain management benefit the most from AI. 55% of Statista survey participants confirmed that with it, they have cut costs by 10% or more. How do they achieve such results? How is AI in logistics improving vital processes, and what is the future of the industry?

The present and future of AI

The McKinsey report The State of AI in 2020 says that businesses are using technology to increase profits by 20% or more. Leading organizations are striving to increase investment in AI in logistics. The rest of the firms are making progress in embracing AI. But they still need time to understand innovations properly and implement them in their in-house processes.

Half of the participants in the report said that they included at least one AI function in their workflows. McKinsey has identified industries leading in the adoption of AI:

• AI solutions development, including relevant products and services (21-24%);
• service operations (19-24%);
• marketing and sales (14-17%);
• risk management (12-16%);
• manufacturing (12-15%);
• human resource management (7-10%);
• supply chain management (9%);
• strategic and corporate finance (6-8%).

Any business benefits from the use of AI. The main thing is to find a successful and experienced AI development company that will help you effectively implement this striking technology and work with it.

COVID-19 has not affected the desire of numerous organizations to invest in artificial intelligence. This has become especially relevant for the automotive, healthcare, pharmaceutical and medical supplies industries. But with the sudden closure of borders, transportation was cancelled to a large extent. Traffic volumes fell abruptly, and so did the demand for transport services. This influenced the financial position of logistics firms. Companies survived by taking the following measures:

1. they ensured supply chain transparency to foresee potential problems and respond to them effectively;
2. invested in relationship management with logistics service providers (LSPs) that supported them and offered maximum discounts;
3. leveraged innovation and the help of AI development companies to adapt to new market conditions.

Participants in the Forbes Insights study noted that logistics, supply chain and transportation are undergoing an era of profound transformation driven by AI and ML technologies. By 2035, artificial intelligence is expected to increase the productivity of enterprises by 38-40%. The AI market is projected by MarketsandMarkets to grow to $10.3 billion by 2030 at an annual growth rate of 17.87%. This is 10 times more than in 2016.

Areas of AI application in logistics

There are five common ways AI is transforming logistics:

1. Robotization.

The use of warehouse and logistics robots is the most obvious manifestation of AI in logistics. They function thanks to deep learning algorithms and therefore can perform different operations. For example, they seamlessly find, track and transport inventory indoors. They also transport and sort small loads in distribution centers.

An autonomous mobile robot from Fetch Robotics tracks the location of goods in the warehouse. The machine searches and moves inventory in warehouses, factories, or distribution centers. AMR also collects data on goods, so it is convenient to carry out an inventory with it. Fizyr robots determine the type of package in less than 0.2 seconds and transport the goods to an appropriate location.

2. Big data.

Logistics firms deal with large amounts of data but do not know how to process and use all this information properly. Efficiency is difficult to measure because data is collected from several sources, and not all pieces of it are useful for business.

To train AI in logistics, you will need 5-10% of relevant and correct data so that it can analyze and “filter” incoming information. It can be successfully applied to forecasting and control of the supply chain.

An AI-based program studies the history of transportation and collects information about road conditions, weather, news, and other parameters. The algorithm compares information and predicts potential scenarios when something goes wrong.

According to the 2020 24th Annual Third-Party Logistics Study, 97% of shippers and 93% of 3PLs believe that AI-enhanced data management determines the success of their supply chain.

3. Computer vision.

The scenarios of AI use related to photo or video analysis are also relevant for the logistics industry. The technology “inspects” trucks, evaluates their condition, and determines whether the equipment needs to be repaired. The IBM Watson system is a striking example of the use of innovation. Thanks to cameras mounted on railroad tracks, this software analyzes images of railcars and determines which one needs repairs before it breaks down.

4. Autonomous cars.

Although autonomous cars are already being tested, experts say it will take decades for them to start driving on a regular basis. Highways are not good enough to support unmanned vehicles, which is the primary reason for such delay. In addition, such a car requires a wireless connection to interact with the outside world. Specialists will also have to adjust the rules of the road for the new type of vehicle. For example, they need to establish favorable weather conditions when autonomous cars can appear on the roads. One must keep in mind that they can break down in the rain or snow.

For the logistics industry, the development of AI solutions for autonomous vehicles will eliminate the driver shortage problem. The International Road Transport Union claims that the shortage of specialists will increase by 25% in the organization’s member countries. Self-propelled cars will also help save fuel and reduce carbon emissions into the air.

5. Smart roads.

Some companies rely on the power of AI to build smart roads. They might increase traffic safety, remove traffic jams and other obstacles to the transportation of goods.

Smart roads are equipped with solar panels and LED lights. Due to them, highways are lit up at night and the asphalt warms up so that there is no ice on it in winter. Sensors installed along the way collect information about road traffic and traffic patterns at different times of the day. Logistics firms know when traffic jams occur and when it’s best to send drivers to the point of destination. A smart system evaluates the situation on the highway and, in case of a collision, reports the incident to the emergency service.

Benefits of AI in logistics

AI is helping logistics firms create an intelligent supply chain:

Optimizing routes

For large logistics companies such as Andersen, with thousands of trucks in their fleet, it is difficult to calculate the possibilities for optimizing routes. For instance, a company services 1000 facilities per day. They need to be counted as one common pool and optimally distributed among 200 trucks and buses. To make such calculations and choose the appropriate option, you need to “sort through” about a million pairs of movements along the transport network. Then it will be possible to determine the cost of delivery from point A to point B in minutes.

Traditional algorithms won’t be able to do this because they can’t handle complex situations, such as

• traffic seasonality;
• preferred routes and driving style of the driver;
• multimodal movement in the urban environment;
• parking search time specific for each destination;
• vehicle features and other parameters.

AI considers these parameters and increases the speed of route cost estimation by 1000 times. Much faster than the Origin Destination Cost Matrix does. Earlier, 20–30 servers were needed for calculation. A neural network that runs on one server calculates these routes quickly.

AI companies can teach the algorithm very complex things. For example, how far one can travel in 25 minutes from the city center at a certain time of the day.

The system from DHL considers 58 parameters for the analysis of air transportation. Freight forwarders know what the average daily travel time will be in advance. They understand whether the delivery will be delayed because of the weather. Such an AI solution is valued in the air travel sector, which accounts for just 1% of global trade by tonnage and 35% by value.

Accurate demand forecasting and supply chain planning

When a logistics software company brings unclaimed goods to a warehouse, this leads to overstocking. The areas that could be used for popular products are occupied. Best-selling goods arrive in smaller quantities than required. And if they are out of stock, the person who ordered them will have to wait longer for their order to arrive. Businesses experience certain troubles then: lack of room – inefficient use of equipment – high handling costs – poor quality of customer service.

To prevent this from happening, companies rely on AI to predict demand, place products in a warehouse, and ship them. A program signals which goods need to be brought in larger quantities to cover the seasonal demand of consumers. Demanded products arrive at the warehouse on time and are successfully sent to the customer.

Computer vision-enabled IoT sensors efficiently manage inventory, help workers quickly locate items, and monitor product status.

Improving customer service

Timely delivery, speed of order fulfillment, product condition and accurate documentation are the major criteria for the quality of service in logistics. When logistics processes occur without hitches and errors, a customer receives cargo on time. AI in logistics affects every stage of the supply chain, from back-office operations and forecasting to vehicle maintenance and route planning.

Poor service will scare off customers. A positive experience will help not only retain them but also gain new ones. Loyal customers are the key to business growth. Keeping regular clients costs 4-10 times less than acquiring new ones.

Preventing equipment and vehicle breakdowns

When specialists inspect a car manually, they carry out the following tasks:

• they change oil;
• check the shaft line
• change the belt
• inspect brakes and tires;
• change the coolant;
• change the engine air filter and so on.

To assess tear and wear, experts rely on manufacturers’ instructions and personal experience. But the following indicators also affect the wear rate: the peculiarities of a driver’s skills and the conditions of car maintenance. In addition, specialists carry out preventive examinations whenever necessary, and not around the clock.

Computer vision technology, IoT sensors, and digital twins make it possible to “monitor” the fleet and look for damage in a vehicle long before it breaks down. For monitoring, a company uses oil and grease sensors, thermal imaging devices, meters for vibration, sound and ultrasonic analysis and similar devices. The system studies the data coming from the instruments and predicts the behavior of the machine in certain situations.

A company that sends a truck with goods can be sure that it will not break down on the road and the order will arrive on time. The McKinsey Global Institute estimates that preventive maintenance will save businesses up to $630 billion by 2025.

Conclusion

Artificial intelligence has many potential scenarios of use in logistics. By implementing this striking and ever-evolving technology, the industry is improving supply chain processes, increasing office productivity, and increasing customer satisfaction. AI makes businesses competitive, especially in the face of rapidly expanding digitalization.