AI in Logistics – Moving With the Times for Businesses and Customers in 2023

Logistics can be complex, costly, and time-consuming. However, with AI playing a key role, the landscape is undergoing rapid transformation. For many companies, it is now a priority to invest in AI for more efficient processes, increased customer satisfaction, and better employee experience.

If you’re a logistics industry professional, keeping up with the AI trends, studying use cases, and becoming aware of future implications are crucial for your professional development. We’re looking at AI in logistics now and into to future so you can understand how this technology is disrupting the sector.

How is AI Used in Logistics?

Logistics professionals use different types of artificial intelligence for different aspects of their jobs. In fact, more than 75% of companies plan on implementing AI technologies for supply chains.

Data from both Statista and MHI show where logistics companies would like to use AI:

• Inventory management
• Quality control
• Customer care
• Monitoring and diagnostics
• Cybersecurity
• Fraud detection
• Shipping and transport
• Supplier selection and due diligence
• Consumer behavior tracking
• Contracts and pricing
• Demand forecasting, planning, and warehouse management
• Product development
• Manufacturing
• Procurement
• Supply chain security

Machine Learning (ML)

Machine learning facilitates data-driven decision-making by allowing software applications to learn from data. It can help in areas such as demand forecasting. Here, ML looks at data to predict customers’ future purchases. It takes different variables into account, such as the customer’s past purchases, seasonality, promotions, marketing campaigns, and more.

One example of a company using AI for demand forecasting is Amazon. For example, when a customer buys a sci-fi book each year as a birthday present, the algorithm will be capable of suggesting similar titles before the birthday.

Amazon’s forecasting service, Amazon Forecast analyzes two types of data:

1. Historical data, such as sales, web traffic, inventory numbers, and cashflow
2. Related data, such as holidays, product descriptions, and promotions

It then uses the data to forecast customer behavior, as shown in the figure below:

Another example of AI in logistics is route optimization. The traveling salesman problem is prevalent not only in computer science but also in logistics. The problem talks about a salesman who is looking for the shortest route between the various cities they have to visit. What’s the most efficient way when it comes to time and money? Or in other words, what’s the best delivery route for logistics trucks that will minimize the distance and the costs?

By using historic data and updates on weather conditions and road closures, ML helps find the closest route from the warehouse to customer. It can also be used inside the warehouse so that employees don’t spend too much time picking orders.

Machine learning can also improve supply chain risk management. Machine learning techniques and AI models can analyze historical and real-time data to help companies make informed decisions against supply chain risks.

These risks include natural disasters, production delays, changes in demand, and anything else that can have an impact on the supply chain. Similarly, ML algorithms can be used to identify patterns of normal and abnormal behavior which can be useful in cybersecurity. When AI is trained to recognize user behavior over time, security threat alerts can be more targeted and reliable.

Robotics and Automation

In 2022, the market size of warehouse robotics was valued at $6.09 billion. It is expected to be worth $15.66 billion by 2030. This directly impacts logistics professionals, requiring working alongside robots and knowing how to use them.

Robotics and automation use AI to increase efficiency, productivity, and safety in the logistics landscape. For example, in 2022, Amazon’s warehouse had 520,000 robotic drive units. Ever since the tech giant acquired the robotics company Kiva in 2012, there have been big changes in the warehouse. Robots work beside employees, move heavy packages around, and provide a safer environment for workers.

Automation of scheduling, tracking, and reporting is another use case of AI in logistics. AI systems can be used to schedule and track shipments in real-time, auto-generate reports, and allocate resources more effectively. This reduces the possibility of human error and provides reliable, data-driven insights for decision-makers.

Driverless Vehicles and Drones

From life-saving delivery drones to self-driving cars, automated vehicles have the capacity to transform the entire logistics industry, although it’s still an emerging branch of AI.

A  2022 McKinsey analysis predicted that by 2035, autonomous driving had the potential to create $300 billion to $400 billion in revenue. Similarly, delivery drones can have life-saving potential, such as in healthcare, delivery drones ensure the quick transport of goods.

Fast and compact, they can deliver items with a short shelf life span and go to remote regions quickly. Alain Hodak‘s life was saved in 2021 thanks to a delivery drone that transported lungs for a transplant. His doctor, Dr. Shaf Keshavjee said that it would be just the beginning, and drone organ delivery could be the future.

Natural Language Processing (NLP)

Natural language processing makes it possible for computers to understand and imitate human language. In logistics, NLP can help improve customer satisfaction, communications, and data extraction.

Customer service AI chatbots can be in operation 24/7. Logistics companies can use them as an AI-powered virtual assistant, handling customer inquiries and answering questions.

In an MHI report, more than 20% of companies reported they were either using or planning to use AI for document processing, such as pricing and contracts. NLP can extract data from invoices, contracts, and agreements, making it faster to understand the clauses and take action.

Generative AI

Generative AI is capable of producing content of different sorts, including text, images, and audio. Listed as one of the emerging technologies of 2023 in a World Economic Forum report, for logistics “Generative AI can create across all media, so text, video, audio, pictures – every digital medium can be powered by generative AI,” said Nina Schick, generative AI expert, in a Yahoo Live video. In logistics, this can translate to supporting other AI models to help with reports, graphics, or visuals of warehouse designs.

Who Is Using AI in Logistics?

Amazon

Amazon has pioneered the use of AI in logistics. It uses machine learning to optimize in-stock availability, automate processes, and forecast product demand. ML also helps it adjust to lower or higher-demand periods, enabling them to have a better-planned inventory.

In Amazon warehouses, employees work with robots that help with scanning, storing, and moving inventory.

DHL

As one of the leading logistics companies delivering couriers, mail, and packages, DHL is using AI to improve its bottom line. Its AI-powered digital assistant helps track packages, find shipping solutions, and more.

The company is able to give 24/7 support to customers who might be waiting for an important parcel. Similar to Amazon, DHL also uses robots in their warehouses.

DHL’s in-house AI solution OptiCarton aids the company to reach its goal to reduce packaging by 50%, and so reducing the company’s carbon footprint. It is designed to maximize the use of space in standard packaging boxes offering a computer game-like visualisation of how contents should be packed.

Microsoft

Microsoft Supply Chain Center has an AI system that comes into effect when there are external issues, such as weather or financial burdens. In case of an emergency, AI automatically drafts an email to relevant partners or stakeholders, so that they can take action.

Companies such as Grupo Bimbo, Mercedes Benz and Tillamook are using the powerful tools from Microsoft to improve their supply chains.

Uber Freight

In June 2022, Uber Freight announced a long-term partnership with Waymo, formerly known as the Google self-driving car project. Besides allowing human drivers and autonomous trucks to collaborate, the venture also aimed to provide solutions to problems such as expensive fuel costs, lack of enough drivers, and increasing demand.

For example, Project Lasso has been able to improve geofencing using large datasets of GPS information. Geofences work to determine arrival and departure times, and the project was able to reduce geofences to 0.1 to 0.3 miles of loading docks, compared to the standard 1.5-mile radius. The company has seen a 60% decrease in obviously incorrect behavior of loads. 

Walmart

36 Walmart stores in the US have drone delivery hubs. In 2022, the hypermarket chain made 6,000 drone deliveries, only a year after announcing its drone delivery service. With drones, Walmart is offering fast delivery at competitive prices.

The company has also been experimenting with an AI-powered voice assistant for store associates called Ask Sam. The tools can help a team member call up their schedule, check store stock levels, and provide real-time store financial information. Deep learning is also harnessed to provide better substitutions for customers who order online and items are out of stock.

Benefits of AI in Logistics

Robots Reduce the Risk of Injuries 

Referring back to Amazon, in its warehouses robots are in charge of heavy lifting, packing, and reaching for high objects. This way, employees spend less time on these tasks, reducing the risk of injuries due to physical work. By automating the robots’ work and making them collaborate with employees, you not only make the workplace safer but also more time-efficient.

In a study published in Labor Economics, it was found that increasing robot use in the workplace reduced workplace injury rates by 1.2 in every 1,000 workers in the US. The same study found that in Germany, when more robots were being used, physical job intensity dropped 4% and disability reports dropped 5%.

Automation Helps Employees Save Time

A Microsoft report found that 9 out of 10 employees want AI to automate repetitive tasks to be able to focus on more meaningful tasks. Some examples of these tasks would be data entry, tracking, customer service FAQs, and report generation. If AI takes on repetitive tasks, employees will have more time for strategic work and training.

Managers Make Data-Driven Decisions

Managers working in logistics have a lot of complex data to analyze and technical decisions to make. What is the most efficient route from point A to point B? How can you organize the warehouse in the best way possible for employees to work better? AI can gather different types of data together in a short period of time, allowing managers to look for patterns and trends to make a decision.

AI Makes Work More Efficient

By finding the best routes, automating repetitive tasks, and predicting inventory needs, AI contributes to a smoother running of the warehouse. Especially with machine learning technologies, it can adapt and evolve based on real-time data. AI’s capabilities also reduce the chance of human error.

Some academics predict that the integration of AI across various links in the supply chain can increase logistics efficiency by 77-99%. Meanwhile, a factory in China was able to increase order-picking efficiency by 50-70% by using smart robots to bring factory shelves to workers.

Reduction in Carbon Footprint

When AI is applied to logistics, it can find alternative ways of doing things, such as a better route or more optimal packaging, it also suggests a way of spending less fuel, meaning lower costs for the company and less pollution for the environment.

IBM’s Deep Thunder project is working on improving localized weather forecasting, working in conjunction with data from NOAA. Martime logistics companies will be able to use more accurate and detailed weather reports to plan the most efficient shipping routes to avoid storms, for example.

Increase Customer Satisfaction

Using AI chatbots to help customers interact with logistics companies at any time and in multiple languages, can increase customer satisfaction. In a Userlike survey in 2022, most chatbot users reported being happy that their query was answered quickly and their query was answered successfully.

Challenges of AI in Logistics

AI is changing the landscape for many industries, including logistics. With technological progress comes challenges and concerns for workers and industry leaders.

Privacy Concerns

A Pew Research Center report looked at companies that implemented AI systems to monitor employees’ work performance. Its results showed that more Americans oppose the use of AI for this purpose rather than favoring it, including delivery and long-haul drivers in the sample.

Costs

Some AI models, such as autonomous mobile robots, might be too expensive for small companies. Although these robots have the potential to save costs in the long run, they may not be accessible to everyone. Creating an AI chatbot can cost anywhere between $10,000 to $150,000, with staff and server maintenance costs to consider in the long term.

Data Availability

AI requires a large amount of data to provide accurate results. Due to the complex workflows of logistics teams, obtaining data might be challenging. Industry standards say that you should have ten times the data rows in a dataset as there are columns in your output, which can be challenging depending on the size of the company looking to build a machine learning tool.

Change in the Nature of Work

McKinsey has predicted that by 2030, 30% of current workloads in the US could be automated, especially with the emergence of generative AI. As a result, the nature of some jobs would change while others decline. In the logistics industry, managers’ roles could involve more strategic thinking and coaching, while demand for production work could decline.

Data from the MHI report also shows that 57% of supply chain companies struggle to hire and retain qualified employees. AI has optimized and automized many tasks but at the same time, it changed them. As a result, companies have a new need — staff that is proficient with AI technologies. As AI becomes a core part of the companies’ operations system, it will become even more important to hire and retain skilled employees.

Environmental Impact

To operate, AI uses a lot of electricity and physical resources, requiring a lot of energy, Naturally, this raises questions about AI’s environmental impact.

“With a better understanding of how much energy AI systems consume, companies and developers can make choices about the trade-offs they are willing to make between pollution and costs,” said researcher Sasha Luccioni to MIT Technology Review. Companies need to be more aware of AI’s environmental impacts and look for ways to make it more sustainable.

The Future of AI in Logistics

A 2021 Gartner survey reported that CEOs expect AI to have the highest impact on logistics and supply chains through 2025.

Another survey by PwC had similar findings. In this survey, 22% of supply chain executives said that they were planning to invest at least $5 million in these technologies. Driving growth and reducing costs were the main goals of these executives, suggesting the importance of AI in achieving these goals.

Generative AI is the Next Era

Software companies are already investing in generative AI, and will continue to do so in the future. For example, Microsoft launched generative AI-powered Dynamics 365 Copilot in 2023. Integrated into customer relationship management (CRM) and enterprise resource planning (ERP), it aims to improve customer experience, employee experience, and efficiency.

Generative AI’s implications will expand to many areas of logistics. For example, its ability to produce images can be useful for product design teams when they assess if the product meets customers’ expectations.

Digital Supply Chains Will be the Norm by 2033

According to the MHI report, 90% of manufacturing and supply chain professionals are planning on investing in digital supply chain technologies. Given this data, by 2033, autonomous digital supply chains are likely to be the norm. Companies will benefit from accurate data, fast systems, and connected processes.

Driverless Vehicles Will Continue Developing

Uber has joined forces with the self-driving car company Waymo. Tesla’s Autopilot has driver assistance features including self-driving capabilities. In the future, we will be hearing more about self-driving vehicles.

Although there is a lot of interest in this topic, consumers look more hesitant than before. A 2023 McKinsey found that customers are less willing to consider driving a fully autonomous vehicle compared to five years ago. This hesitancy is relevant to the logistics industry since trucking accounts for most of the overland freight movement in the US. Future developments in the self-driving vehicle industry and the public’s perceptions will be important.

Data-Driven Automation Will Increase

The chart below illustrates the expected increase in decision-making automation between 2021 and 2024, according to Gartner.

CEOs expect to see an increase in all logistics areas, especially planning, customer fulfillment, and transportation. Keeping the data in mind, it’s more important than ever to train staff on how to use AI.

Job Descriptions Will Change

AI’s ability to automate complex processes like demand forecasting comes with consequences. While it means less time and more efficient results for the companies, it also implies that junior logistics professionals will have fewer opportunities to learn how to generate forecasts manually.

Here is what Gartner’s report suggests on ways people can adapt to working with AI:

• Crowdsourcing: Take the best of humans and AI. In this case, it would be humans’ insights and knowledge, and AI’s algorithms and analytics. Training employees on how to make use of AI would also be helpful.
• Process mining: By using this procedure, logistics companies can make better decisions and find ways to improve and automate tasks. It would also show what areas AI can automate.
• Data literacy: AI needs large volumes of data to operate, meaning employees need to be proficient in data and analytics. This way, they will know their way around machines and be more comfortable working with AI on a daily basis.

FAQs

References

• Statista
• Amazon Forecast
• Amazon
• McKinsey
• CBC
• World Economic Forum
• Companies Market Cap
• Microsoft
• Uber Freight
• Uber Freight
• Walmart
• Fox Business
• Science Direct
• Researchgate
• Journal of Physics
• IBM
• Userlike
• Microsoft
• Pew Research Center
• McKinsey
• MIT Technology Review
• PwC
• Tesla
• Mercedes Benz
• Statista