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Top 15 Logistics AI Use Cases and Applications in 2024

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Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Top 15 Logistics AI Use Cases and Applications in 2024Top 15 Logistics AI Use Cases and Applications in 2024

As organizations overcome the pandemic-induced disruptions, they need to focus more on strengthening their supply chain and logistics capacity. Leveraging AI can be an effective way of doing that. According to McKinsey, the successful implementation of AI has helped businesses improve logistics costs by 15%, Inventory levels by 35%, and service levels by 65%.

Another research by McKinsey estimates that logistics companies will generate $1.3-$2 trillion per year for the next 20 years in economic value by adopting AI into their processes.

In this article, we explore top AI use cases in the logistics industry and how they improve logistics operations.

Logistics Planning

Logistics requires significant planning that requires coordinating suppliers, customers, and different units within the company. Machine learning solutions can facilitate planning activities as they are good at dealing with scenario analysis and numerical analytics, both of which are crucial for planning.

1. Demand forecasting

AI capabilities enable organizations to use real-time data in their forecasting efforts. Therefore, AI-powered demand forecasting methods reduce error rates significantly compared to traditional forecasting methods such as ARIMA, AutoRegressive Integrated Moving Average, and exponential smoothing methods.

With improved accuracy in demand prediction,

  • manufacturers can better optimize the number of dispatched vehicles to local warehouses and reduce operational costs since they improve their manpower planning
  • local warehouses/ retailers can reduce the holding costs (opportunity cost of holding the item instead of investing the money elsewhere)
  • customers are less likely to experience stockouts that reduce customer satisfaction

You can also see our list of AI tools and services:

2. Supply planning

Artificial intelligence helps businesses analyze demand in real-time so that organizations update their supply planning parameters dynamically to optimize supply chain flow. With dynamic supply planning, businesses use fewer resources since dynamic planning minimizes waste.

Automated Warehousing

Figure 1. The MHI annual industry report projects that by 2026 the adoption of AI-powered warehouse solutions by businesses will reach 60+% as compared to 2020.

A bar graph showing the adoption of AI-powered warehouse solutions by businesses will reach 60+% by 2026 as compared to 2020
Source: MHI/Deloitte

3. Warehouse robots

Warehouse robots are another AI technology that is invested heavily to enhance businesses’ supply chain management. The warehouse robotics market was valued at USD 4.7 billion in 2021 and is expected to grow at a CAGR of 14% between 2021 and 2026.

For example, the retail giant Amazon acquired Kiva Systems in 2012 and changed its name to Amazon Robotics in 2015. Today, Amazon has 200,000 robots working in their warehouses. In 26 of Amazon’s 175 fulfillment centers, robots help humans in picking, sorting, transporting, and stowing packages.

4. Damage detection / Visual Inspection

Damaged products can lead to unsatisfied customers and churn. Computer vision technology enables businesses to identify damages and ensure quality control in warehouse operations. Logistics managers can determine the size and type of damage and take action to reduce further damage.

5. Predictive maintenance

Predictive maintenance is predicting potential machine failures in the factory by analyzing real-time data collected from IoT sensors in machines. Machine learning-powered analytics tools enhance predictive analytics and identify patterns in sensor data so that technicians can take action before the failure occurs.

Autonomous Things

Autonomous things are devices that work without human interaction with the help of AI. Autonomous things include self-driving vehicles, drones, and robotics. We should expect to see more autonomous devices in the logistics industry due to the industry’s suitability for AI.

6. Self-driving vehicles

Self-driving cars have the potential to transform logistics by decreasing heavy dependence on human drivers. Recent surveys suggest that real-life use cases of autonomous cars and trucks will be seen in the near future (see figure 2). Technologies such as platooning support drivers’ health and safety while reducing carbon emissions and fuel usage of vehicles. Tesla, Google, and Mercedes Benz are investing heavily in the concept of autonomous vehicles, it is only a matter of time before autonomous trucks are seen on roads around the world. However, according to BCG estimations, only around 10 % of light trucks will drive autonomously by 2030.

Figure 2. L4 use cases for autonomous vehicles are expected to emerge by 2024 or 2025

Image shows when use cases of autonomous vehicles are liley to emerge.
Source: Mckinsey

7. Delivery drones

For the logistics of products, delivery drones are useful machines when businesses deliver products to places where a ground transfer is not possible, safe, reliable, or sustainable. Especially in the healthcare industry, where pharmaceutical products have a short shelf life span, delivery drones can help businesses reduce waste costs and prevent investments in costly storage facilities.

Analytics

8. Dynamic Pricing

Dynamic pricing is real-time pricing, where the price of a product responds to changes in demand, supply, competition price, and subsidiary product prices. Pricing software mostly uses machine learning algorithms to analyze customers’ historical data in real-team so that it can respond to demand fluctuations faster by adjusting prices.

9. Route optimization / Freight management

AI models help businesses to analyze existing routing and track route optimization. Route optimization uses shortest-path algorithms in graph analytics discipline to identify the most efficient route for logistics trucks.

Therefore, the business will be able to reduce shipping costs and speed up the shipping process. For example, Valerann‘s Smart Road System is an AI web-based traffic management platform that delivers information about road conditions to autonomous vehicles and users.

Route optimizers are also effective tools for reducing corporate carbon footprint.

Back office

Every business unit has back-office tasks and logistics are no different. For example, there are numerous logistics-related forms like a bill of lading from which structured data needs to be manually extracted. Most businesses do this manually.

10. Automating document processing

Invoice/bill of lading/rate sheet documents helps communication between the buyers, suppliers, and logistics service providers. Document automation technologies can be used to increase the efficiency of processing these documents by automating data input, error reconciliation, and document processing.

11. Automating other manual office tasks

Hyperautomation, also referred to as intelligent business process automation, means using a combination of AI, robotic process automation (RPA), process mining, and other technologies to automate processes in an end-to-end manner. With these technologies, businesses can automate several back-office tasks, such as

  • Scheduling and tracking: AI systems can schedule transportation, organize pipelines for cargo, assign and manage various employees to particular stations, and track packages in the warehouse.
  • Report generation: Logistics companies can use RPA tools to auto-generate regular reports that are required to inform managers and ensure everyone in the company is aligned. RPA solutions can easily auto-generate reports, analyze their contents and, based on the contents, email them to relevant stakeholders.
  • Email processing: Based on contents in auto-generated reports, RPA bots can analyze the content and send emails to relevant stakeholders.

For more RPA and hyperautomation use cases for businesses’ back-office tasks, feel free to read our articles:

12. Customer service chatbot

Customer service plays an important role in logistics companies since customers will contact companies for any issue they experience in delivery. Customer service chatbots are capable of handling low-to-medium call center tasks such as:

  • Requesting a delivery
  • Amending an order
  • Tracking shipment
  • Responding to a FAQ

Chatbots are also valuable tech to analyze customer experience; chatbot analytics metrics enable businesses to understand their customers better so that they can enhance the customer journey they deliver.

To learn more about AI applications in customer service, feel free to read our article: 11 AI Usecases in Customer Service.

Sales & marketing

Sales and marketing activities of logistics service providers can also be enhanced by artificial intelligence. Some applications are:

13. Lead scoring

Lead scoring means enabling sales reps to focus on the right prospects. AI-powered tools can be used to help automatically assign scores to leads based on their profiles, behavior, and interests. AI-based lead scoring systems utilize machine learning algorithms to quickly process data and accurately determine which leads are most likely to convert into paying customers.

14. Routine marketing

AI can be used to help logistics service providers automate routine marketing tasks, such as email marketing and content creation.

15. Sales and marketing analytics

AI can offer more precise sales and marketing analytics. AI-powered tools can be used to help logistics service providers analyze customer behavior and use predictive analytics to better understand what their customers are likely to do next. AI-enabled systems can also be utilized to monitor changes in the market, enabling logistics service providers to stay ahead of the competition and make data-driven decisions that will result in greater efficiency.

For more AI applications in sales and marketing, you can check our articles:

To learn more

Leave us a comment if you know of other applications of AI in logistics. Here is a list of more AI-related articles you might be interested in:

If you still have questions about AI and/or logistics, we would like to help:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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1 Comments
Usa Wuttisilp
Mar 03, 2021 at 08:13

Good job!

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