How Is AI in Logistics Changing the Tech Game?

Almost 38% of the businesses in the logistics and manufacturing industry are utilizing AI in some form or another. Besides, over 50% of enterprises in this domain believe that investing in AI can be productive for them.

Well, the trust in this technology shows how AI is becoming a game changer in the logistics industry. Moreover, with efficient AI application development services, the ever-rising abilities of this technology can be used to process data and make informed decisions. 

This blog will highlight the prominent ways in which businesses can leverage AI in logistics operations, boosting efficiency and meeting the ever-changing market demands.

What is the Role of AI in Logistics?

The role of artificial intelligence in logistics is simple — to streamline time-consuming tasks across order processing, inventory management, supply chain, and distribution networks.

AI uses algorithms and subsets like machine learning and deep learning to automate customary tasks and offer useful insights. Apart from this, AI in logistics also helps in monitoring and tracking parcels and route optimization, providing real-time status of the shipments.

Digital transformation in the logistics industry has remarkably increased the productivity and efficiency of businesses. Now with AI at the forefront, this changeover becomes completely unpredictable. 

Major Applications and Use Cases of AI in Logistics

Several features of AI such as predictive analytics, workflow automation, computer vision, and robotics have proved to be advantageous in the logistics domain. Below are some of the useful applications of AI in transportation and logistics.

Automated Warehouse Solutions

Warehouse systems powered by AI and cloud computing can easily identify patterns and dependencies from unstructured data. Based on the dynamic conditions, they can later adjust to a new environment throughout the complete logistic network.

A research study concludes that almost 60% of warehouses will be automated by 2026. Besides this, by using IoT apps for business automation, several repetitive tasks could be automated for better efficiency.

Use Case:

China-based Cainiao, a billion-dollar subsidiary of e-commerce behemoth Alibaba, has revolutionized its warehouse management systems by integrating AI solutions. The company has turned its warehouses “smart” by equipping over 100 Wi-Fi-based, self-charging AGVs (automated guided vehicles) to manage inventories.

Route Planning and Optimization

According to a study, route optimization can save fuel costs by at least 20%. That’s why around 73% of logistics managers across the globe are utilizing efficient fleet management and telematics solutions to maintain fleet sustainability.

By utilizing the power of artificial intelligence in logistics and fleet management, businesses can unlock smart route-planning approaches backed by predictive analytics. This can help fleet managers integrate data from various sources, helping fleet drivers make smart judgments regarding travel routes and accelerate deliveries.

Use Case:

United Parcel Service, an American multinational shipping and logistics company uses dynamic On-Road Integrated Optimization and Navigation technology (ORION) for its drivers.

The technology helps optimize the routes and assist drivers in delivering shipments timely. Since 2012, UPS has saved over 100 million miles and 10 million gallons of gasoline using AI-powered ORION technology.

Efficient Demand Forecasting

In order to run a successful logistics business, one of the essential requirements is the ability to predict demand and supply trends. If you run low on inventory levels, you will lose sales, revenue, and eventually your customers. However, if the inventory is overstocked, the chances of wastage and mismanagement increase significantly.

The integration of AI in logistics can substantially help overcome these challenges. AI-based algorithms can better predict the future regarding the demands and market trends, even than experts. These AI logistics solutions can not only forecast the demand but also measure the related parameters precisely and quickly. Consequently, you get a simplified warehouse system and well-managed inventory.

Use Case:

Shell and Equinor collaborated together to form a digital product – Shell Inventory Optimiser – that leverages predictive analytics to optimize inventory levels. According to Equinor, this tool was efficient enough to lessen inventory inflow by at least 13%, thus saving millions of dollars.

Smart Roads Development

Another notable use case of AI in logistics is smart roads or highways. This is one of the latest ways modern technologies like artificial intelligence are used and incorporated into roads, improving the operational efficiency of connected fleets and autonomous vehicles.

Besides assisting logistics vehicles, this AI logistics solution is also effective in monitoring the conditions of roads, tracking street lighting, and managing traffic. With this collaborative transportation artificial intelligence approach, players in the supply chain business can expect faster deliveries with reduced delays. Moreover, as per Market Research Future, the global smart roads market is projected to hit the $39 billion mark by the end of this year.

Use Case:

Countries in the North American region have already started constructing smart roads and highways. The prominent reason behind this is the traffic congestion which creates a substantial demand for an effective digitized solution.

Challenges Associated with the Adoption of AI in Logistics

The adoption of AI in warehouse and inventory management is one of the prominent benefits in the logistics industry. However, despite the advantages of merging logistics and artificial intelligence, businesses face several challenges that can be barriers to adopting AI technology. 

Cost of implementation and maintenance

Integrating AI in logistics often requires significant investment in hardware, software, and infrastructure, which can be expensive. Moreover, proper maintenance and training may be required to ensure that the AI systems are functioning properly and providing accurate results. 

This is also important considering logistics and order processing mismanagement could incur additional issues, increasing overall costs. For smaller businesses having limited budgets, these expenses can prevent them from completely adopting AI technology. 

Lack of resources and expertise

Although artificial intelligence has been there for quite a long time, many of its subsets are still relatively advanced. To operate and function with them, businesses might lack the necessary technical expertise or resources that are crucial for the effective implementation and management of AI systems. 

According to a study by McKinsey & Company, the United States — which is one of the superpowers in the world — faced a shortage of 140K to 190K people with profound analytical skills. Apart from this, the study also revealed that about 1.5 million managers and analysts might need more understanding to leverage big data and make effective decisions. 

Concerns around security and privacy

Security and privacy have always been a prior concern while using AI systems. Historical cybersecurity breaches and cyber-attacks have left businesses lacking confidence regarding incorporating advanced technology solutions.

For instance, in April 2021, beverages giant C&C Group subsidiary shut down its IT systems following a cybersecurity incident. Similarly, Facebook also faced a major data breach in 2021 when the contact information of millions of users was dropped over the dark web.

The Future of AI in Logistics and Supply Chain Management

Given how AI technology has enhanced customer satisfaction by automating numerous time-consuming activities, the future of the logistics sector with AI is bright. From logistics planning to warehouse automation, the benefits of logistics mobile apps are wide-ranging.

However, the technology alone cannot fix the supply chain and logistics problems. Digital transformation with AI requires three critical ingredients:

• the competent talented resources
• adjusted business models and processes
• the right mix of advanced technologies

Only with this perfect combination, businesses can attain the desired goals. Also, it is crucial to utilize its potential in a way that can overcome challenges associated with leveraging AI in transportation and logistics.

How Can Appventurez Help Integrate AI Solutions in the Logistics Industry?

With the successful implementation of logistics AI solutions, businesses observed a significant improvement in expenses by 15%, inventory levels by 35%, and service areas by 65%. However, to attain these, you must collaborate with an AI app development company having expertise in AI tools and technologies.

At Appventurez, we have worked with several companies looking to optimize their supply chain and provided them with the best AI logistics solutions. As a professional logistics app development company, our team ensures to comprehensively understand the requirements of our clients and provide them with the most efficient services.