Intelligent Logistics: Why Use Big Data, AI And IoT To Differentiate

Competitiveness forces companies to seek innovative alternatives to differentiate themselves. The logistics sector, in particular, has led to the search for intelligent logistics powered by technologies such as IoT, big data, and AI.

This approach is the only way to meet the challenges of the segment, which, according to the Next-Generation Wireless in Logistics study by DHL, are the following:

• Achieve accurate end-to-end visibility;
• Fragmented data collection from inherently heterogeneous supply chains.

Faced with these obstacles, the use of Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) can improve agility, traceability, visibility of processes, flexibility, and cost efficiency in the logistics sector.

Its applications allow obtaining quality data for informed decision-making, computer vision, monitoring the productivity of machines, using internal and external predictive analyses, and tracking delivery vehicles, among others – integrating partners, suppliers, and customers.
In this article, we will explore how the logistics sector can automate its processes using hyperconnectivity technologies, understand the main bottlenecks and possible solutions through technology, and finally, how it is possible to reduce costs through their use.

How AI Improves Productivity and Reduces Human Errors

AI can help reduce human error in repetitive and routine operations. With these benefits, managers can focus their expertise on strategic activities rather than on remediation of failures and reworks, optimising their time and gaining greater efficiency.

In other words, AI offers scalable and standardized production and operations. According to the objectives and goals defined by the manager, it can interpret data and may identify processes that support these objectives.

How AI, Big Data, and IoT Improve End-to-End Visibility

Supply chain management is complex because it involves many relationships: from monitoring the flow of raw material procurement to distributing the final product to customers.
Therefore, it has a high potential to present bottlenecks. Mapping and identifying these bottlenecks can be complicated through phone calls and emails and requires end-to-end monitoring to address them.

• Cargo transport: Traffic, poor road infrastructure, inspections, weather conditions, and accidents are common problems in transport. AI, IoT, and Big Data can help address these challenges by capturing real-time information about road conditions and weather and calculating suitable routes. In addition, they help keep documentation up-to-date and digital to streamline the inspection process.
• Inventory control: Demand fluctuations, safety-stock requirements, communication failures between the departments involved, and loss of merchandise can be minimised through predictive analytics and even solutions such as automatically triggering recurring customer orders.
• Broad business visibility: Silos between people, technologies, and processes can cause a distorted view of demand needs and hinder rapid response to risks. With big data and IoT, we capture and analyse information in real time, providing visibility into processes across the sectors involved in operations.

How IoT, Big Data, and AI Reduce Logistics Costs

AI can help reduce operational costs in logistics. You can identify suitable vehicle routes in the transport process, which can help reduce fuel consumption. Repetitive internal activities are automated, reducing rework and optimising staff time. By installing sensors in machinery and equipment, predictive analysis is promoted, which helps reduce repair costs.

The storage sector can be monitored and controlled according to the quantities demanded by customers, avoiding waste and making the production chain cleaner. Logically, implementing technologies in industrial logistics faces challenges in becoming a natural part of companies. Managers and CIOs must look at their needs and find best practices to introduce disruptive technologies into their teams. It takes planning, expert support staff, and time to adapt.

Challenges of Implementing AI, Big Data, and IoT in Logistics

And the main challenges raised are:

1. Lack of knowledge: Industries still face a lack of familiarity with the technological innovations of Industry 4.0. The lack of digital culture and incentives for modernisation on the part of companies can delay their progress and affect competitiveness.
2. High cost of implementation: One of the industry’s main complaints is the lack of availability of specific lines of investment for technological performance by financial institutions and public bodies.
3. Qualified professionals: The increase in data and connectivity brought by IoT, big data, and AI requires in-depth data science knowledge to lead to new applications. Digital culture has not yet reached high levels within organisations, which requires action to attract professionals.

One solution to these challenges is to find specialised companies that can help managers and CIOs to disseminate technological knowledge within their teams.

Conclusion

You might wonder, how do these technologies work for logistics companies? Then here is the answer: through the effective use of Big Data, Internet of Things (IoT) and AI, logistics companies gain greater visibility, automate decision-making, minimize errors and control costs. But implementation requires strategy, skilled professionals and business-specific adjustments.