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What is AI for Logistics and (most importantly) What It Isn’t


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Logistics professionals, data experts, and synthetic biologists are all talking about AI being the next transformation for human civilization. How it’s on par with discoveries like the wheel and the ability to harness electricity — changing the way we operate at a pace faster than before.

It’s no surprise, then, that professionals across the board are feeling alarmed by headlines discussing how AI is coming for our jobs. But the reality is that 66% of leaders are ambivalent or dissatisfied with their progress on AI and GenAI.

That’s because AI is not a magic wand with a one-size-fits-all solution, and for a successful rollout, realistic expectations need to be set. Here are some common misconceptions about AI in logistics and how teams can benefit with the right mindset and solution adaptations. 

What Are the Misconceptions About AI for Logistics?

There are several aspects of AI in logistics that people may not fully understand:

❌AI can fully automate all aspects of logistics operations immediately.

✅Different logistics processes require specific AI applications, with certain tasks requiring gradual implementation and continuous human oversight.

Implementing AI in logistics requires significant investment in technology, infrastructure, and skilled personnel. It’s not a plug-and-play solution and often involves integrating AI systems with existing processes and technologies.

❌Mass unemployment in logistics will result from AI.

✅While AI can automate repetitive tasks, like driving trucks, for example, it typically works best in conjunction with human expertise. 

Humans are still essential for decision-making, problem-solving, and handling exceptions that AI systems may not be equipped to handle, such as governance and training. Roles will adapt, and new roles, such as AI system monitoring and data analysis, will arise. 

❌AI is error-proof.

✅AI is as good as the data it is trained on — without clean, relevant data, AI systems may not perform as expected. However, the quality and availability of data in logistics can be a challenge. Data privacy, security, and bias must also be considered. People may not fully understand the implications of using AI in areas like route optimization, predictive maintenance, or customer data analysis.

Even with high-quality data and compliant data practices, continuous monitoring and adjustment are needed as circumstances change over time.

❌AI is a universal solution for all logistics challenges.

✅When applied strategically, AI can support many logistics processes. Still, it must be catered to the scenario. 

Not to mention changes in job roles, workforce dynamics, and business models, are not fully understood. People may underestimate the extent or long-term impacts of these changes and the need for adaptation and upskilling.

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Which AI Technologies Are Actually Changing the Sector?

From enriching data quality to predictive analytics to strategic asset positioning, the benefits of AI for logistics are outstanding. Let’s take a closer look at some of the technologies impacting the space and how logistics professionals can adapt these for specific company objectives.

Machine Learning

Data cleansing is essential for implementing any new logistics software, and logistics data teams are usually handcuffed for a year manually preparing for it. However, using machine learning (ML) in the data cleansing process can reduce that year to a few weeks.

Say your objective is to improve your demand forecasting, but you need your ML to detect and clean data from anomalies. You can train the ML with historical logistics data, including variables such as order volume, delivery times, and other relevant factors, ensuring the dataset includes normal and abnormal instances.

Still, fine-tuning parameters and adjusting training data are crucial for optimal ML performance. This involves selecting features that may influence demand, including time of day, special holidays and external events, labeling instances where anomalies occurred, and periodically retraining the model to adapt to evolving patterns.

This example uses an Isolation Forest for anomaly detection. Logistics professionals must adjust the features, hyperparameters, and evaluation metrics based on your specific requirements and dataset characteristics for accurate ML-powered anomaly detection and cleansing.

Computer Vision

For logistics companies operating distribution centers that process large volumes of packages daily, computer vision-powered package sorting can significantly improve the speed and accuracy of this operation. Cameras are installed along the conveyor belts to capture images of each passing package.

At the same time, data teams can train computer vision algorithms to recognize and classify packages based on their size, shape, labels, and unique identifiers such as barcodes or QR codes. The AI then instructs the sorting mechanism, directing each package to the appropriate chute or container based on its destination.

Remember, successful implementation relies on logistics teams to check and retrain the system with new data, such as package size or label changes, ensuring appropriate lighting conditions and camera alignment to guarantee it continues to identify packages correctly.

Optimization Algorithms

Optimization algorithms are revolutionizing the logistics space by improving efficiency and cost-effectiveness. They streamline complex decision-making processes, such as route planning, inventory management, and resource allocation. 

For instance, logistics firms managing intricate delivery networks face difficulty identifying efficient routes. Optimization algorithms can swiftly analyze historical data, traffic patterns, weather conditions, and other pertinent factors to generate real-time optimized routes. This capability aids in reducing fuel consumption costs and substantially shortening delivery times for enhanced operational efficiency.

Nevertheless, with these vast data sources, the technology requires huge computing power to find the right scenarios and suggestions. Regularly updating and fine-tuning these algorithms ensures you optimize computing power while also aligning parameters with evolving business objectives and changing market dynamics.

Generative AI

Logistics professionals can leverage natural language processing (NLP) to enhance logistics processes, communication, and decision-making. Its usefulness is so broad that generative AI for logistics market is expected to be worth around $13.9 billion by 2032.

Take inventory management, for example. NLP could boost your performance by analyzing supplier communications, contracts, and other textual data to extract relevant information about product availability, lead time changes, or any disruptions that may affect inventory levels.

Nevertheless, human involvement is essential for tasks such as data preprocessing, providing contextual understanding, and making decisions in ambiguous situations, contributing to the system’s overall robustness.

If you’d like to know more about AI for Logistics, check out our blog!