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9 Use Cases of Machine Learning in Logistics


Global market competitive forces have placed high expectations on logistics operations regarding speed and dependability.

At today's fast-changing market, the whole logistics system is customer-service focused. Customer service is defined by the timely, dependable and accurate delivery of undamaged items.

Today, traditional logistics operations are being transformed by technology, forcing supply chain leaders to adapt. One of the technologies that transform this sector is machine learning.

Machine learning enables logistics service providers to analyze vast amounts of data and improve logistics management. Machine Learning (ML) is a popular type of artificial intelligence (AI). Businesses worldwide are adopting this technology fast in order to change various business operations. As a result of this digital revolution, goods, services and workplaces are using machine learning to simplify, automate and improve their processes.

Facts and Figures

The Global AI Market is anticipated to achieve a revenue of 118 billion

dollars by 2025, according to Tractica, a market research organization.

Amazon automates picking and packing items in a warehouse logistics setting using a machine learning algorithm. Thanks to machine learning, Amazon's average 'click to ship' time was reduced by 225%, from 60-75 minutes to 15 minutes (Forbes).

91% of leading businesses are currently investing in AI (Businesswire).

44% of organizations reduced business costs with AI (McKinsey).

The Azure Machine Learning framework predicts stock market peaks and troughs with a 62% accuracy (Microsoft).

According to McKinsey, implementing AI has helped companies to improve logistics costs by 15%, inventory levels by 35% and service levels by 65%.

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Why Use Machine Learning in Logistics?

When it comes to supply chain, AI and machine learning in logistics may be pretty beneficial. It is feasible to use it to optimize operations, minimize mistakes people can make or miss, and foresee future possibilities and difficulties.

An efficient and flexible supply chain is a significant advantage in a highly competitive industry. As a result, businesses are seeking tools to assist them in optimizing their operations and making decisions to increase operational efficiency and customer happiness,as well as to decrease economic and environmental costs. The most significant development in this industry is the digital transformation of the supply chain, which continues to be a big issue for many transport operators.

According to Gartner, more than 75% of commercial supply chain management application providers will deliver artificial intelligence (AI) and data science by 2026.

Machine learning can improve numerous business segments. Some of them are:

Supply Chain Planning

spply chain planning

Balancing demand and supply, delivery process optimization. Your team will balance demand and supply while optimizing delivery procedures by analyzing massive data sets and applying sophisticated algorithms. Using machine learning, your supply chain decision-making processes can be considerably improved.

Warehouse Management

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Inventory optimization, avoiding over and under-stocking. Machine learning is used in warehouses to automate manual work, identify potential difficulties and reduce paperwork for warehouse employees. Machine learning also helps to program robots used in warehouses. Additionally, computer vision helps in detecting arriving packages, scanning barcodes, etc.

Warehouse robots

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The warehouse robotics market revenue accounted for USD 4.7 Billion in 2021 and is projected to reach USD 9.1 Billion by 2026, at a CAGR of 14.0% during the forecast period.

The online retail and shipping sectors have progressively automated their warehouses more over the last decade, with big players like Amazon leading the way.

Another example is Alibaba. They have the world's largest automated warehouse, where robots can collect and pack products for delivery to clients. These robots currently perform 70% of the work in the warehouse. The robots can hold up to 500 kilos above them as they move. Every robot has specific sensors to prevent clashing with one another, and they have Wi-Fi so that workers may summon them at any moment.

Warehouse Analysis

warehouse analysis

Monitoring warehouse perimeter, automating barcode reading, tracking employees and preventing thefts and violations. Security cameras' analytics and AI allow them to monitor particular circumstances. When the system detects a match, it takes action specified by the programming instructions. For example, it identifies someone entering the property after hours when no one should be there. It notifies the operator, who issues an audible warning to the invader. During the day, analytics may ensure that only authorized personnel enter a warehouse.

Demand Forecasting

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Predict and improve demand forecasting, analyzing factors that influence the market. Machine learning applies mathematical algorithms to find patterns, recognize demand signals and recognize correlations between variables in huge datasets. Aside from processing massive amounts of data, the smart system regularly updates and retrains the model. As a result, it adjusts to changing situations, addressing volatility and performing effectively in diverse settings. According to a McKinsey analysis, AI and ML-based supply chain solutions can cut prediction mistakes by up to 50%.

Logistics Route Optimization

logistics route optimization

According to ATRI’s report, the annual cost to the trucking industry from traffic congestion is $73.5 billion, with the sector losing 1.2 billion hours in productivity due to highway delays – the equivalent to 425,533 truck drivers sitting idle for a year.

Route optimization is determining the most effective and quickest order for stops while minimizing driving time and distance. It is becoming a more simplified procedure as artificial intelligence evolves. 

Additionally, artificial intelligence and machine learning can monitor and forecast traffic and other factors that may affect shipment time, like peak hours in logistics centers. With machine learning, companies can predict and, therefore, avoid them.

Predictive Analytics

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Previously, logistics planners assessed the condition of operations manually, primarily using pen and paper. Even with years of expertise, logistics specialists cannot guarantee efficiency, especially in today's day and age. Furthermore, these processes are time-consuming, and the variation of each component only increases during peak hours or as a company becomes bigger.

That's where AI comes in. AI can execute these logistics tasks while adding more important external aspects to its information extrapolation by collecting more precise data. As a result, logistics technology companies can better predict future demands and embrace proactivity.

Smart Inventory Management

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Inventory management is a complicated matter, and keeping the correct product in the right place at the right time can be challenging. Companies can use machine learning to help them adopt better inventory management. Machine learning algorithms can forecast the demands of the whole network and propose efficient actions based on historical data, orders and current supplies (purchase, transfer, etc.). It will determine which goods are selling the quickest or slowest and will generate a more accurate inventory. This enables inventory modifications to be made to minimize shortages and control overstocks.

Quality Control

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Nothing is more disappointing to a consumer than opening an order only to discover that the product inside is damaged, which generally results in bad company reviews. Machine learning solutions are ideal for visual pattern recognition, and there are many possible applications for physical inspection across the supply chain network. Machine learning algorithms that can quickly find common patterns in large amounts of data have shown to be quite helpful in quality management automation in logistics centers by separating packages containing damaged items.


Machine learning improves supply chain management by improving customer experience, cutting costs, optimizing inventory planning and assuring error-free delivery. Supply chain companies that use machine learning are already witnessing double-digit gains in demand planning productivity and on-time delivery.

Machine learning technology is a helpful tool for logistics companies since it can be applied at any point along the supply chain, from production to product delivery. Its objective is to do mechanical and repetitive chores while assisting workers in decision-making, allowing them to focus on jobs with higher added value.

If you are looking for a business consultant, or even an entire team of experts who are already familiar with the best practices and who have seen what strategies work in different places and projects, feel free to reach out.

If you’d like to learn more about Machine Learning, download the whitepaper below.

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