Machine Learning in Logistics: Top Use Cases & Implementation

by Aleksei Shinkarenko, BI Architect

The rapidly increasing globalization made the development of international logistics has never been more important. As our reliance on international freight transportation continues to grow, effective and technology-driven supply chain management becomes the backbone of staying on top of the competition. That is why more and more logistic companies are seeking machine learning advisory.

Machine learning in logistics: a summary

Machine learning in logistics is the use of advanced statistical models to optimize routing decisions, predict demand for materials and supplies, automate order fulfillment, reduce transportation costs, forecast supply chain disruptions, and identify customer demand patterns.

In this article, we will discuss how machine learning solutions can revolutionize the logistics industry and maximize operational performance. We’ll also explore the different ways logistics companies are applying machine learning, provide implementation advice, and look at the technology’s potential for years to come in the logistics industry.

Machine learning in logistics market statistics

of logistics industry leaders consider technology as a competitive advantage

Gartner

of commercial supply chain management vendors will use AI and data science by 2026

Gartner

projected CAGR of AI in the supply chain market from 2021 to 2028

BlueWave Consulting

Best 10 ML use cases in logistics

Warehouse management

Machine learning can help streamline warehouse management by providing insights into inventory levels, stock availability, fulfillment rates, shipment time frames, and other essential metrics. While some companies use robotic process automation for this task, ML models are far superior as they can autonomously learn, improve and adapt over time without human involvement. With ML-driven automation systems in place, warehouses can become more efficient, and managers can eliminate manual errors that lead to delays or lost shipments.

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Real-life examples of ML in logistics

Plus is a California-based company that develops self-driving systems for long-haul trucks, which prove to be safer and more environmentally friendly while helping overcome driver shortage. With the help of proprietary multimodal sensor systems, Plus-augmented trucks can autonomously overtake other vehicles, handle stop-and-go traffic, change lanes, and perform other basic maneuvers on the road. Importantly, the system also optimizes fuel usage, saving around 10% of energy costs. In the last few years, Plus partnered with some of the biggest companies in the industry including IVECO, FAW, and Amazon.

As the trucking industry faces a continued driver shortage, the demand for autonomous trucks that enhance the safety, efficiency, and sustainability of long-haul trucking is increasing.

Liu Zhiyuan

CEO and Founder of Guangzhou Zhihong

Machine learning technologies used in logistics

Predictive analytics

Predictive analytics is a data mining technique that uses statistical models and algorithms to analyze current and historical data sets and make predictions about future outcomes. Applied in logistics and supply chain, predictive analytics tools use data from customer orders, delivery times, transportation costs, and other data to identify patterns, help anticipate problems before they arise, and take provisional actions.

Computer vision

Computer vision is a form of machine learning that allows computers to identify objects in images and videos without human intervention. It has many applications in the world of logistics, such as tracking inventory levels or recognizing damaged parcels. In addition, image recognition algorithms can be used in robotics navigation systems, helping robots transport items in warehouses. By using this technology, companies can reduce their operational costs, optimize resources, and improve customer satisfaction.

The Internet of Things

The combination of ML and IoT technologies has the potential to revolutionize the logistics industry. Together, they can provide a powerful solution that collects, processes and analyzes vast amounts of data from connected devices and makes decisions automatically. In logistics, ML and IoT can be applied for optimizing supply chain routes, predicting demand for goods and services, anticipating disruptions in the flow of goods, and improving customer experience.

Implementation roadmap

1

Define the problem

ML implementation should always be driven by a real business need. It’s paramount for technology leaders to collaborate with supply chain experts to figure out which problems prove to be most disruptive to their company and if emerging technologies like ML can help solve these issues.

2

Assess your ML readiness

Next, companies should check their readiness for integrating ML. This includes the impact of ML adoption on workforce and workflows, identifying personnel gaps, machine learning ROI in the long term, and implementation expectations.

3

Establish data governance standards

Given that machine learning shines only when fed enough data, logistics companies need to sort out where and how they acquire data. After determining what issue they want to address with ML, companies should identify relevant data sets, the more the better, for model training.

4

Collaborate with other companies

The key to reaping maximum benefit from ML lies in building an ecosystem of partners that mutually benefit from exchanging region- and niche-specific information. With an abundance of actionable data, ML models have the highest chances to add value.

5

Involve industry experts

Explaining the variability in data with the help of ML is a complex initiative. The development of accurate forecasting models requires not only data science and programming expertise but also industry-specific knowledge. This is why it's critical to involve supply chain professionals early in the process and during model development in particular.

Benefits of machine learning in logistics

Increased visibility

Improved customer service

Enhanced decision- making

Automated processes

Increased scalability

Improved delivery times

Automated data analysis

Improved forecasting

Reduced logistics risks

Faster response times

ML in logistics

Provides greater transparency into the supply chain by tracking data throughout the entire shipment journey.

Personalizes the customer experience by automatically predicting customers’ needs and resolving their common inquiries.

Helps to make better decisions faster and more efficiently based on large volumes of disparate data.

Automates manual tasks such as route optimization, task allocation, and inventory management, reducing the need for manual labor.

Improves operational scalability by automating processes that usually require human input.

Reduces delivery times by optimizing routes, managing inventory levels, and providing accurate customer information in real time.

Generates detailed reports on customer behaviors and trends, used to optimize logistics operations.

Accurately forecast demand to help companies manage inventory levels and improve supply chain performance.

Minimizes logistics risks by tracking shipments and providing accurate customer information in real time.

Speeds up response times by automatically detecting problems and offering solutions to them much faster than humans.

Solving key logistics challenges using ML techniques

Challenge

Solution

Inefficiency due to manual processes

Manual processes are prone to human errors and delays, but ML can automate routine tasks, increasing their efficiency and accuracy while reducing costs.

High transportation costs

Transportation costs are the major logistics expenditure item. ML can be used to identify the most cost-effective shipping routes and carriers, helping businesses reduce expenses on transportation.

Limited visibility

Keeping track of shipments is essential for effective shipping management. ML can make supply chain smarter and more transparent, providing logistics companies with real-time insights into their operations.

Streamline supply chain management with ML

A growing number of customers who demand swiftly and error-free delivery is forcing supply chain companies to rethink their approach to operations. Due to ML’s ability to quickly process large amounts of data from a myriad of sources and suggest solutions for emerging operational challenges within minutes, the technology will inevitably become a staple in logistics. If you want to optimize your supply chain operations with the help of machine learning, contact us to make their logistics operations more efficient.

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FAQs

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