6 Machine Learning Use Cases in Logistics and Supply Chain

6 min readAug 16, 2024

The advancements in machine learning and supply chain management go hand in hand, improving operational efficiency with real-time delivery updates and automated data processing.

As automation technologies advance, the transportation industry is undergoing significant changes, with the role of machine learning in logistics becoming irreplaceable. The demand for precise, fast, and cost-effective deliveries with comprehensive tracking capabilities has become the standard. Thus, over 50% of supply chain businesses are already investing in AI and ML algorithms to withstand competition and boost efficiency.

Acropolium

With over nine years of experience in the transportation sector, Acropolium has developed more than 23 logistics software solutions incorporating machine learning for automation. Today, we want to share the most disruptive use cases of machine learning in supply chain operations, exploring its tech benefits and sharing success stories.

Machine Learning and Supply Chain: Market Projections & Benefits

More and more logistics businesses are adopting machine learning in supply chain operations, which significantly impacts the ML market growth.

Machine learning (ML) is an instrumental subset of artificial intelligence (AI) used for processing vast amounts of data to generate actionable business insights. In 2024, the global ML market was valued at $44.07B and is expected to surge to $626.64B by 2032, with a projected CAGR of 35.09% from 2023 to 2032.

By implementing machine learning for logistics operations, businesses revolutionize the supply chain landscape by enhancing automation and demand forecasting. This technology provides valuable insights into route optimization, inventory management, security, and risk management, making it one of the top trends in logistics technology.

With automation of routine tasks and real-time transparency, one can also uncover numerous business opportunities:

Improved efficiency. ML in logistics predicts disruptions, minimizes human error risks, and optimizes operations through real-time data analysis, enhancing asset and fleet utilization.
Cost reduction. Real-time software offers logistics route optimization using machine learning based on traffic and weather, which reduces vehicle wear and tear and contributes to reduced operational costs.
Enhanced customer experience. Features like live tracking in warehouses, along with automated notifications, boost customer satisfaction and improve delivery efficiency.
Better decision-making. Constant learning from data aids in precise demand forecasting and planning, helping companies unlock inefficient patterns and improve overall workflows.
Scalability and adaptability. Custom ML solutions offer scalability and adaptability to meet specific logistics needs, where you can adjust the functionality of your software as your business expands.
Environmental impact. ML improves sustainability by forecasting demand accurately, reducing waste, and tracking emissions.

6 Machine Learning Use Cases in Logistics, Inventory, and Warehouse Management

Explore the common use cases of machine learning in logistics and supply chain management to see how you can grow your inventory, warehousing, and shipping operations.

The application of machine learning in supply chains varies greatly based on the scale of operations. Larger enterprises might utilize ML for comprehensive fleet and inventory management automation, optimizing routes, and predictive maintenance. In contrast, smaller logistics operators may primarily use ML-enhanced GPS tracking systems to streamline operations and improve delivery accuracy.

The scope of ML and AI use cases in logistics expands with the complexity and needs of the business, offering tailored solutions for efficiency and cost-effectiveness across the industry.

Supply Chain Planning

Modern companies use machine learning in supply chain planning to analyze extensive datasets and balance demand and supply effectively. It identifies patterns that lead to the development of advanced algorithms, optimizing delivery procedures.

Logistics Route Optimization

Machine learning algorithms aid in identifying the most efficient paths and minimizing driving time. This technology also predicts traffic patterns, helping businesses anticipate and mitigate delivery challenges. For instance, UPS uses ML to optimize package delivery, efficiently allocate resources, and improve customer satisfaction.

Warehouse Management

By adopting machine learning in warehouse management , businesses can automate manual tasks, predict issues, and significantly reduce paperwork. It also enables the programming of robots for better product placement and monitoring of equipment. Amazon, for example, uses ML in its warehousing to improve operational efficiency and accuracy.

Demand Prediction

Machine learning technology enhances demand forecasting by identifying patterns and correlations in large datasets. This contributes to a better awareness of market trends and customer behaviors, enabling smarter decision-making. C.H. Robinson uses ML to incorporate real-time data for swift responses to changing demand.

Predictive Maintenance

ML-based predictive maintenance systems use sensors and data analysis to forecast maintenance needs, reducing downtime and extending equipment lifespan. DHL is another example of a company using machine learning in shipping industry activities. They employ this technology in their transportation management system to proactively schedule maintenance, improving efficiency and reducing costs.

Smart Inventory Management

Machine learning models in inventory management predict demand and recommend actions to avoid shortages and overstock. They analyze supply levels and automatically trigger restocking when necessary. IBM’s Watson Supply Chain employs ML to monitor inventory and manage restocking processes efficiently, ensuring product availability without manual oversight.

Leveraging Machine Learning in Supply Chains with Acropolium

Acropolium, a certified provider of supply chain management software, has over 10 years of experience in delivering tailored solutions for the logistics industry. We specialize in modernizing and building products, including carrier procurement systems, fleet tracking applications, and serverless transportation management platforms.

Through years of exploring machine learning and logistics, we have helped companies realize their goals while providing solutions to common ML implementation challenges:

Machine learning relies on high-quality data, but supply chain data can be fragmented and inconsistent. We leverage cloud-based logistics systems to provide our clients with accessible and centralized data storage.
The costs associated with ML implementation, including infrastructure and staffing, can be high. Acropolium helps companies craft cost-effective solutions tailored to their needs.
Resistance to new technologies requires effective change management and training, necessitating additional resources. Thus, our teams provide training programs when delivering software solutions.
Ensuring robust security measures is crucial for handling sensitive data and preventing breaches, and Acropolium takes pride in crafting compliant solutions with the latest security protocols.

ML Success Stories

Our deep expertise in automation empowers businesses to expand their operational capabilities. Here are some machine learning in supply chain case study examples that highlight how our solutions have optimized business processes.

Tracking Mobile App Development

A large shipping service company needed an MVP mobile app to track driver hours and ensure accurate payments. We faced tight deadlines and had to address visibility issues. Our team identified critical issues in the existing Android app, prioritized optimization, and built an iOS app, implementing real-time geolocation tracking and a responsive UI.

The project resulted in a 90% increase in security and a 28% improvement in platform session speed.

Big Data Processing App Development

We also collaborated with a targeted proteomics company to integrate AI and ML for biomaterial processing and optimize their backend. Despite challenges with limited external developer communication and low stakeholder engagement, we structured the development using Scrum, facilitating better communication and progress tracking.

This led to a 40% increase in processing analysis accuracy and a 38% reduction in processing time, achieving a 30% reduction in project launch time.

Final Words

Machine learning can significantly enhance efficiency in logistics and supply chain operations for businesses of all sizes by automating data processing and analysis. This leads to intelligent decision-making, cost savings, better route planning, and improved response management.

At Acropolium, we create custom IoT, AI, and ML-based software solutions to modernize logistics. Whether you need a transportation system upgrade or complete product development, we offer flexible, subscription-based pricing to suit any budget.