Machine learning in logistics: technology breakdown & 10 use cases

Machine learning in logistics: technology breakdown & 10 use cases

January 25, 2023

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.

Route optimization

ML and AI tools for transportation can analyze data such as traffic patterns and distances between locations and work out the most efficient routes for freight delivery in real-time. Combined with predictive analytics, ML can also help logistics companies save time and money by alerting them about costly traffic jams or delays caused by weather or other unforeseen circumstances.

Workforce planning

Machine learning can also streamline workforce planning and optimize staffing levels. For instance, analyzing historical data sets on traffic patterns, customer orders, and shipment demand, machine learning algorithms can predict the best times for delivery. With this information on hand, logistics companies can ensure they have enough staff during peak periods and prevent overstaffing during off-peak periods.

Fraud detection in payment systems

By analyzing patterns and data across multiple sources, ML models can detect suspicious activity and fraudulent payments more accurately and quickly than humans. Additionally, these models can help identify new types of fraudulent activities that may have gone undetected before. With ML-based fraud detection technology in place, companies can also craft new, more efficient fraud prevention strategies to prevent financial losses as well as potential customer data breaches.

Demand prediction

ML can improve demand prediction by providing more reliable forecasts than traditional ones based on historical data only. Machine learning algorithms can crunch data on past orders, traffic patterns, customer behavior, inventory trends, weather conditions, and other market drivers to create models that accurately predict demand changes. With their help, businesses can better prepare for sudden spikes or drops in demand, timely adjusting their operations.

Predictive maintenance for vehicles

ML can analyze data from on-freight sensors and external factors like road conditions to identify upcoming maintenance needs. Relying on such a predictive maintenance solution, logistic companies can service vehicles at the optimal time and avoid costly breakdowns or unexpected repairs.

Self-driving delivery vehicles

Equipped with modern-day ML-powered sensors, autonomous vehicles can navigate complex cityscapes and rural areas with little to no human intervention. Their implementation for delivery can reduce labor costs, optimize fuel usage, help drivers handle unexpected road conditions, improve drivers’ security, plan optimal routes for deliveries, and even predict traffic patterns to avoid delays.

Autonomous drones for package delivery

The use of machine learning-powered autonomous drones in logistics can transform package delivery. Drone fleets, equipped with advanced navigation and control systems, could drastically reduce delivery duration while increasing its safety and accuracy. By leveraging real-time data such as traffic patterns, obstacles, weather conditions, and terrain maps, these autonomous drones can be trained using ML algorithms to navigate end-to-end routes efficiently and autonomously.

Supplier relationship management

Machine learning solutions can be used to improve supplier relationship management (SRM) in the logistics industry. Complemented with predictive analytics, ML algorithms can help identify customer behaviors and preferences that may be valuable for SRM. This data can then be used to gain insights into demand patterns, customer loyalty, and supplier performance over time. Additionally, ML-based automation solutions can streamline order management and delivery scheduling.

Dynamic pricing

ML can enable dynamic pricing, which is the practice of changing prices based on market demand. By using algorithms and predictive analytics, ML helps companies adjust shipment rates according to real-time data. In the long run, businesses can optimize their pricing strategies and improve customer satisfaction by offering competitive prices.

Looking for reliable machine learning experts?

Turn to Itransition

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

Liu Zhiyuan

CEO and Founder of Guangzhou Zhihong

Coupa is a California-based company providing ML-powered tools for data-driven decision-making for logistics companies. Air Mobility Command of the US Air Force manages a fleet of 1100 aircrafts. Due to inherent challenges with cargo demand forecasting, the Command turned to Coupa to employ its AI-based Demand Modeling tool. After analyzing enormous amounts of Command’s data with the help of ML, Coupa managed to identify critical demand drivers. This allowed the company to accurately predict how many aircrafts will be necessary at any given time within the next 12 months.

Image title: Coupa supply chain demand modeler
Data source: coupa.com — Demand Modeler

Coupa supply chain demand modeler

Pfizer, a multinational pharmaceutical industry giant, uses AI and ML to handle sensitive inventory during transportation. Together with Controlant, a software development company that helps pharmaceutical companies to have better visibility into their supply chains, Pfizer developed a platform that guarantees safe and timely vaccine delivery and ensures compliance with many regulatory standards. By collecting large amounts of data from IoT-based sensors installed on shipping containers, Pfizer’s ML-powered solution provides critical data about vaccine conditions, allowing the company to make adjustments to their supply chain in real time. As a result, 99.99% of the Pfizer vaccines have been successfully delivered to the intended locations.

Image title: Controlant’s app in action
Data source: controlant.com — Controlant now providing monitoring and Supply Chain Visibility for Pfizer-BioNTech COVID-19 Vaccine distribution and storage

Controlant’s app in action

The team at National Healthcare Service Blood and Transplant (NHSBT) collaborated with London-based software development company Kortical for a smarter demand planning system for blood products, notorious for their short shelf life. Kortical implemented an ML model to predict demand for 40 blood products across 15 distribution hubs. Next, to ensure adequate blood product availability, the NHS wanted to accurately predict blood supply coming from the general British public. This was a complex task because of no-shows and a varying quantity of platelets from donor to donor. By analyzing huge amounts of historic supply data, Kortical managed to build an accurate ML-based tool for predicting platelet supply. As a result of ML implementation, NHSBT achieved a 54% reduction in expired platelets and a 100% reduction in costly ad hoc transport.

Image title: NHSBT supply and demand planning app
Data source: kortical.com — AI supply chain optimisation for platelets to reduce costs

NHSBT supply and demand planning app

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

Similar to the combination of IoT and AI in the architectural domain, the mix 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 guidelines

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.

Increased visibility

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

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

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.

Streamline supply chain management with ML

Want to streamline your logistics operations with machine learning?

We can help

FAQs

How machine learning is used in the supply chain?

Machine learning in the supply chain can be applied for demand forecasting, route optimization, inventory management, workforce and supply chain planning, and vehicle predictive maintenance.

How can machine learning be used in warehouses?

Machine learning can be used in warehouses to better manage inventory, track shipments, predict customer demands, and suggest optimal stocking levels for each item, which can result in reduced costs and improved delivery times.

What other techniques of artificial intelligence are used in logistics?

Other types of artificial intelligence commonly used in logistics include natural language processing, computer vision, and predictive analytics.

Machine learning consulting services & solutions we deliver

Service

Machine learning consulting services & solutions we deliver

Explore our range of machine learning consulting services, along with related technologies, use cases, implementation roadmap, and payoffs.

Logistics software development

Service

Logistics software development

Explore Itransition’s range of logistics software development services, along with top solutions, technologies, and implementation guidelines.

Machine learning in manufacturing: key applications, examples & adoption guidelines

Insights

Machine learning in manufacturing: key applications, examples & adoption guidelines

Learn how machine learning can help manufacturers to improve operational efficiency, discover real-life examples, and learn when and how to implement it.

AI in the automotive industry: use cases, success stories & adoption guidelines

Insights

AI in the automotive industry: use cases, success stories & adoption guidelines

Explore key use cases, payoffs, and real-life examples of AI in the automotive industry, along with common adoption challenges and tips to address them.

Robotic process automation for a global logistics company

Case study

Robotic process automation for a global logistics company

Learn how Itransition automated three labor-intensive logistics processes using robotic process automation, improving the company's operational efficiency.