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Predictive Analytics in Logistics: Applications & Use Cases


Predictive Analytics in Logistics Cover Image Blog Article

Predictive analytics is a logistics necessity in an industry where time and resource allocation can make or break a company’s bottom line. The modern logistics market is more demanding than ever: Businesses across the supply chain today must adjust to shipment patterns easily, predict customers’ buying behaviors, and provide on-time deliveries — through the most efficient routes possible — all while reducing the risks of cargo inventory errors and miscalculations.

However, predictive analytics is helping logistics and supply chain companies meet these increasing demands: A study by the Council of Supply Chain Management Professionals revealed that 96% of 3PLs and 86% of shippers have migrated to the cloud while 80% of 3PLs and 77% of shippers are investing in tools like predictive analytics that maximize Internet of Things (IoT) data.

So what exactly is predictive analytics, and why has it become so important in logistics and supply chain? 

Predictive models use historical and transactional data to identify patterns for risks and opportunities within a particular set of conditions, which helps guide decision-makers and anticipate specific events. A predictive solution serves multiple needs but brings the most value when tailored with rules and restrictions for each specific operation. These solutions can benefit from loading a single food truck at full capacity to successfully updating entire supply chains to operate just in time (JIT).

Let’s review predictive analytics use cases in logistics, the predictive solutions developed by industry giants such as DHL, Maersk, and UPS, and the best predictive analytics tools logistics startups offer.

Better Supply Chain Visibility

Visibility into the shipment lifecycle has vastly evolved. McKinsey reports visibility as the first of three critical “resilient supply chain planning” ingredients. Improving shipment status and location visibility, by monitoring devices like truck telematics, helps 3PLs avoid late or off-schedule shipments and related costs while creating new business opportunities by meeting service level agreements (SLAs). 


With predictive analytics, anyone can prepare weeks or months to plan inventory and shipments. The toolkit contains historical data on previous shipments, external factors that influence trends and seasonality — and demand forecasting software

Companies can make the right operational decisions by using predictive solutions to generate supply and demand forecasts based on historical and real-time data. This approach allows for the rebalancing of assets across any logistic network at a minimal cost, thus ensuring less waste and more on-time deliveries.

Transportation Management Systems (TMS)

Logistics service providers depend on TMS to track and manage shipments and lead times. However, predictive analytics helps logistics providers take a proactive rather than reactive approach. A predictive analytics-powered TMS can forecast future disruptions before they happen so logistics companies can manage their operations seamlessly, eliminating bottlenecks. 

Predictive analytics can also create new visibility into seasonal buying patterns and forecasts to help suppliers make more informed decisions.

Predictive Maintenance

This is a cost-effective solution gained by implementing predictive AI algorithms. Suppliers and logistics companies can detect failure patterns and anomalies, learn from those patterns, and then predict future failures of machine components so that they can be replaced before they fail. This is improving the supply chain’s efficiency and maximizing equipment uptime.

Last-Mile Delivery

The ever-troublesome last-mile delivery problem is another area in which predictive analytics can considerably impact — Transport emissions make up over 27% of the EU’s carbon footprint; and in the US, 35% of heavy truck miles are driven empty. However, by using predictive analytics solutions in route optimization, and anticipatory shipping, logistics teams can make real and quantifiable improvements to boost sustainability.

Here’s how some of the major logistics and supply chain companies are implementing predictive analytics and data to improve operations.

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Real-life Use Cases of Predictive Analytics in Logistics

Some of the biggest logistics and supply chain players have been making moves to ensure they’re taking advantage of predictive analytics solutions in their operations. 

DHL invested $350 million to digitize its operations and built an end-to-end visibility solution, MySupplyChain, which uses predictive analytics to optimize the supply chain and digitize its operations globally. The investment continues in 2024 as the logistics leader creates a central nervous system for its operations, optimizing resource allocation, automating workflows, and dynamically adapting to real-time changes in demand. 

In Q2, revenue grew to $21.7 billion for the largest shipping company in the world, Maersk. Operating 15.3% of the global container ship fleet, this shipping giant readily adapts to changing times by embracing data and predictive analytics. It uses automation and IoT enablement to deliver unprecedented visibility, predictability, and control in its predictive maintenance tools and business intelligence. The company uses these tools to gain the visibility needed to determine which of its ships needs to be more utilized. The better repositioning of empty containers will save Maersk millions of dollars.

Amazon’s famous anticipatory shipping predicts when, where, and which items customers will purchase based on buying habit history in particular areas. In other words, when an Amazon client orders a popular product, it will be sent from a nearby hub in a much shorter time due to its ensured availability. This also helps Amazon to accurately predict how many delivery drivers will be needed at a given time, determine the number of shipments, and choose the most efficient delivery vehicle storage, leading to significant improvement in last-mile sustainability.Another excellent predictive analytics use case comes from UPS. To compete with the new consumer demand for almost immediate package delivery, UPS enhanced its award-winning On-Road Integrated Optimization and Navigation (ORION) platform with Dynamic Optimization, which recalculates individual package delivery routes throughout the day as traffic conditions, pickup commitments, and delivery orders change. The platform uses real-time data and analytics to improve operational efficiency and saves the company $100-200 million in costs.

How Transmetrics Leverages Predictive Analytics in Logistics

Transmetrics has over 10 years of experience as a supply chain and logistics data expert, supporting organizations such as Kuehne + Nagel, DPD, DHL, Matson, and TIP Trailers in optimizing operations, increasing efficiency, and gaining a competitive edge. 

Our predictive analytics-based platform combines historical data with relevant external factors to build highly accurate and reliable forecasts. This enables a proactive response to dynamic market conditions and optimization of operations. Transmetrics’ solutions help solve challenges related to capacity, volatility, and margins by leveraging predictive planning and becoming truly data-driven.

For instance, using proprietary predictive optimization algorithms, Transmetrics helped:

  • NileDutch (acquired by Hapag Lloyd): With a fleet of over 30 chartered container vessels and a capacity of 80,000 TEU, NileDutch was one of the leading shipping companies focused on the Africa region. However, empty container storage costs and their maintenance were taking up over 12% of operating costs. The company was able to significantly lower total empty container management costs and reduce its container fleet size by increasing cost visibility and operating with predictive analytics.
  • Speedy (DPD Bulgaria): Managing 700+ vehicles and handling 16+ million parcels annually, Speedy controls one-third of the courier/express market in the country. Transmetrics supported the company in gaining load factor visibility, preparing for holiday seasons ahead of time, and accurately assigning costs for nonstandard-sized items.

A New (and Predictable) Future for Logistics

It’s clear that for logistics and the supply chain, predictive analytics is the key to opening new doors of cost-savings and efficiency. Additionally, these solutions are transforming the industry from human-driven to data-driven decision-making, a huge factor in the digitization of the industry as a whole.

So, what can businesses do to get started with implementing predictive analytics solutions? The first port of call is to clarify your objectives for using predictive analytics and the scope of work. This includes reviewing the state and location of data sources required to train your analytics model — it can help to appoint a Chief Digital Officer, hire data scientists, or work with well-established logistics technology providers to get the best results.

Once you have the right people to help you gain visibility into your supply chain, you’ll need to ensure all your data is cleaned up and qualified for use by machine learning algorithms. Data quality is the main barrier to effective digital technology. It helps to standardize data recording processes to make collaborating and extracting insights from various data sources straightforward.

Ultimately, logistics giants and consumers continue raising expectations for faster and cheaper shipments. Predictive analytics is essential to keep logistics and supply chain businesses surviving in today’s demanding market. The good news is that it is already available for any logistics organization.