Select Page

How Predictive Analytics Transforms Logistics and Supply Chain


How Predictive Analytics is Transforming Logistics and Supply Chain

In an industry where time and resources can make or break a company’s bottom line, predictive analytics is no longer just a helpful bonus feature to have in logistics; it’s a necessity. The modern logistics market is more demanding than ever before: Businesses across the supply chain are now expected to easily adjust to shipment patterns, predict customers’ buying behaviors, provide on-time deliveries through the most efficient routes possible, and reduce the risks of cargo inventory errors and miscalculations.

However, the introduction of predictive analytics is helping logistics and supply chain companies meet these increasing demands. In fact, the logistics industry has identified predictive analytics as having the biggest impact on the supply chain this decade. This movement towards anticipatory logistics is already widely accepted among industry decision-makers: A study by the Council of Supply Chain Management Professionals revealed that 93% of shippers and 98% of third-party logistics firms feel like data-driven decision-making is crucial to supply chain activities, and 71% of them believe that big data improves quality and performance.

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 to guide decision-makers and anticipate specific future events. A predictive solution can serve a wide array of different needs but brings the most value when it’s tailored to a particular type of operation and based on a set of rules and restrictions made for that specific operation. These solutions can bring benefits to different levels, from a single warehouse to even an entire supply chain.

In this article we will go over a wide variety of predictive analytics use cases in logistics; deep dive into the predictive solutions developed by such logistics giants as DHL, Maersk, and UPS; and talk about the best predictive analytics tools offered by logistics startups.

Better Supply Chain Visibility

In this new era, both shippers and suppliers have entirely updated ranges of visibility into the shipment lifecycle. Research has shown exactly how predictive analytics is creating new supply chain visibility – helping 3PLs avoid late shipments by monitoring devices; improving the visibility of shipment status and location; avoiding costs related to late or off-schedule shipments; and creating new business opportunities by meeting visibility requirements. 


Now, anyone can prepare weeks or even months in advance to plan inventory and shipments based on customer demand and buying behavior, thus ensuring less waste and more on-time deliveries. By using predictive solutions to generate supply and demand forecasts, companies will be able to make the right operational decisions in a proactive manner. This approach can also allow for the rebalancing of assets across any logistic network at a minimal cost.

Transportation Management Systems (TMS)

Logistics service providers largely depend on transportation management systems to track and manage shipments and lead times. With predictive analytics, many TMS can now predict future disruptions before they happen and help logistics companies manage their operations proactively, rather than reactively. Predictive analytics can also create new visibility into seasonal buying patterns and forecasts to help suppliers make more informed decisions.

Unexpected Conditions

Organizations can better prepare for short-term behavioral changes that affect the supply chain and logistics such as news, weather, shortages, and manufacturing promotions. By utilizing predictive analytics models to detect unexpected conditions, they can better adjust shipments and inventory in response to specific, time-sensitive changes in routes or inventory.

Predictive Maintenance

This is a new 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 even 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 have a huge impact. Carbon dioxide emissions from freight transportation account for 30% of all transportation-related carbon emissions from fuel combustion. But by using predictive analytics solutions in the areas of route optimization, robotics, and anticipatory shipping, real and quantifiable improvements can be made on sustainability in last-mile delivery.

Let’s take a look at how other logistics and supply chain companies are implementing predictive analytics and data to improve operations.

Logistics Giants

Some of the biggest players in the logistics and supply chain industries have been making moves just as big to ensure they’re taking advantage of predictive analytics solutions for their operations. DHL Supply Chain announced in 2018 they would be investing $350 million in deploying emerging technologies, across 340 of their locations in an effort to cut costs and optimize productivity on a mass scale. These emerging technologies include its proprietary end-to-end visibility solution, MySupplyChain, which uses predictive analytics to optimize the supply chain and digitize its operations globally. “We need to continue to invest and to be an industry leader in digitalization. This is important if we want to be an employer of choice,” the company told Forbes.

Danish shipping company Maersk Line operates in over 130 countries and owns more than 600 container vessels. The largest shipping company in the world, Maersk transports goods with an estimated yearly value of $675 billion. Carrying 15% of the world’s annual GDP, this shipping giant is readily adapting to changing times by embracing data and predictive analytics. The company uses these tools to gain the visibility needed to determine which of its ships were being underutilized, or even wasteful. The better repositioning of empty containers will save millions of dollars, according to Jan Voetmann, engagement director at Maersk Analytics.

Amazon’s famous anticipatory shipping predicts when, where and which items will be purchased by customers based on the history of buying habits in a particular area. In other words, when an Amazon client orders a popular product, it will be sent from a nearby hub in a much shorter time frame due to its ensured availability there. This also helps Amazon to accurately predict how many delivery drivers will be needed at a given time, to determine the number of shipments and the most efficient way to store them in a delivery vehicle — leading to a major improvement in last-mile sustainability.

Another great use case of predictive analytics comes from UPS. On an average day, the company handles 19 million packages with 96,000 vehicles on the road — and eliminating just one mile from every driver’s route per day could save $50 million. If the company can save that much money with just one mile eliminated, imagine how much they could impact their bottom line when using predictive analytics! That’s exactly why UPS invests $1 billion annually in technology. To compete with the new consumer demand for almost immediate package delivery, UPS recognizes that it’s crucial to use technology like predictive analytics to make their operations as efficient as possible. UPS’s new Network Planning Tools software aimed to be fully deployed by 2020 in the U.S., uses real-time data and analytics to improve operational efficiency and potentially save the company $100-200 million in costs.

Meanwhile, DB Schenker, a global logistics provider, has also adapted to the modern area with the introduction of its Decision Support Tool. This software, used at DB’s individual warehouse locations since 2016, simulates daily scheduling and processes to optimize operations. DB Schenker has also created an “Industrial Data Space” to allow for secure data exchanges between companies who are using predictive analytics to enable predictive maintenance.

Generally, predictive analytics can enable predictive maintenance by delivering a required replacement to the machine prior to actual maintenance or to alert the company when a part is expected to wear down, which can significantly reduce downtime. However, the prerequisite for predictive maintenance is that the spare parts logistics provider must be involved in the data exchange. This is where DB’s Industrial Data Space platform is coming in — using the platform, companies can enable cost-saving predictive maintenance without worrying about the safety of their data.

How Transmetrics Leverages Predictive Analytics

Predictive analytics is also at the core of Transmetrics, a logtech company which offers AI-driven predictive planning tools exclusively for logistics service providers. Taking into account all customer requirements and business constraints, Transmetrics’ AI algorithms recognize patterns that can help to predict fluctuations in transport demand. With more accurate logistics demand forecast, cargo transport companies have a better vision of their business and can further use Transmetrics tools to plan their operations more easily and efficiently.

For instance, by using proprietary predictive optimization algorithms, Transmetrics helped NileDutch, one of the leading shipping companies focused on the Africa region, to significantly lower the total costs for managing empty containers and reduce its container fleet size.

A New Future for Logistics and Supply Chain

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.

According to Gartner’s 2017 CSCO survey, over three-quarters of chief supply chain officers admit that their digital transformation projects are still not aligned. So, what can businesses do to get started with implementing predictive analytics solutions? In some cases, the first step could be to hire or appoint a Chief Digital Officer to guide the digital transformation of your business and build an information-driven supply chain. Positions such as digital officers or data scientists who can work with predictive analytics are ever-increasing in demand, but not every business can afford to scout and hire such talent. Instead, the second option is to work with a well-established technology provider who can provide predictive analytics products and services tailored for logistics.

Once you have the right people in place to help you gain visibility into your supply chain, you’ll need to ensure all of your data is cleaned up and qualified to be used by machine learning algorithms. A Deloitte study in 2017 found that data quality was the main barrier to the effective application of digital technology in logistics organizations, for almost half of chief procurement officers surveyed. With this in mind, it’s important to try to standardize processes for recording data in your organization and ensure the data is cleansed before using it for your predictive analytics solution.

At the end of the day, investing in a predictive analytics solution is no longer an option – it has become a necessity to maintain competitiveness with all of the players mentioned above (and many more) who already benefit from emerging predictive technologies. Both consumers and organizations continue raising expectations to receive their shipments faster and cheaper. Therefore, those logistics and supply chain businesses that don’t invest in predictive technologies within their operations, might simply not survive in today’s demanding market.


EU Logo

The Transmetrics project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 945610.