Top 10 Supply Chain and Logistics Technology Trends in 2020

logistics technology trends

Increasing technology innovations are making big waves across industries, and logistics and the supply chain may be one of the most impacted sectors. Notorious for its heavy use of manual processes and large amounts of data stored in different ways and in different places, the logistics industry has perhaps the most to gain from implementing new technologies and following the most innovative Supply Chain and Logistics technology trends.

Recent years have seen massive advancement for the logistics industry in areas like artificial and augmented intelligence, advanced analytics, and automation, to name just a few. These technologies have evolved faster than ever while startups with even newer solutions and innovations continue popping up at a rapid rate. But attached to these innovations are new expectations and standards, forcing logistics companies to either adapt or fall behind. Much pressure comes from customers in the form of individuals and enterprises, all of who are demanding their products or services come faster and cheaper than ever before.

But advancements in technologies aren’t the only big changes influencing the industry. From new shipping regulations to growing global tensions and trade wars, and a predicted economic recession, logistics companies will need to be alert and prepared for 2020. For example, carriers are already working hard to meet the global 0.5% Sulphur cap, which goes into effect on January 1, 2020. It would affect up to 70,000 ships, according to IMO estimates, and could lead to a 20-30% increase in total fuel costs, which would ultimately be passed on to customers.

Global trade wars and tensions like that of China and the U.S. have continued affecting logistics operations. In 2018, trade tariffs affected $34 billion worth of Chinese products imported into the U.S., with China also taking costly countermeasures on U.S. imports. The European economy has also been going into a downturn as Brexit concerns continue weighing heavily on European countries, and the U.S. economy has also been growing weaker. All of these issues are signaling a possible global recession in 2020, which would make things much more difficult for logistics companies.

There is much to consider as 2020 quickly approaches. Companies within the logistics and supply chain sphere must continue getting ready for all of these bigger changes with innovations. From digital twins to blockchain to real-time supply chain visibility, Transmetrics has identified the Top 10 important logistics technology trends your company should be keeping an eye on in 2020: 

Two Approaches to Tackle the Repositioning of Empty Containers

Repositioning of Empty Containers

The global economy, as it functions today, has become completely dependent on container shipping to succeed. 95% of all manufactured goods in the world arrive at their destinations courtesy of this massive industry. But despite the growing reliance on container shipping, there’s one industry-wide problem that remains to be solved: the repositioning of empty containers. Unfortunately, one out of three containers being moved is empty — estimating around 60 million empty container moves per year at an annual cost of $20 billion to the industry. Apart from the enormous profit wastes, these empty containers are also come at a big cost to the environment, due to the extra fuel consumption, congestion, and shipping emissions.

Logistics Demand Forecasting: The Benefits of AI & How to Implement It

Logistics Demand Forecasting

For many logistics companies, the road to digital transformation and AI implementation is not an easy one. In an industry that has largely been run by pen, paper, and phone for decades, the transition to using modern software and tools can seem challenging and even overwhelming. What many of these companies don’t realize, however, is that they are creating an even bigger challenge for themselves by not implementing some of this cutting-edge technology into their operations. 

Companies who don’t use logistics demand forecasting find that it makes the operational planning of assets very difficult. The multi-faceted problem requires businesses to consider how many assets they need, whether or not those assets are positioned correctly at any given moment in time, and how to best plan the technical breaks.

This is a very complicated problem to solve, as it requires a large volume of interdependent information. Luckily, logistics companies already generate a tremendous amount of data internally and have access to even more data from public sources. Nevertheless, the challenge remains that only a few tools currently exist which allow companies to synthesize all of this information and enable data-driven decision-making in conjunction with the experience and instincts of their managers.

But with the help of modern predictive optimization tools, logistics companies can shift to an anticipatory strategy based on accurate demand forecasting, and thereby achieve far greater operational efficiency. Let’s take a look at what exactly logistics demand forecasting does, how it works and its many benefits for logistics companies.

Augmented Intelligence for Logistics Planning

Augmented Intelligence for Logistics

This article is based on the presentation by Asparuh Koev, Founder and CEO at Transmetrics, at the “Logistics meets Innovation” conference. The topic of the conference was “AI in Logistics: from Theory to Practice” and you can read the full overview of the event here: “AI in Logistics: from Theory to Practice” – Transmetrics’ Conference Summary.

Artificial Intelligence (AI) is already a widespread term in logistics, thanks to the implementation of exciting AI technologies like automated warehouses by Amazon, autonomous trucks by Einride, drone deliveries by Zipline, last-mile delivery robots by Starship, and more. These are the types of technologies that can potentially replace some manual jobs that don’t require complex skills (e.g. warehouse sorting, last-mile delivery person, truck driver, etc.).

However, the situation is different when Artificial Intelligence is used to deal with high-skilled positions like logistics planners. That’s where Augmented Intelligence enters the scene. By combining human intelligence with processes automated by AI, companies can save time, reduce operating expenses, and eliminate manual errors, while human employees can focus more on analytical and complex duties. In this article, we will uncover what exactly Augmented Intelligence is and how it can enhance logistics planning capabilities.

How Predictive Analytics Is Transforming Logistics and Supply Chain

Predictive Analytics

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 operations and based on a set of rules and restrictions made for that specific operation. These solutions can bring benefit 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.

“AI in Logistics: from Theory to Practice” – Transmetrics’ Conference Summary

“AI in Logistics: from Theory to Practice” – Transmetrics’ Conference Summary

One of the most exciting aspects of Artificial Intelligence (AI) in logistics is that there is a huge number of applications impacting the industry ranging from data cleansing, demand forecasting and price optimization to autonomous trucks, last-mile delivery robots, and more.

With the ongoing evolution in supply chain digitization, more companies are already trying to implement AI-driven technologies into their operations in order to become more efficient and cost-effective. A cross-industry study on AI adoption by McKinsey & Co. found that early adopters with a proactive AI strategy in the transportation and logistics sector enjoyed higher profit margins.

That is why Transmetrics decided to make the focus of this year’s conference on exploring both the basics of AI, machine learning and data science, as well as successful use-cases to show how some of the logistics industry innovators are getting benefits from AI already today. The fourth Logistics meets Innovation conference took place in Brussels on May 28th, 2019 on the Vlerick Business School campus. The event gathered 50+ senior executives from logistics and supply chain companies including Amazon, DHL, ECS, Kuehne + Nagel, LKW Walter, Lufthansa Cargo, NileDutch, Panalpina, Pfizer, Novartis, and many others.

Artificial Intelligence in Logistics: Two Approaches to Improve Planning

Artificial Intelligence in Logistics: Two Approaches to Improve Planning

The real-world use cases of Artificial Intelligence (AI) are expanding rapidly: from e-commerce to healthcare to security and fraud detection, AI seems to have something to offer virtually for every industry. It’s a bit surprising that AI’s potential hasn’t become more realized in core logistics operations, but that time is fast approaching.

To best leverage AI in logistics operations, decision-makers need to understand the basics. Best to start with the two AI approaches with the potential to bring the biggest impact on logistics.

Digitalizing Post and Parcel: A Peek Into the Future

Post and Parcel

Wondering what’s ahead in the Post and Parcel industry in the next few years? Autonomous Vehicles delivering packages… robots handling warehouse tasks… IoT making the operations smoother… data analytics for managing contingencies… It all may sound a bit futuristic, but companies are already pushing the limits by bringing these concepts closer to reality.

The rise of the eCommerce and the growing demand for returns are driving exponential growth in the Post and Parcel industry. Trying to secure a niche in the market while adapting to ever-changing consumer behavior may seem overwhelming. But companies have a potent toolkit at their disposal: namely, digital technologies. The way Post and Parcel companies use artificial intelligence, IoT, robots, and other innovations will determine their relevance in the future.

How Artificial Intelligence Can Improve Sustainability in Last-Mile Delivery

Sustainability in Last-Mile Delivery

Modern technology and logistics infrastructure have made sending a package anywhere in the world easier than ever imagined. The most visible part of logistics – the last mile – is as important as ever, and yet it continues to be complicated.

In major cities, commonplace obstacles like road closures, construction, heavy traffic, and even parking restrictions make the last mile remarkably time- and energy-consuming. For example, London recently announced the expansion of its ultra-low emission zone, making the deployment of last-mile delivery vehicles even more complicated for logistics companies. On the other hand, in more remote areas, the infrastructure (or lack thereof) as well as a low volume of deliveries often render the logistics excessively inefficient.