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.
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.
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.
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.
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.
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.
Container shipping transports 95% of all manufactured goods around the world. It is the industry that underpins the global economy – the quantity of goods carried by containers has risen from around 102 million metric tons in 1980 to about 1.83 billion metric tons in 2017, which is now worth over $4 trillion of products.
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.
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.
2018 was rich for the discussions about logistics innovations. The industry demonstrated its readiness to move forward and try out new technologies in order to solve the long-going challenges of inefficiency, under-utilization of logistics assets, and limited capabilities of the IT infrastructure.
AI and Big Data, Autonomous Transportation, Blockchain and Predictive Logistics were just a few topics covered by Transmetrics Blog in 2018. The following articles proved to be the most engaging in 2018 among our readers.