Forbes Insights research shows that 65% of senior transportation-focused executives believe logistics, supply chain and transportation processes are in the midst of a renaissance—an era of profound transformation. But of the most visible forces of change, perhaps none carries more potential for innovation and even disruption than the evolution of artificial intelligence (AI), machine learning (ML) and related technologies.
Leading companies are already harnessing artificial intelligence and machine learning to inform and fine-tune core strategies, such as warehouse locations, as well as to enhance real-time decision making related to issues like availability, costs, inventories, carriers, vehicles and personnel. While these new technologies bring about truckloads of data, the transportation industry has been capturing data for years. Decades ago, trucking, rail and sea cargo began being tracked by satellite via telematics, and versions of electronic driver logs have been around for nearly 20 years. The industry has also for many years now applied high-level decision theory to optimize the costs and transit times associated with high-value vehicles and often even higher-value cargoes.
AI, ML and associated technologies promise to enable leaders to focus IoT and myriad other data feeds on achieving greater optimization and responsiveness across the whole of their logistics, supply chain and transportation footprint. Consider examples such as:
Augmented real-time decision making: Logistics teams often handle a wide range of complex but repeatable tasks that require large amounts of input data in order to make the best choices. Optimal carrier selection, for example, means combing through thousands of possible candidates, routes and schedules. In practice, workers often require 10 minutes or more to gather the needed information. But with AI and associated tools, supply chain professionals can automate the analysis and narrow their selections to just two or three within a matter of seconds. Human intuition then closes the deal.
1. Predictive analysis: When will customers be ready to order? Of course the sales team wants to know, but this is also vital information for logistics, supply chain and transportation planning—an example where an AI platform could collaborate closely with sales and marketing. Looking specifically at transportation needs, telematics/IoT can help determine when a vehicle might need preventative maintenance, thus avoiding breakdowns and reducing the risk of failing to meet customer needs and expectations.
For example, DHL analyzes 58 different parameters of internal data to create a machine learning model for air freight. Rather than subjective guesswork, this method allows freight forwarders to predict if the average daily transit time is expected to rise or fall up to a week in advance. Furthermore, this solution can identify other factors which could influence shipment delays like climate and operational variables. Such insights are incredibly valuable in a sector like air freight, where it accounts for only 1 percent of global trade in terms of tonnage but 35 percent in terms of value.
In general, the predictive analytics solutions in logistics and supply chain are on the rise. However, while the technology is available, there is still a scarcity of people who can make sense out of the incomplete and low-quality data, the case commonly presented in the logistics industry.
2. Strategic optimization: Where, when and how? Leaders in these disciplines are learning how to gather and comb information to make the best decisions regarding the deployment of not only inventories but also the transportation assets needed to connect all the dots from origin to customer location. Where are the drivers? Where are the vehicles? What commitments have been made? Where are the customers?
These and related variables can be fed to AI and machine learning engines that can crunch the data and then present a range of scenarios for optimization. With sophisticated tools that continuously learn and improve, industry professionals are able to make better, up-to-the-minute decisions as well as more informed longer-term, strategic choices, such as warehouse locations, fleet size/specifications, etc.
3. Robotics: No conversation about Artificial Intelligence is complete without mentioning the field of robotics. While they may sound like a futuristic concept, they are already embedded inside the supply chain. Tractica Research estimates that the worldwide sales of warehousing and logistics robots will reach $22.4 billion by the end of 2021. Robots are locating, tracking, and moving inventory inside warehouses, they are conveying and sorting oversized packages at ground distribution hubs.
A good example of supply chain robotics is the work of startup Fizyr. The Dutch deep tech company is in the business of automating logistics globally and putting robots to work. Fizyr incorporates their deep learning algorithms into robotics and brings autonomous decision-making to processes that involve identifying, analyzing, counting, picking and manipulating goods. Picking is one of the most labor-intensive parts of the logistic process, so Fizyr has crafted a solution which allows the robot to identify package-type – in less than less than 0.2 seconds – and physically move the item to the desired location.
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But these and related examples are merely the tip of the iceberg. No doubt as AI and machine learning become more widely used, practitioners will find an ever-expanding array of use cases—some evolutionary and others potentially disruptive.