Top 5 Benefits of Integrating AI in Logistics
In the current landscape, it is no secret that Artificial Intelligence (AI) has deeply seeded itself into our society. From finishing our sentences through automated suggestions to drone-delivered packages from Amazon, all the way to automated groceries—AI has demonstrated its extremely innovative potential for helping to maximize efficiency in many different areas of our lives.
AI’s innovations have advanced the world of business across many industries as well. For the supply and demand chain and logistics operations, AI has been a game-changer. In fact, a report by McKinsey predicts that AI will create an entirely new “logistics paradigm” by 2030 as it continues to outperform humans at repetitive but mission-critical tasks. With unforeseen challenges in shipping currently taking place, solutions for advancing logistics technologies are more and more needed.
Let’s look at the top 5 benefits of integrating Artificial Intelligence in Logistics.
Enriching Data Quality
First and foremost, the primary benefit of AI’s application in logistics will always be the quality of the data it helps to ascertain. Capacities within AI, such as Natural Language Processing (NLP) and Machine Learning (ML), help accurately obtain and organize information that pours into logistics companies at insurmountable volumes every day. NLP can understand terms, phrases, and even jargon that is commonly repeated, and ML can draw connections between these key points—creating context and learning as these words evolve.
Better, faster global connections allow AI to be more accurate than ever before, and when applied to logistics, it will enable optimal data usage within shipping. Avoiding risks and creating more efficient solutions, AI maximizes resources and slashes costs. With synergy between man and machine, enriched data can help tell a company where they need the precise amount of people and assets when they need them, and for how long—even in multiple locations.
With improved information authenticity and consolidation, more insight is gained, and logistics companies can make needed refinements across the board for day-to-day operations in a high-demand era.
Improved Predictive Analytics
In the past, logistics planners assessed the state of operations manually—mostly by hand on pen and paper. Even with years of experience through trial and error and shrewd gut feeling, there is no way that logistics professionals can ensure optimization alone in today’s day and age. Sorting through data analytics, capacity estimation, and network analyses makes it nearly impossible for the human brain to know how to optimally transport a single shipment from point A to point B at any point in time. Not to mention that these processes are very time-intensive, and the variance of every component only escalates in peak times or as a company grows larger.
That is where a solution powered by AI and predictive analytics comes in. By compiling more accurate data, AI can perform all of these logistics procedures, while adding even more relevant external factors to its information extrapolation—better estimating the upcoming demand and helping logistics technology companies to embrace proactivity.
An example of this is seen through the international transportation leader DHL, whose platform monitors more than eight million online and social media posts to identify potential supply chain problems. Thanks to ML and NLP, the system extracts information from online conversations and identifies potential material shortages, access issues, and supplier statuses. With an ability to accurately see what’s coming, logistics companies can remain ahead of the demand curve—helping them glean a competitive edge in the transportation industry and cut down on unnecessary costs.
Better Logistics Forecasting
It was projected that AI would increase productivity in logistics by more than 40% by 2035. Efficient output in the supply and demand chain is especially make-or-break now, with E-commerce exploding—up 33% to $792 billion in 2021 alone.
Today, having a crystal ball capacity to forecast where assets are needed is a huge advantage. AI can be linked into a multitude of databases along the transportation chain, procuring information on all available components and opportunities, and providing a roadmap to enhanced feasibility.
AI-driven software can provide demand forecasting per asset type and location up to 12 weeks in advance, giving logistics companies insight to route rail and vehicles more efficiently, optimize storage on ocean shipments, and potentially avoid any bottlenecks at ports or areas of common production delays.
This is vital during an increasingly chaotic era in the transportation industry, as seen via the historic stoppage in the ports of Los Angeles and Long Beach. Through satellite images, it was seen that the shortages of truckers, shipping containers, and other equipment have been causing ships to wait offshore for their turn in line at the port—reverberating further delays up the transport line.
Strategic Asset Positioning
With it costing more than $20,000 to ship a standard 40-foot container from China to the east coast of the US in 2021, up from less than $3,000 only two years ago, it is imperative that logistics companies fortify all of their available assets.
An AI analysis can increase visibility into fleet performance, help planners and logistics professionals strategically position their assets and safeguard against unnecessary risk. AI algorithms support companies in the utilization of predictive capacity matching—decreasing the nonessential shipment of empty containers and trailers and even mitigating the number of vehicles on the road.
By decreasing the total number of vehicles needed for transport and directing them to the locations where the demand is expected, asset positioning can ensure efficiency while also achieving significantly lower operational costs. This helps to ensure that a shipping company always has the correct number of assets at the optimal location, at all times.
Optimized Linehaul and Last-Mile Planning
In 2021, there was a 24.3% increase in the parcel and last-mile segment of transportation from growth in E-commerce and home delivery. Rather than subscribing to subjective guesswork, logistics companies need to apply AI to linehaul planning in order to streamline and enhance overall operations.
Oftentimes in shipping, transport networks are complex, and figuring out the supply chain never is a singular equation. AI leverages the potential of shipment data, allowing companies to derive informative insights and better optimize their fleets. These innovations can take all user requirements and business constraints into account and provide the optimal linehaul capacity to help automate and schedule logistics plans for superior functionality.
With the capacity to predict forthcoming volumes on the most granular level, every leg of a shipment’s journey can be solidified. This is especially prevalent in last-mile planning, which brings the currently dwindling element of service level into play.
By understanding all the external factors that might occur along the arduous line of a shipment, AI can help logistics technology companies plan around unnecessary roadblocks and remain efficient—especially in the last mile of transportation. Unlocking informed data for linehaul planning through AI will set companies apart from their competitors because they can prove in real time that they can both literally and figuratively deliver.
How to leverage AI for Logistics?
Recent years have been game-changing for logistics technology companies. AI is due to grow from 12% to 60% within logistics by 2026—showing how important it is for logistics players to get on board with these innovations.
In order to stay competitive and leverage physical assets and infrastructure to the fullest logistics businesses need to take their digital organization as the first priority. Watch the following webinar to learn:
- Why are AI and data analytics the centerpiece of the modern logistics organization?
- How can Artificial Intelligence empower human jobs instead of replacing them?
- Where can exactly your organization leverage from adopting state-of-the-art AI?
- What are the first steps to introduce this technology to your business?