The supply chain logistics industry continues its march into digitization as cloud-based applications for all kinds of logistics management functions enjoy increasing adoption. The current hot topic in supply chain logistics is the use of artificial intelligence (AI) and machine learning (ML). These powerful new technologies are, for some, a source of confusion and mystery. If you’re not clear on what AI and ML can do for your operation, the good news is it's rather easy to understand what this technology does, and even easier to see why there’s no time to waste in getting started applying it to your business.  

Five to ten years ago, supply chain digitization was an initiative being embraced primarily by only the largest organizations. Today however, the imperative has become mission-critical to shippers, carriers and brokers of all sizes as adoption of cloud solutions like transportation management systems (TMS), visibility apps, yard management systems and many other logistics tech tools have become the norm. With so many integrated systems capturing so much operational data, the natural next step in the evolution of logistics technology involves the utilization of AI and ML, to deliver dramatic improvements to efficient supply chain planning and execution. Yet, for many operations, AI and ML are still regarded as a future-state goal to be addressed at some unspecified time.  

The plain truth is simple. The technology has grown mature. The expectations of consumers and the markets serving them have grown as well.   The benefits of embracing logistics technologies (including AI and ML) have been proven by the early adopters. All that remains in the way of embracing and adopting this technology among supply chain logistics operations is the perception of complexity and the overblown notion of risk. To combat this barrier to technological advancement, here’s some straight talk on AI technology – what it means, how it works and how you can leverage it, today, to drive superior business results.  

First, let’s decode and demystify AI. What does it really mean?

According to Merriam-Webster, AI is commonly defined as “the capability of a machine to imitate intelligent human behavior.” In fact, the machine mind is far better equipped than the human brain to identify hidden patterns in the massive data sets generated by applications already widely used by the freight industry. It would take a human logistics planner months to review the vast data sets that AI can do in mere seconds. Within the logistics organizations already harnessing AI, the machine is already actively mining the data and providing insights to its human counterparts who apply the business intelligence in ways that significantly streamline operations. Recent research from Accenture reveals AI is projected to increase productivity by more than 40% by 2035.

When it comes to embracing AI, much of the hesitancy among transportation logistics operators stems from the belief that it is too advanced to be easily engaged. The reality is, AI is no more dependent on highly specialized tech experts than any other technology being used in logistics management operations. Organizations are already driving value through AI to customers, business partners, salespeople and others. Accelerated investment in AI is making this powerful tech increasingly accessible, and a variety of user groups within the supply chain industry are successfully adopting AI programs to propel their companies forward. The growth of AI-powered applications is delivering the power of AI to logistics organizations without any need to keep an expert on staff.

Shipping and logistics professionals are overcoming perennial planning and execution challenges using AI to address things like insufficient lead time, opaque transit time, poor spend visibility and negative impact on the environment due to inefficient equipment utilization. For example, AI is applied to historical shipment data to identify lanes where there is a higher-than-normal occurrence of deadhead miles/empty loads. It is useful at deriving accurate, predicted/estimated arrival times, using historic transit time data captured in TMS. AI can be applied to identify locations where there is a chronic pattern of loading/unloading delays to help improve throughput at distribution centers (DC) and warehouses.  

Yet, AI can be applied to reviewing a near limitless array of data points stored in a TMS and associated systems. For trucking companies, AI can find patterns in driver safety and on-time performance to improve fleet safety. It can identify the consignees with the fewest invoice disputes as well as those with the timeliest payment histories. For shippers, AI in the TMS can identify carriers with the highest tender acceptance figures and lowest, unplanned accessorial charges. The possibilities for uncovering ways to streamline and strengthen operations are endless. 

Shippers, carriers and brokerages who’ve adopted this technology have dramatically improved logistics operations. According to research from InData Labs, early adopters of AI in transportation and logistics already enjoy profit margins greater than 5%. And all of them would confirm they were able to achieve these benefits using AI-powered applications – not by adding AI experts to their staff.

This all prompts the logical question, “what is involved in implementing an AI tech strategy for my business?” The first and most important step in embracing AI is to set goals and expectations regarding what logistics problems your operation most needs to correct. When creating a plan to implement AI, address both short- and long-term objectives, and it will be easier to build a strategic deployment that yields tangible results.

Next, consider the secondary benefits that flow from the implementation of AI. AI can manage volumes of data and extract critical information across multiple logistics functions – from sales/demand planning to logistics planning and execution, through billing/settlement.  The significant time invested into developing insights and actionable plans by your human workforce is, instead, handled by the AI. This frees up a significant volume of man-hours to be redirected into revenue building activities instead which boosts bottom line earnings. 

To keep things simple, AI technologies imitate humans to exceed efficiency when it comes to data-driven decisions. What matters most for trucking companies and shippers’ supply chain operations is that AI enables less spending, more freight throughput and better service to customers. AI and ML help optimize operations, increase profits, and will continue to be critical to competitive advantage for years to come. 

Now is the time for the logistics industry to embrace AI and engage trusted tech partners who understand how to apply AI to supply chain logistics, or risk being left behind by those that do.