The Cutting Edge of Innovation
Today, it’s hard to remember when we couldn’t get one-click, same-day delivery for almost any product.
Modern consumer expectations (e.g., superior services, same-day delivery, lowest costs) have pushed the logistics and transportation (LT) industry to the brink. To survive, companies have worked to maximize efficiency—and the never-ending succession of new technology means that this process is never complete.
However, efficiency comes at a human cost. According to the U.S. Bureau of Labor Statistics, warehousing and transportation account for the second-highest number of fatal workplace accidents across industries. Despite the efforts of logistics and transportation services providers (LTSPs) to safeguard their workers, mistakes are inevitable—and often deadly.
However, there is a solution.
Over the last several decades, technology has emerged that can protect workers without sacrificing efficiency. In 2021, this technology—artificial intelligence (AI)—is helping LTSPs keep their workers safe from harm, reach their desired outcomes, and resolve their pain points—simultaneously and more sustainably than ever before.
The prevalence of repetitive and data-heavy tasks within the LT industry makes it well-suited for AI adoption. This article covers how AI will help craft a better future not only for LTSPs but for everyone else, too.
LTSPs Desired Outcomes
According to research by McKinsey & Company, LTSPs primarily rely on two characteristics to attract clients and remain competitive:
1. Superior services. Modern consumers expect rapid (even same-day) shipping. They also expect omnichannel distribution: the option to purchase, receive, and return orders through multiple channels through one platform.
For small to mid-sized LTSPs (for whom efficiency through scale is unfeasible), superior services provide a competitive edge. They distinguish innovative LTSPs with value-adding offerings such as package tracking.
2. Efficiency through scale. Expansion benefits LTSPs by increasing their efficiency. Large-scale warehouses and transportation fleets can be automated for high-ROI so that efficiency increases with scale.
However, scaling up creates two acute pain points for LTSPs:
- A growing shortage of labor. Today, not enough individuals seek employment as warehouse workers or truck drivers, and these sectors suffer from high attrition rates and challenging working conditions.
- Difficulty on-and-off-loading labor to meet demand. The e-commerce market, which now dominates the LT industry, is extremely volatile. Demand spikes sharply during the holiday season (Christmas, Black Friday, et cetera)—when extra workers are most difficult to pick up.
Fortunately, these pain points are solvable with AI, as we discuss below.
So far, we’ve considered two specific pain points that affect large-scale LTSPs: (1) a growing shortage of labor and (2) difficulty on-and-off-loading labor. For the sake of brevity, we explain the other top LTSP pain points in terms of the following categories:
1. Cost. As we noted above, e-commerce now dominates the LT industry. Indeed, e-commerce’s share of total retail sales in the U.S. has risen steadily, from less than 6 percent in 2013 to almost 15 percent in 2020. This number will continue to climb as many brick-and-mortar retailers used the “time off” during the pandemic to create e-commerce sites for their stores to satisfy customer demand.
The COVID-19 pandemic has supercharged e-commerce, with virus-fearing consumers turning to the Internet to purchase their necessities. These consumers will further accelerate e-commerce’s takeover of retail and, by extension, e-commerce’s takeover of the LT industry.
However, this power-shift has dire implications for LT. the largest e-commerce companies (e.g., Amazon) have significant buying power, and many have developed their own logistics capabilities. These industry giants dominate the most profitable areas of the LT market. In the process, they have strong-armed smaller LTSPs into occupying lower-margin spaces.
To survive in these low-margin areas, LTSPs must carefully manage costs. They must predict and circumvent financial issues and process inefficiencies.
2. Labor. For decades, there has been a growing shortage of labor in LT.
The COVID-19 pandemic has temporarily made labor more accessible for LTSPs, as a growing number of unemployed workers rush to re-enter the labor force. However, this boom will not last. The American Trucking Associations estimates that the truck-driver shortage, currently 63,000, could increase to 174,000 by 2026. Similar shortfall projections show warehouse labor also in jeopardy.
Of course, LTSPs face various specific pain points, from customer service challenges to fuel costs. However, these vary between LTSPs—no two companies face the same issues, and, therefore, no one solution will fit all companies. For this reason, we approached pain points in terms of broad categories—and will do the same for their AI solutions.
1. Automation. Warehouse automation is already a reality in multiple forms, and trucking automation isn’t far behind. McKinsey estimates that trucking will be driverless by 2027, and some warehouses are already automated.
The benefits of automation are clear: robots can work faster, longer, and more consistently than human workers. They aren’t subject to workplace regulations, such as those requiring truckers to rest at regular intervals.
Additionally, robots provide a level of labor security that humans cannot. LTSPs that fully automate their warehouses can rest assured that they won’t need to onboard new workers en-masse during peak seasons. And, as a bonus, many automation solutions can be switched on and off at will. This way, they can be deactivated when demand is low to save on costs.
However, the one thing robots can’t do is work without assistance and supervision. AI can’t replicate uniquely human assets such as creativity, common sense, and interpersonal skills; and workers in the LT industry should welcome robots as a tool that will make their jobs easier, not take them away.
2. Data aggregation and analysis. Data, when properly utilized, provides incredible value to LTSPs. Data aggregation and analysis—in addition to enabling customized services such as differentiated packing, effective returns management, and package tracking—enables predictive analytics.
“Predictive analytics” use data, statistical algorithms, and machine learning (AI) to identify the likelihood of future outcomes based on historical data. For example, predictive analytics can forecast what products a consumer will want, how much, and when based on said consumer’s past buying habits.
Several LTSPs (and e-commerce companies with in-house logistics capabilities) already leverage predictive analytics to great effect. Amazon, for example, “has obtained a patent for what it calls ‘anticipatory shipping’—a system of delivering products to customers before they place an order,” according to Forbes.
In addition to customized services predictive analytics, data aggregation and analysis also also allows LTSPs to gain insight into the efficiency of their processes and increase said efficiency when applicable. “Smart” fleet management is one example of a data-directed solution that allows LTSPs to (1) track present efficiency, and (2) improve said efficiency with actionable insights.
3. Connectivity. As noted above, modern consumers expect (and, with their buying choices, effectively demand) omnichannel distribution. This and similar networks that require multi-channel integration create a need for intelligent connectivity, which refers to connection between digital channels or elements.
Connectivity has several applications, many of which overlap with those of automation and data aggregation and analysis. Indeed, connectivity “connects” the elements of these applications that create value (robots, insight-producing AI, etc.) to the resources that allow them to create value (multi-form data).
Together, connectivity and data can help LTSPs generate actionable insights to lower costs and improve efficiency. Similarly, connectivity and automation can help LTSPs reduce labor demands and—you guessed it—also improve efficiency.
Every second of every day, our world grows more interconnected.
Our world economy’s interdependence — its strengths and its inherent vulnerabilities — has never been more apparent than the past year. Supply-chains stretch between continents, supplying a diverse range of people with everything from life-saving medicine to educational supplies. And, in the background, AI is enabling this expansion across industries—increasing efficiency toward a more interconnected, greener future.
LTSPs can ride this wave of digitization by investing in AI solutions. They must act sooner rather than later, as early AI-adopters like Amazon and UPS are leading the market in innovation driven by data, and the rest of the industry isn’t far behind.
If you are an LTSP looking to leverage AI, Synaptiq is here to assist you. We help our clients stay on top of their competition and under their budgets by starting our engagements with low-cost, proof-of-concept projects. And our case study catalog demonstrates a proven track record of success.
We look forward to hearing from you.