EKA Solutions recently launched MPlace, which allows larger shippers and brokers to create private marketplaces where they can trade with “trusted providers in a more precise, automated and real-time manner,” the company said in a release.
“We provide technology solutions that are very efficient and at a lower cost,” JJ Singh, founder and chief executive of EKA, told FreightWaves. “We already focus on small and medium-sized brokers, shippers and carriers, so we thought larger brokers and shippers could benefit from our solutions.”
The company launched its unified, cloud-based Omni-TMS (transportation management system) platform for small and medium-sized brokers, carriers and shippers in 2018. Its is now offering some of the capabilities of its software platform to larger shippers, brokers and carriers “in a way that can complement their existing TMS platforms and help them meet today’s more dynamic logistics environment,” the company said.
For example, if a large shipper needs a load hauled in a new freight lane, one of its employees can go onto MPlace and find partners in its own private space and not have to go out to the spot market or load boards,” Singh said.
“Utilizing the EKA MPlace, customers reduce direct labor, transportation spend and contracting risk while providing end-to-end visibility, audit and analysis,” Singh said.
Chart of the Week: Operating Ratio — Dry Van Carriers Company Fleet
Like travelers walking through the desert that found an oasis, carriers found a wellspring of freight in 2017 and 2018 and expanded their operations. Unfortunately for the carriers, the pool has dried, leaving many dying of thirst. 2019 has been a lesson in how trucking markets can overheat just like the economy, leading to an uncomfortable period of contraction. Operating ratios (ORs) for dry van carriers in the Truckload Carriers Association (TCA) Truckload Indexes program have averaged over 100% since the start of the year as a result of this contracting growth.
In recent weeks there has been a lot of dispute in the media and over Twitter about the state of the trucking market. We’ve described the market as getting “bloody” earlier this year, while others have talked about how “strong” the trucking business is. Recently, we developed a view that the market was about to turn for the better, based on volume data we have seen in the market.
Regardless of the words you use, it is important to inform these views with data. The TCA benchmarking program is the first and only program in the history of truckload that provides monthly benchmarking of hundreds of competitive truckload carriers, ranging from mid-size to enterprise (smallest fleet 75 trucks, largest is 7000). In total, 73,000 trucks are counted in the data sample, representing 8% of the total truck count of medium and large trucking fleets operating in the entire U.S.
The fleets submit monthly financial data into a benchmarking software program that compiles aggregated financial reports for the industry, sliced by a number of variables. While the truckload carriers submit 500+ points of data, only 31 are published in an aggregated basis inside of SONAR. With this data we can use data and not bias to determine how the industry as a whole are doing. Prior to the data being offered on an aggregated basis, the only data points non-insiders would get about the state of the market is through the public truckload carrier earnings reports that would come out quarterly and offer up 8-10 operational KPIs. Now we have over 30 and they come up out monthly from over 200 different fleet profiles.
Operating ratios are a measure of operational efficiency. The formula is operating costs/operating revenue. A 100 OR indicates that for every dollar made in revenue, 100% of it goes to funding the cost of doing business, leaving nothing for debt or investment. In trucking operating costs are things like driver wages, back office support, and maintenance costs. Debt and interest payments are not included in these costs.
Most carriers carry some amount of debt in order to fund some of their growth as many trucking companies are low on cash. Purchasing equipment and buildings are some of the more commonly financed items. In general, many carriers consider making five to ten cents on the dollar a success, or a 90 to 95 OR.
The issue for carriers in 2019 has been more about the oversupply than the lack of demand, although both are present. As carriers saw margins expand in 2018, they decided to invest in growing their fleets as was demonstrated by the record number of class 8 truck orders last year. About the time that most of the orders were being placed the market started to cool. Daily truckload volumes have averaged roughly 3% under 2018 from March through July, but the most brutal hit came in May and June when they were over 4% under previous year volumes.
The trucking industry is extremely competitive with relatively low barriers to entry. All it takes is a commercial driver’s license (CDL), a truck, and a willingness to drive to start a trucking company. Many drivers will quit larger operations to start their own venture after a time. FreightWaves has studied the Federal Motor Carrier Safety Administration (FMCSA) data and found that smaller fleets are still growing when the lager fleets (100+ trucks) have contracted over the past several months.
Many of these drivers have developed relationships with shippers over the years, making them a reliable option. Smaller carriers have lower overhead costs and can drop rates under the larger carriers whose costs are filled with building leases and back office costs. The influx of smaller carriers has a deflationary impact to both spot and contract rates.
In 2017, carrier ORs averaged 99%. Many of the contracted rates were made based on 2016 activity, which was the last freight recession. Seeing as most freight contracts are made on an annual cycle, these rates were in place throughout most of 2017 and early 2018, which kept profit margins low.
Late in 2017 into early 2018, carriers started falling out of their contracted obligations to service higher paying spot market freight. Demand grew so fast that spot rates were well above contract. Carriers, at times, could get more than double the price of hauling for their contracted shippers. The spread between spot and contract was too much to ignore.
Recently, volumes have recovered but have not had significant impact to rates. This should provide relief for some carriers and extend their life for a time, but winter is coming when freight volumes typically plummet. Carriers normally build reserves in the summer to keep them afloat during the slower months. The true test of carrier resolve has yet to come.
$5.6B of venture capital has been invested in FreightTech in 2019 to-date
FreightTech venture investing is super hot, but most of these startups continue to lose money. Is this a fad or something else?
Last year, FreightWaves created a FreightTech Venture Index to track the venture capital (VC) investing in FreightTech startups. The term FreightTech is defined loosely as software companies and other technologies that aid in the movement of freight or management of supply chains through logistics.
In prior studies, we eliminated companies that were involved in on-demand mobility (Uber for instance), because much of the investment was targeted towards personal mobility and not freight movement.
Recently, mobility and e-commerce companies have started to play a much bigger role in the freight innovation map, with significant resources deployed to expand their on-demand mobility networks and experience into the logistics sector. With companies like Uber, Amazon, JD.com, Alibaba and others building out freight networks and looking more like logistics powerhouses, the lines between personal mobility and freight movement are starting to blur.
Going forward, we will include venture funded on-demand mobility companies in our studies, as long as they include freight logistics as a core product offering. These firms have been some of the biggest freight innovators of all and dismissing them because they do other types of mobility businesses leaves total investment under represented.
The logistics sector (defined as the movement or management of freight) is a $9.6 trillion sector, globally. In the U.S. alone, logistics represents $1.6 trillion, or approximately 8 percent of domestic GDP. Compared to financial services revenues, logistics is bigger, with financial services generating $1.5 trillion, or 7.4 percent of domestic GDP.
FinTech has been one of the hottest venture sectors for the past decade, while FreightTech has only recently become a core focus of Silicon Valley investors. FinTech describes the software services and other technology used to support or enable banking and financial services. Payments, money movement, capital markets or insurance technologies are sub-segments of the FinTech industry.
In 2018, FinTech received $40.5 billion in VC investment, while FreightTech received $10.4 billion. More remarkably, however, FreightTech saw an explosion of interest from VC, growing by more than 400 percent, from $2.3 billion in 2017. FinTech doubled between 2017 and 2018. Since 2014, FinTech has grown by almost 500 percent, while FreightTech has grown by almost 1,000 percent, according to an analysis by FreightWaves using Pitchbook data.
FreightTech Venture Capital investing measured by total VC dollar investments:
So far in 2019, FreightTech startups have raised $5.6 billion of venture capital, while FinTech startups have raised $19.1 billion of venture capital.
Venture investing momentum in FreightTech is unlikely to slow down anytime soon. VCs tend to be momentum-driven investors, following their peers, but also looking at broader market trends. With companies around the world making significant investments in delivery and logistics networks to remain competitive, VCs will want an outsized share of the upside.
Over the next decade, companies that fail to invest in their logistics networks will find themselves disinter-mediated by companies that do. Consumers and companies alike will want real-time visibility, custody tracking and sourcing information, combined with near instant on-demand fulfillment.
Restaurants, retailers, distributors and manufacturers that fail to adapt to these demands will be as endangered as a niche media outlet that generates a large percent of its contribution margins from paywall subscriptions.
There is only one thing stronger than all the armies of the world: and that is an idea whose time has come. -Victor Hugo
Existing incumbents that have maintained their go-to-market strategies for years or decades with little value add to their clients will be displaced by venture-backed startups.
Incumbents that have a dated understanding of established business cycles and go-to-market strategies will be forced to adapt to a new way of thinking. Simply blowing off venture startups and their founders as idealistic, impractical and cocky is foolish and demonstrates historical ignorance or context.
Blockbuster versus Netflix is perhaps the greatest example of a market leader that pretended that a scrappy VC-backed company couldn’t displace them. Blockbuster had the chance to buy Netflix on multiple occasions (for as low as $50 million), but miscalculated where the market was headed and underestimated the advantages of Netflix’s business model and tech team.
Incumbent company execs interpret VC fundraising success as grandstanding for follow-on venture funding, without understanding how or why these same startups attract investment to begin with. They dismiss their business models as “unsustainable” or “ill-conceived”, assuming that the founder is clueless, arrogant, or living in a fantasy world.
In the early days of a startup’s funding cycle, Seed or Series A, a company doesn’t have to generate revenue. Often times, a good idea, charismatic entrepreneur, and a large total addressable market (TAM) size is all you need in the earliest days of startup. For FreightTech companies, the enormous size of the total logistics market ($9.6 trillion) is so massive, even in specialized areas, that investors know if the company has early traction in the market, it can grow to a big enough size for a large exit.
The amount of investment is often small in these early days (a few million dollars), just enough to get the company to a stage where the first couple of paying customers will buy the product.
Later stage companies that raise larger rounds (Series B and beyond) require product market fit, which means they need paying customers and high revenue growth. The biggest risk to a startup is running out of money, and tech-enabled startups that have high revenue growth and favorable unit economics almost never run out of willing investors to support the company.
Startups that are not growing fast (40+ percent year-over-year) face pressure by investors for a premature exit, often to a larger company, where the startup becomes a bolt-on product or feature of the acquirer’s core business. Lack of revenue growth is death for a startup, lack of profit is not.
In order for a startup to generate revenue, it must deliver real value to customers. The assumption that startups only play for venture capitalists and not for customers is usually held by the existing incumbents that are confused by the new entrants’ business model and go-to-market strategy. The tactics of the disrupter are usually drastically different than the incumbents, so the legacy companies write the new player off as a flash in the pan or assume the startup is given market credit that is neither deserved nor earned (“hype”).
Paying customers will have a different perspective, however.
Fast revenue growth is a sign of an under served client need identified by the new entrant. If the incumbents were serving the client’s needs, the startup wouldn’t have an opportunity to gain a foothold in the market and wouldn’t make it past a Series A funding.
In what is considered by many to be the most important book in Silicon Valley, Innovator’s Dilemma, incumbents misinterpret the opportunity, often dismissing it as a small and uninteresting niche. This allows the startup to establish a beachhead without any push back from the establishment.
“New entrants (often founded by frustrated ex-employees of the incumbents) with little or nothing to lose when they enter the market. Initially these small upstarts don’t pose a threat — the new entrants find new markets to apply these technologies largely by trial and error, at low margins. Their nimbleness and low cost structures allow them to operate sustainably where incumbents could not.
However, the error in valuing these technologies comes from what happens next. By finding the right application use and market, the upstarts advance rapidly and hit the steep part of the classic “S” curve, eventually entering the more mature markets of the incumbents and disrupting them.
In essence, the smaller markets are the guinea-pigs and test labs that help the technologies advance enough to play in the big boys league. In many cases the entry-point markets are left behind as the new technologies move into higher margin upmarket territory disrupting due to their superior performance.”
In order for the startup to grow, it must continue to add new revenue and client satisfaction throughout the engagement. The startup doesn’t enjoy the longer history or distribution of incumbents and must innovate to gain customers. Simply using a playbook of much larger and entrenched incumbents just means competitors will run the same playbook with a lot more resources than the startup.
As the company scales, client satisfaction is paramount. Usually this means that the startup is solving a major issue that the prior incumbents were ignoring. In later stage companies, venture investors want to see product market fit (real customer traction), high revenue growth and high paying customer satisfaction (usually tracked through NPS scores).
Venture investors are not concerned about profits if the startup is growing revenues quickly. This confuses a lot of people that don’t understand the venture investment model. Many of the most successful technology companies burn millions of dollars each year while they are growing fast. The biggest venture exits are usually companies that have high recurring (or reocurring) revenue growth, but with substantial losses. VC investors have a great deal of experience in seeing these companies become the dominant leaders of the next generation.
Even public companies can have big losses, so long as their revenues are growing. For software-enabled tech companies, investors have created the “Rule of 40,” which means that a healthy company should have a combined profit margin and growth rate in excess of 40 percent. Under this guidance, companies can lose 100 percent, but grow by 140 percent and still be considered “healthy.” ‘
Venture-backed startups are encouraged to sacrifice short-term profits, if the pursuit of profits sacrifices growth. The logic behind this is actually quite simple: fast growing revenue companies are far more valuable than slow growth, but profitable companies.
In Grow Fast or Die Slow, McKinsey studied 3,000 tech-companies between 1980 and 2012. Their conclusions were something that venture capitalists instinctively already knew, but defied conventional wisdom held by traditional business model thinkers, reporters and executives. Since the freight space has never seen the level of tech disruption that is going on currently, it is understandable that there would be a reluctance to accept it.
In the report, the management consulting firm stated:
Three pieces of evidence attest to the paramount importance of growth. First, growth yields greater returns. High-growth companies offer a return to shareholders five times greater than medium-growth companies. Second, growth predicts long-term success. “Supergrowers”—companies whose growth was greater than 60 percent when they reached $100 million in revenues—were eight times more likely to reach $1 billion in revenues than those growing less than 20 percent. Additionally, growth matters more than margin or cost structure. Increases in revenue growth rates drive twice as much market-capitalization gain as margin improvements for companies with less than $4 billion in revenues. Further, we observed no correlation between cost structure and growth rates.
VCs also have defined exit time horizons; their investment will only be in the startup for a few years. If the startup chooses to make a profit, it isn’t investing as much in marketing, product features or market expansion. With tech-enabled businesses valued at a multiple of revenues, venture investors want revenue growth above all to maximize their returns.
Customers are usually the winners when venture startups join the market. Often, startups build their businesses with more favorable unit economics for buyers, more flexible terms, better features and service.
Plus, with a focus on customer retention above all, client satisfaction and success is built into the startup’s DNA. Existing legacy companies that are measured on quarterly profits alone are more challenged to compete and will struggle to fend off the eventual pressure of the new FreightTech startups.
If a startup demonstrates an ability to raise multiple rounds of funding from reputable venture investors with solid track records, that signal alone is usually a sign of product market fit and high revenue growth.
For executives where their core business is under siege, it is a difficult place to be, especially if you are apart of an enterprise where innovation funding is not readily available. The instinct is to lash out and assume the startups will either flame out or lack long term sustainable business models. In other words, when surrounded, they just shoot everyone.
But for the freight executives that understand the current investment trend is just getting started, the best chance for survival is to take the startups very credible, assume that customers are as well, innovate internally, find ways to partner, or acquire.
Sitting angry, defiant, and idle is death- just ask Blockbuster. And venture investing in the freight space is just getting started.
Tom Curee says artificial intelligence can help transportation companies deal with the “analysis paralysis” of overwhelming data.
Photo by Deborah Lockridge
If the words “artificial intelligence” conjure up an image of Skynet in the Terminator movies, it’s time to think again. There are real-world transportation companies using AI to do their work better, faster, and more effectively – and they could be your competition.
Artificial intelligence is not building computers in such a way to wipe out the human race, said Tom Curee, vice president of strategic development at Kingsgate Logistics, during a session on using AI to solve problems held during McLeod Software’s 2019 user conference in Denver.
“Really, it’s just the development of systems to perform tasks that normally require human intelligence,” he explained. In fact, AI is a concept that’s been around since the 1950s. It only has gained steam in the last few years with the availability of big data, he said.
“I’m getting data from so many sources, I can’t keep up with them. What has brought AI back to the forefront is that you can train it and teach it and code it to evaluate the data for you and make decisions.”
There are subsets of artificial intelligence called machine learning and deep learning. In machine learning, algorithms and software can automatically learn and improve from experience without being explicitly programmed. Deep learning goes beyond that and gets into artificial neural networks, algorithms inspired by the human brain, that learn from large amounts of data.
Matching trucks and loads is a major way artificial intelligence can be used in transportation and logistics.
Photo by Deborah Lockridge
Curee cited as an example how AI can be used by brokers or shippers what want to find the best truck to move a particular shipment. The computer can be taught to make a prediction for which carrier is the best match, based on factors such as location, type of equipment, profitability, driver hours of service remaining, driver preferences, and so on. With machine learning, the algorithm also learns over time and adjusts its recommendations based on which carrier or driver you chose.
“Over the years, we’ve made so many decisions off of our gut, and now we have data that may tell us something different,” Curee said.
Real-World Examples of AI in Transportation
Kingsgate Logistics is a non-asset-based freight broker, working with both truckload and less-than-truckload carriers. Curee notes, however, that he actually first got interested in AI when trying to address a serious driver turnover problem for a refrigerated carrier.
Kingsgate, which has 90 people in its brokerage business, has in the past year made a serious investment in leveraging technology. In April, Curee said, the technology team consisted of three people. Today, it’s 14, “because we see the need of what we’re trying to do with technology to keep up.”
The company is already using AI in six areas and has three more it has identified to work on. Curee shared examples of some of the ways Kingsgate is using artificial intelligence and machine learning, many of which would apply to asset-based trucking fleets as well as to brokerage operations.
Recruiting: It’s not just truck drivers who are hard to recruit. With the tight labor market, all types of positions are a challenge to fill – and sifting through hundreds of applications or resumes is time consuming. In fact, by the time you find someone who looks like the perfect fit, he or she may already have taken a job elsewhere. With AI, you can tell it what parameters you’re looking for, such as the types of previous job titles, skills, experience, etc.
Think about how this could work: Look at the people who have been most successful in your organization, who worked best with the team, at their skillsets and backgrounds, and teach it what you’re looking for. In addition, you also give a thumbs-up or thumbs-down to the recommendations it comes up with to help it learn further.
Sales: One of the ways Kingsgate is leveraging AI in sales is simply by figuring out the best time of day to call a prospect. It pulls data such as social media activity, when the prospect opened an email from McLeod, when they have visited the website, etc.
Ai is similarly being leveraged to identify new customer prospects that are a good match for Kinggate. And it’s being used to create a prediction score on how likely customers are to be a fit for the company. “How much value would you have if you could stop your sales reps two months earlier on an account that just isn’t ever going to be a fit?” Curee said.
Kingsgate is also using AI to analyze recorded calls that have been transcribed via a speech-to-text program. They are analyzing what the company’s most successful sales reps do and coaching less-successful reps in these areas. For instance, it’s finding that the most successful reps spend less time on small talk and more time talking about market insights.
Social Media: In its marketing efforts, Kingsgate uses the technology to choose the best times to publish its social media posts based on the audience it is trying to reach.
Personalized Websites: Still in the early stages is dynamic personalization of its website. When people come to the website, technology tracks their IP addresses and can often cross-reference to the company’s CRM (customer relationship management) data to identify what industry they’re in. So a flatbed carrier would get shown a different version of the website than a refrigerated carrier, each optimized to appeal to that specific market.
IT Support: Although it started as an internal function, Kingsgate is in the process of making this customer-facing as well. The AI can process tech support requests and make a suggested answer based on the company’s knowledge base as well as on previous similar tech requests. Right now it still takes a human in the support department to OK that answer and send it to the person needing help, but in the future, Curee said, it will handle some of these questions automatically.
The Future is Now
“There’s virtually no major industry that modern AI hasn’t already affected,” Curee said. “We have so much data flowing at us now, and everyone is asking, ‘How do I mobilize this data, how do I make decisions from it? AI makes your data actionable. It’s not always making a decision for you; it’s helping you be able to make the decisions you need to make.”
In fact, he said, your company’s ability to adopt and adapt to this technology will be crucial to your success in the next five to 10 years.
A big part of that is going to be “reskilling” your workforce, he said. “It’s not good enough that the tech team can implement it.” Front-line workers don’t need to know how to program it, any more than they need to be able to build a computer in order to use it, but they do need to understand what artificial intelligence is and how to use it to improve their day-to-day experience.
Each driver was placed in a car simulator capable of handling both human and autonomous driving.
Photo courtesy of MTI.
As autonomous vehicles become more commonplace and automakers invest millions into the development of such technologies, consumers and researchers alike are left to ponder the implications of such technology failing during operation.
The study looks at how humans interact with AV’s if it loses control while on the road. It measured how quickly and deliberately the car alerted drivers and how efficiently the driver could effectively react and take control.
The study tested 40 individuals starting at 18 years old to 55 years plus. Each driver was placed in a car simulator capable of handling both human and autonomous driving. The test measured response times and vehicle drift from a centerline under several different scenarios.
The report takes into consideration how AVs can affect roadways and drivers.
Photo courtesy of MTI.
Key results showed that between two different speed settings (high speed of 65 mph and a low speed of 55 mph), the lower speed yielded better performance. Out of all the three different age groups tested, the older participants performed better in overall driving and driving after autonomous technology failure.
Researchers also found that individuals tended to increase their speed and steer after taking control of the vehicles, as opposed to braking. About half of the drivers also reported not seeing the visual warning on the central console but did hear the auditory warnings.
The report takes into consideration how AVs can affect roadways and drivers. It gives suggestions on what mobility and infrastructure changes may need to be implemented to ensure safety while operating autonomous technologies
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