Freight Automation: TaaS (Transportation as a Service)

Published on Aug 30, 2019

Updated on Nov 6, 2019

Governments are keen on introducing high capacity vehicles (HCV) to limit traffic congestion and reduce carbon footprints through freight transportation. But, truckers struggle with finding their next load on the backhaul and, of course, want to clear payments as fast as possible.

E-commerce has brought about a 5% increase in urban shipment demand. But, the situation is- retailers complain of goods not reaching the customer in time because of trucker shortage. And transporters claim- they suffer significant losses due to deadhead miles. Ironically, the load trucks are rolling, but without loads or lesser goads than their capacity, which leads to the transporter’s loss.

This article highlights how freight automation can maximize load bookings to bring a favourable impact on the transportation and logistics industry.

Logistics & Transport Service Challenges

The traditional shipping process involves contacting third party brokers and vetting the shipper manually. Despite being at the core of the supply chain, transportation services lack innovations to improve operational efficiency. The following are some crucial challenges that the logistics industry faces, even today!

Deadhead Miles

The trucks operating without load contribute to dead miles. Dead miles can occur when a carrier travels from location A to location B to pick items or it returns empty from location C to location A after dispatching the load.

According to the American Transportation Research Institute (ATRI) survey report 2017, it costs $66.65 per hour to operate a truck.

Traditionally, small trucking companies call freight brokers, who in turn call up warehouses to find if there’s freight ready for hauling. Unfortunately, about 15%-25% of the time, truckers end up carrying zero freight.

Therefore, deadhead miles certainly bring a huge loss, especially because freight services generally operate interstate.

Lack of Price Transparency

The transportation sector has been struggling with inflexible prices and backhaul charges. Fleet operators often demand deadhead miles charges for the shipment. Thus, irrespective of cargo capacity (or the volume to it’s full), the operator can charge sellers any amount.

Trucker Shortage

Trucking companies have reported truck driver shortage as their top industry issue in 2017-18. The American Trucking Associations state- the industry needs to recruit and train 898,000 new truckers by 2026.

Manual Booking

On average, a logistics company may waste 4000 to 6000+ hours to manually create bookings via phone calls, emails, and coordinating with drivers and manufacturers.

Benefits of Freight Automation

Transportation-as-a-Service (TaaS) can bring manufacturers/sellers, shippers, and carriers on a common platform. Automation solutions can bring the following benefits-

Route Matching and Optimization

Traditional backhauls include unused available capacity, causing deadhead mileage.

With route matching feature of a freight automation system, instead of travelling back and forth from location A to location B, and then starting a new haul from location A to location C; trucker can find the best route to reach location C enroute.

Efficiently Managing Fleet Operations

Traditionally, equipment tracking was dependent on manual data entry from drivers, shippers, and consignees. The process was not only cumbersome but also error-prone. Transportation supply chain automation helps in managing fleet operations in the following ways-

Lodging truckers’ start and end time automatically add to the accuracy of HOS (Hours of Service) records.
Vehicle tracking can identify bottlenecks and provide instant support in case of accidents, fuel shortage, roadblocks, or other unanticipated highway incidents.
Route guidance enables efficient haul plans.
It can reduce idling time and thus improve fleet productivity.

Transparent Pricing

Transparency in pricing can make freight transport robust and reliable.

For instance, Uber Freight has introduced Lane Explorer, which shows real-time market-based rates, up to two weeks in advance.

Online Processes

In any logistics and transport organization, the manual payment cycle requires 40%-60% more time and effort than its automation counterpart. Freight bill automation can solve the heavy-haul truckers’ problem of receiving payments faster. Eliminating manual processes can improve overall supply chain efficiency.

Collaboration Between Fleet Brokers

OECD states– Truck platooning can save over 10% in operational costs. Platooning is driving a group of vehicles together to increase road capacity via an automated highway system.

At the same time, HCVs (High Capacity Vehicles) that carry 50% more load than traditional trucks can save up to 20% cost/km.

However, truck platooning and utilizing complete HCVs capacity requires collaboration between shippers, carriers, and freight brokers. Automation can bring different stakeholders from the freight and logistics industry on a common platform to work together.

Product Spotlight

HwyHaul, a leading California-based freight brokerage startup uses transportation automation to connect enterprises with truckers. It simplifies the ‘load booking’ process for shippers and seamlessly empowers them with a state of the art Transportation as a Service (TaaS) solution.

Currently serving Reefer, Dry Van, and Flatbed loads, HwyHaul connects shippers and carriers on a common platform. The distinct features that freight-logistics management platform brings are-

Shipping enterprises can create and track their freight from booking to end-of-delivery.
Trucking companies (carriers) can manage their fleet and drivers.
Internal operations team can oversee and govern backend processes.
Truckers can use HwyHaul app to book and deliver loads without having to wait for telephonic communication.

We specialize in developing industry-specific and logistics & freight automation products. Contact us at hello@mantralabsglobal.com to learn more.

Bottom Line

Load bookings and freight brokerage automation solutions can contribute to reducing carbon footprint and improve fleet productivity to a great extent.

PwC 2019 report says by 2030, automation will shorten delivery lead times by 40% and reduce logistics costs for standardized transport by 47%. With newer disruptions like driverless trucks, relay-as-a-service model and automatic freight scheduling on the horizon, the transportation and logistics industry is on the cusp of unlocking new revenues across the value chain.

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

Maximizing Load Bookings with Freight Transport Automation