As we work our way through the discussion of Managed TMS, we invariably are asked what we believe are the best transportation management software (TMS) platforms in the industry.

To help buyers through their TMS selection process of upgrading their supply chain technology, we want to be as transparent and upfront as possible on the other TMS software options so they can work through what system will be the best fit for them because there is not a one size fits all answer on the topic.

With that said, let’s go through the list and then have a short discussion on how to assemble a requirements document.

Top 13 Transportation Management Systems (TMS)

• 3Gtms
• e2open
• Cloud Logistics
• Descartes
• Blue Yonder
• Manhattan
• MercuryGate
• Oracle
• SAP
• TMC – A Division of C.H. Robinson
• Trimble
• Transplace

TMS Requirements Document

With the top transportation management systems outlined, it is imperative for buyers to assemble a requirements document to make an objective decision.  Too often we see companies make their decision on the flash and excitement, which causes them to make a suboptimal decision.

Technical innovators are creating new markets and services by the aggregation, standardization and orchestration of these transactional services, while adding more analytical, industrial-strength APIs into the equation. When combined with progress in data science and the Internet of Things, technology companies that add value to direct-to-carrier APIs and combine them with high-power data analytics will create new concepts for the information economy.

Join us to learn more about:

• Transactional API’s versus Analytical API’s for Speed and Ease of Use
• Positive Impacts of Message Standardization for Data Harmonization and System Integration
• Benefits of Orchestration to Your Business
• Exception-based Workflows

This raises the issue of how cargo airlines can make sure that the “back to normal” is not a “hard landing”. In this environment, a new approach to revenue management could be the key that allows airlines to adjust their commercial strategies and continue to benefit from opportunities in the market.

Over the past three years, the cargo market has been capacity-driven and airlines with significant capacity pulled ahead of competitors. Recently, there seems to be a transition back to a demand-driven market: yields have declined, demand has slowed, and belly capacity continues to recover (Exhibit 1). Moving forward, rates are expected to decline further, although will likely remain above 2019 levels.

What this means is that new ways of working may be required for individual cargo airlines to remain competitive in this changing market. As belly capacity returns, the market will likely become increasingly competitive, and airlines that don’t have a robust commercial and revenue-management strategy in place might lose out and see their yields diminish faster than the average.

At the same time, many cargo airlines have invested considerably in their digital strategies since the pandemic began. In particular, online sales have boomed, and consequently, cargo airlines have access to much more data than was possible three years ago. A recent Freightos WebCargo report found that digitized air capacity across the industry reached 57 percent in Q1 2023, compared to 38 percent in Q1 2022, and only 3 percent in Q1 2019.

Taken together, the turning point in the market and the rise of digitization in the industry point to today being a crucial time to formulate next-generation revenue management for air cargo.

This article details three areas where cargo airlines can focus their efforts to re-think revenue management, specifically by relying on accurate forecasting to form actionable insights; using real-time monitoring for fast decision-making; and taking a customer-centric approach.

Using new tech to improve forecasts

Forecasting demand and supply is the starting point for a cargo pricing and revenue-management strategy. However, cargo demand is extremely challenging to forecast, for several reasons.

First, booking tends to be a last-minute process and late bookings are a consistent feature in this environment. Typically, two weeks before departure, less than 40 percent of an airline’s capacity has been booked. Second, the market is volatile. Air freight is often used by shippers as a last-minute restocking option, which depends on many economic factors, so the need for air freight can change almost overnight. Third, the air freight market is composed of dozens of industries, and thousands of commodities, each with different drivers that make demand difficult to predict.

But, airlines can leverage technological advances to improve demand forecasts and deal with volatility. The availability of more granular data sources, and the advance of Machine Learning (ML) algorithms, make it possible for cargo airlines to pursue better demand forecasting solutions to gain deeper insights—and ultimately make more nimble revenue decisions.

For instance, due to the increase in online sales, cargo airlines have more data available about their customers’ behavior. This is particularly the case for airlines that have their own sales portals. Through digitalization, the air cargo industry has an opportunity to build a 360-degree view of demand across the entire customer journey which includes data that is above the sales funnel, such as which flights customers search for, lead times, how the cargo request was made, how long it took to fulfill, and if there was a cancelation or modification. Airlines can also look at step-based conversion rates showing how the airline performs at each stage of the sales funnel (discovery, flight selection, product selection, price offer, etcetera). Having all of this data in one place means that cargo airlines can improve their customer experience: better understand what customers want, and when they are likely to want it. This is the type of insight that companies in B2C industries, such as passenger airlines or hotels, typically have access to and cargo airlines could consider using a similar approach and leaning into the e-commerce aspect of sales.

It’s clear that Artificial Intelligence (AI) and ML are transforming sectors and industries across the world—and cargo airlines could harness the power of AI to better predict demand. A McKinsey Global Institute study identified that the travel, transport, and logistics sector has the most potential for incremental value from AI, amounting to $1.8 trillion in value. Within this sector, roughly half of this value is likely to come from commercial applications such as customer service and pricing.

Cargo airlines are well positioned to increase forecasting accuracy through AI. For example, AI could make sense of the thousand or more commodities, as well as their inter-dependencies, within the supply chain. For instance, AI could determine how trends in raw materials and semi-manufactured products in one country could lead to a growth or decline in specific finished products in another—and how this would influence cargo demand.

There are a few pointers airlines could keep in mind when using AI for demand forecasting. It is important to select the right data as input, as it needs to be sufficiently granular. And using a blend of internal and external data can lead to greater forecasting accuracy as early as two weeks out, despite very few bookings being made at that time. Internal historical data is very important for improving forecasting quality, which tends to be overlooked.

Considering that the accuracy of ML algorithms increases with the amount of quality data being used, airlines will probably find that AI-enabled forecasts get more accurate over time. One cargo airline managed to improve its ability to predict demand significantly through the use of AI. Initially, the AI tool reduced the airline’s forecasting error from around 20 percent to 14 percent, and once it went live it continued to improve in accuracy.

The airline found that the AI model was much better at predicting seasonality patterns through multi-layered algorithms than traditional models. This allowed it to predict volume patterns to a high degree of accuracy from one to four weeks before departure. Furthermore, incorporating data on trends such as booking cancellations improved final volume predictions.

There are other untapped opportunities to leverage internal data, such as by predicting no-show rates for bookings by lane and by customer. Another airline followed this approach which led to better capacity management and, ultimately, improved profitability. Predicting cancellations allowed the airline to increase “overbooking” while still controlling for the risk of penalties (Exhibit 2). This, together with other specific use cases, helped to uplift load factors by around 8 percent after a 12-week pilot. Based on this success, the airline was able to identify potential network-wide savings worth tens of millions of dollars.

U.S. railroads originated 952,074 carloads in October 2022, up 0.5 percent, or 5,121 carloads, from October 2021. U.S. railroads also originated 1,062,422 containers and trailers in October 2022, down 1.4 percent, or 15,095 units, from the same month last year. Combined U.S. carload and intermodal originations in October 2022 were 2,014,496, down 0.5 percent, or 9,974 carloads and intermodal units from October 2021.

In October 2022, seven of the 20 carload commodity categories tracked by the AAR each month saw carload gains compared with October 2021. These included: coal, up 14,937 carloads or 5.8 percent; crushed stone, sand & gravel, up 8,615 carloads or 10.7 percent; and motor vehicles & parts, up 5,998 carloads or 11.4 percent. Commodities that saw declines in October 2022 from October 2021 included: chemicals, down 6,195 carloads or 4.8 percent; primary metal products, down 4,645 carloads or 13.2 percent; and all other carloads, down 4,209 carloads or 16.8 percent.

“October is usually one of the highest-volume months of the year for rail carloads, and it’s the top month so far this year,” said AAR Senior Vice President John T. Gray. “Carloads of grain surged upward as U.S. producers sought alternatives to the Mississippi River constraints while motor vehicles had one of their better months since pre-pandemic times. Carloads of chemicals were down in part because of high natural gas feedstock prices. U.S. intermodal volumes remained subdued in October thanks largely to high inventories at many retailers, lower port volumes and still-scarce warehouse capacity for many rail intermodal customers.”

Excluding coal, carloads were down 9,816 carloads, or 1.4 percent, in October 2022 from October 2021. Excluding coal and grain, carloads were down 8,950 carloads, or 1.5 percent.

Total U.S. carload traffic for the first 10 months of 2022 was 9,971,376 carloads, up 0.1 percent, or 14,912 carloads, from the same period last year; and 11,321,976 intermodal units, down 4.8 percent, or 567,366 containers and trailers, from last year.

Total combined U.S. traffic for the first 43 weeks of 2022 was 21,293,352 carloads and intermodal units, a decrease of 2.5 percent compared to last year.

Week Ending October 29, 2022

Total U.S. weekly rail traffic was 514,457 carloads and intermodal units, up 0.8 percent compared with the same week last year.

Total carloads for the week ending October 29 were 244,425 carloads, up 2.6 percent compared with the same week in 2021, while U.S. weekly intermodal volume was 270,032 containers and trailers, down 0.7 percent compared to 2021.

Five of the 10 carload commodity groups posted an increase compared with the same week in 2021. They included coal, up 4,036 carloads, to 70,984; nonmetallic minerals, up 3,631 carloads, to 34,438; and motor vehicles and parts, up 2,365 carloads, to 15,002. Commodity groups that posted decreases compared with the same week in 2021 included chemicals, down 2,147 carloads, to 31,092; metallic ores and metals, down 1,916 carloads, to 22,108; and forest products, down 805 carloads, to 8,908.

North American rail volume for the week ending October 29, 2022, on 12 reporting U.S., Canadian and Mexican railroads totaled 347,651 carloads, up 2.2 percent compared with the same week last year, and 355,130 intermodal units, down 0.9 percent compared with last year. Total combined weekly rail traffic in North America was 702,781 carloads and intermodal units, up 0.6 percent. North American rail volume for the first 43 weeks of 2022 was 29,189,693 carloads and intermodal units, down 2 percent compared with 2021.

Canadian railroads reported 80,084 carloads for the week, up 0.4 percent, and 67,951 intermodal units, down 2.3 percent compared with the same week in 2021. For the first 43 weeks of 2022, Canadian railroads reported cumulative rail traffic volume of 6,270,727 carloads, containers and trailers, down 1.8 percent.

Mexican railroads reported 23,142 carloads for the week, up 4.7 percent compared with the same week last year, and 17,147 intermodal units, up 1.8 percent. Cumulative volume on Mexican railroads for the first 43 weeks of 2022 was 1,625,614 carloads and intermodal containers and trailers, up 3.9 percent from the same point last year.