The chaos of 2020 wreaked unprecedented havoc on the freight transport industry. A true Black Swan event, the COVID-19 pandemic derailed every phase of the supply chain and created backlogs some shippers are still working through today. As the ripples continue to affect global trade, a new problem is emerging: the challenge of pulling accurate comparisons for future data forecasting. The numbers from 2020, 2021, and 2022 are heavily skewed, which makes it difficult to look forward with any real clarity.

This presents a real problem for shippers. Without good comps, it’s difficult to facilitate freight operations. How do you budget accordingly? Or benchmark rate averages? How do you negotiate rate contracts in good faith? It’s difficult, but not impossible.

2020-22 Skewed Comparisons and Much More

Freight comparisons have been all over the place over the past few years. If you pull numbers from early 2020, there’s likely some consistency from past years, but pull numbers from over the past 24–30 months, and you’ll find major lows. The third quarter saw rates spike, and they’ve only continued to rise since. Clearly, there’s no good way to pull prior-year comps.

To make matters worse, shippers can’t even rely on sales data from 2020 through 2022 to forecast freight demand. Guidance regarding sales projections is even worse, which only adds to the confusion as shippers and carriers scramble to plan for the end of 2022 and the start of 2023.

Realigning Comparisons Amidst Disruption

So, how can shippers and carriers come to terms with rates? Without the ability to extrapolate from prior-year data in conjunction with forecasts, shippers are turning to more creative modeling methods. The best methods for realigning comparisons to approach accurate benchmarking include:

• Year-over-year comparison: Shippers can either take aggregated monthly data for trailing years and average it or use total year-over-year growth to project forward-looking comps from the current month. In either case, 2020–2022 can be viewed as an outlier.
• Sequential comparison: This method of benchmarking involves looking at consecutive quarters for figures affecting freight demand, such as sales forecasting and GDP data. These comps tend to be more reliable than year-over-year data.
• Stacked comparison: Two- and three-year stacked comps are useful for benchmarking if shippers examine the same time frames, determine variables, and factor them in with current circumstances.

While these methods are useful for projecting comparison data, they’re often complicated and cumbersome to calculate.

Machine Learning Provides a Smart Solution

This complexity has many shippers turning to machine learning and artificial intelligence (AI) to carry the calculation burden for them. A transportation management system (TMS) with features for automation and data analytics gives shippers access to prior data and the current rate environment. The result is a data-driven approach that incorporates historical data and current insights to fuel forward-looking comps.