Transportation procurement remains a major problem for enterprises faced with the need for faster and more extensive capacity. According to Richard Howells of Forbes, the 2020 Oxford Economics survey found “49% of supply chain leaders (the top 12 % of respondents) can capture real-time data insights and act on them immediately, while 51% use AI and predictive analytics to capture insights. This allows supply chain leaders to react in real time to changing conditions – from wide-scale disruptions to individual customer complaints.”
Part of acting on them immediately derives from the size of the data collected and how it can be transformed into meaningful insights. Let’s take a closer look at the five key hallmarks of quality data sets that can improve transportation procurement.
The first thing to know about freight market data value is the importance of size and scope. Freight market data size and its consistency are critical factors in quickly normalizing that data and making use of it. This measure of consistency prevents deterioration of the data and allows for faster processing. For instance, near-real-time electronic tender data can have a profound impact on whether a carrier chooses to hire new drivers or the freight rates shippers submit for their tenders. It is all very specific to the unique needs of each freight market segment.
Another common characteristic of effective market data sets involves their diversity. Yes, consistency describes a reliable view of data. However, that data should be reflective of the whole freight market, including its granularities. A wider net of data also helps to avoid isolating data to localized “bubbles,” which provides for additional ways and insight into how an organization can avoid and mitigate transportation disruption risk.
With an emphasis on truckload data, the data size should also offer an expansive view of all over the road trucking (OTR) modes and their contributing factors. Take a moment to look at the final part of that sentence, contributing factors and how they relate to ground shipping management. Those factors harken to the need to look beyond a sole OTR mode for insight into what is happening, will happen and what should happen to achieve an optimum result. In other words, it may be necessary to consider ocean shipping data, rail data and air cargo data to understand the correlations and maximize the use of such data.
In recent years, most freight market articles have focused on the endless battle of spot versus contract rates. However, the idea of a singular decision between two opposites rarely pans out in reality. The shipping industry needs a new market metric – a way to gauge the actual conditions and how stable individual markets are. After all, the best lane contracts do little good if carriers cannot oblige tender requests. It is that information that can provide information for many bids and help everyday shippers and carriers maximize their operating margins.
Lastly, data analytics should align with observed values. Think about the basic principle behind analytics – identifying the full shipment lifecycle and preempting problems before they occur. That sounds great on paper, but it is only as valuable as how accurately those predictive capabilities are observed as time passes. For that specific reason, freight analytics is evolving again, using the power of analytics to assess how well analytics stood up to the true tests of today’s world. In fact, FreightWaves Scientific ticker indices do just that, showing whether predictive rates aligned with observed conditions and helping transportation managers justify their investment into freight market forecasting resources.
There’s a true battle raging in the pursuit of increased data use and validity within transportation management and procurement. FreightWaves SONAR and FreightWaves SONAR SCI, set to launch in mere days, will be integral tools in the truly effective toolkit of the modern shipper, broker or carrier. Request a FreightWaves SONAR demo by clicking the button below to get started.