Tender rejections and their crucial role in dynamic spot pricing 

Coping with and minimising the impact of tender rejections is easy when you have a solution that leverages automation and machine learning. 

In a perfect world, shippers could rely solely on their routing guide and contracted rates to best orchestrate specific shipments between modes, lanes and preferred carriers. Alas, the current state of trucking and supply chain is far from perfect and tender rejections are common, as carriers by-pass contracted rates for better opportunities elsewhere.  

Shippers correctly consider rejections a result of poor contracting, tight spot freight market conditions, or both. But good buyers also embrace a healthy rejection rate as a sign of effective procurement. Too high a rate indicates there’s a discord between shipper and market, while too few rejections mean a shipper is leaving money on the table.  

But how to strike that balance and keep operational burdens of rejection at a minimum? This blog covers how an automated, artificial intelligence-enabled approach to spot freight procurement can use rejection signals to reframe and adjust pricing, in real time, to help shippers get to another tender quicker and smarter. This piece also touches on the impact of tender rejections, why they are on the rise, and the power of automation and AI in spot freight procurement. 

What does a tender rejection mean?

A tender rejection occurs when a carrier refuses a load offer from a shipper. Tracking capacity and tender rejection rates are the best indicators of the trucking industry’s financial health. In other words, are shippers and carriers making or losing money? In general, as capacity tightens and tender rejections increase, spot rates tend to move up, and vice versa. 

Why tender rejections are rising

To understand why tender rejections are increasing in today’s market, consider everything affecting the transportation and supply chain industries. Tight capacity and rising consumer demands are nothing new; those have been common throughout the pandemic over the past two years. The combination of unprecedented port congestion, peak season pressures, surcharges and general rate increases have created significant downstream impacts on trucking. 

As a result, shippers and carriers are switching ports. Because the routes and laneways may be different to their normal ones, the shipper might not know what the reasonable rates are. It becomes a situation where both sides are trying to feel each other out, and there is little, if any, experience in dealing with this sort of change. 

The Impacts of Rejection

For shippers, a tender rejection has real operational and financial impacts. They’ve lost lead time and are under pressure to find a partner outside of their preferred carrier group. Each rejection is a dead-end conversation; the longer the search continues, the more time the shipper’s staff needs to dedicate to that effort, resulting in higher costs and reduced productivity. Even worse, rejections directly impact the shipper’s bottom line. To ensure parts/materials reach manufacturers on time or finished products get to customers, often the only option a shipper has is to increase the offer to prospective carriers. Once shippers transition to spot, they are at the mercy of the market. Carriers can be selective; their available options are plentiful. 

The power of automated, AI-enabled tendering

By introducing data science, automation and machine learning into the equation, the spot market becomes a fluid, dynamic pricing environment. Rather than a broadcast approach, solutions like Transporeon Autonomous Procurement make decisions based on data science and behavioral science, enabling dynamic offers to preferred carriers. Essentially, the software is continuously reading signals to be used as inputs to further a shipper’s pricing strategy.  

If the targeted carrier does not respond or declines the offer, algorithms assess the reason for the tender rejection. For example, the carrier wasn’t interested in that price, that lane, or fills capacity earlier in the day. Over time, machine learning profiles the carrier for potential future offers. The details help shippers determine things like, “This carrier would be a good fit here, “They are good in this lane,” or “They are more likely to accept an offer in this price range”. 

The software is also able to manage many of these bilateral carrier negotiations in parallel. Rejection by one carrier is only a small data input influencing the many other ongoing tenders. 

Carriers also benefit from machine learning and an AI-based approach. They are trying to plan their day and optimise routes. They’re getting deluged with quote requests and want to accept only the freight-hauling opportunities that best fit their strategic plans. It takes time to evaluate and provide quotes for each request, but accepting a reasonable offer is as easy as clicking a button. If the carrier wants to negotiate, machine learning makes that easy as well. If it’s a reasonable bid that cannot be beaten, the machine accepts the offer on behalf of the shipper. 

Moving away from hard tender rejections

With machine learning, shippers are not just broadcasting broadly to carriers and hoping someone accepts. They’re starting to get more targeted and offering a match-now price that provides both sides with instant feedback and faster results. Tender rejections, in the standard sense, are significantly reduced, as pricing becomes a fluid, on-going series of dynamic offers. This yields the following benefits: 

• Easy identification of your preferred carriers 

• Faster negotiation cycles and matches to capacity   

• Ability to accurately target a wider range of carrier partners with relevant loads  

• Deeper, mutually beneficial relationships with carriers  

• Strategic Orchestration + AI is greater than tactical management 

When shippers turn to the spot freight market looking to secure coveted capacity, Transporeon Autonomous Procurement solution can make sure they minimise tender rejections by using algorithms and machine learning to predict correct prices. Autonomous procurement systems, such as ours, ensure strategic decisions are in the hands of human leadership, while tactical decisions are in the realm of AI software. This translates into giving the shipper’s procurement team more time to focus on other priorities, while also helping to build stronger carrier relationships due to less haggling over each offer/bid.