A portfolio approach to freight procurement 

02/23/2023 | 3 min

It is widely believed that flexibility improves decision outcomes when faced with uncertainty. But does it always? 

To combat supply- and demand-sided market uncertainty, the FTL industry uses non-binding volume contracts that allow shippers and carriers the flexibility to fluctuate between the amount of volume offered or accepted without direct repercussions. However, what happens when it is taken too far? In some cases, shippers’ value placed on flexibility can result in misleading behaviour, which can impact relationships with carriers and ultimately cost them more.

The relationship spectrum 

The set of relationships that shippers and their carriers have can take on many forms. These relationships lie on a spectrum (see Figure 1). At one extreme sits full vertical integration with a firm’s private fleet or partial integration with dedicated capacity. If available to a shipper, these options usually cover consistent, reliable demand. This is stable business for both sides. At the other extreme is the pure transactional relationship in the dynamic spot market. This is often how variable, irregular demand is covered with dynamic pricing determined by the current market. And in the middle, we have a wide range of contractual relationships where the price for a set of expected demand on each lane is agreed upon during the strategic procurement process. 

Firms that outsource their transportation needs must decide the optimal mix of spot and contract. However, often transportation buyers rely heavily on contracts to mitigate potential price volatility and service uncertainty commonly associated with the spot market. 

In the first masterclass instalment of our Science-based transportation logistics [insert link to webinars lp?] series, we explore a common outcome of this “coverage” procurement approach: ghost lanes. On average, 70% of FTL contracts are never used, resulting in ghost lanes – lanes for which a contract is in place with a carrier, but where no volume ever materialises (see Figure 2). 

This can create problems for both shippers and carriers. For carriers, being awarded ghost lanes is problematic because they have secured or allocate capacity to those lanes. They expect business on them and the associated revenue. Additionally, they may have built up their networks including backhauls around these lanes. They have also of course spent time and resources determining and submitting their bid prices. 

These efforts invested in the procurement event are significant. From forecasting demand on each lane, to sending out RFQs, sifting through submitted bids on every lane, requesting increasingly complex winner determination problem formulations of optimization tools, and post-processing decisions and negotiations – the procurement process can take months of shippers’ time. And the costs increase with each additional lane included. This means that by establishing contracts on unnecessary lanes, the shipper community is incurring these upfront administrative costs in the hope that the cost, effort, and time to find capacity later, for the few lanes that do materialise, will be much higher. 

Reasons for the coverage procurement approach – in theory and in practice 

But first, why do ghost lanes exist in the first place? They are a result of a procurement approach by shippers to reduce uncertainty and lock in prices. Shippers use the procurement process to vet their potential suppliers, lock in competitive contract prices – often information that is needed for finance teams for budgeting and by sales teams for customer quotes – and often used as a safety net to ensure a carrier and a price are established in the TMS if shipments materialise over the next year. 

Interestingly, human behavioural research and game theory explain why we see these behaviours on a more fundamental level. Real options theory – a subset of game theory – explains that establishing flexibility by creating and then exercising or not exercising certain options later on is advantageous when facing uncertainty. The non-binding nature of transportation contracts enables the coverage strategy, which in turn gives shippers the flexibility to offer shipments to their contracted carriers if and when it materialises, or to never use the contract, resulting in ghost lanes. 

Furthermore, research has shown that people value flexibility so much that they are willing to pay more – in fact, knowingly overinvest – up front in order to enable flexible options later on. This explains shipper behaviour around continuing to include high uncertainty lanes that never materialise from year to year. 

Finally, another set of research offers an additional potential explanation of the tendency to continue pursuing a coverage strategy: budget slack. Business units tend to overstate their expected spend and budgetary requirements to add some padding as a means to hedge future uncertainty. This occurs more often during favourable economic conditions as protection against the moment when markets turn in the future. The cyclical nature of transportation markets suggests we should expect ghost lanes to occur more often during soft markets. 

How ghost lanes can haunt your procurement strategy 

While the intended benefits to shippers explain why a coverage procurement strategy is so prevalent, how effective the strategy is (and the potentially unintended consequences) had previously not yet been explored. Working with a series of datasets representing the outcome of each strategic bid event and every subsequent load offer for dozens of FTL shippers in the US over a five-year time period, we modelled shipper and carrier behaviours to isolate the effects of ghost lanes.

We had three research question in mind: 

Question 1: Are there price and/or service repercussions in the future for shippers that have a high rate of ghost lanes for their carriers? 

To answer this first question, we followed shipper-carrier pairs year to year. We measured the percentage of shippers’ lanes awarded to carriers that ended up as ghost lanes over the course of one year, and determined how that impacts carriers’ capacity availability for individual shippers, and its contract price competitiveness for that shipper the following year. The results showed that there was in fact a statistically significant decrease in capacity availability in that year and an increase in the carrier’s contract prices the following year.  

Question 2: How can we predict whether a lane will become a ghost lane before it is procured? 

For this research question, we develop a prediction model that takes as input characteristics of lanes before they are procured, and tells us the likelihood of the lane ending up as a ghost lane. We find that the strongest indicator of whether a lane will become a ghost lane is if it is new, meaning it was not included in the procurement event the previous year. In fact, 85% of ghost lanes were new that year (see Figure 3). 

In addition, of the ghost lanes that were procured the previous year, the next highest category was also ghost lanes the previous year – meaning we have recurring ghost lanes year to year. The rest of the ghost lanes had very few loads materialise on them throughout the previous year. 

We also find that lanes that typically have less outbound demand – and as a result have higher spot prices – are more likely to be ghost lanes. This suggest shippers are trying to lock in contract prices for lanes that they know will have high spot prices otherwise. 

Finally we find that ghost lanes appear more often during soft market conditions rather than tight markets. As noted by previous research, firms tend to create budget slack – here by creating more contracted lanes – during these more favourable economic, soft market conditions as protection for when things go poorly in the future. 

Question 3: Is the coverage procurement approach reducing cost escalations for shippers as intended, or would it be better to remove potential ghost lanes from the procurement event? 

To answer our third research question, we model the price differential between contract and spot prices on lanes that are likely to become ghost lanes, as identified by our previous model. While shippers’ intentions in procuring contracts on these lanes is to establish lower contract prices than the spot prices they would otherwise be exposed to, we find that in fact, these contracts are priced higher than their lane-specific spot prices. For example, new lane contracts are between 13% and 40% higher than spot prices, and lanes that are previously procured but turn out to be ghost lanes are between 7% and 11% higher than spot prices. 

This suggests that the uncertainty in volume demand for carriers on these lanes is being accounted for when they submit bid prices for contracts. Not only are shippers not seeing the cost savings they expect by establishing contracts on these lanes, they’re actually overpaying by quite a lot. 

Rethinking the portfolio balance  

This research suggests that we shift the traditional balance away from an extreme reliance on the traditional long-term, fixed-price contract and take more of a wait-and-see approach before setting prices. This allows both shipper and carrier to better assess the demand and capacity needs as they become known. In this way, we can incorporate the spot market more strategically into the relationship portfolio. Once more information about demand becomes available, we can better determine if establishing contracts with carriers for that lanes that do materialize is the best option and set a price that is more competitive and suits those lanes better. 

Reflections: gather market information without ghosting 

Often, we see disingenuous or misleading activity, such as ghost lanes resulting from a lack of information. Shippers lack price transparency, particularly in the FTL space. Unlike LTL pricing, where rate tables are constructed that more clearly indicate what transportation buyers will pay for their freight, FTL contract prices are the result of auctions and negotiations for each individual lane. In some cases, shippers may use the bid process to gather pricing information and check the pulse of the current market without intending to instate or honour contracts they establish. This process of setting up contracts for highly uncertain lanes may be done to provide price information required by sales and finance teams. 

However, there are alternative options for shippers that need market information. For example, more reliable lane-level pricing information exists from a number of transportation market analytics benchmark providers. These providers aggregate much larger samples of contract and spot prices, offering more consistent information. Moreover, freight markets are cyclical. Most shippers have experienced the situation where in the time between running the bid and when rates go live, prices already need to be adjusted. This underpins the unreliability of price information a shipper receives from its individual bid: it only represents a single snapshot in time. Analytical tools from benchmark providers, for example, can provide faster, more reliable pricing information.

In summary, high supply- and demand-sided uncertainty and a lack of information drive dishonest behaviour – whether intentional or unintentional. However, this has negative efficiency and cost repercussions for both shippers and carriers. The research suggests a need for more intentional use of historical demand information, incorporation of expectations of the market, and available analytical tools to inform today’s strategic procurement decisions to find the right portfolio mix. 

About the Author

Dr. Angela Acocella is a Researcher in Transportation, Logistics, and Operations Management at the School of Economics and Management of Tilburg University, and at the Center for Transportation & Logistics at the Massachusetts Institute of Technology (MIT). She incorporates market dynamics into empirical behavioural models to help companies improve freight transportation procurement strategies and contracting decisions. She applies data analytics and machine learning methods to large-scale data in collaboration with industry partners.


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