Balancing Act: The Trade-off Between Load Optimization Savings and Carrier Capacity
Carrier capacity has been an issue for quite some time. If you’ve been in transportation for the past decade, you know the story too well. The problem was temporarily mitigated with the softening of the economy which caused transportation demand to drop. Now that the economy has rebounded, freight volumes have increased while new Hours of Service regulations have further reduced truck productivity. So, once again, the issue of truckload capacity has become a hot topic among industry professionals. The problem is both compelling and expensive, and doesn’t appear to be going away; factually it’s getting worse—at least for now.
We all know that capacity is hard to find, particularly as we head in to the holiday shipping season, but have you given any serious consideration to its impact on your budget? What about, more importantly, how you can change your company’s practices to lessen that cost impact? Steve Banker of ARC recently wrote a piece for Logistics Viewpoints called, “What Price Do You Pay after your Truckload Tender is Rejected?” In it he referenced a 2013 MIT study on truckload pricing and tender rejection rates from researcher Yoo Jin Kim. Banker’s review of the study highlighted several key findings:
- On average it takes 1.4 tenders to get a load covered
- The number of tenders required to cover a load increases for loads over 100 miles as carriers are looking to minimize the potential of running significant empty backhaul miles
- The shorter the lead time the more likely the load will be rejected on initial tender
- The average cost increase when the initial tender is rejected is 15%
- This increases to 25% when a company needs to go 3 or more carriers deep on the tender list
These are startling stats that deserve consideration. Clearly, planning loads that are more carrier-friendly, and therefore more likely to be accepted on the first tender, is of primary importance.
For decades, companies have used Load Optimization as a way to generate transportation savings that often range from 3 – > 20% depending on the mix of freight and density of network. Many companies use load optimization as a mitigation strategy for dealing with both cost increases and limited capacity. In essence, do more consolidation to save more money and use less capacity. But the data presented in the study above put an interesting bend to it. Think of this; if the anticipated savings from optimization is 15% or less, consequently in the current environment there’s a good chance those savings will be eroded at best, if not reversed, if the first tender is rejected. That multi-stop load that saved 10, 15, 20% might actually end up being less expensive as multiple LTL shipments if the load is not accepted by the lowest cost truckload carrier.
Am I advocating against employing Load Optimization as a means to save? Absolutely not! Load Optimization remains the single largest opportunity for reducing transportation expense. However, this begs the question, how to implement more carrier-friendly planning practices that balance the trade-off between anticipated savings through Load Optimization and the actual cost to move the load in this environment?
The answer is found in adopting an integrated, adaptive Transportation Management System (TMS) platform. A solution that uniquely blurs the lines between planning & execution and produces results that reflect the reality in which you operate. This is required to improve the likelihood of first tender acceptance and minimizes erosion of anticipated savings as you execute your loads. Here are some things to look for:
Planning constraints for executable and carrier-friendly loads
- Adheres to defined maximum load duration & distance
- Adheres to defined maximum stop-offs
- Plans routes that minimize “out-of-route” miles and “route wander”
- Accurately reflects Hours of Service regulations in to the plan
- Transportation planning & execution that considers available carrier capacity
- Accurately factors stop “activity” time in to transit time of a load
- Employs load optimization techniques that use a model rate that is more reflective of the actual cost to move the load rather than assumes the load will always move at the lowest possible rate
Adaptive tendering practices
- Automatically tender the “no brainers,” the simple loads that don’t need to be reviewed, right away to maximize tender lead time
- Specify a maximum cost increase for an automated re-tender after rejection
- Leverage the spot market and Load Boards as part of your tendering process
- While many shippers are hesitant to use the spot market due to safety and insurance liability concerns, risks can be minimized with integrated carrier on-boarding
- Aggregated lane analysis – being able to determine the most likely carrier to accept your load based on historical activity on a transaction by transaction basis
- Suspend automated re-tendering to re-evaluate mode selection and load consolidation after rejected tender.
The following are some enhanced features and advanced programs that will take more time and analysis to implement. If not currently available in the TMS they should at least be on the product roadmap for continued process improvements:
- Establish a continuous move program with your carrier partners to help minimize your carrier’s empty backhaul miles
- Offer loads as backhauls to carriers that are already moving freight into the region
- Identify backhauls within your own network for your carriers
- Develop a comprehensive collaborative, capacity planning program with your core carriers
The carrier capacity problem is not going away overnight. Nor in the foreseeable future without a lot more equipment being manufactured, a lot more drivers entering the job market and a sudden reversal of HoS regulations –very unlikely scenarios in the near term. That said, there are excellent and effective alternatives that can be put in place by using a TMS with a blended planning and execution platform. This has proven to mitigate the effect on your operations while preserving load savings.