Transportation procurement, the process of identifying and evaluating logistics providers, is a game of options. The business asks, “Who will transport our goods this year?” and, in turn, you have to ask, “What are the options?”
So you turn to the market, building an RFP with hundreds of lanes, and inviting tens or hundreds of carriers. Once you receive offers, you may wonder how many options are on the table. What you are really asking here is how many permutations exist based on these variables. As a simple example, if you have 100 lanes with 10 offers per lane, that number would be 10010, or 100,000,000,000,000,000,000.
With that many options, how do you determine which one is the best? How do you rule out the options that simply don’t make sense for your business?
Combinatorial optimization can lighten the load
The resources required to solve those questions manually are simply unfeasible. As a result, we make educated guesses based on a mixture of cherry-picking (giving business to the lowest cost vendor wherever possible), status-quo (keeping a certain percent of business with the incumbent) and supplier performance feedback (not choosing a certain supplier if you have any reason to think they’re not a great match for your business).
This sounds easy enough, but anyone who has executed a transportation RFP will tell you that trying to do this using spreadsheets results in painting your scenario with a very broad brush. You can be left with no option but to bundle packages of lanes with a roughly known total spend, and spend a few days switching around service provider allocations until you are able to reach the totals you’re looking for—or at least get as close as possible.
The more you complicate scenario-building with operational constraints, the longer the process takes (days become weeks) and the less likely it will be that you can meet every one of those constraints.
Case Study: 470 Lanes
Consider a case like this: your global logistics team wants to strategically consolidate all global freight business to a pool of four core providers and might allow two additional new providers to ensure full lane coverage, but the operational team in one of your regions has an adamant reason to maintain one of their niche service providers. Added to that, there is a global directive to reduce outbound logistics complexity, and therefore only two suppliers per facility may be awarded export business. And, finally, no new supplier may receive more than 5% of the global volume. Could you build a scenario like this, identifying the lowest price that matches all these constraints, using Excel (in under a year)? This is where combinatorial optimization can help.
Here is another example based on a real-world project that we just completed. On behalf of a U.S.-based global automotive component and tire manufacturer, we recently evaluated an RFP with 470 lanes and 60 supplier offers, equating to 47060 options (a 2 followed by 160 zeroes).
The cherry-picking scenario, which simply awarded each lane to the lowest-cost carrier, resulted in a 25.6% savings, equating to a very healthy cost reduction of $5.9 million. But there was a hitch. This savings required our customer to use an unmanageable total of 37 suppliers.
We then inverted the analysis to run a minimal carrier award scenario and found this required only two suppliers, which resulted in a different kind of problem. Instead of substantial financial savings, this scenario pushed prices up by an unacceptable 5%.
So we looked again—and this time we applied our proprietary combinatorial solver. By changing the criteria to balance price reduction with carrier numbers, it took less than ten minutes to identify an award scenario with five suppliers on a national level and three suppliers per origin site, and still achieve an extremely favorable savings of 16.3%.
(For more insights like these, download our e-book, "Visualizing complex bid scenarios to optimize logistics costs")
A solution found with combinatorics
All the stakeholders were satisfied—logistics, who needed to maintain a couple of their incumbents at one of the sites; procurement, who over-achieved their savings targets; customer service, who had fewer vendors to get in touch with moving forward; and of course management, who secured an incredible ROI when comparing the Ticontract fees with the savings and optimization results.
If you have an RFP coming up, and you want to find the ideal award scenario in a matter of minutes, contact us now for more information on combinatorial optimization. Let our platform do the math—so that you don’t have to!