Last week I summarized my comments on how Sometimes 80% is enough here on Sourcing Innovation. I did this for multiple reasons – it seems that not everyone gets the point that with regards to optimization, not only is 100% unattainable, but even striving for 100% is often ludicrous.

The reason for this is that you are never optimizing against actual data, but estimated data. Remember, when you are sourcing, you are sourcing against forecasted needs, on forecasted schedules, with forecasted shipment levels associated with forecasted freight costs. Your demand probably will vary slightly, and may vary significantly, your schedules will need to be accelerated or decelerated when demand spikes or drops, your shipment sizes will also vary with seasonal demand variations, and with freight surcharges the norm these days, your freight rates will never be locked in stone. Thus, even an “optimal” solution is not optimal.

Moreover, striving for an optimal solution instead of settling for a (very) near optimal solution may actually decrease the quality of your solution. For example, let’s say your supplier gives you a significant discount (in the form of a rebate) of 10% if you buy 60,000 units, and your anticipated demand is precisely 60,000 units. Let’s say you award the supplier the business, but your forecast was over by 5% and you only buy 57,000 units. Let’s also say that the second cheapest supplier was only 3% less expensive. In this situation, your search for the ultimate solution cost you 7%!

As another example, let’s say a certain carrier will beat every other carrier’s truckload rate by 10%, where the truckload rate applies if you fill 75% or more of the truck. Let’s also say that we have the situation where your expected shipment is 80% of a truckload, that 25% of a truckload costs 20% more than the average shipping cost across your other carriers, and that your shipment size varies significantly by season and promotion (because you are in the food service industry, for example). One week you’ll ship 80%, the next week you’ll ship 60%, and the week after you’ll ship 120%. Chances are good that, in reality, you will not be shipping truckload half the time and paying on average 10% more. (If you are paying 20% more half the time, you’re paying 10% more over all.)

So this brings me back to the title of my post – the right way to handle freight. First of all, let’s note that when dealing with freight, you have one of five situations:

- Freight is a small percentage of total spend, less than 20%
- Freight is a moderate percentage of total spend, 20% to 40%
- Freight is more or less equal to total spend, 40% to 60%
- Freight is a large percentage of total spend, 60% to 80%
- Freight is a majority percentage of total spend, greater than 80%

The first case is the most important case. Why? Because it is this case that I find to be the most mishandled and misunderstood. I know for a fact that many corporations have thrown away millions, if not tens or hundreds of millions, of dollars because of their belief that freight optimization needs to be perfect even when it falls into this case and have put off acquiring a decision optimization solution in hopes that the perfect solution will come along soon.

This is the case where the “sometimes 80% is enough” rule comes into play. If someone provides you with an optimization solution that can handle your buy almost perfectly but only handle freight 80%, don’t dismiss it as imperfect and pass up an opportunity to save millions just because it’s not perfect in your eyes. Do the math! If freight is at most 20% of your spend, and the solution is expected to be at least 80% accurate, then the solution computed by the optimizer will be at least 96%. If freight is at most 10% of your spend, then the solution computed by the optimizer will be at least 98% optimal. If your non-optimization assisted solution doesn’t even approach 90% of optimal, why would you pass up an opportunity to save an extra 6%-9%? After all, as per my arguments above, I’d argue you are never going to achieve more than 98% (on average) in reality anyway! So don’t look for perfection when evaluating optimization solutions – chances are you will not find it (even though some solutions might come quite close) as it’s still a maturing and improving technology.

What about the other cases? The fifth case, where freight is the majority of your spend is also easy – you simply invert the problem and source freight lanes, and treat the product buy as freight.

The middle cases are harder. As for cases two and four, where freight is a moderate or large percentage of spend, the best way to handle these cases is to combine the categories with similar categories that can, or will in all likelihood, be shipped on the same trucks or in the same lanes. Preferably, those categories where, in the second case, freight is significantly lower and bumps you back into the first case or where, in the fourth case, freight is significantly higher and bumps you up into the fifth case, as we already know how to handle these cases. Case three is the toughie – product cost and freight are almost equal. What do you focus on?

This is the case where you do enterprise-wide freight optimization. You optimize all of the product buys, amalgamate all the freight requirements, and then optimize the freight. Unless, of course, your spend is significant enough, your pocketbook deep enough, and your patience long enough to throw CombineNet’s top-end optimization platform at it. (It really depends on your organization size – if you are a large organization, the cost of CombineNet should be inconsequential, especially considering the potential savings. If you are a small organization, the difference between the cost of the solution and the expected savings is not likely to be significant. If you are a mid-size, it depends on category size and characteristics.) On an ultra-high end server, their platform can certainly handle most of the problems you can throw at it, but not all problems solve in less than a second … a large and complex enough problem will take minutes, hours, or even days regardless of how good your optimization platform is. (However, if it takes more than a few hours, chances are your model is not the right one.) Also, since their solution is not part of any suite where your data resides, there will be some integration time. (But that’s a small price to pay to save $$$!)