The Applicator

It’s a plain fact that there are certain kinds of math problems that even bright people don’t solve very well and computers solve better than we do. Some of these look surprisingly simple. If you’re a retailer, when should you mark down product and by how much? Or if you’ve got to make six deliveries, should you use one truck and make six stops, two trucks with three each, etc.

Entrepeneurs in the world of enterprise software have recognized this fact for a long, long time and have seen an opportunity there. They develop so-called optimization software, which solves an essentially mathematical problem and then provide it in various forms to the market. A few have provided only the engine itself, most of them ending up in I-Log, and many more that provide optimization solutions that address specific problems, like supply chain optimization, markdown optimization, or the subject of this post, sourcing optimization.

Now if there’s one thing that’s completely clear about optimization software, it is that the track record has been disappointing. I’m not just talking about the overpromising and underdelivering that afflicted the big-name supply chain optimization vendors. (No names, but you know who you are.) I’m talking about really wonderful products, like Joe Shamir’s ToolsGroup, which have struggled to put their tools in the hands of people who need it.

Sourcing is no exception to the general rule. In sourcing, optimization came to the fore when companies like FreeMarkets or Digital Freight wanted to set up sourcing “events,” where possibly hundreds of bidders could bid on thousands of line items, offering discounts for bundles of selected lines. What if A bids X for lines 1, 2, and 3, and B bids Y for lines 2, 3, and 4, etc.? This is one of those math problems that linear programming solutions solve better than human beings do.

A number of vendors still in the market place (you know who you are) proceeded to develop solutions for setting up and managing these events, which of course did a lot more than just figuring out which bids to accept. And to some extent, they worked; they did change the way many companies do business, and sourcing events are now fairly normal, with several large companies being big proponents.

But in my (admittedly somewhat limited) view, the optimization piece has been relatively unimportant. Certainly some companies have developed some good optimization algorithms, and they are used, but in the events I’ve seen or heard about, it’s not clear to me that the solutions they offered were actually all that optimal, and the reaction of buyers that I know to the solutions they offered have been tepid, shall we say.

I’m not surprised, as I say, because I’ve seen similar reactions from users of other optimization software.

Now I have a degree in math, and I’ve taken a course in linear programming. I know that the solutions they give you are better than what people can come up with by themselves. So are the people who don’t like these solutions just grousing? That’s certainly what most vendors think.

Well, the other day, I got a glimmer of the answer from some terrific research done by Vishal Gaur of Cornell University. (See my earlier post, “An Automated Replenishment System.”) He found in his case study that a solution produced by optimization software had a highly suboptimal operational effect because it was optimizing around the wrong things. The math was done perfectly, but the problem had been set up incorrectly.

This is always a problem in principle when you set optimization software chugging away. The software draws a box around the problem and finds the point inside the box with the best available solution. If you draw a different box (usually a larger one), the solution will be different, and will take longer to find.

In Professor Gaur’s case, the software developer had drawn the wrong box around the problem. Now what’s interesting here is that the software developer never knew that he’d done anything wrong. He delivered the software, and the IT people who were using it expressed satisfaction. And you might think, “Well, so what, even if it drew slightly the wrong box, it got roughly the right answer.” But in fact, when you change the box, you change the answer completely. So in this case, the users had to replace (manually) all the solutions the software offered (manually) with better and completely different solutions.

If you have optimization software, in other words, you have to draw the right box, or the answers that it gives you are not better than what you can provide, they are worse, often much, much worse.

Now look at how this applies to sourcing optimization. Let’s say that you set up the problem in one way. (You only allow bids from certain vendors, for instance, and you require that they offer discounts of a certain percentage.) If you draw the box differently, you will come up with quite different answers.

I was discussing this problem with Garry Mansell, president of TradeExtensions, a small sourcing optimization vendor. He argues that the only way of solving this problem is to provide a lot of services, along with the software. The experts that he provides (it just so happens, of course, that he’s in the business of providing this pairing) can keep the sourcing on track and make sure the right box is drawn.

Now I react to most honeyed words from vendors the way I react to offers of honey for my tea. (Hint: “No, thank you.”) But this struck me as roughly right. The problems that I’ve seen with implementations of myriad kinds of optimization software wouldn’t be solved, then, by fixing the software. It would be solved by helping people to make sure that they’re drawing the right box around the problem.

So who knows. Maybe Mansell is right. (Or maybe he’s just selling software. Or both.) But it seems to me to be a useful thing to keep in mind. If you’re looking at sourcing optimization software (or, more generally, any optimization software), don’t buy it and don’t use it, unless you have some serious experts, who can help you make sure that you drew the right box.