Yesterday I told you that not only could CombineNet support all of the basic cost and constraint categories required for true decision support, but that the model they generate accurately represents all of the costs and constraints they support and they can solve the model faster than all of their competitors the vast majority of the time. I also told you that today I’d highlight where this unique capability comes from.

Paul highlighted it in his “Now that’s an Electric Engine …” recent post on CombineNotes [WayBackMachine]. CombineNet’s ClearBox uses sophisticated tree-search algorithms to find the optimal allocation. Furthermore, the algorithms are ‘anytime algorithms’; they provide better and better solutions during the search process. And, CombineNet has also invented a host of proprietary techniques in tree search algorithms, including new branching strategies, custom cutting plane families, cutting plane generation and selection techniques, and machine learning methods for predicting what techniques will perform well on the instance at hand (for use in dynamically selecting a technique).

Even though every model can be built and solved on an off-the-shelf optimizer, the reality is that we are dealing with NP-Complete problems, which means that solve time generally increases exponentially with problem size. This means that for any given solver and any given model class, there is a limit on the average model size that can be solved in any given time window. Although an efficient model formulation combined with an appropriately tweaked off-the-shelf solver can solve a very decently sized problem, one must not ignore the fact that generic solvers use generic algorithms which are not always optimized for the problem at hand. This indicates that it is likely that one could create an appropriately defined custom designed algorithm that is likely to solve the model faster, if not significantly faster.

What is not as obvious is the degree of difficulty often associated with the development of these custom algorithms for strategic sourcing decision optimization. The nature of these problems is that it is very hard to determine for any given solution strategy and any given model instance, whether it is more or less likely to solve the model faster than another similar solution strategy. It’s the fundamental nature of NP-Complete. If we knew how to do it, we’d likely be in P-Space.

As highlighted in the post, CombineNet began to develop its algorithms in 1997, and it has 16 people working on the algorithms. We have tested hundreds of techniques to find those that shorten solve time for Expressive Commerce clearing problems. Some of the techniques are specific to market clearing, and others apply to combinatorial optimization more broadly. And that’s where the strength of CombineNet is – 10 years of research focussed on determining how to solve the combinatorial optimization problems that underlie strategic sourcing decision optimization problems quickly and optimally. Everything else is just icing on the cake.

We ended our last post by noting that what is important in strategic sourcing decision optimization is:

1. The ability to support all of the relevant costs and cost tiers.
2. The ability to support all of the fundamental constraint types required for true strategic sourcing decision optimization.
3. The ability to generate a model that accurately represents all of the relevant costs and constraints.
4. The ability to optimally solve the model in a realistic time frame.

Other requirements, which should go without saying, are:

• Solid Mathematical Foundations
• What If? Capability

Today we are going to discuss each of these points in detail and explain where the true power of a BoB solution like CombineNet is and what’s just confusing marketing hype.

1. The ability to support all of the relevant costs and cost tiers.

Fundamentally, in order to be a true solution for strategic sourcing decision optimization, the application has to support fixed and variable costs, and, furthermore, the application should allow those costs to be bid in a tiered or layered fashion or as discounts, so that a buyer can use the bidding structures that are natural to the commodity or industry they are in. This includes the ability to define unit costs, transportation costs, usage costs, and impact costs as well as sophisticated supplier discounts along the lines of “If you buy 10,000 forks, I’ll give you a discount on 10,000 spoons”.

2. The ability to support all of the fundamental constraint types required for true strategic sourcing decision optimization.

Fundamentally, the optimization solution must support, at a minimum, four basic categories of constraints: (a) capacity constraints, (b) flexible allocation, (c) risk mitigation, and (d) qualitative constraints in order for it to be a real strategic sourcing decision optimization product.

You need to take into account all of your supplier capacities, you need to be able to account for your current contracts and business rules, you need to insure that sole sourcing risks are addressed when required, and you need to be able to take into account your non-cost requirements such as quality, delivery time, and durability (etc.).

3. The ability to generate a model that accurately represents all of the relevant costs and constraints.

Many solutions exist that let you define whatever you want, but under the hood the costs and constraints are simplified and only an approximate representation is used.

4. The ability to optimally solve the model in a realistic time frame.

This is actually a two part requirement. The first requirement is that the system optimally solves the defined model, and not an approximation of the model. The second requirement is is that the system solves the model in a realistic time frame. A small model should not take more than a few minutes. A medium sized model should not take more than a few hours. A large model should solve overnight. Any longer and the usefulness of the solution is limited, especially when sourcing cycles are now completed in weeks, and not months, and all that a buyer may have to make an award decision is a few days.

Furthermore, as I mentioned in my last post, where CombineNet really stands apart from the rest of the pack is:

3. Their ability to generate a model that accurately represents all of the relevant costs and constraints.

4. Their ability to optimally solve the model in a realistic time frame.

5. Their ability to solve larger models than the majority of their competitors.

Simply put, not only can they support all of the basic cost and constraint categories required for true decision support, but the model they generate accurately represents all of the costs and constraints they support and they can solve the model faster than all of their competitors the vast majority of the time. Furthermore, they have the capability to solve larger models than the vast majority of their competitors. And tomorrow we’ll discuss where this unique capability comes from and why that, and not Expressive-Bidding, Expressive-Commerce, Comprehensive Bidding, or whatever-you-want-to-call-it-today-bidding, is what makes CombineNet BoB.

I know I ended my last post indicating that my next post would put BoB in perspective by extolling the virtues of POE, but I’m getting really tired of CombineNet over-hyping Expressive Bidding and so I’m going to explain why Expressive Bidding and Expressive Commerce has nothing to do with the price of fish when we’re talking about BoB. (Don’t worry, this does not have to be a series of finite length, so I will discuss the virtues of POE eventually so that you can put the virtues of BoB in perspective.)

In Paul’s “2007 – The Year of the Supplier” post on CombineNotes [WayBackMachine], he says Expressive Bids can include conditional (if/then) offers, volume discounts, packages of items (bundles), and other creative offers that take advantage of their strengths and/or recent innovations and with Expressive Bidding, suppliers drive the inefficiencies out of their own business and share the savings with buyers, simultaneously strengthening strategic relationships for long-term supply chain efficiencies and competitive advantage.

First of all, there’s nothing here that you couldn’t do self-serve with MindFlow’s application back in 2000/2001, which was two years before CombineNet started using the terminology and filing for trademarks / copyrights / etc. Secondly, pieces of this functionality existed before that in Emptoris’ offering, FreeMarket’s failed effort, i2’s early technology, etc. Thirdly, operation researchers have known how to do if-then constraints for at least two decades using the Theory of Logical Variables and its precursor instantiations. Fourthly, the same holds true for tiered bids (and every discount can be transformed to a tiered bid with a minimum buy if-then constraint and vice versa), which operations researchers have been accomplishing for even longer using piece-wise linear constraints. Fifthly, these bid styles existed long before the introduction of optimization technology, so there is fundamentally nothing new about what is being supported. Sixthly they’re not the first company to come up with a wizard-like interface (although it looks like theirs may be better than most). I could go on, but you get the point.

I’m not saying that Expressive Bidding, Real-World Bidding, Comprehensive Bidding, or whatever-you-want-to-call-it-today-bidding is not important, it is, because, without it, any optimization application with any degree of sophistication will be quite difficult to use, just that it’s not the greatest thing since sliced-bread, which CombineNet’s marketing materials would leave to believe.

What’s important is:

1. The ability to support all of the relevant costs and cost tiers.
2. The ability to support all of the fundamental constraint types required for true strategic sourcing decision optimization.
3. The ability to generate a model that accurately represents all of the relevant costs and constraints.
4. The ability to optimally solve the model in a realistic time frame.

Where CombineNet really stands apart from the rest of the pack is with respect to:

3. Their ability to generate a model that accurately represents all of the relevant costs and constraints.
4. Their ability to optimally solve the model in a realistic time frame.
5. Solve larger models than the majority of their competitors.

So tomorrow we’ll discuss these required capabilities and what BoB truly is, and, more importantly, what CombineNet’s offering really is and what is just annoying marketing hype.