Monthly Archives: November 2006

Six Sigma IV: Achievement

The recent Insight from Aberdeen Group, “Technologies Enable Six Sigma” (now Aberdeen Access Members Only) does a great job at demonstrating why we need Six Sigma, or 99.9997% accuracy, and why 99.9% accuracy is not good enough. At a 99.9% quality level, in the United States alone, this would mean:

  • no electricity for nearly an hour each month
  • unsafe drinking water once per week
  • 2,000 lost articles of mail per hour
  • 2 short or long landings at most airports each week
  • 20,000 wrong drug prescriptions per year
  • 500 wrong surgical procedures a week

The insight also makes it a point to note that Six Sigma is not just for manufacturing, since the metrics can apply universally. Whether you measure bad parts coming off an assembly line, or errors made in processing orders, a process is a process and a defect is a defect.

Based on a recent study, the insight notes an interesting fact – those companies that measure PPM (parts per million) and DPMO (defects per million opportunities) achieve better performance than those that simply measure defect rates in terms of %good or %defective. Although none of the Best in Class performers achieved true six sigma quality, those that measured PPM and DPMO were very close to five sigma performance (99.98%).

Why? I think it is partly due to the lessons offered in the Aberdeen Insight, “what gets measured, gets managed” and Six Sigma relies on accurate and accessible data which requires the proper technology, and partly due to psychology. We’ve been trained by the media’s overuse of statistics to accept 99% as very good and 99.9% as excellent and 99.99% as absolutely fantastic. After all, Ivory is 99.99% pure, and we’re supposed to accept that to be as good as it gets, but the reality is this is barely five sigma, which we know to be unacceptable in any critical operation.

As implied in the lessons learned, Aberdeen found a direct correlation between the application of technology and Best in Class performance. In all but one category of IT solutions (non-conformance reporting), adoption rate increases with performance. Although this does not decisively prove cause and effect, there is a strong correlation between top performers and supporting technology, and what’s more important – a detailed cause and effects analysis or being in the “better” pool?

Of course, six sigma is more than just measuring defect rates. Six Sigma employs a structured approach to problem-solving and requires the management of projects which have the potential of having significant impact on the business. To maximize the benefits of these projects, it is necessary to provide an infrastructure that facilitates the adoption of Six Sigma across the organization, manage individual projects, share information effectively and manage financial information in such a way as to gain acceptance on an enterprise-wide basis. While, strictly speaking, tools such as spreadsheets and generic desktop tools, and even pencil and paper, can assist in early stages of Six Sigma implementation, standardization and collaboration are necessary to achieve the next level of benefits.

By implementing project management tools that support collaborative efforts and provide workflow automation, Six Sigma practitioners are able to focus on the analytical aspects of the methodology to drive to true results.

For more information on Six Sigma, I refer you back to parts I through III of the series which appeared on e-Sourcing Forum [WayBackMachine] back in September.

  • I: An Introduction
    II: Innovative Quality
    III: Value Based Strategic Sourcing

Your Favorite Weasels …

Last Month I was kind enough to remind you that Scott Adams had begun his annual weasel poll.
The results are in! Check them out! Your favorite weasels are:

Category Winner!
Weaseliest Pundit/Reporter Michael Moore
Weaseliest Celebrity Tom Cruise
Weaseliest Sports Person Barry Bonds
Weaseliest Politician George W. Bush
Weaseliest Country United States
Weaseliest Organization Republican Party
Weaseliest Company Haliburton
Weaseliest Industry Oil

CombineNet Communiqué III: Differences

Disclaimer: This blog, including this post, is not sponsored by CombineNet (acquired by Jaggaer). The author is not employed by, contractually engaged with, or affiliated with CombineNet. Any and all opinions expressed herein are solely those of the author. Furthermore, the opinions expressed herein should be contrasted with the opinions of other educated professionals before the reader forms his or her own opinion. Finally, the author is neither endorsing nor dissenting the use of CombineNet’s products or services – merely trying to spread awareness on the importance of optimization and the relative uniqueness of an offering like that of CombineNet. This disclaimer holds true for each post in this multi-part series and will be repeated.

Yesterday we discussed CombineNet’s post “The Make vs. Buy Dilemma” and indicated that although this was a very good post … it did not quite meet its goal because this is a problem that could be more than adequately solved by way of a leading platform optimization engine and indicated that there are problems that the platform optimization engines cannot solve.

We noted that Paul was on the right path with the make versus buy example. This was primarily because there were three options at different levels of complexity:

  • source individual components (make)
  • source assembled components (buy)
  • source sub-assembles

and each of these options could define a sophisticated model on its own.

Now we’ll shift from make vs. buy to logistics, and, specifically, transportation network design. Consider a buyer who needs to transport product from a supplier’s facility. The buyer often has at least three options: let the supplier transport the product from their facility to the buyer facility, let a third party transport the product from the supplier’s facility to the buyer’s facility, or have the supplier transport the product to a centralized distribution center and then have a preferred third party transport the product to the buyer’s facility. Sounds easy, doesn’t it? Sounds like something we could probably do by hand since there will only be a small number of legs associated with each lane, each with a small number of costs and besides shipping costs, we only need to consider tariffs.

Now consider a buyer who needs to transport product from fifty supplier facilities. Instead of having at least three choices, you now have at least one hundred and fifty choices. Now you might be thinking that you could simply solve for each supplier location separately, which you could easily do with most platform optimization engines, but this is not likely to be the optimal solution since (a) there will likely be discounts from suppliers and freight carriers if sufficient volume is allocated and (b) if intermediate DCs are used, then products from the same category can be bundled and better rates obtained.

Of course, you might note that although most sourcing models either assume that the supplier is providing freight or that a single carrier is being used, at least one provider will soon offer a model where you can simultaneously associate multiple freight options with each product and that an intermediate distribution center is just another option (with adjusted costs) and that such a model could, with the proper formulation, costs, and adjustments likely handle such a model. Well, yes and no. It could handle a basic representation, but we haven’t considered bundling concerns – not all products from a centralized DC can be shipped on the same truck, multiple freight brackets and discounts – which could greatly increase the computational complexity, and the fact that the best solution in the design of an international transportation network might involve using multiple levels of centralized distribution centers. Even though a platform optimization engine with a really good embedded model could probably handle bundling reasonably well by way of grouping and exclusion constraints and freight brackets and discounts by way of appropriately implemented discounts and penalties, the reality is that your standard embedded sourcing, or even logistical model, is not going to be able to fully handle a dynamic multi-level transportation network.

So does this mean that best of breed wins out? Not necessarily. You’re not going to redesign your transportation network for every sourcing event. In fact, you’re probably not going to make significant changes to your network more than every couple of years. In addition, most of the time your problems are not going to be anywhere near this level of complexity.

So does this mean that leading platform optimization engines are your best choice? Not necessarily. As I indicated in my first post, on a high value category, even a couple of extra points can mean millions of dollars.

The answer is that if you are a large company, you probably need to follow the advice of the SpendFool and use both – they are not mutually exclusive. Use your (super-charged) honda-powered platform optimization engine from your leading Software-as-a-Service provider for your average sourcing event, since this is probably your highest ROI, but bring in the big lear-powered jet for your high-value complex events with very large numbers of decisions that need to be made simultaneously, particularly those that involve network design or really complicated make versus buy decisions (where you are not just considering a seat but the sourcing of the BOM for an entire car – and once you know what you are going to make versus what you are going to buy, you can probably revert to the honda-powered platform for the individual sourcing events). (As for how often will you need the lear jet, that depends on your specific needs. If you’re not sure, I would start with the honda and see how far it takes you, or, better yet, bring in a third party with expertise in optimization and market offerings to help you decide.) In my view, there’s plenty of room in the market, and plenty of need, for both BoB and POE because, when it comes to optimization, there really is no one-size-fits-all and the best you are going to find is a one-size-fits-most for any category of problems that you have.

Is the story over? Not by a mile. This series is titled CombineNet Communiqué and the next part of this series* will discuss their (public) solution offerings now that we’ve illustrated a scenario where a platform optimization engine might not be enough.

* After a brief hiatus.

CombineNet Communiqué II: Comparisons

Disclaimer: This blog, including this post, is not sponsored by CombineNet (acquired by Jaggaer). The author is not employed by, contractually engaged with, or affiliated with CombineNet. Any and all opinions expressed herein are solely those of the author. Furthermore, the opinions expressed herein should be contrasted with the opinions of other educated professionals before the reader forms his or her own opinion. Finally, the author is neither endorsing nor dissenting the use of CombineNet’s products or services – merely trying to spread awareness on the importance of optimization and the relative uniqueness of an offering like that of CombineNet. This disclaimer holds true for each post in this multi-part series and will be repeated.

Yesterday we recounted The Story To Date, and in response to the statements of CombineNet’s representatives that their product is designed for “everything in the bucket sourcing”, their optimization is easily accessible, their speed allows for more scenarios to be run in the same time frame – increasing the probability of a successful event -, they are now a hybrid of BoB (Best of Breed) and POE (Platform Optimization Engine), offering their clients the best of both worlds with their preconfigured templates, and even Jay Reddy, founder of MindFlow, openly acknowledged CombineNet’s optimization capabilities were without peer I noted that the reality was, more or less, (much?) better than it was (but easy is a relative term), definitely, more or less (but that’s not necessarily a bad thing), and pseudo revisionist history.

Today I’m going to indirectly address these issues by tackling CombineNet’s post The Make vs. Buy Dilemma, which was in response to my comments on their Analytics Support Negotiations posting.

In this post, Paul Martyn endeavors to offer a specific example of make versus buy to illustrate the saving potential an optimization-enabled sourcing process can unlock to demonstrate how Expressive Bidding unleashes savings and offers new insight into supply plans.

In this example, Paul uses the example of a seat assembly to illustrate that in addition to:

  • sourcing the individual components and assembling them (make)
  • sourcing the assembled components (buy)

one can take a hybrid approach where one considers

  • sourcing bundles of parts in combination with individual parts.

In his example, Paul illustrates a situation where sourcing individual parts (make) costs $129, sourcing the final product (buy) costs $120, but sourcing subassemblies, which may consist of the odd individual part, only costs $92.

Although this was a very good post, and one of the clearest posts out there on the power of optimization, it did not quite meet its goal because this is a problem that could be more than adequately solved by way of a leading platform optimization engine, such as Iasta’s, although it would take three scenarios instead of one (and possibly a few extra milliseconds of solve time), and a problem that could have been solved with a single scenario using an unreleased version of MindFlow’s optimizer, the former leader in the platform optimization engine category. In other words, it might take the right approach, a little creativity, or a little extra work, but some make vs. buy analysis can be done with platform optimization engines.

Does this mean that you don’t need best of breed? Not necessarily. There are problems that the platform optimization engines cannot solve. However, these tend to fall into two categories: deep and complex or highly specific. However, as it was defined, this was not one of them. But Paul was closing in on the right path, and tomorrow we will discuss a problem that you would be (very) hard pressed to solve using a platform optimization engine.