Optimization is the Future … And the Future is Now!

Even before Aberdeen came out with its “Success Strategies in Advanced Sourcing and Negotiations: Optimizing Total Costs and Total Value for the Next Wave of E-sourcing Savings” in June of 2005 where they determined that “the application of optimization tools to analyze total costs, and of flexible bidding functionality to uncover creative supplier solutions has enabled early adopters to identify average incremental savings of 12% above those that basic, price-focused auctions alone have generated“, some of us already knew that decision optimization was the future of strategic sourcing.

However, even though Aberdeen confirmed this result in their January 2007 follow-up entitled “The Advanced Sourcing and Negotiation Benchmark Report: The Art and Science of the Deal” which found that “enterprises that are employing advanced sourcing techniques are still identifying an average savings of 11.9% per sourcing event” and that “best-in-class enterprises are identifying an average savings of 13.7% per event“, I still need to ask the question Why Aren’t You Optimizing Your Sourcing Decisions?. Despite the hundreds of success stories that can be culled from previous customers of some of the leading vendors, only 21% of companies plan to consider such technologies in the next two years. I can not help but ask why?

Many innovative service and solution companies in the e-sourcing marketplace have been betting for the last five to ten years that optimization is the wave of the future. It’s true that many have met with limited success to date, and that many more are out of business, but this is simply what happens in an emerging industry, especially when it’s on the tail end of a boom. The reality is that, in their haste to get something to market, many of these companies did not understand what optimization really was, did not have the expertise or skills in house to pull it off, did not focus on usability, did not enable their customers in the collection and cleansing of the large amounts of data required, did not integrate well with the other sourcing products that encapsulate the processes that come before and come after decision optimization, or failed in all these regards. Furthermore, many customers were not ready as you need a good process in place, need the tools to collect the data, and need the right training in strategic sourcing to maximize benefit from such a tool.

But we’re not in the late nineties anymore – this is the late naughts – and many things have changed. Many leading organizations not only make use of leading e-Sourcing technologies in their end-to-end sourcing process, but have good e-Procurement and data management technologies as well. They have highly capable people, have squeezed a lot of the fat out of supplier margins through e-Auctions, and have sufficient data to do meaningful spend analysis to spot opportunities. But even though they are prime for it, they not only are not using, but are not even considering decision optimization technology. And I’m puzzled!

Is it because they think it’s not appropriate to their situation? Is it because they think you need a Ph.D. to utilize such technologies? Is it because they think the products are just not where they need to be? As I’ve argued before, optimization is always appropriate – even if just a sanity check on the intended award. There are a few situations where it will not save you money, but these are very few and far between and neither I, nor any company I’ve worked with, have ever encountered a situation where it didn’t eek out at least a couple of additional percentage points of potential savings on any moderately complex scenario. Although you often need a PhD to design and build these tools, there’s no reason the user interface needs to be any more complicated than your run-of-the-mill business intelligence tool that you use every day – and the good providers realize that and have spent a lot of time, effort, and money, making the tools easy to use. Although many of the tools even five years ago were just not where they needed to be in at least one respect, that’s not the case today. Many are where they need to be, or close enough, for many common scenarios that their application will realize the savings that Aberdeen has found.

With rising raw material and energy prices, rising transportation costs, lengthening lead times due to labor force shortages and increased regulation, and constant consumer demand for shorter product cycles and lower costs, the time for decision optimization is now. If you have it as part of your e-Sourcing, e-Procurement, or e-Logistics suite, use it. If not, get it. There are good solutions out there now, and no reason not to use them.

For more information on decision optimization, and strategic sourcing decision optimization, check out the Strategic Sourcing Decision Optimization: The Inefficiency Eliminator wiki-paper over on the e-Sourcing Wiki, the two-part What is Supply Chain Optimization? podcast over on Next Level Purchasing (Part I Transcript and Part II Transcript), or my Decision Optimization posts over on Sourcing Innovation.

One Response to Optimization is the Future … And the Future is Now!

  1. You can also use your spend analysis system to quickly determine what optimization models will be most productive. Just dump the RFx results into your spend analysis system and build a quick cube.

    As Michael Lamoureux said (http://blog.sourcinginnovation.com/2007/03/27/analytics-vs-optimization.aspx) last March:

    “What if your organization had a spend analysis product that allowed you to build a spend cube any time you wanted – on any data you wanted – on any dimensions you wanted – and then throw it away when you’re done? Then there would be nothing to stop you from building a cube on your RFP or Auction data, building reports by supplier, by cost, or by property (minority supplier, quality, historical on time delivery), building cross tabs and tree maps, and then changing the cube to look at the data a different way.

    You wouldn’t need optimization or a plethora of deterministic reports to find out who the lowest cost supplier was, who the highest quality supplier was, who the lowest cost supplier was relative to your quality metric, or any other query that can easily be answered by rank and cross-tab queries.

    You’d still need optimization, because it couldn’t tell you the best way to make the 50-30-20 split between three top suppliers subject to your qualitative and on-time delivery requirements when your freight costs vary to each local ship to location, but it would greatly simplify the optimization process. First of all, you could easily see which suppliers do not make the cut in quality or in on-time delivery metrics and eliminate them with a couple of rankings. Then you could quickly analyze total cost rankings based on presumed 100% awards to each suppliers and quickly determine that you could only do the split between three of the top five bids, since the rest of the bids are just too high consider. Furthermore, you could eliminate the need for the quality or on-time delivery constraints since you have eliminated all suppliers that do not meet the requirements. Now you have reduced model size and model complexity, and significantly decreased solve time. ”

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