Comments on: Optimization is the Future … And the Future is Now! http://www.esourcingforum.com/archives/2007/10/08/optimization-is-the-future-and-the-future-is-now/ The source of information and best practices in strategic sourcing. Sun, 20 Jul 2008 04:26:11 +0000 http://wordpress.org/?v=2.3.2 By: Eric Strovink http://www.esourcingforum.com/archives/2007/10/08/optimization-is-the-future-and-the-future-is-now/#comment-8374 Eric Strovink Mon, 08 Oct 2007 13:01:16 +0000 http://www.esourcingforum.com/archives/2007/10/08/optimization-is-the-future-and-the-future-is-now/#comment-8374 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: <i> "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. " </i> 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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