The Role of Optimization in Strategic Sourcing – Optimization and Reverse Auctions

This series discusses the recent report from CAPS Research on the role of optimization in strategic sourcing. The primary goal is to highlight, clarify, and, in some cases, correct parts of the report that are important, confusing, or incorrect to insure that you have the best introduction to strategic sourcing decision optimization that one can have.

In this chapter, the benefits of using optimization with reverse auctions are discussed and a number of case studies are presented. Specifically:

  • Fasteners #1
    Before the event, which was conducted as a reverse auction followed by an optimization-based analysis, the suppliers were projecting a 20%+ price increase. After the two-stage event, the end result was an increase of 11%, which was split among one new supplier and two incumbents, while two incumbents lost business.
  • Fasteners #2
    A company decided to centralize its buy across eight business units. A reverse auction followed by optimization-based analysis identified savings of over $80,000.
  • Shelving
    A shelving buy for 35 stores covering 150 items from 10 different sources realized a total savings of 10% when optimization was applied after a reverse auction.

Next, the chapter discussed the challenge of tiered and bundled bids. They are challenging in a number of respects — they are a challenge to define, they can be a challenge to explain, they are often a challenge to “normalize”, and they can be a big challenge to implement for even sophisticated developers — but not as challenging as the report would have you believe. After all, a few providers support both of these bid-types, and at least two do so in their self-service tools.

The statement that only after the model is solved can it be discovered if the business allocated to a supplier would have been sufficient to earn a discount is false! While the specific solution being used, by the company in the example, may not have supported discounts, a number of solutions on the market fully support tiered and volume discounts, which include the type described within the example. These solutions support models which dynamically update the total cost when a threshold is reached. (I have personally designed and implemented two solutions with this capability, one of which is still on the market.)

The one thing that should have been noted, but wasn’t, is that implementing these discounts usually requires a sophisticated set of binary equations. If discounts are required in bulk, the size and complexity of the model will increase significantly and this can negatively impact solve time in a big way.

In addition, not only are tiered and bundled bids the most common form of creative bidding supported by many optimization applications, but they are also the most powerful when combined with discounts and used appropriately.

Finally, there’s no reason that the optimization cannot be applied on-line, in real-time, during the auction. If you’re buying a commodity, or if you can completely specify your business rules and constraints up-front, you can run an optimization-enhanced auction and make (automated) contract offers immediately after the optimization completes. While most providers don’t yet have this capability, Trade Extensions, for example, does. Now, the model has to be of a size and complexity that can be solved in real-time during the auction, but thanks to the advances in processing power and solution algorithms that have materialized over the past five years, you’d be surprised just how big the model can get and still solve in the 15 to 30 minutes typically allocated for a mid-size real-time auction.

Next Part VIII: Challenges / Issues

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