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Decision Optimization

As defined in the Strategic Sourcing Decision Optimization wiki-paper, decision optimization is the application of rigorous analytical techniques to a well-defined scenario to arrive at the absolute best decision out of a multitude of possible alternatives in a rigorous, repeatable, and provable fashion. Furthermore, the analytical technique must be capable of analyzing every possible solution to the scenario (complete) and be capable of performing such analysis accurately (sound).

Strategic sourcing decision optimization is an important part of your technology toolkit because it can significantly reduce cycle times, enable significant realizable cost savings, and guide a buyer to the best overall total value management sourcing decision. Analyses that take weeks or months in spreadsheets like Excel can often be done in a few hours, and sometimes even a few minutes, and the savings can be substantial. Aberdeen, in two back-to-back studies in 2005 and 2007, found that the application of optimization tools in the analysis of total cost and the usage of flexible bidding functionality enabled early adopters of the technology to identify an average incremental savings of 12% beyond the savings that could be realized with basic price-focussed auction and e-RFX technology.

There are a number of requirements that need to be met before a solution can be identified as a true strategic sourcing decision optimization solution. In addition to solid mathematical foundations (such as Mixed Integer Linear Programming, or MILP), it must support true cost modeling, sophisticated constraint definition in multiple core categories, and "what if" analysis capabilities.

The system must be able to accurately model the complex cost of goods sold calculations that are common in today's global sourcing environment. It must be able to account for all of the fixed costs (such as production line set up, support fees, etc.) and variable costs (such as cost per unit, duty, etc.) that are associated with each product level, regardless if the cost is a raw material usage cost, energy cost, labor cost, trade cost, distribution cost, or trade cost. In addition, it must also support the definition of discounts and rebates that suppliers will offer for volume purchases or multiple product purchases.

Since the lowest bid is not always the best solution (as there are quality issues, supply reliability risks, and associated costs to take into account in the total cost and total value equations), it is imperative that the system be able to support the plethora of regulatory, business, and strategic quantitative and qualitative constraints that a buyer needs to truly arrive at an optimal, implementable, solution. At a minimum, the system needs to support capacity and limit constraints, basic allocation constraints, risk mitigation (meta) allocation constraints, and qualitative constraints.

Capacity Constraints allow for the specification of real world limits on the amount of product a supplier can supply and on the amount of product that a warehouse can receive. They also allow a buyer to restrict the supply base according to business rules, regulatory constraints, and strategic decisions.

Basic Allocation Constraints allow a user to specify that a certain supplier, or group of suppliers, must receive a minimum award of one or more products, and may also be used to specify a maximum award that a supplier, or set of suppliers, may receive. They can be used to capture pre-existing agreements, define preferred suppliers, and implement global sourcing strategy constraints.

Risk Mitigation Allocation Constraints allow a user to specify that at least one supplier in a group must receive a minimum, or maximum allocation, and are generally used to mitigate supply risk or ensure compliance with regulations.

Qualitative Constraints allow for the definition, and imposition, of an absolute or average minimum, or maximum, qualitative score on each product, or product bundle, sourced. They allow for engineering requirements, marketing and customer satisfaction goals, and other non-quantitive business constraints to be included in the model.

For a more complete definition of decision optimization, see the Strategic Sourcing Decision Optimization wiki-paper. For a detailed discussion, see the Next Level Purchasing podcasts on "What is Supply Optimization" and the associated transcripts, indexed below. For savings benchmarks, see the Aberdeen white-papers, also indexed below. Finally, the following posts offer some good starting points as well:

Next Level Purchasing Podcasts and Transcripts

Aberdeen Studies


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