Gartner Research

Should-Cost Modelling in Data-Poor Categories

Published: 04 January 2017

ID: G00386046

Analyst(s): Procurement Research Team


What is a good methodology for increasing the depth of Procurement’s should-cost models when limited data exists? In data-scarce environments, leading organizations focus on increasing buyers’ comfort with estimates by: Breaking up cost drivers by level of staff confidence (fact-based, estimate and best guess) to provide staff an “out” for potentially erroneous estimates and guesses. Sharing application guidelines on how to use models that are populated primarily by guesses and estimates.

Table Of Contents

More Detail

Create Cost Data Accuracy Levels

Provide Application Guidelines for Different Levels of Cost Data Accuracy

Additional Resource

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