The parametric approach
By leading method — transparent, parametic, proven
The parametric approach in cost structure analysis asks: "Is this a black box or a conscious orientation toward value?" analycess answers that question with a resounding commitment to transparency.
The method is grounded in multidimensional linear regression — a mathematically rigorous, well-understood technique that procurement professionals, controllers, and engineers can all explain and defend.
Every result is traceable to the underlying data. Every target price can be justified — not just asserted.
What makes it leading
Multidimensional from the ground up
- Considers multiple value drivers simultaneously — not one factor at a time
- Takes correlations between value drivers into account automatically
- Requires very few data points: just 5 more than the number of value drivers
- Delivers a linear model that is easy to communicate to suppliers and internal stakeholders
- Applicable to materials, parts, and services across all industries
- Validated by years of use with leading industrial companies
Scale and standardisation
The same method, everywhere
At the core of analycess lies a regression mechanism that reflects state-of-the-art mathematical research. It will find the statistically best approximation function for the given prices, based on the observed product characteristics. Especially in the case of product characteristics that are correlated among each other (e.g. size and weight), simpler regression mechanisms often are not able to find a statistically valid approximation function. Statistics like Adjusted R2 are mathematical quality criteria against which the performance of analycess can be measured.
The concrete result of the application of analycess to a range of products in industrial procurement is a linear approximation formula that describes the price sufficiently. The deeper added value of analycess for the strategic buyer is to appreciate the real impact of various product characteristics on the price.
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analycess — Technical Value & Statistical Validity