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The GE matrix helps a strategic business unit evaluate its overall strength. Each product , brand, service , or potential product is mapped in this industry attractiveness/business strength space. The GE multi-factor model or "nine-box matrix" was first developed by McKinsey for General Electric in the early 1970s.
The growth–share matrix [2] (also known as the product portfolio matrix, [3] Boston Box, BCG-matrix, Boston matrix, Boston Consulting Group portfolio analysis and portfolio diagram) is a matrix used to help corporations to analyze their business units, that is, their product lines.
A vitality curve is a performance management practice that calls for individuals to be ranked or rated against their coworkers. It is also called stack ranking, forced ranking, and rank and yank.
A matrix effect value of less than 100 indicates suppression, while a value larger than 100 is a sign of matrix enhancement. An alternative definition of matrix effect utilizes the formula: M E = 100 ( A ( e x t r a c t ) A ( s t a n d a r d ) ) − 100 {\displaystyle ME=100\left({\frac {A(extract)}{A(standard)}}\right)-100}
The Hayes-Wheelwright Matrix, also known as the product-process matrix, is a tool to analyze the fit between a chosen product positioning and manufacturing process. The first dimension of the matrix, the product lifecycle, is a measure of the maturity of the product or market. It ranges from highly customized products with low volumes, to ...
An issue tree, also called logic tree, is a graphical breakdown of a question that dissects it into its different components vertically and that progresses into details as it reads to the right.
Risk is the lack of certainty about the outcome of making a particular choice. Statistically, the level of downside risk can be calculated as the product of the probability that harm occurs (e.g., that an accident happens) multiplied by the severity of that harm (i.e., the average amount of harm or more conservatively the maximum credible amount of harm).
The sample covariance matrix (SCM) is an unbiased and efficient estimator of the covariance matrix if the space of covariance matrices is viewed as an extrinsic convex cone in R p×p; however, measured using the intrinsic geometry of positive-definite matrices, the SCM is a biased and inefficient estimator. [1]