Listing all projects in priority order in the Paf.com backlog provides visibility into what is currently ongoing, helping coordinate the work of multiple Scrum teams. As a result of introducing a structured portfolio management process, the number of ongoing projects has dramatically reduced, from over 200 to 30, reducing thrashing. No structured way of starting projects was enforced company-wide, and too many parallel projects got started. Also, there was lack of visibility into projects entering and progressing in the development pipeline. Paf.com had experienced problems with long time-to-market due to thrashing, which was caused by frequently changing priorities due to an ad-hoc prioritization process and handovers. It is one of the most commonly used inter-dependency techniques and is used when the relevant set of variables shows a systematic inter-dependence and the objective is to find out the latent factors that create a commonality.This paper is a descriptive case study of how one department at Paf, Paf.com, introduced portfolio management to help support scaling agile software development. It may help to deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables. Factor analysis is commonly used in psychometrics, personality psychology, biology, marketing, product management, operations research, finance, and machine learning. Ī common rationale behind factor analytic methods is that the information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Simply put, the factor loading of a variable quantifies the extent to which the variable is related to a given factor. The observed variables are modelled as linear combinations of the potential factors plus " error" terms, hence factor analysis can be thought of as a special case of errors-in-variables models. Factor analysis searches for such joint variations in response to unobserved latent variables. For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables. For factorial design, see Factorial experiment.įactor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.
0 kommentar(er)
