Companies face emerging external complexities that they must respond to with internal complexity to be able to perform on a superior performance level. On that account, an application-oriented methodology to support the context specific selection of appropriate complexity management methods for accomplishing the optimal level of internal complexity is lacking. A complexity management model is introduced that tackles this deficiency. Based on the identification of 37 complexity drivers that determine corporate complexity and 81 complexity management methods from literature, an assignment matrix with 2,997 relations between complexity drivers and methods is stretched. A scoring algorithm uses these relations to generate a sorted list of appropriate management methods for a specific complexity context determined by relevant complexity drivers. The approach is operationalized by a software prototype and evaluated through six interviews with experts from the field who confirmed practical relevance, appropriateness, and value-added of the provided management recommendation.
Keywords: complexity management, recommendation model, complexity drivers, law of requisite variety, scoring algorithm