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Despite the emergence of event driven business process management, smartbusiness networks, social networks, etc. as important research areas inmanagement, for all the attractiveness of these concepts, two major challengesremain around their design and the partner selection rules while learning frominteraction events.While smart business networks should provide advantages dueto the quick connect of business partners for selected functions in a processcommon to several parties, literature does not provide constructive methodswhereby the selection of temporary partners and functions can be done. Mostdiscussions only rely solely on human judgment. This paper introduces bothcomputational geometry, and genetic programming, as systematic methods wherebyto identify, characterize, and then display on a continuing basis from eventmonitoring such possible partnerships; such techniques also allow to plan fortheir effect on the organizations and thus to carry out selection. The twomethods are being put in the context of emergence theory. Tessellations addressthe identification and categorization issues; business maps address the displayand monitoring challenge with the use of Voronoii diagrams. Cellular automatamimicking living bodies, with genetic algorithms of which parameters areestimated by learning, address the selection and effect issues. To illustratethe approach, some experimental results from the sourcing function in a hightech industry, are discussed; they address the case of how to determine theselection process for a systems integrator to set up joint ventures withsmaller technology suppliers.

Smart business networks and business genetics with a high tech communications supplier selection industry case