A Bayesian belief network approach to operationalization of multi-scenario technology roadmap

The strategic importance of business and technology planning has been highlighted in the era of growing uncertainty in which markets shift rapidly and new technologies proliferate unlimitedly. In this respect, among others, multi-scenario technology roadmap has been one of the most frequently adopted tools. Despite all the possibilities offered by the multi-scenario technology roadmap, however, it is subject to limitations that stem from difficulties in operationalization. The vital requisites for operationalizing the multi-scenario technology roadmap are to deal with it as a complex process comprised of interrelated activities and to select the most appropriate migration path for given scenarios. The tenet of this study is the requisites can be achieved through the Bayesian belief network (BBN). The distinct strengths of BBN, vis-à-vis others, lie in modeling and analyzing a complex problem that is characterized by direct/indirect effects and uncertainty. Specifically, a network topology of the BBN is first constructed based on the multi-scenario technology roadmap. The causal relations are then derived by pairwise comparisons. Finally, in-depth analysis is carried out to obtain the fitness of individual migration path for given scenarios. The proposed BBN approach is expected to help organizations to overcome the challenge of keeping a technology roadmap alive by improving its analytic power.