In an increasingly global and competitive business landscape, firms must collaborate and partner with other firms to ensure survival, growth, and innovation. Understanding the evolutionary composition of a firm's relationship portfolio and the underlying formation strategy is a difficult task given the multidimensional, temporal nature of the data. In collaboration with senior executives, we have designed and implemented an interactive visualization system that enables decision makers to gain both systemic (macro) and detailed (micro) insights into a firm's relationship activities and discover patterns of multidimensional relationship formation. Our system provides sequential/temporal representation modes, a rich set of additive crosslinked filters, the ability to stack multiple enterprise genomes, and a dynamically updated Markov model visualization to inform decision makers of past and likely future strategy moves.
The Computational Enterprise Science Lab focuses on the design, analysis, and management of complex enterprise systems (e.g. organizations, supply chains, business ecosystems) using information visualization, modeling/simulation, and system science approaches.
