Application: Corporate Excellence

Corporations face a stream of shocks both exogenously from their external environment (funding, tasking) and endogenously from interactions between their subsystems and groups. Confronted with these perturbations, decisions are made at scales from the individual, to sub-groups, to corporation. These decisions influence global corporate health (generically defined by the ability of the corporation to excel within its environment). When implementing policy or rules, funding internal projects, bringing in new externally funded projects, hiring new people, or creating initiatives, it is difficult to know how these decisions influence the corporation’s health either in the short or long term.

• Goal /Aspiration for Project
• Define and characterize corporate health using network measures that intrinsically embody structure and function
• Build computational model that will allow the evaluation of corporate policy such that it is both robust to uncertainty and yields corporate resilience within context of perturbations
• Approach/Methods/Models
• Render the corporation's network as a set of multiply overlapping functional dependencies (flows of influence, funds, and communications to yield production) built from:
• organizational charts (corporate hierarchy, formal influence)
• labor charging (task groups, funding streams)
• communication (email, telephone, and meetings)
• products (patents, reports, presentations, widgets and software)
• Investigate structure and statistics in time; characterize changes in network structure to past perturbations (reorganizations of corporate hierarchy or business units)
• Develop network measures of organizational Health
• Conceptual modeling…
• Assemble a set of generic threats to corporate health and possible policies that may mitigate the threats through interaction with corporate specialists
• Rank the mitigation policies relative to their effectiveness
• Identify uncertainty in model parameters, model form and assumptions to determine policies that are robust to these uncertainties
• Acknowledgements
• Funded by the Sandia National Laboratories Laboratory Directed Research and Development (LDRD) program