Most decision-supports methods are dedicated to the identification and characterization of risks and opportunities. The concrete exploitation of these risks and opportunities is generally depending on the ability of users to analyze multi-dimensional situations, to mobilize their experience and to foresee consequences. In this article, a new and original data science-based vision of risk and opportunity management for decision-making purpose is introduced. The main expected benefit of this vision is to enable decision makers to manage the performance trajectory of a considered system by visualizing and combining the impact of risks and opportunity.
Management Science tries to enable managers and decision-makers to take the desired solutions to guide systems toward their objectives. This requires identifying the different dimensions of the system. Organizations and enterprises are complex systems associated with uncertainties in dynamic business contexts, that interact with their environments. Due to pressures such as collaborations with their customers, suppliers, their environment, the seek for innovations, etc., the performance may be changed by internal and external risks and opportunities that push and pull the enterprises like forces. Thanks to Physics of Decision (PoD), by identifying these pressures according to the organization's features and objectives, unstable conditions due to the forces, can be detected and identified as risks and opportunities. This article attempts to present a time-dependent dynamic framework, based on a physical approach to identify risks and opportunities seen as forces applied on Organizations and Enterprises.
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