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.
Managing a Collaborative Network (such as a supply chain) requires setting and pursuing objectives. These can be represented and evaluated by formal Key Performance Indicators (KPIs). Managing a supply chain aims to stretch its KPIs towards target values. Therefore, any Collaborative Network's goal is to monitor its trajectory within the framework of its KPIs. Currently potentiality (risk or opportunity) management is based on the capacity of managers to analyze increasingly complex situations. The new approach presented in this paper opens the door to a new methodology for supply chain potentiality management. It offers an innovative data-driven approach that takes data as input and applies physical principles for supporting decision-making processes to monitor supply chain's performance. With that approach, potentialities are seen as forces that push or pull the network within its multidimensional KPI space.
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