Evidence-based Prescriptive Analytics, CAUSAL Digital Twin and a Learning Estimation Algorithm
This work addresses the need for evidence-based prescriptive analytics in business operations, though it appears incremental as it builds on existing digital twin and causal methods.
The paper tackled the problem of determining optimal operational set-points for business productivity by developing a Learning Causal Digital Twin (LCDT) algorithm that uses a modified recurrent neural network with causal graph simulation. Results from a proof-of-principle using real vibration data showed encouraging outcomes in causal factor estimation, what-if analysis, and counterfactual experiments.
Evidence-based Prescriptive Analytics (EbPA) is necessary to determine optimal operational set-points that will improve business productivity. EbPA results from what-if analysis and counterfactual experimentation on CAUSAL Digital Twins (CDTs) that quantify cause-effect relationships in the DYNAMICS of a system of connected assets. We describe the basics of Causality and Causal Graphs and develop a Learning Causal Digital Twin (LCDT) solution; our algorithm uses a simple recurrent neural network with some innovative modifications incorporating Causal Graph simulation. Since LCDT is a learning digital twin where parameters are learned online in real-time with minimal pre-configuration, the work of deploying digital twins will be significantly simplified. A proof-of-principle of LCDT was conducted using real vibration data from a system of bearings; results of causal factor estimation, what-if analysis study and counterfactual experiment are very encouraging.