Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We present an approach that combines ideas from modeling & simulation, reinforcement learning and control theory to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. The paper describes illustration of approach in two real world use cases.
|Title of host publication||Proceedings of the 2019 Winter Simulation Conference|
|Number of pages||15|
|Publication status||Published - 11 Dec 2019|
|Event||Winter Simulation Conference 2019 - National Harbor, United States|
Duration: 8 Dec 2019 → 11 Dec 2019
|Conference||Winter Simulation Conference 2019|
|Period||8/12/19 → 11/12/19|