Modelling and enterprises-the past, the present and the future.

Industry has been practicing model-driven development in various flavours. In general it can be said that modelling and use of models have delivered on the promises of platform independence, enhanced productivity, and delivery certainty as regards development of software-intensive systems. Globalization market forces, increased regulatory compliance, ever-increasing penetration of internet, and rapid advance of technology are some of the key drivers leading to increased business dynamics. Increased number of factors impacting the decision and interdependency amongst the key drivers is leading to increased complexity in making business decisions. Also, enterprise software systems need to commensurately change to quickly support the business decisions. The paper presents synthesis of our experience over a decade and half in developing model-driven development technology and using it to deliver several business-critical software systems worldwide

TOWARDS ADAPTIVE ENTERPRISES USING DIGITAL TWINS

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