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

Full Paper: Neural Text Generators in Enterprise Modeling: Can Chatgpt be Used as Proxy Domain Expert?

Enterprise modeling is concerned with the systematic development of a comprehensive and holistic representation of an enterprise (an enterprise model) to support organisational initiatives. Domain experts have an essential role in enterprise modeling projects (EM), as they provide the required domain knowledge or specifics of the organisation under consideration. The paper investigates if neural text generators (large language models) can reduce the dependency on domain experts for certain tasks in enterprise modeling. The main contributions of this paper are (1) a systematic literature analysis on neural text generator use in EM, (2) the identification of potential for applying large language models in EM, and (3) findings from quasi-experiments comparing output of ChatGPT and domain experts for the same EM task

The construction and interrogation of actor based simulation histories

Large socio-technical systems are complex to comprehend in their entirety because information exchanges between system compo- nents lend an emergent nature to the overall system behaviour. Although Individual system component behaviour may be known at the outset, such components may exhibit uncertainty and further exacerbate issues of a priori prediction of the overall system behaviour. Multi-agent sys- tems and the use of simulation is a possible recourse in such situations however, simulation results need to be correctly interpreted so as to nudge the overall system behaviour towards a desired objective. We pro- pose a solution wherein the system is modelled as a set of actors ex- changing messages, a simulation engine producing execution trace for an actor as its history, and a querying mechanism to identify patterns that may span across individual actor histories to ascertain property of the overall system. The proposed solution is evaluated using a representative sample from real life

A model-based approach to systematic reviews of research literature

A systematic approach to develop a literature review is attractive because it aims to achieve a repeatable, unbiased and evidence-based outcome. However the existing form of systematic review such as Systematic Literature Review (SLR) and Systematic Mapping Study (SMS) are known to be an effort, time, and intellectual intensive endeavour. To address these issues, this paper proposes a model-based approach to Systematic Review (SR) production. The approach uses a domain-specific language expressed as a meta-model to represent research literature, a meta-model to specify SR constructs in a uniform manner, and an associated development process all of which can benefit from computer-based support. The meta-models and process are validated using real-life case study. We claim that the use of meta-modeling and model synthesis lead to a reduction in time, effort and the current dependence on human expertise

Actor monitors for adaptive behaviour

This paper describes a structured approach to encoding monitors in an actor language. Within a configuration of actors, each of which publishes a history, a monitor is an independent actor that triggers an action based on patterns occurring in the actor histories. The paper defines a model of monitors using features of an actor language called ESL including time, static types and higher-order functions. An implementation of monitors is evaluated in the context of a simple case study based on competitive bidding

A model based realisation of actor model to conceptualise an aid for complex dynamic decision-making

Effective decision-making of modern organisation requires deep understanding of various aspects of organisation such as its goals, structure, business-as-usual operational processes etc. The large size and complex structure of organisations, socio-technical characteristics, and fast business dynamics make this decision-making a challenging endeavour. The state-of-practice of decision-making that relies heavily on human experts is often reported as ineffective, imprecise and lacking in agility. This paper evaluates a set of candidate technologies and makes a case for using actor based simulation techniques as an aid for complex dynamic decision-making. The approach is justified by enumeration of basic requirements of complex dynamic decision-making and the conducting a suitability of analysis of state-of-the-art enterprise modelling techniques. The research contributes a conceptual meta-model that represents necessary aspects of organisation for complex dynamic decision-making together with a realisation in terms of a meta model that extends Actor model of computation. The proposed approach is illustrated using a real life case study from business process outsourcing industr

Querying histories of organisation simulations

Industrial Dynamics involves system modelling, simulation and evaluation leading to policy making. Traditional approaches to industrial dynamics use expert knowledge to build top-down models that have been criticised as not taking into account the adaptability and sociotechnical features of modern organisations. Furthermore, such models require a-priori knowledge of policy-making theorems. This paper advances recent research on bottom-up agent-based organisational modelling for Industrial Dynamics by presenting a framework where simulations produce histories that can be used to establish a range of policy-based theorems. The framework is presented and evaluated using a case study that has been implemented using a toolset called ES