Adaptive Control of Enterprise

TL;DR

This paper discusses an adaptive control framework for enterprises using AI-driven diagnostics, prediction, and decision-making, employing semantic representations to handle complex system control and critical event management.

Contribution

It introduces a novel approach integrating semantic representations with adaptive algorithms for enterprise risk assessment and response.

Findings

Effective risk prediction for critical enterprise events

Semantic representations improve problem formulation and resolution

Adaptive algorithms enhance enterprise resilience

Abstract

Modern progress in artificial intelligence permits to realize algorithms of adaptation for critical events (in addition to ERP). A production emergence, an appearance of new competitive goods, a major change in financial state of partners, a radical change in exchange rate, a change in custom and tax legislation, a political and energy crisis, an ecocatastrophe can lead up to a decrease of profit or bankruptcy of enterprise. Therefore it is necessary to assess a probability of threat and to take preventive actions. If a critical event took place, one must estimate restoration expenses and possible consequences as well as to prepare appropriate propositions. This is provided using modern methods of diagnostics, prediction, and decision making as well as an inference engine and semantic analysis. Mathematical methods in use are called in algorithms of adaptation automatically. Because the enterprise is a complex system, to overcome complexity of control it is necessary to apply semantic representations. Such representations are formed from descriptions of events, facts, persons, organizations, goods, operations, scripts on a natural language. Semantic representations permit as well to formulate actual problems and to find ways to resolve these problems.