Adaptive Control of Enterprise
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.
💡 Research Summary
The paper proposes an “adaptive control” framework that integrates artificial‑intelligence‑driven adaptation algorithms with traditional Enterprise Resource Planning (ERP) systems to manage a wide spectrum of external threats faced by modern enterprises. While ERP excels at internal process optimization, it lacks real‑time responsiveness to disruptive events such as the launch of a competing product, sudden exchange‑rate swings, regulatory changes, partner insolvency, political or energy crises, and ecological catastrophes. To fill this gap, the authors introduce a multi‑layered architecture composed of (1) probabilistic risk assessment, (2) semantic representation of both structured and unstructured data, and (3) a three‑stage engine for diagnosis, prediction, and decision making.
Risk assessment treats each identified threat as a stochastic event characterized by an occurrence probability and a loss function. Bayesian networks and time‑series forecasting ingest live market prices, transaction volumes, news feeds, and policy updates to continuously update these probabilities. When a probability exceeds a predefined threshold, an alert triggers the adaptive control pipeline.
The semantic layer converts natural‑language descriptions of events, facts, actors, organizations, products, operations, and scripts into an ontology‑based graph. Natural‑language processing extracts entities and relations, which are then stored as nodes and edges. This graph enables inference over non‑numeric information—e.g., detecting a new regulation that could affect customs duties—thereby expanding the risk horizon beyond what ERP’s relational tables can capture.
The core engine consists of three tightly coupled modules. The Diagnosis module cross‑checks the current state of the ERP (production rates, inventory levels, cash flow, contract terms) against the semantic graph to pinpoint anomalies. The Prediction module runs scenario‑based simulations, employing Monte‑Carlo sampling, reinforcement‑learning policy evaluation, and what‑if analysis to estimate restoration costs, recovery time, market‑share impact, and other key performance indicators. The Decision module formulates optimal response strategies using multi‑objective optimization (e.g., genetic algorithms, Pareto front exploration) that simultaneously minimize cost, limit loss, and preserve growth opportunities. The resulting strategies are automatically compiled into actionable recommendation reports for senior management.
A key innovation is the system’s ability to “self‑adapt.” When a new threat type emerges or an existing threat’s characteristics evolve, only the relevant module is retrained, leveraging meta‑learning techniques to keep overall re‑training overhead low. This modularity ensures that the framework can scale with the evolving risk landscape without requiring a complete system overhaul.
The authors validate the approach with a simulated manufacturing enterprise. Three stress scenarios—exchange‑rate shock, entry of a rival product, and partner bankruptcy—are injected. Compared with a baseline ERP‑only configuration, the adaptive control system reduces average profit loss from 12 % to under 4 %, improves restoration‑cost forecasts to within 5 % error, and cuts decision‑making latency from roughly 30 minutes to 5 minutes.
Limitations identified include the high cost of building and maintaining the semantic ontology, the computational intensity of real‑time streaming analytics combined with large‑scale simulations, and the need for transparent human‑AI interaction mechanisms to foster trust. Future work will explore automated ontology expansion, edge‑computing for low‑latency processing, and user‑centric interfaces that blend expert judgment with AI recommendations.
In summary, the paper demonstrates that coupling AI‑driven adaptation algorithms with semantic representations and ERP creates a unified, proactive risk‑management platform. This platform not only detects and quantifies emerging threats but also automatically generates optimized mitigation plans, thereby enhancing enterprise resilience in volatile economic, political, and environmental environments.