AIoT-Driven Strategic Decision Support Systems as Enablers of Supply Chain Resilience, Circular Economy Integration, and Business Model Innovation

Description

This paper presents a comprehensive business analytics and development strategy for the deployment of Artificial Intelligence of Things (AIoT)-driven Strategic Decision Support Systems (SDSS) as enabling infrastructure for supply chain resilience, circular economy integration, and business model innovation. Drawing on systems theory, dynamic capabilities theory, and digital transformation literature, the study develops a five-pillar architecture — Sensing Infrastructure, Analytical Intelligence, Decision Orchestration, Circular Value Loops, and Adaptive Business Models — and maps each pillar to measurable operational outcomes and financial value levers. The strategy synthesizes current technological capabilities across edge computing, federated data mesh architectures, machine learning operations, and generative AI co-pilots into an integrated decision support framework operable in real-time at enterprise scale. The circular economy integration dimension addresses material passport design, reverse logistics intelligence, and Scope 1-3 carbon accounting enabled by primary IoT activity data — responding to the regulatory imperative of frameworks including the EU Corporate Sustainability Reporting Directive (CSRD) and the SEC climate disclosure rules. The business model innovation dimension maps five AIoT-enabled archetypes — from performance-based Servitization to sustainability intelligence marketplace — with financial modeling and horizon-based portfolio governance.

Steps to reproduce

The Industrial Internet of Things (IIoT) has matured from a point-solution technology into a systemic platform capability. When layered with advanced machine learning, edge computing, and cloud-native analytics, it becomes the Artificial Intelligence of Things (AIoT) — a dynamic environment where physical assets continuously generate intelligence that informs, validates, and sometimes autonomously executes strategic decisions. Traditional decision support systems relied on periodic reporting cycles, static dashboards, and human-in-the-loop interpretation. In contrast, AIoT-driven SDSS operates on a continuous sensing-to-action architecture, capable of processing millions of data events per second, identifying anomalies in sub-second timeframes, and triggering corrective workflows before human perception would register a problem.

Institutions

• North South University

  Dhaka Division, Dhaka

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