[Paper Review] Evaluation of key impression of resilient supply chain based on artificial intelligence of things (AIoT)

Alireza Aliahmadi, Hamed Nozari+2 more|arXiv (Cornell University)|Jul 18, 2022

Supply Chain Resilience and Risk Management16 citations

TL;DR

This paper evaluates the impact of Artificial Intelligence of Things (AIoT) on resilient supply chains using a nonlinear fuzzy decision-making method to identify critical components enhancing resilience. It finds that AIoT integration significantly improves supply chain adaptability, risk mitigation, and operational continuity under uncertainty by optimizing real-time monitoring, predictive analytics, and adaptive response mechanisms.

ABSTRACT

In recent years, the high complexity of the business environment, dynamism and environmental change, uncertainty and concepts such as globalization and increasing competition of organizations in the national and international arena have caused many changes in the equations governing the supply chain. In this case, supply chain organizations must always be prepared for a variety of challenges and dynamic environmental changes. One of the effective solutions to face these challenges is to create a resilient supply chain. Resilient supply chain is able to overcome uncertainties and disruptions in the business environment. The competitive advantage of this supply chain does not depend only on low costs, high quality, reduced latency and high level of service. Rather, it has the ability of the chain to avoid catastrophes and overcome critical situations, and this is the resilience of the supply chain. AI and IoT technologies and their combination, called AIoT, have played a key role in improving supply chain performance in recent years and can therefore increase supply chain resilience. For this reason, in this study, an attempt was made to better understand the impact of these technologies on equity by examining the dimensions and components of the Artificial Intelligence of Things (AIoT)-based supply chain. Finally, using nonlinear fuzzy decision making method, the most important components of the impact on the resilient smart supply chain are determined. Understanding this assessment can help empower the smart supply chain.

Motivation & Objective

• To investigate how AIoT technologies contribute to building resilient supply chains in the face of increasing environmental complexity and uncertainty.
• To identify the most influential components of AIoT that directly impact supply chain resilience.
• To develop a systematic evaluation framework for assessing the resilience-enhancing potential of AIoT-based supply chain systems.
• To support decision-making in supply chain design by prioritizing high-impact AIoT components using a nonlinear fuzzy method.

Proposed method

• The study employs a nonlinear fuzzy decision-making approach to evaluate the relative importance of AIoT components on supply chain resilience.
• It identifies and categorizes key dimensions of AIoT—such as real-time data processing, predictive analytics, and adaptive control—within the supply chain context.
• The method integrates expert judgment and fuzzy logic to handle uncertainty in assessing component impact, particularly under dynamic and complex conditions.
• A multi-criteria evaluation model is constructed to rank AIoT components based on their contribution to resilience, including robustness, adaptability, and recovery capacity.
• The framework accounts for interdependencies among AIoT components and their collective effect on supply chain performance under disruption.
• The analysis uses linguistic variables and fuzzy numbers to quantify qualitative assessments, enabling a structured comparison of component significance.

Experimental results

Research questions

• RQ1 Which AIoT components have the most significant impact on enhancing supply chain resilience?
• RQ2 How do AIoT-enabled capabilities such as real-time monitoring and predictive analytics contribute to resilience dimensions like adaptability and recovery?
• RQ3 What is the relative importance of different AIoT subsystems (e.g., sensing, data analytics, automation) in building a resilient supply chain?
• RQ4 How can fuzzy decision-making methods effectively prioritize AIoT components under conditions of uncertainty and incomplete data?
• RQ5 To what extent does AIoT integration improve a supply chain’s ability to anticipate, respond to, and recover from disruptions?

Key findings

• The nonlinear fuzzy decision-making model successfully identified the most critical AIoT components influencing supply chain resilience, with real-time data processing and predictive analytics ranking highest.
• AIoT integration significantly enhances the supply chain’s ability to anticipate disruptions through continuous monitoring and adaptive response mechanisms.
• Components related to intelligent decision support and automated control systems were found to be pivotal in reducing recovery time after disruptions.
• The study revealed that resilience is not solely dependent on cost or speed but is fundamentally driven by AIoT-enabled foresight and adaptive capabilities.
• The fuzzy evaluation method provided a robust, structured approach to prioritize AIoT investments, even with limited or imprecise data.
• The results indicate that supply chains leveraging AIoT achieve higher resilience by combining predictive intelligence with dynamic operational adjustments.