AIoT-Enabled Blockchain Integration in Dual-Product Music Supply Chains: Optimizing Sustainable Production Decisions Under Consumer Eco-Awareness Heterogeneity

The increased environmental concerns drive the global music industry to adopt sustainable practices. Sourcing raw materials for audio instruments and distributing audio content are all parts of the music industry supply chain. However, consumer eco-awareness varies widely across market segments, creating operational complexities for manufacturers seeking to optimize dual-product strategies. This characteristic challenge operations management in devising creative alternatives for sustainability. The combination of the Artificial Intelligence of Things (AIoT) and blockchain enables the enhancement of eco-sustainability, transparency, and traceability in the dual product supply chains of the music industry. In this article, we propose an AIoT blockchain enabled music supply chain (ABEMSC) framework to analyze how music manufacturers simultaneously produce eco-friendly digital streaming services and traditional physical music products to meet differentiated consumer demands with varying eco-awareness levels. We establish mathematical models to examine manufacturers’ decision-making processes with and without ABEMSC integration. Our analysis establishes precise cost thresholds for ABEMSC adoption and demonstrates how manufacturers can optimize pricing strategies for both digital and physical music products while enhancing supply chain transparency. When digital music trust deficit is high and conventional product valuation discount is low, manufacturers should prioritize ABEMSC implementation even at lower cost thresholds. Performance analysis demonstrates ABEMSC’s superiority over baseline methods in verification capabilities and edge computing efficiency, providing optimal AIoT sensor deployment strategies for balancing real-time authentication with energy efficiency.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-08-08
DOI
https://doi.org/10.1109/jiot.2025.3597139
Primary Topic
Energy Efficiency and Management
Type
article
Field-Weighted Citation Impact
0.00

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article

AIoT-Enabled Blockchain Integration in Dual-Product Music Supply Chains: Optimizing Sustainable Production Decisions Under Consumer Eco-Awareness Heterogeneity

Zhonghua Liu, Qian Zhu
IEEE Internet of Things Journal
Energy Efficiency and Management
article

AIoT-Enabled Blockchain Integration in Dual-Product Music Supply Chains: Optimizing Sustainable Production Decisions Under Consumer Eco-Awareness Heterogeneity

Zhonghua Liu, Qian Zhu
article en

Abstract

The increased environmental concerns drive the global music industry to adopt sustainable practices. Sourcing raw materials for audio instruments and distributing audio content are all parts of the music industry supply chain. However, consumer eco-awareness varies widely across market segments, creating operational complexities for manufacturers seeking to optimize dual-product strategies. This characteristic challenge operations management in devising creative alternatives for sustainability. The combination of the Artificial Intelligence of Things (AIoT) and blockchain enables the enhancement of eco-sustainability, transparency, and traceability in the dual product supply chains of the music industry. In this article, we propose an AIoT blockchain enabled music supply chain (ABEMSC) framework to analyze how music manufacturers simultaneously produce eco-friendly digital streaming services and traditional physical music products to meet differentiated consumer demands with varying eco-awareness levels. We establish mathematical models to examine manufacturers’ decision-making processes with and without ABEMSC integration. Our analysis establishes precise cost thresholds for ABEMSC adoption and demonstrates how manufacturers can optimize pricing strategies for both digital and physical music products while enhancing supply chain transparency. When digital music trust deficit is high and conventional product valuation discount is low, manufacturers should prioritize ABEMSC implementation even at lower cost thresholds. Performance analysis demonstrates ABEMSC’s superiority over baseline methods in verification capabilities and edge computing efficiency, providing optimal AIoT sensor deployment strategies for balancing real-time authentication with energy efficiency.

IEEE Internet of Things JournalVol. 12(19)
Xinxiang University (CN), China Medical University (CN)
National Natural Science Foundation of China
Responsible consumption and production
Openalex Percentile: Top 87%
Energy Efficiency and Management
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