Quality upgrade strategies in a supply chain with advanced disclosure under AI technology

Qi Zheng; Keke Xie; Qi Zheng; Keke Xie

doi:10.3934/jimo.2026046

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 3: 1244-1283. doi: 10.3934/jimo.2026046

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Research article Special Issues

Quality upgrade strategies in a supply chain with advanced disclosure under AI technology

Qi Zheng ,
Keke Xie ^,

School of Management, Shanghai University of Engineering Science, Shanghai 201620, China

Received: 25 July 2025 Revised: 15 January 2026 Accepted: 30 January 2026 Published: 09 February 2026
90B06, 91A35

This study develops a two-period supply chain model consisting of a supplier, a retailer, and consumers to explore the interplay between artificial intelligence (AI)-driven quality upgrades and information disclosure strategies. The supplier decides on the level of AI-driven quality upgrade, while the retailer determines whether to disclose the product's quality information. The results indicate that AI upgrades by the supplier should only be implemented when the resultant performance improvement surpasses a critical threshold, effectively offsetting the associated costs; marginal upgrades should be deferred to prevent potential profit erosion. Conversely, retailers should disclose the product's quality information when consumer quality preference is high or when uncertainty regarding product quality is low. In situations of high uncertainty, retailers are advised to withhold information to discourage strategic waiting by consumers. Furthermore, when AI investment is endogenously determined within the Bayesian trust‑updating framework, information disclosure not only enhances market transparency but also activates a trust‑amplification feedback loop, which significantly strengthens the supplier's investment incentives by boosting the perceived value of AI‑driven quality improvements. Notably, dual-rollover strategies are shown to yield higher profitability at both extremely low and extremely high AI upgrade levels, while single-rollover strategies demonstrate superior performance at moderate upgrade levels. These findings offer actionable insights for aligning AI investment, pricing strategies, and disclosure decisions. They also provide policy guidance for the design of balanced AI subsidies and transparency frameworks, thereby promoting sustainable supply chain innovation.
- AI technology,
- advanced disclosure,
- quality upgrade,
- rollover approach,
- multi-period decision-making
Citation: Qi Zheng, Keke Xie. Quality upgrade strategies in a supply chain with advanced disclosure under AI technology[J]. Journal of Industrial and Management Optimization, 2026, 22(3): 1244-1283. doi: 10.3934/jimo.2026046

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Abstract

This study develops a two-period supply chain model consisting of a supplier, a retailer, and consumers to explore the interplay between artificial intelligence (AI)-driven quality upgrades and information disclosure strategies. The supplier decides on the level of AI-driven quality upgrade, while the retailer determines whether to disclose the product's quality information. The results indicate that AI upgrades by the supplier should only be implemented when the resultant performance improvement surpasses a critical threshold, effectively offsetting the associated costs; marginal upgrades should be deferred to prevent potential profit erosion. Conversely, retailers should disclose the product's quality information when consumer quality preference is high or when uncertainty regarding product quality is low. In situations of high uncertainty, retailers are advised to withhold information to discourage strategic waiting by consumers. Furthermore, when AI investment is endogenously determined within the Bayesian trust‑updating framework, information disclosure not only enhances market transparency but also activates a trust‑amplification feedback loop, which significantly strengthens the supplier's investment incentives by boosting the perceived value of AI‑driven quality improvements. Notably, dual-rollover strategies are shown to yield higher profitability at both extremely low and extremely high AI upgrade levels, while single-rollover strategies demonstrate superior performance at moderate upgrade levels. These findings offer actionable insights for aligning AI investment, pricing strategies, and disclosure decisions. They also provide policy guidance for the design of balanced AI subsidies and transparency frameworks, thereby promoting sustainable supply chain innovation.

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