Evaluating operational AI–enabled supply-chain innovation: A disclosure-aware boundary framework for inventory and working capital in Chinese manufacturing
Artificial intelligence (AI) is embedded in demand forecasting, replenishment, and supply-chain planning, yet evidence on its operating consequences remains scarce. Public disclosures can verify a system as operating during a fiscal year without revealing first internal use, production acceptance, or the exact go-live quarter; coding the report date as adoption would turn disclosure into a fictitious intervention. Therefore, this study investigates a public operating-use milestone, not a deployment event. Four hand-verified operating-use disclosures by Chinese A-share manufacturers, covering operating years 2019–2024, were linked to an audited quarterly CSMAR panel. For each firm, synthetic controls were estimated under first-quarter and fourth-quarter assignments to the disclosed operating year. These are maximal quarterly codings of an annual marker: they test whether a conclusion survives the greatest within-year reclassification that the public record permits, but do not locate deployment or numerically bound effects at interior quarters. The study distinguishes public operating-use evidence from separate accounting-gap evidence. Across the four inventory/deviation cases and three cash-cycle cases, no pooled diagnostic was below 0.05 under both assignments: inventory days (p = 0.670/0.904); industry-median deviation (p = 0.363/0.386); and cash conversion cycle (p = 0.072/0.340). The study does not show that operational AI improves inventory efficiency or working-capital performance. Its principal contribution is methodological: it demonstrates how disclosed operating years should be treated as coarsened time information and subjected to boundary sensitivity rather than converted into precise deployment dates. The results provide descriptive comparative patterns, including a sign-based distinction between inventory scale and industry-relative alignment, without causal or general performance claims. For systematic-innovation research, the contribution is an ex-post, disclosure-aware evaluation protocol for a realised intelligent operating innovation: it links a named operational application, transparent time coding, accounting outcomes, and explicit inference boundaries. It is not a method for generating inventions, a TRIZ extension, or an evaluation of the proprietary AI models firms use.
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