Cost-Benefit Effects and Breakthrough Strategies of AIGC Digital Transformation for Small and Medium Hardware Manufacturing Enterprises
Against the intelligent manufacturing and new quality productive forces context, generative AI (AIGC) has reconstructed manufacturing R&D, production, quality inspection and supply chain links. Most existing AIGC transformation studies concentrate on well-resourced large listed manufacturers, with scarce quantitative empirical evidence for traditional hardware small and medium enterprises (SMEs) regarding transformation gains and cost pressures. Taking 41 Wenzhou hardware SMEs from 2021 to 2025 as balanced panel samples, this research adopts a two-way fixed-effects DID model to quantify the causal effects of AIGC adoption. Benchmark regressions prove AIGC significantly boosts SME performance: cutting new product R&D cycles by 32.6%, lowering unit manufacturing costs by 18.3% and raising total factor productivity (TFP) by 14%. Such benefits are more pronounced for medium firms with 20-50 million RMB annual revenue and 8-15 years of operation; placebo, variable substitution and parallel trend tests validate result robustness. Cost measurement identifies three key burdens: one-off equipment expenses 11.37% of annual net profit, annual system operation fees (6.99%) and compound talent costs (8.97%). Major transformation obstacles cover industrial AI talent shortages, mismatched universal AIGC models, limited profit margins and inadequate cluster digital ecosystems. Drawing on cost-benefit results, this paper puts forward dual micro-macro solutions: lightweight SaaS AIGC subscriptions for firms, plus tiered subsidies, shared industrial AI platforms and school-enterprise talent training schemes for governments. This research fills the SME AIGC transformation research and supplies actionable empirical references for industrial clusters and local policymakers.