LOCALIZATION CONFIGURATIONS AND MARKET OUTCOMES OF CHINESE NEW ENERGY VEHICLE BRANDS IN THAILAND: A COMPARATIVE CASE STUDY OF BYD, MG, AND GWM

Abstract

Thailand has become a strategic production and sales base for Chinese new energy vehicle (NEV) manufacturers, yet firm-level evidence on how localization configurations differ and how they correspond with market outcomes remains fragmented. This study compares BYD, MG (SAIC Motor–CP), and Great Wall Motor (GWM) through a structured documentary case-study design. Evidence from peer-reviewed studies, Thai policy documents, industry statistics, and corporate disclosures was triangulated and coded across five dimensions: production, supply chain, product, marketing and service, and human resources. The analysis distinguishes verified observations from managerial inference and uses 2025 company-wide Thai vehicle sales as a transparent, comparable outcome rather than unreported profitability or brand-equity measures. The cases reveal three configurations. BYD combines a large newly built manufacturing base with rapid product and channel deployment; MG combines an early Thai joint venture, established manufacturing, and high workforce localization; and GWM combines acquisition-led production with a multi-powertrain, multi-brand portfolio. In 2025, BYD sold 39,856 vehicles in Thailand, MG 27,007, and GWM 18,096, equivalent to approximately 6.42%, 4.35%, and 2.91% of the total new-vehicle market, respectively. These descriptive outcomes are consistent with, but do not prove, a relationship between deeper host-country commitments and market access. Policy-contingent production obligations, supplier capability, charging and service coverage, resale confidence, and cross-cultural coordination remain important constraints. The study extends the ownership–location–internalization perspective by treating localization as a portfolio of interdependent managerial choices rather than a single investment decision, and it proposes an auditable localization information dashboard for industrial managers. The findings are bounded by public-data availability and should not be interpreted causally.