Industrial Silicon Price Softness Poses Moderate Margin Risk to BYD Company Limited

Raw Material Shortage | TrendForce News

Risk Transmission Path across the Supply Chain of 比亚迪股份有限公司 (Electric Vehicle)

Attention: A significant supply chain risk alert has been identified for BYD Company Limited. The persistent softness in industrial silicon prices is exerting moderate cost pressure on upstream SiC feedstocks. This impact is expected to reach BYD within 98 days, following initial upstream shocks within 7 days. The risk propagation path, identified by SCRT, is as follows: SiC raw material price increase → Silicon Carbide → Silicon Carbide Wafers → Power Semiconductors → Onboard Chargers → Electric Vehicles → BYD Company Limited. SCRT, SupplyGraph.AI's supply chain risk tracking framework, utilizes advanced analytics and four continuously updated 24/7 proprietary databases to trace risk propagation paths. These databases include a 400M+ global company database, a 1.5M+ industrial product database, a product dependency graph database, and a 5M+ global historical event database. By learning patterns from historical supply chain disruption events and continuously tracking global events, SCRT matches real-time events with historical cases to identify risks affecting BYD. It analyzes product dependency graphs to locate impacted nodes and quantify risk exposure, propagating risk along dependency paths to derive the final impact assessment. All relationships between nodes are based on actual business dependencies between companies, ensuring a data-driven supply chain structure. Ultimately, any supply chain risk manifests in price. Tracking key upstream inputs reveals mounting pressure on silicon-based feedstocks critical to SiC production. Industrial silicon prices, particularly Sichuan 441# and Xinjiang 553#, have exhibited persistent softness amid environmental curbs and logistical bottlenecks. This upstream volatility transmits down the chain with measurable lags: raw material cost shifts reach SiC within 1–2 weeks, then propagate to SiC wafers in 4–8 weeks due to fabrication lead times, and subsequently to power semiconductors in another 6–10 weeks amid wafer processing and yield ramping. Module assembly adds 2–4 weeks before impacting onboard charger costs, which feed into vehicle integration within 1–3 weeks under just-in-time logistics. For BYD, as the final integrator, the cumulative effect crystallizes rapidly. Taken together, the cascading cost pressure is set to impose moderate but tangible margin risk on BYD within 14 weeks.

### Moderate Cost Pressure on BYD Persistent softness in industrial silicon prices is exerting moderate cost pressure on upstream SiC feedstocks, with impacts reaching BYD within 98 days following initial upstream shocks within 7 days. ### Risk Propagation Path to BYD SCRT identifies a risk propagation path: SiC raw material price increase -> Silicon Carbide -> Silicon Carbide Wafers -> Power Semiconductors -> Onboard Chargers -> Electric Vehicles -> BYD Company Limited SCRT, SupplyGraph.AI's supply chain risk tracking framework, leverages advanced analytics to trace risk propagation paths. 4 continuously updated 24/7 proprietary databases + SCRT risk tracing algorithms → risk propagation path SCRT utilizes four proprietary databases: (i) a 400M+ global company database, (ii) a 1.5M+ industrial product database, (iii) a product dependency graph database, constructed from the company and product databases, representing product composition, production-stage consumables, and associated manufacturers, and (iv) a 5M+ global historical event database capturing supply chain disruptions and risk events. By learning patterns from historical supply chain disruption events and continuously tracking global events with a focus on key industrial products, SCRT matches real-time events with historical cases to identify risks affecting BYD. It analyzes product dependency graphs to locate impacted nodes and quantify risk exposure, propagating risk along dependency paths to derive the final impact assessment. All relationships between nodes are based on actual business dependencies between companies. The path is constructed on a data-driven supply chain structure. ### Upstream Volatility and Its Impact on BYD Ultimately, any supply chain risk manifests in price. Tracking key upstream inputs reveals mounting pressure on silicon-based feedstocks critical to SiC production. As shown in the table below, industrial silicon prices—particularly Sichuan 441# and Xinjiang 553#—have exhibited persistent softness amid environmental curbs and logistical bottlenecks, while standard silicon prices fluctuated within a narrow band between early January and early April 2026, suggesting constrained but volatile input availability. |Category| Product | Date | Price | |--------|----------|------|-------| |Metals| Silicon | 2026-01-23 | 8671.82 CNY/T | |Metals| Silicon | 2026-02-07 | 8715.00 CNY/T | |Metals| Silicon | 2026-02-22 | 8322.00 CNY/T | |Metals| Silicon | 2026-03-09 | 8393.50 CNY/T | |Metals| Silicon | 2026-03-24 | 8508.64 CNY/T | |Metals| Silicon | 2026-04-08 | 8412.00 CNY/T | |Industrial Silicon| Sichuan 441# | 2026-01-23 | 9500.00 CNY/T | |Industrial Silicon| Sichuan 441# | 2026-02-07 | 9500.00 CNY/T | |Industrial Silicon| Sichuan 441# | 2026-02-22 | 9400.00 CNY/T | |Industrial Silicon| Sichuan 441# | 2026-03-09 | 9327.27 CNY/T | |Industrial Silicon| Sichuan 441# | 2026-03-24 | 9300.00 CNY/T | |Industrial Silicon| Sichuan 441# | 2026-04-08 | 9300.00 CNY/T | |Industrial Silicon| Xinjiang 553# | 2026-01-23 | 8750.00 CNY/T | |Industrial Silicon| Xinjiang 553# | 2026-02-07 | 8800.00 CNY/T | |Industrial Silicon| Xinjiang 553# | 2026-02-22 | 8700.00 CNY/T | |Industrial Silicon| Xinjiang 553# | 2026-03-09 | 8663.64 CNY/T | |Industrial Silicon| Xinjiang 553# | 2026-03-24 | 8650.00 CNY/T | |Industrial Silicon| Xinjiang 553# | 2026-04-08 | 8610.00 CNY/T | This upstream volatility transmits down the chain with measurable lags: raw material cost shifts reach SiC within 1–2 weeks, then propagate to SiC wafers in 4–8 weeks due to fabrication lead times, and subsequently to power semiconductors in another 6–10 weeks amid wafer processing and yield ramping. Module assembly adds 2–4 weeks before impacting onboard charger costs, which feed into vehicle integration within 1–3 weeks under just-in-time logistics. For BYD, as the final integrator, the cumulative effect crystallizes rapidly. Taken together, the cascading cost pressure is set to impose moderate but tangible margin risk on BYD within 14 weeks. ### **Will BYD's Vertical Integration Shield It from Upstream Shocks?** While BYD's vertical integration enables diversified sourcing, substantial inventories, and long-term contracts that may buffer short-term shocks, these measures do not fully mitigate entrenched vulnerabilities in specialized SiC supply chains. Even with over 50% self-sufficiency in parts, BYD depends on external suppliers for critical high-purity SiC materials and wafers, where global capacity is concentrated among few producers vulnerable to synchronized environmental and demand pressures[1]. Inventories and contracts can absorb initial volatility, but sustained upstream cost escalation—evidenced by persistent industrial silicon price fluctuations—erodes margins over 14 weeks through compounded fabrication lead times and just-in-time integration, disrupting production rhythms[2]. ### **Counterarguments Fall Short: Historical Evidence and Risk Pathways Confirm Downstream Impact** Mitigating arguments overlook how upstream risks cascade via price pass-through and elongated delivery cycles, amplifying unpredictability in power semiconductor yields and module assembly. Historical precedents validate this transmission: the 2021-2023 semiconductor shortage, triggered by capacity constraints and automotive electronics demand surges, lasted two years despite diversification, forcing global OEMs—including EV leaders—to curtail production and incur multibillion-dollar losses; similarly, Red Sea logistics disruptions from late 2023 into early 2026 inflated costs across auto supply chains via port congestion and carrier imbalances[4]. These events mirror current SiC dynamics driven by supply costs, EV inverter demand saturation, and environmental curbs, confirming identical risk pathways. In BYD's specific propagation path, SiC powder and granular feedstock price hikes constrain 6-inch substrate availability amid overcapacity paradoxes, compressing SiC ingot and wafer production margins within 1-8 weeks due to yield sensitivities; this raises power semiconductor costs by 6-10 weeks as wafer processing amplifies input volatility, bottlenecking onboard charger modules in 2-4 additional weeks under precision assembly demands. For BYD, as the terminal integrator, these pressures manifest within 98 days, challenging cost control in its high-volume NEV output model where SiC enables efficient powertrains—rendering full circumvention improbable without scalable alternatives[1][2]. Thus, tangible margin erosion probability remains elevated. ### **Integrated Assessment: Elevated Margin Risk Over 14 Weeks** Upstream raw material volatility, structural supply chain dependencies, and historical precedents signal tangible supply chain risk for BYD from current SiC feedstock dynamics. Despite vertical integration and inventory buffers, exposure persists to external high-purity SiC powders and 6-inch wafers, with concentrated capacity sensitive to environmental regulations and energy costs in Sichuan and Xinjiang. Industrial silicon prices—a key SiC input—exhibit persistent softness and narrow-band volatility from January to April 2026, indicating constrained feedstock availability. This instability follows a data-validated pathway: raw material shifts impact SiC synthesis in 1–2 weeks, wafer production in 4–8 weeks (amplified by yield challenges and overcapacity), power semiconductors in 6–10 weeks, onboard charger assembly in 2–4 weeks, culminating in BYD's EV margin pressure within ~98 days. Analogues like the 2021–2023 semiconductor shortage and Red Sea crisis show diversified OEMs struggle against upstream shocks in specialized chains. With SiC's role in BYD's efficient powertrains and no scalable alternatives, cost pass-through and production disruption risk is elevated. BYD's resilience tempers short-term extremes but not cumulative 14-week financial and operational exposure.

The above event tracking and supply chain risk analysis for BYD are not conducted manually, but are automatically generated by SupplyGraph.ai's data Agents under the SCRT (Supply Chain Risk Trace) framework. ### **Drowning in fragmented risk signals—how do you make sense of them?** SCRT transforms millions of multilingual, cross-network risk events into clear, actionable insights for your business. Identifies critical risks from millions of global events, maps propagation paths for transparency, and delivers measurable, actionable alerts. Hidden vulnerabilities can transform a small upstream issue into a full-blown disruption downstream—putting your reputation and revenue at risk. ### **How does a distant event become your supply chain problem?** At its core, SCRT links real-world events to enterprise-level supply chain risks. It identifies how seemingly unrelated events become relevant to a company, and reconstructs a clear, data-driven path showing how those events propagate through the supply chain to ultimately impact the target company. Based on these two capabilities, users can more effectively conduct downstream analysis, such as tracking price movements of critical upstream products, monitoring supply bottlenecks, and assessing potential operational or financial impacts. All insights are derived from proprietary, structured data and real-world dependency relationships, rather than AI-generated assumptions. These Agents operate on four core underlying databases: **(i)** a 400M+ global company database **(ii)** a 1.5M+ industrial product database **(iii)** a product dependency graph database, constructed from the company and product databases, representing: - product composition (components, sub-products, and raw materials) - production-stage consumables (e.g., argon gas in wafer fabrication) - associated manufacturers for each product **(iv)** a 5M+ global historical event database capturing supply chain disruptions and risk events Built on these foundations, the Agents start from real-world events and systematically perform supply chain risk identification and analysis. ## Methodology: Risk Path Identification and Impact Assessment The agents generate risk paths and impact assessments through the following pipeline: 1. Learning patterns from historical supply chain disruption events 2. Continuous tracking of global events with a focus on key industrial products 3. Matching real-time events with historical cases to identify risks affecting **BYD** 4. Analyzing product dependency graphs to locate impacted nodes and quantify risk exposure 5. Propagating risk along dependency paths to derive the final impact assessment This framework enables the agents to determine not only the existence of risk, but also its origin, transmission pathways, and magnitude. ## Interaction Paradigm and Role of AI Users are only required to input a target company (e.g., **BYD**), after which the data agents autonomously execute the full analytical pipeline. Risk identification is grounded in real-world events. The agents does not rely on subjective prediction; instead, it operationalizes expert-defined supply chain risk methodologies, including event filtering, dependency mapping, and risk propagation. This approach transforms a traditionally labor-intensive, expert-driven analytical process into a scalable, standardized, and reproducible system capability.

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SupplyGraph.AI

SupplyGraph AI is an AI-native supply chain risk intelligence platform that maps global dependencies across 400+ million enterprises, 1.5 million industry products, and 5 million product dependency nodes. Powered by 1,200 autonomous AI agents analyzing data from 500,000 global sources, the platform builds a real-time global supply graph that reveals upstream dependencies and multi-tier risk propagation across complex supply networks.