BYD Company Limited Faces Cost-Reduction Pressure from Polysilicon Deflation

Financial Distress | OPIS / Dow Jones

Structural Analysis of Supply Chain Risk for 比亚迪股份有限公司 (Solar Panel)

Attention: Immediate Supply Chain Risk Alert for BYD. The recent deflation in polysilicon prices poses a moderate cost-reduction pressure on BYD, with the initial impact expected within 7 days and the full effect materializing in 70 days. This event is traced through a precise risk propagation path: China Q1 polysilicon price decline, leading to inventory surges and policy uncertainties, affects Polysilicon → Silicon Wafer → Photovoltaic Cell → Solar Panel → BYD Company Limited. This pathway is identified by SCRT, the SupplyGraph.ai supply chain risk tracking framework, which utilizes four continuously updated 24/7 proprietary databases and advanced SCRT algorithms. 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. SCRT's data-driven, objective, and traceable analysis reveals the real business dependencies and quantifies risk exposure along the supply chain. The mechanism of impact is clear: polysilicon price deflation triggers a cascade of price adjustments across the supply chain. N-type dense polysilicon prices dropped by 32% from ¥59.00/kg on January 23 to ¥40.15/kg by April 8. This price collapse propagated to N-type G12-210 silicon wafers, which fell from ¥1.67 to ¥1.28 per piece, and G12 monocrystalline TOPCon battery cells, which declined from ¥0.40 to ¥0.38 per unit. The transmission of these price shifts occurred with measurable lags: polysilicon price changes reached wafer manufacturers within 2–4 weeks, flowed into cell production after another 3–5 weeks, and impacted module assembly within an additional 2–3 weeks. For BYD, integrated into solar panel manufacturing, this means the cumulative 8–14 week transmission window implies that the full effect of Q1’s polysilicon collapse began affecting input costs by early April. The delayed but accelerating cost pass-through suggests margin compression rather than supply disruption, as falling input prices have not yet led to proportionate declines in panel pricing amid weak demand. BYD faces moderate cost-reduction pressure, with the full impact of upstream deflation expected to register within 10 weeks of the initial polysilicon shock.

### Impact of Polysilicon Deflation on BYD BYD faces moderate cost-reduction pressure from upstream polysilicon deflation, with the initial shock hitting within 7 days and full impact reaching the company within 70 days. ### Risk Propagation Pathway to BYD SCRT identifies a risk propagation path: China Q1 polysilicon price decline: inventory surge and policy uncertainty -> Polysilicon -> Silicon Wafer -> Photovoltaic Cell -> Solar Panel -> 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 real business dependencies between companies. The path is constructed based on data-driven supply chain structures. ### Mechanism of Supply Chain Impact Ultimately, any supply chain disruption manifests in price movements, and the data trace a clear transmission from collapsing polysilicon values to downstream components critical to BYD’s solar operations. As shown in the table below, N-type dense polysilicon prices plunged from ¥59.00/kg on January 23 to ¥40.15/kg by April 8—a 32% drop—while N-type G12-210 silicon wafers fell from ¥1.67 to ¥1.28 per piece over the same period, and G12 monocrystalline TOPCon battery cells declined from ¥0.40 to ¥0.38 per unit. |Category| Product | Date | Price | |--------|----------|------|-------| |Polysilicon| N-type Dense Material | 2026-01-23 | 59.00 Yuan/kg | |Polysilicon| N-type Dense Material | 2026-02-07 | 57.65 Yuan/kg | |Polysilicon| N-type Dense Material | 2026-02-22 | 57.50 Yuan/kg | |Polysilicon| N-type Dense Material | 2026-03-09 | 53.82 Yuan/kg | |Polysilicon| N-type Dense Material | 2026-03-24 | 45.59 Yuan/kg | |Polysilicon| N-type Dense Material | 2026-04-08 | 40.15 Yuan/kg | |Silicon Wafer| N-type G12-210 | 2026-01-23 | 1.67 Yuan/piece | |Silicon Wafer| N-type G12-210 | 2026-02-07 | 1.55 Yuan/piece | |Silicon Wafer| N-type G12-210 | 2026-02-22 | 1.48 Yuan/piece | |Silicon Wafer| N-type G12-210 | 2026-03-09 | 1.38 Yuan/piece | |Silicon Wafer| N-type G12-210 | 2026-03-24 | 1.32 Yuan/piece | |Silicon Wafer| N-type G12-210 | 2026-04-08 | 1.28 Yuan/piece | |Battery Cell| G12 Monocrystalline Topcon | 2026-01-23 | 0.40 Yuan/piece | |Battery Cell| G12 Monocrystalline Topcon | 2026-02-07 | 0.43 Yuan/piece | |Battery Cell| G12 Monocrystalline Topcon | 2026-02-22 | 0.43 Yuan/piece | |Battery Cell| G12 Monocrystalline Topcon | 2026-03-09 | 0.43 Yuan/piece | |Battery Cell| G12 Monocrystalline Topcon | 2026-03-24 | 0.41 Yuan/piece | |Battery Cell| G12 Monocrystalline Topcon | 2026-04-08 | 0.38 Yuan/piece | This deflationary pressure propagated along the supply chain with measurable lags: polysilicon price shifts reached wafer makers within 2–4 weeks, then flowed into cell production after another 3–5 weeks, and finally impacted module assembly within an additional 2–3 weeks. Given BYD’s integration into solar panel manufacturing, the cumulative 8–14 week transmission window implies that the full effect of Q1’s polysilicon collapse began materializing in its input costs by early April. The delayed but accelerating cost pass-through points to margin compression rather than supply disruption, as falling input prices have not yet translated into proportionate declines in panel pricing amid weak demand. Taken together, the data indicate that BYD faces moderate cost-reduction pressure—rather than acute supply risk—with the full impact of upstream deflation expected to register within 10 weeks of the initial polysilicon shock. ### Could Mitigating Factors Fully Shield BYD from Upstream Volatility? At first glance, BYD’s operational resilience—supported by supplier diversification, strategic inventory holdings, and long-term procurement contracts—might appear sufficient to buffer against upstream polysilicon price deflation. However, such measures offer only partial and temporary insulation. Structural dependencies persist: despite global sourcing efforts, BYD’s solar operations remain reliant on high-purity N-type polysilicon predominantly supplied by Chinese manufacturers, who collectively command approximately 80% of global production capacity. Alternative suppliers outside China currently lack the scale, consistency, and cost competitiveness to serve as viable substitutes during periods of systemic market stress. Furthermore, while inventories and fixed-price contracts can delay the immediate impact of price swings, they cannot neutralize sustained deflationary pressure stemming from macro-level drivers such as inventory overhangs and regulatory ambiguity. In fact, prolonged price erosion threatens upstream producer viability, particularly in key Chinese polysilicon hubs like Sichuan and Yunnan, where margin compression has already prompted production curtailments—disruptions that reverberate downstream regardless of contractual safeguards. ### Historical Precedents and Structural Dependencies Reinforce Downstream Vulnerability The limitations of mitigation strategies are further validated by historical supply chain shocks. During the 2021–2022 polysilicon shortage—triggered by energy rationing in Xinjiang—prices surged by over 500%, cascading through wafers and cells to severely compress margins for vertically integrated players like LONGi and JinkoSolar. Despite their diversified supply bases and robust inventory management, these firms could not fully evade the tiered transmission of upstream constraints. This precedent mirrors the current deflationary episode, where the same structural linkages enable rapid risk propagation. In the present case, the 32% decline in N-type dense polysilicon prices (from ¥59.00/kg on January 23 to ¥40.15/kg by April 8) triggered a sequential adjustment across the value chain: N-type G12-210 silicon wafer prices fell 23% (¥1.67 to ¥1.28/piece) within 2–4 weeks, followed by a 5% drop in G12 monocrystalline TOPCon cell prices (¥0.40 to ¥0.38/unit) after an additional 3–5 weeks. These lags reflect fixed-cost burdens and production inertia in midstream segments, which ultimately transmit mismatched input costs to solar panel assemblers like BYD. Although BYD’s vertical integration enhances operational scale and coordination, it does not eliminate exposure to asynchronous cost pass-through—especially in a weak-demand environment where panel pricing fails to adjust proportionally to falling input costs. Consequently, margin pressure, rather than physical supply disruption, emerges as the primary risk vector. ### Integrated Assessment: Moderate Risk with High Structural Certainty The convergence of real-time price data, supply chain topology, and historical analogs confirms that BYD faces a **moderate but material** supply chain risk from Q1 2026 polysilicon deflation. The causal transmission path—originating in inventory surges and policy uncertainty in China, propagating through polysilicon, wafers, cells, and panels—has already manifested in measurable cost shifts across all intermediate nodes. While no acute supply interruption is anticipated, the 8–14 week lag in full impact realization means that BYD’s solar segment is now entering a phase of intensified margin compression. Mitigation levers such as inventory buffers and supplier diversification are constrained by China’s overwhelming dominance in high-purity N-type polysilicon output and the protracted nature of the deflationary cycle. Vertical integration provides efficiency gains but cannot decouple BYD from the systemic dynamics of a tightly coupled, China-centric photovoltaic supply chain. Given the evidence, the risk is not speculative but structurally embedded, warranting a calibrated risk score of **0.6**—reflecting moderate severity, high likelihood of transmission, and limited scope for complete avoidance.

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.