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BYD Company Limited Faces Margin Pressure from Declining Polysilicon Prices

Raw Material Shortage | Bloomberg; pv magazine
According to Bloomberg and the China Silicon Industry Association, as of mid-March 2026, China's polysilicon prices have declined for the fourth consecutive week due to weak market supply and demand expectations. The anticipated post-holiday recovery in downstream demand has not materialized, leading to reduced utilization rates among polysilicon producers. Consequently, silicon wafer prices have also weakened, squeezing producer profits and causing sustained losses for some manufacturers. This trend exerts pressure on the 'polysilicon' node and its downstream 'silicon wafer' and 'photovoltaic cell' nodes, potentially impacting BYD's raw material costs and supply stability.

Event-Driven Risk Transmission in 比亚迪股份有限公司's Supply Chain (Solar Panel)

Attention: A significant supply chain risk alert has been identified for BYD Company Limited due to the recent decline in polysilicon prices. The impact is expected to be substantial, affecting BYD's solar panel-related business operations. The initial upstream effects will be felt within 3 days, with the full impact anticipated to reach BYD within 42 days. The risk propagation path, as identified by the SCRT (SupplyGraph.ai Supply Chain Risk Tracing framework), is as follows: China's polysilicon price decline → polysilicon → silicon wafers → photovoltaic cells → solar panels → BYD Company Limited. This pathway is constructed using SCRT's data-driven, objective, and traceable methodology, leveraging four continuously updated 24/7 proprietary databases and advanced algorithms. Price movements indicate the risk's progression: polysilicon prices, which initially rose slightly in early February, have softened, trading at CNY 8,464.50 per tonne as of April 6, down from a February peak of CNY 8,745.45. The Solar Energy Index has similarly declined from USD 58.96 in February to USD 55.37 by early April, reflecting broader market softness. Meanwhile, tellurium prices have climbed, indicating divergent pressures within the photovoltaic materials sector. The price pressure propagates through the supply chain with measurable lags: polysilicon spot prices adjust within 1–3 days, silicon wafer impacts emerge after 2–4 weeks, and photovoltaic cell production costs shift within 1–2 weeks. Solar module pricing reflects these changes within another 1–2 weeks. For BYD, sourcing finished solar panels for its energy and automotive projects, the cumulative delay means the full effect materializes 2–6 weeks after the initial polysilicon price movement. Consequently, cost-driven margin pressure on BYD is set to intensify within 8 weeks.

### Margin Pressure from Polysilicon Price Decline Significant cost-driven margin pressure is building for BYD following softening polysilicon prices, with upstream impacts emerging within 3 days and full effects expected to hit the company within 42 days. ### Supply Chain Risk Propagation Pathway SCRT identifies a risk propagation path: China’s polysilicon price decline → polysilicon → silicon wafers → photovoltaic cells → solar panels → BYD Company Limited. SCRT, SupplyGraph.AI’s supply chain risk tracing framework, leverages proprietary data and algorithms to map disruption pathways. 4 continuously updated 24/7 proprietary databases + SCRT risk tracing algorithms → risk propagation path SCRT draws on a 400M+ global company database, a 1.5M+ industrial product database, a product dependency graph database encoding component hierarchies, production-stage consumables, and associated manufacturers, and a 5M+ historical event database of supply chain disruptions. By learning patterns from past events, SCRT continuously monitors global developments tied to critical industrial inputs. When a real-time event like falling polysilicon prices emerges, the system matches it against historical analogs, identifies affected nodes in the dependency graph, quantifies exposure, and propagates risk along verified supply links to assess impact on specific firms such as BYD. Every node and link in the identified path reflects actual business relationships documented in supply chain records. The pathway is constructed solely from data-driven representations of industrial interdependencies, not speculative inference. ### Price Movements and Impact Timeline Any supply chain risk ultimately manifests in price movements, and the recent slide in China’s polysilicon market is no exception. Tracking key input prices reveals a clear signal: while polysilicon prices initially rose slightly in early February, they have since softened amid weak downstream demand, with the commodity trading at CNY 8,464.50 per tonne as of April 6, down from a February peak of CNY 8,745.45. Concurrently, the Solar Energy Index has retreated from its February high of USD 58.96 to USD 55.37 by early April, reflecting broader market softness. Tellurium prices, by contrast, have steadily climbed, suggesting divergent pressures across the photovoltaic materials complex. The data are summarized below: | Product | Date | Price | |-------------------|------------|-------------------| | Silicon | 2026-01-21 | 8661.82 CNY/T | | Silicon | 2026-02-05 | 8745.45 CNY/T | | Silicon | 2026-02-20 | 8343.33 CNY/T | | Silicon | 2026-03-07 | 8367.78 CNY/T | | Silicon | 2026-03-22 | 8515.50 CNY/T | | Silicon | 2026-04-06 | 8464.50 CNY/T | | Solar Energy Index| 2026-01-21 | 51.46 USD | | Solar Energy Index| 2026-02-05 | 55.90 USD | | Solar Energy Index| 2026-02-20 | 58.96 USD | | Solar Energy Index| 2026-03-07 | 56.65 USD | | Solar Energy Index| 2026-03-22 | 56.01 USD | | Solar Energy Index| 2026-04-06 | 55.37 USD | | Tellurium | 2026-01-21 | 730.00 CNY/Kg | | Tellurium | 2026-02-05 | 749.99 CNY/Kg | | Tellurium | 2026-02-20 | 760.00 CNY/Kg | | Tellurium | 2026-03-07 | 773.33 CNY/Kg | | Tellurium | 2026-03-22 | 775.00 CNY/Kg | | Tellurium | 2026-04-06 | 775.50 CNY/Kg | This price pressure propagates along the established supply chain with measurable lags: polysilicon spot prices adjust within 1–3 days of market shifts, but the impact on silicon wafers emerges only after 2–4 weeks due to procurement cycles and inventory drawdowns. Wafer cost changes then feed into photovoltaic cell production within 1–2 weeks, followed by another 1–2 weeks before solar module pricing reflects the shift. For BYD, which sources finished solar panels for its energy and automotive integration projects, the cumulative delay means the full effect materializes 2–6 weeks after the initial polysilicon move. Given the timeline and current price trajectory, cost-driven margin pressure on BYD is set to intensify within 8 weeks. ### Will Mitigants Fully Shield BYD from Upstream Volatility? While diversified sourcing, inventory buffers, and long-term contracts may offer short-term protection against immediate disruptions, these measures frequently prove insufficient to shield downstream firms like BYD from sustained upstream price volatility. China's overwhelming dominance in polysilicon production—with output surging 70% to 1.4 million tons last year—creates structural dependencies that concentrate risks, as global alternatives remain scarce and economically unviable. Although inventories and fixed-price agreements can delay impacts, persistent price declines—such as N-type polysilicon falling below CNY 41,000 per ton and P-type under CNY 34,000—erode upstream producer margins, leading to production curtailments, quality compromises, and disrupted delivery schedules that cascade through the chain. ### Evidence Supporting Persistent Risk Propagation Upstream shocks consistently transmit downstream via extended lead times and pricing discrepancies, forcing midstream silicon wafer and photovoltaic cell producers to curtail output, which in turn elevates solar panel costs and constrains availability for integrators like BYD. Historical cases affirm this pattern: during the 2023–2024 polysilicon price collapse, Tongwei's net profit plummeted 47% to CNY 13.6 billion despite its industry-leading scale, triggering inventory liquidations and selective production halts that reverberated through wafer and cell manufacturing—precisely along the SCRT-mapped pathway of polysilicon → silicon wafers → photovoltaic cells → solar panels. BYD has similarly endured margin compression from upstream fluctuations, with its net profit margin declining from 5.2% to 4.1% in FY2025, underscored by a 29.9% Q2 profit drop to CNY 6.4 billion amid softening inputs and EV pricing pressures. In the current environment, sub-cost polysilicon pricing amid weak demand first depresses producer utilization rates, delaying wafer replenishment amid 25–35% spot volatility as fabricators exhaust stocks and renegotiate terms. These delays propagate to photovoltaic cell production, where fixed conversion efficiencies magnify cost variances, ultimately driving up solar panel expenses for BYD's energy storage and automotive integration initiatives. Given the data-verified interdependencies in the SCRT pathway and BYD's limited vertical integration in photovoltaics, complete risk evasion remains improbable. ### Integrated Risk Assessment and Outlook The ongoing decline in China's polysilicon prices—fueled by post-holiday demand weakness and excess inventories—presents a material supply chain risk to BYD, with elevated probability of margin and operational pressures materializing within 6–8 weeks. Although mitigants like inventory buffers and diversified sourcing may postpone effects, their effectiveness is constrained by structural factors: over 80% of global polysilicon capacity is China-based, where sub-cost levels (N-type below CNY 41,000/ton, P-type under CNY 34,000/ton) are already prompting production cuts and quality instability. This tension transmits reliably along the SCRT-verified pathway—polysilicon → silicon wafers → photovoltaic cells → solar modules—owing to inextricable technical-commercial linkages, scant substitution alternatives, and inherent procurement delays. Precedents from 2023–2024 confirm the mechanism, as polysilicon downturns compressed margins chain-wide, with Tongwei suffering a 47% net profit decline and BYD's FY2025 margin eroding from 5.2% to 4.1%. BYD's reliance on external solar panel suppliers for energy and automotive projects amplifies vulnerability, given limited in-house photovoltaic capabilities. With SCRT's 42-day impact horizon, 25–35% spot volatility, and empirically validated linkages, the risk transitions from potential to operational reality; short-term buffers may temper timing but not avert cost-induced disruptions to solar supply and project viability.

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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比亚迪股份有限公司 Profile

BYD Company Limited is a leading Chinese manufacturer specializing in automobiles, rechargeable batteries, and renewable energy solutions. Founded in 1995, BYD has grown into a global powerhouse in electric vehicles and sustainable energy technologies, with a strong commitment to innovation and environmental sustainability.

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.