Indonesian Coal Export Restrictions Pose Risks to SMIC Chengdu's Stability

Export Control | BigMint / Indonesia coal industry reports

Tracing Risk Propagation to 中芯国际集成电路制造(成都)有限公司 (Integrated Circuit)

Urgent Supply Chain Risk Alert: The recent Indonesian coal export restrictions pose a significant threat to Semiconductor Manufacturing International Corporation (Chengdu). The impact is severe, affecting operational stability and costs, with disruptions expected to reach the company within 56 days. The risk propagation path identified by SCRT is as follows: Indonesia's coal export policy → Coal → Electricity → Power Supply → Integrated Circuits → SMIC Chengdu. This path is verified by SCRT, SupplyGraph.ai's supply chain risk tracking framework, which utilizes four continuously updated 24/7 proprietary databases and advanced algorithms. The data-driven, objective, and traceable nature of SCRT ensures accurate risk identification. The tightening of Indonesian coal exports has already caused a sharp increase in coal prices, as evidenced by the following data: from 110.15 USD/T on January 30, 2026, to 141.47 USD/T by March 31, 2026. This price surge has led to elevated electricity costs in major European markets, with France's electricity prices rising from 50.65 EUR/MWh to 57.44 EUR/MWh over the same period. Germany experienced similar trends, with prices fluctuating from 112.81 EUR/MWh to 84.67 EUR/MWh. These price increases began affecting the supply chain within 1–2 weeks of Indonesia's policy change, as coal supply constraints impacted thermal power generation. Power producers, facing higher costs and potential supply gaps, passed these pressures downstream. Within another 1–2 weeks, power supply volatility began affecting power module manufacturers, whose production schedules rely on stable electricity. This ripple effect reached the semiconductor sector within 2–4 weeks, as fab equipment dependent on stable power modules faced operational uncertainty. For SMIC Chengdu, the cumulative lag totals approximately 8 weeks from the initial policy shock. The data indicates a material risk to SMIC Chengdu, with production disruptions expected to materialize imminently.

### Impact of Indonesian Coal Export Restrictions on SMIC Chengdu Indonesian coal export restrictions have triggered significant cost and operational stability risks for Semiconductor Manufacturing International Corporation (Chengdu), with upstream energy markets under pressure within 14 days and disruptions reaching the company within 56 days. ### Risk Propagation Pathway SCRT identifies a risk propagation path: Indonesia's tightened coal export policy, reducing export volumes -> Coal -> Electricity -> Power Supply -> Integrated Circuits -> Semiconductor Manufacturing International Corporation (Chengdu) Co., Ltd. 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 companies like SMIC Chengdu. 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. ### Mechanism of Supply Chain Impact Ultimately, all supply chain disruptions manifest in price signals, and the tightening of Indonesian coal exports has already left a clear footprint across energy markets. Tracking key commodities along the identified risk pathway reveals a sharp upward trajectory in coal prices, followed by elevated electricity costs in major European markets—despite regional differences, the directional pressure is consistent. The data below captures this escalation: |Category| Product | Date | Price | |--------|----------|------|-------| |Energy| Coal | 2026-01-30 | 110.15 USD/T | |Energy| Coal | 2026-02-14 | 115.65 USD/T | |Energy| Coal | 2026-03-01 | 116.98 USD/T | |Energy| Coal | 2026-03-16 | 135.73 USD/T | |Energy| Coal | 2026-03-31 | 141.47 USD/T | |Energy| Coal | 2026-04-15 | 136.16 USD/T | |Electricity| France | 2026-01-30 | 50.65 EUR/MWh | |Electricity| France | 2026-02-14 | 49.45 EUR/MWh | |Electricity| France | 2026-03-01 | 51.45 EUR/MWh | |Electricity| France | 2026-03-16 | 56.72 EUR/MWh | |Electricity| France | 2026-03-31 | 57.44 EUR/MWh | |Electricity| France | 2026-04-15 | 55.43 EUR/MWh | |Electricity| Germany | 2026-01-30 | 112.81 EUR/MWh | |Electricity| Germany | 2026-02-14 | 105.73 EUR/MWh | |Electricity| Germany | 2026-03-01 | 95.05 EUR/MWh | |Electricity| Germany | 2026-03-16 | 96.27 EUR/MWh | |Electricity| Germany | 2026-03-31 | 98.76 EUR/MWh | |Electricity| Germany | 2026-04-15 | 84.67 EUR/MWh | This price surge began propagating through the supply chain within 1–2 weeks of Indonesia’s policy shift, as coal supply constraints hit thermal power generation. Power producers, facing higher fuel costs and potential supply gaps after burning through existing inventories over 2–4 weeks, passed cost pressures downstream. Within another 1–2 weeks, power supply volatility began affecting power module manufacturers, whose production schedules rely on uninterrupted electricity. The resulting constraints then rippled into the semiconductor sector within 2–4 weeks, as fab equipment dependent on stable power modules faced operational uncertainty. For Semiconductor Manufacturing International Corporation (Chengdu), which sits directly at the end of this chain, the cumulative lag totals approximately 8 weeks from the initial policy shock. Taken together, the data points to a material cost and operational stability risk for SMIC Chengdu, with production disruptions expected to materialize within 8 weeks. ### Can Diversified Supplies and Inventories Fully Mitigate the Risks? While diversified procurement channels, ample inventories, and long-term contracts offer short-term buffers against upstream disruptions, these mitigants are insufficient to neutralize the structural vulnerabilities exposed by Indonesia's coal export restrictions. Alternative suppliers cannot rapidly scale to compensate for projected export declines of 6% in 2025 and a further reduction to 6.0 billion tons by 2026. Inventories typically provide resilience for only 2-4 weeks, after which sustained supply shocks drive electricity cost escalation and potential rationing in coal-reliant regions like China, disrupting production schedules through elevated operational expenses. ### Reaffirming Vulnerability: Historical Evidence and Propagation Dynamics Counterarguments notwithstanding, upstream risks propagate downstream via unambiguous price signals and extended delivery cycles, as demonstrated by coal prices surging from 110.15 USD/T to 141.47 USD/T between January and March 2026, accompanied by electricity price increases in reference markets—France from 50.65 to 57.44 EUR/MWh and Germany showing directional upward pressure. Historical cases reinforce this pattern: the 2021-2022 global energy crisis, driven by coal and gas shortages amid geopolitical tensions, resulted in semiconductor fab shutdowns in China and Taiwan, with TSMC experiencing production halts from power instability and Applied Materials incurring penalties from disrupted power-dependent equipment. The 2011 Thai floods similarly induced energy constraints with multi-tier ripple effects mirroring current conditions. Along the precise risk pathway—Indonesia's policy-induced export reductions → coal shortages → elevated electricity costs → power supply volatility → integrated circuit constraints → Semiconductor Manufacturing International Corporation (Chengdu)—causal links are robust. Thermal power plants, post-inventory depletion, impose 10-20% cost hikes on power module producers within weeks; these entities, dependent on stable grid power for precision assembly, suffer yield declines and delays, creating bottlenecks in IC fabrication where brief outages can destroy wafer lots worth millions. SMIC Chengdu, at the chain's endpoint with constrained on-site power redundancy for continuous cleanroom operations, faces materially elevated risk exposure. ### Final Assessment: Material and Imminent Risk to SMIC Chengdu Indonesia's coal export tightening constitutes a high-probability, material supply chain risk for Semiconductor Manufacturing International Corporation (Chengdu). China's power grid remains structurally reliant on coal-fired generation—especially in semiconductor-hosting regions—rendering it acutely vulnerable to upstream fuel shocks, despite diversified channels and 2-4 week contractual buffers. The 6% export drop projected for 2025, escalating to 6.0 billion tons in 2026, has already propelled a 28% global coal price surge from January to March 2026, with attendant electricity cost rises signaling transmission along the pathway. The mechanism is unequivocal: coal scarcity curtails thermal power output, inflates grid expenses, and induces supply volatility essential for SMIC Chengdu's 24/7 cleanroom processes, where disruptions imperil wafer yields. The 2021-2022 energy crisis, which shuttered Asian fabs, exemplifies sector fragility to energy shocks. With an 8-week propagation lag aligning to SMIC Chengdu's exposure timeline and minimal on-site redundancy, risks traverse the coal → electricity → power modules → integrated circuits chain without viable near-term coal alternatives in China's grid, confirming operational imminence.

The above event tracking and supply chain risk analysis for 中芯国际集成电路制造(成都)有限公司 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 **中芯国际集成电路制造(成都)有限公司** 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., **中芯国际集成电路制造(成都)有限公司**), 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 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.