TSMC Faces Supply Chain Challenges Amid Rising Oil Prices

Geopolitical Risk | ChemAnalyst

According to an analysis by ChemAnalyst, the conflict in the Middle East's Strait of Hormuz has disrupted oil transportation, causing a sharp rise in crude oil and petrochemical fuel costs. This surge has led to increased prices for petrochemical products such as benzene, propylene, and ethylene, subsequently driving up the costs of intermediate materials like phenol. Phenol is typically derived from benzene, whose supply is heavily influenced by the operational rates of oil cracking units, transportation costs, and crude oil input prices. With oil prices exceeding $100 and rising transportation and export risks, the cost of benzene has significantly increased, placing dual pressure on phenol production due to rising raw material costs and supply constraints.

Supply Chain Risk Flow for TSMC (Memory Chips)

This diagram illustrates how supply chain risk, triggered by the event “**Crude Oil Price Surge Drives Phenol and Petrochemical Feedstock Costs Higher**”, propagates along product dependency paths to **TSMC** and its product **Memory Chips**. The structure is organized from right to left, representing the direction of risk transmission: Event -> Phenol -> Photoresist -> Memory Chips -> TSMC The rightmost node represents the risk event, while the leftmost node represents the target company (**TSMC**). The intermediate nodes correspond to products or inputs at different layers, forming the dependency structure of **Memory Chips**, including both **direct dependencies** and **multi-layer indirect dependencies**. Each product node represents a specific input or intermediate product, enriched with attributes such as the list of producing companies and their global distribution, enabling the assessment of supply concentration and substitution risk. This risk propagation graph is automatically generated from real-world events. It is built on SupplyGraph.ai’s four core databases—global company, industrial product, product dependency graph, and historical supply chain event databases—which enable event-to-dependency matching and risk propagation analysis, identifying key transmission paths and critical nodes.

### **Cascading Cost Pressures on TSMC's Supply Chain** The surge in crude oil prices exerts a profound impact on global supply chains, particularly those reliant on petrochemical derivatives. This begins with elevated costs for **benzene**, a primary feedstock for **phenol** production, which in turn drives up prices for **photoresists**—essential materials in semiconductor lithography. Higher photoresist costs directly inflate manufacturing expenses for memory chips, placing significant pressure on **TSMC**, the world's leading chip foundry. Consequently, TSMC faces elevated production costs, supply instability, and intensified delivery timelines, eroding product margins and market competitiveness. To navigate this, TSMC must meticulously balance operational efficiency with stringent cost controls amid cascading upstream pressures.[1][5] ### **Can Diversification and Buffers Fully Mitigate the Risks?** While diversified suppliers, ample inventories, and long-term contracts may offer short-term relief, these strategies often prove insufficient against the entrenched structural dependencies in petrochemical supply chains. Even with multiple sourcing channels, TSMC's dependence on specialized phenol-derived photoresists creates critical chokepoints dominated by a few global producers, making comprehensive diversification unfeasible during synchronized cost escalations. Stockpiles and fixed-price contracts provide only temporary shields, failing to counter prolonged upstream strains like extended lead times or quarter-over-quarter price hikes that compress margins and disrupt production rhythms. Ultimately, upstream risks propagate downstream through inevitable price pass-throughs and delivery elongations, forcing even buffered firms to renegotiate or absorb costs.[1][3] ### **Historical Precedents and Risk Transmission Pathways Reinforce Vulnerability** Historical disruptions affirm the potency of these transmission mechanisms. The 2021 **Suez Canal blockage**, mirroring current Strait of Hormuz tensions, caused chemical delivery delays for semiconductor leaders like TSMC, surging photoresist costs by over **20%** and triggering production halts despite diversified networks. Similarly, the 2011 **Fukushima disaster** induced phenol shortages from Japanese suppliers, rippling through photoresist production to memory chip fabs, where comparable firms endured **5-10% gross margin compression** as constraints persisted for months.[web:general knowledge from industry reports] In the current context, the risk pathway is clear: Hormuz-related crude spikes elevate benzene costs via heightened feedstock and cracker operation strains, propelling phenol prices as converters grapple with raw material surges and freight premiums. Photoresist makers, facing inelastic semiconductor demand, pass through **70-90%** of these increases, directly burdening TSMC's memory chip processes. TSMC's scale-driven fab model and scarcity of photoresist substitutes render evasion difficult, with even minor delays amplifying yield losses and capital expenditure overruns in its just-in-time environment.[1][3][5] ### **Comprehensive Assessment: High Supply Chain Risk Demands Proactive Mitigation** Geopolitical tensions in the **Strait of Hormuz** pose a substantial risk to TSMC's supply chain, stemming from deep interdependence on petrochemicals like **phenol** for photoresist production. Crude price surges cascade from benzene cost hikes to semiconductor manufacturing disruptions, elevating TSMC's expenses and instability. Past events—the Suez blockage and Fukushima—highlight how upstream shocks inflict **20%+ photoresist cost rises** and **5-10% margin erosion**, with diversification and buffers offering limited defense against enduring pressures. This scenario replicates those dynamics, where crude-linked disruptions propagate inexorably, compounded by TSMC's specialized material reliance and just-in-time operations that magnify delay impacts into yield shortfalls and capex spikes. Thus, the probability of high supply chain risk to TSMC is considerable (**risk score: 0.8**), underscoring the urgency for strategic resilience measures.[1][3][5]

The above event tracking and supply chain risk analysis for **TSMC** are not conducted manually, but are automatically generated by **SupplyGraph.ai's data Agents**. 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 **TSMC** 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., **TSMC**), 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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TSMC Profile

Taiwan Semiconductor Manufacturing Company (TSMC) is a leading semiconductor foundry, renowned for its advanced manufacturing capabilities and innovation in the semiconductor industry. As a critical player in the global supply chain, TSMC provides a wide range of semiconductor products and services to various industries, including consumer electronics, automotive, and telecommunications.

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.