Managing the Bullwhip Effect in Semiconductor Supply Chain: A Case Study of Infineon Technologies AG
- Functional Applications - Inventory Management Systems
- Infrastructure as a Service (IaaS) - Backup & Recovery
- Logistics & Transportation
- Warehouse & Inventory Management
- Demand Planning & Forecasting
- Supply Chain Visibility
- System Integration
- Testing & Certification
Infineon Technologies AG, a global top 10 semiconductor company, faced significant challenges in managing the volatility in demand and the bullwhip effect in their supply chain, especially during the COVID-19 pandemic. The semiconductor industry is characterized by capital intensity and high demand volatility, which is highly dependent on innovation cycles. The bullwhip effect, a phenomenon where order fluctuations are amplified as they move up the supply chain, was a major concern for Infineon. During the pandemic, the demand for automotive semiconductors dropped significantly due to reduced commuting, leading to excess inventory. However, when the market rebounded, the increased demand coincided with a global computer microchip shortage, exacerbating the bullwhip effect.
MCM (Machine Centers Manufacturing)
About The Customer
Infineon Technologies AG is one of the world's largest semiconductor manufacturers, with a workforce of over 50,280 people worldwide. In 2021, the company reported revenue of more than €11 billion. Following the acquisition of the US company Cypress Semiconductor Corporation in April 2020, Infineon became a global top 10 semiconductor company. The company operates in an industry characterized by high capital intensity and demand volatility, with demand being highly dependent on innovation cycles and prone to the bullwhip effect.
To address these challenges, Infineon's supply chain engineers decided to apply system dynamics tools to study the bullwhip effect. They aimed to understand the impact of end-market scenarios on the bullwhip effect throughout the supply chain. The engineers identified end-market demand recovery scenarios, created a system dynamics supply chain model in AnyLogic, tested the model using historical data, and conducted a sensitivity analysis to identify parameters with the most significant impact on results. The supply chain model included four echelons: OEMs, Tier-1 supplier, Tier-2 supplier, and Semiconductor supplier. Each echelon was modeled to pass inputs through several control loops before outputting to the next stage. The model assumed that the semiconductor supplier reserves were infinite, guaranteed by the silicon supplier.
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