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Disruption evaluation in end-to-end semiconductor supply chains via interpretable machine learning.

Friedrich-Maximilian Jaenichen1, Christina J Liepold2, Abdelgafar Ismail1

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Summary
This summary is machine-generated.

This study introduces a hybrid simulation and machine learning model to analyze supply chain disruptions. It offers semiconductor companies strategies to mitigate demand volatility and improve inventory management.

Keywords:
Supervised learningchip shortagecounterfactual analysissemiconductor supply chain

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Artificial Intelligence

Background:

  • The COVID-19 pandemic has severely disrupted global supply chains, particularly impacting industries like automotive and semiconductors.
  • Understanding and mitigating the effects of end-market demand disruptions is critical for economic stability.

Purpose of the Study:

  • To develop and apply a hybrid approach combining simulation modeling and machine learning for analyzing supply chain vulnerabilities.
  • To provide actionable insights for semiconductor companies to manage demand-side risks.

Main Methods:

  • Utilized a supply chain simulation model integrated with born-again tree ensembles, a type of tree-based supervised machine learning classifier.
  • Investigated the influence of varying behavioral and structural parameters on key performance indicators like inventory levels.
  • Employed counterfactual analysis to derive managerial insights and tested mitigation strategies within the simulation.

Main Results:

  • Demonstrated the effectiveness of born-again tree ensembles in analyzing simulation data and identifying critical parameters affecting supply chain performance.
  • Quantified the impact of parameter variations on inventory levels and other key performance indicators.
  • Identified specific managerial strategies to mitigate adverse effects of demand disruptions.

Conclusions:

  • The hybrid simulation-machine learning approach offers a robust framework for understanding and managing supply chain risks.
  • Findings provide semiconductor companies with data-driven insights for enhancing resilience against demand volatility.
  • Integrating counterfactual analysis findings back into the simulation model deepens understanding of multi-echelon supply chain dynamics.