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<p>The increasing frequency and intensity of disruptions have heightened the vulnerability of global supply chains, underscoring the critical need for enhanced Supply Chain Resilience (SCRE). This study addresses this challenge by developing a decision-making framework that strategically aligns Industry 4.0 (I4.0) technologies with specific disruption typologies. Grounded in a Systematic Literature Review that identified 36 relevant I4.0 technologies, the framework classifies disruptions into four strategic scenarios based on their impact and frequency. For each scenario, the model recommends specific technological interventions by linking them to key resilience mechanisms, such as proactive risk management through Big Data Analytics and Artificial Intelligence, or enhanced systemic visibility via the Internet of Things. It also identifies the Key Performance Indicators most affected, providing a clear rationale for targeted investment aimed at tangible benefits like shorter recovery times or lower disruption-related costs. This study’s primary contribution is a prescriptive, scenario-driven framework. Unlike simple taxonomies, it offers managers a direct, actionable link between specific disruption typologies and targeted I4.0 technology investments, enabling a proactive and justified approach to building SCRE.</p>

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A decision-making framework for selecting industry 4.0 technologies to build supply chain resilience

  • 期刊论文

  • 发表日期：

    2025年12月21日

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  • José Miguel Vives-Martínez,
  • Marta M. Marín,
  • Rosario Domingo

摘要

The increasing frequency and intensity of disruptions have heightened the vulnerability of global supply chains, underscoring the critical need for enhanced Supply Chain Resilience (SCRE). This study addresses this challenge by developing a decision-making framework that strategically aligns Industry 4.0 (I4.0) technologies with specific disruption typologies. Grounded in a Systematic Literature Review that identified 36 relevant I4.0 technologies, the framework classifies disruptions into four strategic scenarios based on their impact and frequency. For each scenario, the model recommends specific technological interventions by linking them to key resilience mechanisms, such as proactive risk management through Big Data Analytics and Artificial Intelligence, or enhanced systemic visibility via the Internet of Things. It also identifies the Key Performance Indicators most affected, providing a clear rationale for targeted investment aimed at tangible benefits like shorter recovery times or lower disruption-related costs. This study’s primary contribution is a prescriptive, scenario-driven framework. Unlike simple taxonomies, it offers managers a direct, actionable link between specific disruption typologies and targeted I4.0 technology investments, enabling a proactive and justified approach to building SCRE.