Recent COVID pandemic has revealed the vulnerability of modern manufacturing systems to endure disruptive changes. In response to the distress caused by disruptions, there is a pressing need to improve manufacturing resilience by embracing automation, digitization, and Artificial Intelligence (AI). This paper re-conceptualizes manufacturing resilience from the perspectives of system agility, robustness, and survivability to explain how industrial AI can be leveraged to improve manufacturing resilience. In addition, a unified framework to design resilient manufacturing systems is discussed.

This paper proposes a comprehensive Prognostics and Health Management (PHM) framework for large fleets of geographically distributed assets. The objective of this research study is to optimize spare part inventory according to asset performance, ensuring efficient and consistent production and extended machine life. The concept of asset condition monitoring and performance prediction along with optimizing maintenance operation is proposed by leveraging existing fleet-level PHM and Decision Support Tools (DST). Dynamic clustering methodology is adopted to equip the prediction model with the ability to adaptive update. And the impact of performance degradation to production loss is evaluated through risk assessment to link asset performance with production.