Table 5.
Comparison of resilience metrics based on quantification methods, strengths, and limitations.
| Article | Methods | Strengths | Limitations |
|---|---|---|---|
| Zhang et al.49 | Based on max-plus algebra | Uses a digital twin based automatic resilience evaluation system | It focuses on system internal disruptions only. Needs historical datasets regarding fault modes to evaluate resilience |
| Alexopoulos et al.50 | Generic algorithm (probabilistic) | It combines both technological and economical terms and requires no large and complex amounts of data for calculations. Disruptions are observed as an ignition for system changes. Production-related aspects, such as varying types of products, operational status, and varying demand, can be described and utilized in a common context | Dependent on disruptions occurrence probabilities estimation |
| Song et al.19 | Fuzzy logic and generic algorithm | The resilience model can be also used to solve other combination problems. Considers also cost and reputation factors | Focus on cloud manufacturing only |
| Caputo et al.53 | Generic algorithm (deterministic) | Step by step description of the process explained. Based on resilience, economic loss is calculated. Manufacturing was observed as the quality of service to estimate resilience | Addresses full plant processes and systems, rather than one workstation. No experiment or case study was included |
| Li et al.54 | DMEA and Monte Carlo simulation based (deterministic) | Considers different types of resilience behaviors based on specific disruption | Only considers internal disruptions and needs historical datasets for a bottom-up approach |
| Yoon et al.12 | General algorithm (probabilistic) | It is based on the existing resilience equation in which restoration as one component is considered | Focus and description are on sensor false alarms. Systems using prognostics and health management techniques were considered only |
| Jin et al.55 | Generic algorithms (probabilistic) | Expands the manufacturing resilience approach by defining 3 resilience metrics: performance loss, performance restoration time, and underperformance time | Case study set-up and resilient calculation not described, but only mentioned |
| Gu et al.9 | Generic algorithms (probabilistic) | Expands the manufacturing resilience approach by defining 3 resilience metrics: production loss, throughput settling time, and total underproduction time. Compares resilience to different company policies | Addresses full plant processes and systems, rather than one workstation |