The rampant increase in frequency and complexity of cyber-attacks against manufacturing firms, has motivated the development of identification and mitigation techniques for cyber-physical vulnerabilities in manufacturing. While the field of cybersecurity assessment approaches is expansive, there is no literature aimed at assessing cyber-physical vulnerabilities for manufacturing systems. In response, this paper provides a framework for systematically identifying cyber-physical vulnerabilities in manufacturing systems. The proposed approach employs intersection mapping to identify cyber-physical vulnerabilities in manufacturing. A cyber-physical vulnerability impact analysis using decision trees then provides the manufacturer with a stoplight scale between low, medium, and high levels of cyber-physical vulnerability for each analyzed production process. The stoplight scale allows manufacturers to interpret assessment results in an intuitive way. Finally, the paper provides a case study of the proposed approach at an applied manufacturing research facility and provides general recommendations to securing similar facilities from cyber-physical attacks.

Purpose: With the emergence of the different Industry 4.0 technologies and the interconnectedness between the physical and cyber components within manufacturing systems, the manufacturing environment is becoming more susceptible to unexpected disruptions, and manufacturing systems need to be even more resilient than before. Hence, the purpose of this work is to explore how does incorporating Industry 4.0 into current manufacturing systems affects (positively or negatively) its resiliency.Design/methodology/approach: A Systematic Literature Review (SLR) was performed with a focus on studying the manufacturing system’s resilience when applying Industry 4.0 technologies. The SLR is composed of four phases, which are (1) questions formulation, (2) determining an adequate search strategy, (3) publications filtering and (4) analysis and interpretation.Findings: From the SLR results’ analysis, four potential research opportunities are proposed related to conducting additional research within the research themes in this field, considering less studied Industry 4.0 technologies or more than one technology, investigating the impact of some technologies on manufacturing system’s resilience, exploring more avenues to incorporate resiliency to preserve the state of the system, and suggesting metrics to quantify the resilience of manufacturing systems.Originality/value: Although there are a number of publications discussing the resiliency of manufacturing systems, none fully investigated this topic when different Industry 4.0 technologies have been considered. In addition to determining the current research state-of-art in this relatively new research area and identifying potential future research opportunities, the main value of this work is in providing insights about this research area across three different perspectives/streams: (1) Industry 4.0 technologies, (2) resiliency and (3) manufacturing systems and their intersections.

The traditional implementation of the risk-adjusted Bernoulli cumulative sum (CUSUM) chart for monitoring surgical outcome quality requires waiting a pre-specified period of time after surgery before incorporating patient outcome information. We propose a simple but powerful implementation of the risk-adjusted Bernoulli CUSUM chart that incorporates outcome information as soon as it is available, rather than waiting a pre-specified period of time after surgery. A simulation study is presented that compares the performance of the traditional implementation of the risk-adjusted Bernoulli CUSUM chart to our improved implementation. We show that incorporating patient outcome information as soon as it is available leads to quicker detection of process deterioration. Deterioration of surgical performance could be detected much sooner using our proposed implementation, which could lead to the earlier identification of problems.