Ahmad E. Elhabashy

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The increasing risk and impact of cyber‐physical attacks on the manufacturing industry are driven by vulnerabilities in industrial control systems and digital manufacturing technologies. These attacks can be strategically engineered to modify product dimensions while evading detection by current quality control (QC) tools. With the rise of cyber‐physical attacks, new malicious process shift types are possible. Although QC studies have examined transient shifts from assignable causes, they have not specifically addressed intelligently designed attacks, which can dynamically alter both shift magnitude and duration, causing severe damage. This study aims to evaluate the performance of five control charting schemes in detecting intelligently designed cyber‐physical transient attacks. Three types of transient attacks were developed and investigated: (1) alternating shifts, (2) constant shifts, and (3) trending shifts. These attacks were designed from an attacker's perspective, exploiting control chart vulnerabilities in manufacturing processes by leveraging knowledge of statistical process control methods. The performance of the control charting schemes was assessed using the average run length (ARL), average number of successful attacks (ANSAs), average quality loss (AQL), and detection probability (DP). The design of these attacks considers three critical factors: the magnitude (δ), the duration (), and the frequency or time between attacks (TBA). Our findings indicate that schemes relying solely on the exponentially weighted moving average chart are particularly vulnerable to alternating attacks, recording the highest ARL, ANSA, and AQL compared to other schemes. This vulnerability arises from the inherent characteristics of the chart, specifically its memory. In contrast, schemes that incorporate the and/or charts performed significantly better when subjected to alternating attacks. Additionally, constant shifts resulted in a lower ARL for schemes using and/or charts for the δ values considered in this paper. For trending attacks, the scheme exhibited the lowest ANSA, while the chart scheme demonstrated the highest. Overall, the combination of and showed improved sensitivity and detection time across different 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.

Enhancing industrial organizations' responsiveness to market fluctuations is essential to survive current global supply chain challenges. Better responsiveness can be attained through robust and real-time production planning, especially on the tactical level through Aggregate Production Planning (APP) activities. A key variable in APP is workforce productivity enhancement. However, the dynamic nature of the workforce raises concerns about productivity improvement initiatives. To address this issue, the incorporation of organizational learning and social sustainability into APP has received significant attention from megacorps looking to increase competitiveness. This literature review assesses the prevalence of workforce productivity and social sustainability considerations in the APP problem. The review found that there is a limited number of studies that consider organizational learning in APP, and more research is needed to address the resilience of organizational learning evaluation models. Additionally, there is a lack of studies that focus on social sustainability measures in APP. Finally, it was observed that metaheuristic approaches are commonly used to solve complex APP problems, with genetic algorithms, harmony search algorithms, and simulated annealing being the most frequently employed methods. More research is needed to explore the use of other metaheuristic approaches and to assess their effectiveness in solving APP problems.

With recent technological advancements, production systems have become more susceptible to cyber‐physical attacks. Such attacks can be intelligently designed to both alter product dimensions and avoid detection by current Quality Control (QC) tools. The objective of this work is to assess the performance of random sampling strategies for control charting in detecting cyber‐physical attacks. More specifically, the methodology adopts traditional random sampling approaches used for inspection and applies them to univariate control charts. Different random sampling strategies are discussed and their performances under varying attack scenarios are evaluated. In addition, new control chart performance metrics were developed for a more in‐depth analysis, to guide practitioners in assessing how well these control charts increase their robustness to cyber‐physical attacks.

With the latest advances in computer and networking technologies, the threat of cyber-physical attacks against manufacturing systems is growing. Unlike traditional cyber-attacks, cyber-physical attacks are not limited to intellectual property theft and affect the physical world, which could be devastating to manufacturing, if they are undetected. Relying on traditional quality control to defend against these malicious attacks, manufacturers can choose to either closely monitor a large number of potential quality characteristics or only monitor a specific subset of the characteristics. However, the former choice may be impractical when a large number of potential characteristics exists, whereas the latter might be susceptible to an intelligently designed attack that targets unmonitored characteristics. Therefore, a novel random variable-selection approach that is both resilient to malicious cyber-physical attacks and sensitive to shifts over a small subset of characteristics is proposed in this work. Such an approach is based upon random sampling strategies when using multivariate Hotelling T ² control charts. To assess its usefulness, the proposed approach was compared to an established variable-selection method, using a simplified cost model. The obtained results show that the proposed approach is both cost-effective and well-suited for industrial applications where the number of quality characteristics to monitor is quite significant.

With advances in computer and networking technologies, along with the increasing dependency on interconnected cyber-physical components, the threat of cyber-physical attacks against manufacturing is on the rise. As opposed to traditional cyber-attacks, cyber-physical attacks go beyond intellectual property theft and can affect the physical world. In manufacturing, such attacks can result in changed product designs, manipulated manufacturing equipment, and altered final products. For over a century, manufacturing systems have relied heavily on Quality Control (QC) systems to ensure stable processes and product integrity. However, previous research has suggested that current QC tools could be exploited by an adversary, making it difficult or even impossible to detect attacks. Unfortunately, there has been little to no effort to identify/understand opportunities where QC tools could be exploited, which is an essential step toward developing new cyber-security solutions for manufacturing. In response, this paper establishes a systematic approach to effectively categorize QC tool vulnerabilities. Furthermore, to highlight the importance of this research to the manufacturing community, the negative effects of exploiting QC tools by cyber-physical attacks are demonstrated in this paper. Finally, best practices and guidelines for better cyber-physical security in manufacturing are also presented.