Resilience in the Digital Industry
Entrepreneurial resilience is a critical facet of a business’s ability to weather external shocks or disturbances within the socio-economic and political landscape. It encapsulates the capacity to adapt to new circumstances, remain stable in the face of external forces, and either revert to the initial condition or adopt a new equilibrium. This resilience is essential for tolerating outside pressures and ensuring long-term survival within dynamic markets.
Resilience, in its essence, is the ability to adapt swiftly while retaining the capacity to act. It involves a readiness to fundamentally rethink structures and processes when required. Conversely, robustness expresses a company’s ability to face change without altering its fundamental structure. It is crucial to recognize that those who view a return to the pre-crisis system as necessary and believe that current difficulties have mostly been resolved may hold a fundamental misconception.
Resilience is an ongoing strategic effort by company management to maintain long-term sustainability. It is not a one-time solution but a continuous commitment. Only resilient businesses understanding the need for perpetual adaptation are adequately prepared for the impending challenges that may arise.
The Fourth Industrial Revolution: Driving Forces
As we delve into the dynamics of the Fourth Industrial Revolution, we see that it is propelled by many factors that will inevitably shape its trajectory. One of these factors is the ubiquitous use of mobile devices, particularly smartphones and tablets, in daily life. Emerging applications are envisioned to allow users to remotely control various devices and systems from their mobile devices, thereby further simplifying and integrating technology into our daily lives.
Networking and Internet Technologies are evolving rapidly with IPv6 facilitating faster and more extensive data transfer across computer networks. This advancement anticipates a future where the swift and easy transfer of large amounts of data, video, and music across networks becomes a norm in day-to-day life. The connectivity of nearly every technological device to the internet foresees the emergence of a network society.
Cloud computing systems address the challenges associated with the increasing number of computing systems and devices used daily. These systems grapple with the need to store the vast amount of data these devices generate. The resolution of this challenge involves leveraging cloud computing technologies for comprehensive, resilient, complete, and dependable data storage. The data stored in the cloud holds immense value due to its perpetual accessibility and immunity to loss or corruption.
The advent of big data is a direct outcome of automated processes that accumulate vast datasets beyond the capacity of manual management. Collecting, processing, and presenting data in extensive systems, such as e-government, necessitate in-depth work. This involves the identification and analysis of data with the necessary attributes. The introduction of a particular type of system called the System of Systems (SoS) represents a paradigm shift. SoS refers to associations of operationally and managerially independent, software-intensive systems distributed across various contexts. These systems, characterized as software-intensive, information-intensive, embedded, and ultra-large, collaborate to achieve high-level missions that cannot be accomplished by any single system alone. The need for collaboration emphasizes the development of architectures like Media Arch within the SoS framework.
The integration of Artificial Intelligence (AI) and Industrial Robots into various industries is witnessing exponential growth. Robots equipped with AI can communicate with one another and actively contribute to the production process. The future envisions robots dominating every production sector due to diverse applications of advanced decision-making algorithms.
Three-dimensional (3D) Printers mark a departure from traditional printers, enabling the rapid production of any industrial product. The versatility of these printers allows for the construction of spare parts and necessary modifications using three-dimensional drawings. This innovation has significant implications for the manufacturing industry. For instance, Ford utilized 3D printers during the development of the 2017 Mustang in the United States, underscoring the cost-effectiveness of this production method.
Smart Factories and Industry 4.0
In the realm of Industry 4.0, the goal is to realize smart factories that incorporate advanced automation, robotics, big data analytics, and the Internet of Things (IoT). This paradigm shift is anticipated to revolutionize manufacturing processes from design and production to the management and operation of factories.
The connectivity of machines, production lines, warehouses, and other elements of the factory environment to the Internet enables real-time insights into production processes. This connectivity facilitates the identification of areas where efficiency can be improved. By embracing Industry 4.0, manufacturers can streamline processes, reduce costs, and enhance productivity.
Beyond the operational improvements, Industry 4.0 offers several additional benefits. Using data and analytics allows for more accurate forecasting of demand and production. This, in turn, enables manufacturers to better meet customer needs while simultaneously reducing inventory costs. The integration of the latest advances in robotics and automation has the potential to minimize the need for manual labor, creating safer and more efficient working environments. Importantly, Industry 4.0 is expected to have positive environmental impacts by reducing production’s energy and resource requirements. Additionally, manufacturers can reduce their reliance on harmful chemicals and pollutants through the use of cutting-edge technologies.
In conclusion, entrepreneurial resilience is fundamental for businesses navigating the ever-evolving external landscape. The Fourth Industrial Revolution, propelled by technological advancements, is reshaping industries and manufacturing processes. Each component plays a crucial role in this transformative journey, from the ubiquity of mobile devices to the integration of AI and robotics. Industry 4.0 represents a shift towards smarter factories, promising operational efficiencies, sustainability, and environmental considerations. As businesses and stakeholders chart their course into the future, a nuanced understanding and proactive adaptation to these transformative forces become imperative for sustained success.
Cybersecurity and Resilience in the Digital Industry
The fourth industrial revolution is increasingly developing cyber-physical systems that innovate industrial engineering and traditional control systems with the deployment of advanced technological paradigms such as IoT, machine learning, and artificial big data with the goal of making factories more efficient and with greater manufacturing innovation with a higher degree of self-awareness and self-configuration.
There is currently a push toward the adoption of the Fog Paradigm to improve all engineering applications that reached real-time execution with high reliability, such as minimizing communication errors, and toward the cloud for all applications that require high raw computing powers. Developing particular information infrastructure monitoring and control strategies intended to retain controllability under external disturbances and unforeseen breakdowns can increase the safety and performance of industrial systems.
Security becomes a key worry as these systems become increasingly sophisticated, linked, and connected to physical items. A systematic and organized perspective of security-related data seems necessary for different operations, such as security analysis and creating security controls and architectures.
The introduction of these changes involves a phase of adaptation of present systems, as particular engineering applications require a combination of computational power and latency for which it is required to develop both hardware and software-sided of cyber-physical systems. Industrial cyber-physics aims to ensure efficiency and performance in line with Industry 4.0 requirements while managing control applications that require real-time decision-making processes.
Although cloud interfaces have generally been utilized to facilitate the deployment of cyber-physical systems, there are real limits in terms of consistency, dependability, and external risk factors, such as broadband outages. Although the compute capability of the fog interface is also constrained, these limits might be lessened by inventive design and engineering. On the other hand, the cloud’s inconsistency when it comes to real-time performance is not indicative of the topology or architecture that underlies it. Therefore, industrial cyber-physical systems enabling Industry 4.0 engineering applications and scenarios would seem more naturally adapted to the decentralization, flexibility, and consistency offered by fog computing.
Moreover, Industry 4.0 saw the introduction of the ICS (industrial control system). In this field, cyber-attacks may result in disruptive effects, such as large societal and financial losses. In fact, it has been presented a communized architectural method from the viewpoints of ICS security-related technologies, ICS cyberthreats, and ICS procedures to proactively handle the security issue of ICSs.
Cyber-Physical Systems (CPS) consist of combining the physical world and the cyber one. Research has demonstrated the existence of a hardware-software framework that supports run-time resilience for cyber-physical systems using digital twins to simulate control processes, improve safety at work, and failure models to optimize operation integration, maintenance, and recovering ability for various use cases, particularly for contexts such as intelligent cities and industrial IoT.
CPS are known for being large, heterogeneous, and networked for their interconnection complexity. They are systems whose operations are tracked, coordinated, controlled, and integrated by components that interact with the physical world through computing, transmission, and other means. Cyber-Physical Production Systems (CPPS) are being developed to apply CPS to industrial production systems. CPPS combines a conventional production system and smart equipment, data storage, and fast processors. This enables production facilities to communicate, act, and manage each other in an independent way, thanks to adaptive networks. Thus, it enhances core manufacturing processes when it comes to decentralized decision-making, industrial added value, supply chain and lifecycle management, manufacturing technology, and resource consumption.
Cyber-physical human-centric (CPHS) has evolved to align with the targets of complex industrial plants to be human-centric, resilient, and sustainable. Manufacturing processes based on collaborative robots and large machines allow for improved operator safety and traceability. The proposed use case focuses on a factory floor where human proximity sensing is used to determine when a machine should or should not be running to use resources more efficiently and prevent accidents or incidents involving such machines. A CPHS proposal uses a mixed edge computing structure and intelligent fog computing points that analyze thermal pictures and take industrial safety actions. Experiment results demonstrate that in the chosen real scenario, the algorithm developed for CPHS is capable of quickly and accurately detecting human presence in less than 10 ms with 97.04% accuracy using low-powered equipment such as Raspberry Pi 3B and providing an efficient solution, a good trade-off among efficiency, robustness, and performance that could be implemented in many Industry 5.0 applications.
Human-Centric Approaches in the Digital Industry
The difficulties of having an aging crew can also be mitigated by switching from hazardous on-site, in-vehicle labor to remote, computer-assisted piloting, making the job more appealing to new generations using virtual reality (VR) and augmented reality (AR) for remote control and modern telepresence techniques. People operating the robot or working in a collaborative application must go through a learning and training procedure for safety concerns. The foundation of intelligent automation is the proficient and effective control of robots and the safety of those robots in industrial settings. Simulators are considered a cost-effective alternative for developing fundamental technical skills and workplace design because many businesses cannot afford to buy a robot expressly for training reasons. At this stage, process modeling and engagement using virtual reality technology may offer a realistic experience without any actual hazards.
The traditional work patterns and protocols used in traditional industries will evolve, update, or completely change with the virtualization of data-driven manufacturing, which enables system-wide communication, coordination, prediction, and control. As a result, resource utilization is optimized, system performance is improved, production efficiency is increased, product quality is improved, and profitable business development occurs. As a result, there is an increased need for a specifically qualified and trained workforce, which will increase the likelihood that new employment of all kinds will be created.
To build a virtual depiction of physical systems where both components are linked to share data, the idea of digital twins has been presented. Emerging Digital Twin technologies are progressively pivotal for comprehending system resilience and identifying risks. Initially, the evolution of digital twins prioritized concerns regarding worker safety by leveraging process modeling and system integration. However, the scope of Digital twins has expanded to encompass diverse realms, such as monitoring cash and carbon flows. By employing Digital twins for intricate simulations and analyses, organizations can proactively mitigate risks, optimize operational efficiencies, and gain valuable insights into various aspects of their operations. The integration of Digital twins into broader considerations like financial and environmental sustainability signifies their versatile application in enhancing overall system resilience and risk management strategies.
Data Analytics and AI in the Digital Industry
Data analysis and artificial intelligence research for intelligent firms became an official trend. They target to analyze raw information to find hidden patterns and linkage between various variables. The growth of data storage, computing power, and analytical algorithms, all of which have seen rapid advancement, has occurred concurrently with the development of IoT and CPS technologies. Industry 4.0 paves the way for real-time controlling and synchronization of the physical-virtual link and the networking of CPS components to the virtual realm.
The conventional issues with hardware, software, and networked systems are connected, which poses a significant new difficulty. As a result, a comprehensive systematic investigation of the viability, robustness, performance assessment, and performance optimization of the ICPS monitoring and control techniques becomes essential.
Network security features are crucial for safeguarding important infrastructure. New intelligent network designs are a must for today’s industrial control systems, especially the creation of the Collaborative Robotic Cyber-Physical System (CRCPS), an industrial security framework for a harmless and secured human-robot cooperation (HRC) in an industrial networked production environment. Industrial clients of collaborative robot manufacturers that deal with automatic and semi-automatic manufacturing processes are becoming more and more focused on balancing their manufacturing processes to a level that allows smooth human-robot collaboration. This is especially true for semi-automatic procedures used in the automotive sector but still require human workers to perform some duties manually. The industrial CRCPS’s network security is essential since the system’s goal is to protect workers using its heavy-payload collaborative robots from potentially fatal situations. Important information inside CRCPS must be secure and cannot be compromised as a result of a malicious attack, in addition to worker safety.
Intelligent anomaly detection remains a difficult problem, particularly when working with limited labelled data to ensure cyber-physical security. This is necessary for spotting cyber-physical threats for reaching efficient and safe work. A few-shot learning model with a Siamese convolutional neural network (FSL-SCNN) has been proposed by certain studies to address the over-fitting issue and evolve in precision for intelligent anomalies identification in industrial CPS.
The dependability characteristics of system components, their interconnections, and the structural and behavioral features of the entire system must all be thoroughly understood to conduct a good analysis of the CPS. Such an investigation of intricately entwined system characteristics poses significant hurdles to the suitable modeling and analytic techniques and applicable software tools.
Cybersecurity Challenges in the Digital Industry
It is necessary to have a general understanding of cybersecurity and its key usage for Industry 4.0. Cybersecurity is a challenging procedure that aids in solving numerous hacking problems of Industry 4.0. The emergence of Industry 4.0 technologies is altering how machines and related data are acquired to analyze the information they contain. Cybersecurity is a major concern for Industry 4.0 tools, platforms, and frameworks.
The interplay between the physical and virtual worlds also generates dangers that must be managed. For instance, robots and humans both operate in cramped quarters in highly automated industrial systems. Such arrangements put everyone at risk, even those who work there.
The goal of fault detection systems in current research and technical paths is to set off alarms to properly notify problems’ occurrence as well as their deepest root causes. Nevertheless, there are still open questions about how quickly it needs to be fixed and how much fault-tolerance, maintenance, and fault recovery are required. More analyses are required to assess the effect of the discovered flaw on the overall performance of the plant.
In practically all industrial sectors, equipment maintenance is a critical issue since it affects any production system’s standard security and output. Additionally, rescheduling production frequently because of accidental and unanticipated interruptions can take a lot of time, particularly for centrally managed systems.
Existing solutions do not yet include the psychological dimensions of safety in the area. This deficiency may result in dangerous circumstances impairing the functionality of the functioning system. However, the inherent unpredictability of the underlying sensor networks is frequently disregarded. The flexibility and robustness of the production process are improved by constructing several modular Cyber-Physical Systems (CPSs) that function as a whole in manufacturing processes, such as a production line made up of many