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Review

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

by
Mihai Andronie
1,
George Lăzăroiu
1,2,*,
Mariana Iatagan
1,
Cristian Uță
1,
Roxana Ștefănescu
3 and
Mădălina Cocoșatu
4
1
Department of Economic Sciences, Spiru Haret University, 030045 Bucharest, Romania
2
Institute of Smart Big Data Analytics, New York, NY 11377, USA
3
Department of Juridical Sciences and Economic Sciences, Spiru Haret University, 500152 Brașov, Romania
4
Department of Law, The National University of Political Studies and Public Administration, 012244 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Electronics 2021, 10(20), 2497; https://doi.org/10.3390/electronics10202497
Submission received: 13 September 2021 / Revised: 8 October 2021 / Accepted: 11 October 2021 / Published: 14 October 2021
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Industry 4.0)

Abstract

:
With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

1. Introduction

There is an emergent body of literature in relation to how Cyber-Physical Systems (CPS) address the integration of Internet of Things sensing networks, computational applications, and artificial intelligence-based decision-making algorithms with physical devices, being engineered as an interconnection between cyber and physical components. This is crucial in the advancement of smart manufacturing by use of cloud computing, social networking, and big data [1,2,3]. Cyber-Physical Production Systems (CPPSs) develop out of the deployment of CPS principles to manufacturing environments. CPPSs represent an elaborate and fluid network [4,5,6,7] of services and shop floor components (e.g., sensors and actuators), can adjust swiftly to new manufactured items or product variants, can optimize networking among smart connected devices in the production environment, and can provide self-governance, self-organization, and interoperability across smart networked factories, thus optimizing the resilience of manufacturing systems. CPPSs are crucial in advancing sustainable manufacturing Internet of Things and smart factories [8,9,10,11], by harnessing wireless sensor networks for controlling objectives, facilitating integration of industrial data, and supervising and coordinating real objects and operations.
The purpose of this research is to inspect the recently published material on CPPSs and integrate the understandings associated with Internet of Things-based decision support systems, interconnected sensor networks, deep learning-assisted smart process planning, and automatic big data-driven real-time production logistics. By analyzing the latest (2017–2021) and most important (ProQuest, Scopus, and the Web of Science) sources, our systematic review has attempted to determine that cyber-physical production systems (CPPS) are adequate in managing complexity and flexibility, configuring the smart factory. CPPSs inspect, supervise, and automate business operations, optimizing manufacturing and logistic processes across smart shop floor environments. Smart autonomous devices are pivotal in planning and control systems as Internet of Things elements of CPPSs. The substantiality and uniqueness of the research are highlighted by clarifying how cyber-physical system-based smart factories optimize the networking among equipment, sensors and big data-driven systems, constituting a research issue of high interest. Cyber-physical system-based manufacturing configures knowledge-intensive industrial autonomous settings in which smart customized items are produced through deep learning-assisted smart process planning [12,13,14,15,16,17], real-time advanced analytics, and cognitive automation. Cloud computing, robotic wireless sensor networks, and artificial intelligence data-driven Internet of Things systems are essential in enabling cyber-physical process monitoring systems.
The research topic advanced throughout our research is whether sustainable cyber-physical production systems developed on functional and behavioral patterns [18,19,20,21,22,23] can address the inconveniences of disruptive Industry 4.0-related production environments. Accurate and reliable assessments of product quality through cyber-physical system-based real-time monitoring assist in optimizing manufacturing processes and big data-driven decision-making instantaneously. In this article, previous findings were gathered, indicating that Internet of Things-based real-time production logistics, automated production systems, industrial big data analytics, and deep learning-assisted smart process planning [24,25,26,27,28,29] facilitate continuous monitoring of smart shop floors. Sustainable Industry 4.0 wireless networks can shape effective and robust manufacturing by automatically monitoring production equipment in a flexible fashion. Our chief purpose is to clarify that artificial intelligence data-driven Internet of Things systems necessitate high-performance operations and adjustable production systems [30,31,32,33,34,35] by use of flexible and real-time scheduling. CPPSs and sustainable manufacturing Internet of Things reconfigure how shop floor operations are designed and carried out. This research contributes to the literature on cyber-physical system-based smart factories by clarifying that manufacturing process monitoring systems have advanced as decentralized reconfigurable networked entities by use of cutting-edge intelligent machines. Industrial enterprises advance as wireless sensor networks to constantly control the operations of their plants. We specifically show that cyber-physical production systems develop by the integrative processes of sustainable industrial big data, artificial intelligence-based decision-making algorithms, and Internet of Things sensing networks in cyber-physical system-based smart factories. This systematic review endeavors to elucidate whether the interoperability between Internet of Things-based real-time production logistics, big data-driven decision support systems, and cyber-physical process monitoring systems can decide upon the progression of operations [36,37,38] advancing a system to the intended state in CPPSs. As a result of the advancement of CPPSs, self-governing monitoring of manufacturing processes is indispensable in smart production processes. Our contribution is analyzing and interpreting connected research findings which prove that industrial cyber-physical systems are pivotal in sustainable smart manufacturing, and integrating control engineering with artificial intelligence-based decision-making algorithms to set up cognitive and self-configuring plants. Smart connected devices have heterogeneous processing and manufacturing capabilities and optimization operation mechanisms. We clarify that leveraging artificial intelligence data-driven Internet of Things systems is pivotal in achieving smart industrial value creation through real-time process monitoring, sustainable Industry 4.0 wireless networks, and Industry 4.0-based manufacturing systems. Condition-based monitoring and predictive maintenance require real-time sensor data through Internet of Things-based decision support systems.

2. Methodology

We carried out a systematic review covering recently published material on artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, cognitive automation, and deep learning-assisted smart process management in cyber-physical production systems by harnessing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. We considered only recent original empirical research or review articles (2017–2021) written in English, indexed in ProQuest, Scopus, and the Web of Science, and covering particular search terms. We used the Systematic Review Data Repository, a software program for the acquisition, processing, and analysis of data. The quality of the indicated scientific sources was appraised by deploying the Mixed Method Appraisal Tool. Extracting and inspecting publicly available documents (scholarly articles) as evidence, we required no procedural ethics permission before initiating our research (Figure 1).
We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems”, “smart industrial production processes” and “sustainable Internet of Things-based manufacturing systems”. The search terms were determined as being the most employed words or phrases across the investigated literature. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources (Table 1 and Table 2).

3. Artificial Intelligence-Based Decision-Making Algorithms, Smart Factory Performance, and Industry 4.0-Based Manufacturing Systems in CPPSs

Intelligent plant modules and smart factory automation [39,40] have advanced CPPSs that are pivotal in collision identification, impedance monitoring, and assimilating machine learning-based tasks. For collective decision-making, CPPSs necessitate processing of collective data, taking into account the feasible knowledge, operational analysis, and fluid systems resilient to the instantaneous dynamic alterations across the shop floor and within the networked infrastructure. Because of the growing volume of modular components and systems [41,42,43], interwoven and heterogeneous factory systems are required for big data-driven decision-making processes and collaborative control in sustainable manufacturing routines (e.g., closed-loop supply chains). The inconstant environment necessitates adjustable patterns through learning, while additional mechanisms comprise self-regulation by smart agents. Operational infrastructure should enable loose coupling, cyber-physical system-based real-time monitoring, and supply networks.
In CPPSs, smart connected devices team up automatically to constantly optimize manufacturing processes, manage disturbances, and adjust to variable conditions [44,45,46,47], articulating the relevance of networking and control systems. Increased collected data would lead to configuring resilient manufacturing systems adjustable to market demand and holding patterns in the logistic chains to obtain exemplary values of the intended key performance indicators. Industry 4.0-based manufacturing systems require the networking of all devices integrated across the shop floor. Data processing to collect significant information and physical value creation in the production operations [48,49,50] can be attained through the assimilation of the enterprise resource coordination and manufacturing execution systems. The deployment of networked monitoring systems across smart factories configures big data-driven manufacturing control architecture. The interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. Internet of Things-based decision support systems will enhance the quality of big data-driven decision-making processes, due to the extensive convenience of information regarding smart networked factories. Smart autonomous devices are pivotal in planning and control systems as Internet of Things elements of CPPSs.
CPPSs and sustainable manufacturing Internet of Things reconfigure how shop floor operations are designed and carried out [51,52,53,54], decentralizing production by deploying networked cyber-physical production resources. Heterogeneous smart connected devices and processes across industrial monitoring interact in product decision-making information systems. Industrial cyber-physical systems are pivotal in sustainable smart manufacturing, by integrating control engineering with artificial intelligence-based decision-making algorithms to set up cognitive and self-configuring plants. Cloud computing and service-oriented designs can network and develop physical factory performance to the cyber world in terms of engineering, monitoring and Internet of Things sensing networks [55,56,57] for increased reliability and resilience. The latency and soundness of cyber-physical system-based real-time monitoring implemented by using cloud and fog computing can result in instantaneous integrated deep learning-engineering applications. High accuracy production necessitates the leverage of cutting-edge signal processing and analytics to supervise, handle, and control manufacturing processes. CPPSs are instrumental in instantaneous heterogeneous data collection, inspection, and distribution by use of interoperability support integrated in process monitoring systems. Multi-scalable signal processing and big data-driven decision-making operations can be deployed for both user-driven and partially autonomous manufacturing support systems. CPPSs redesign decision-making processes (e.g., dynamic, distributed, and inverse scheduling) across manufacturing environments, assimilating heterogeneous functionalities in Internet of Things sensing networks.
Cyber-physical system-based manufacturing configures knowledge-intensive industrial autonomous settings [58,59,60,61] in which smart customized items are produced through deep learning-assisted smart process planning, real-time advanced analytics, and cognitive automation. CPPSs inspect, supervise, and automate business operations, optimizing manufacturing and logistic processes across smart shop floor environments: big data performance, cloud services, and cyber-physical predictive decision support connected devices can boost productivity and efficiency. Industrial enterprises advance as wireless sensor networks to constantly control the operations of their plants. Wireless sensor technology monitors manufacturing assets and networked production or logistics business operations in real time [62,63,64], increasing output, improving resource efficiency, diminishing interruptions, or reducing discontinuation. Wireless sensor networks are multipurpose and inexpensive to install for impermanent and gradual gathering of further data points, facilitating rapid continuous integration into industrial production networks comprising Internet of Things smart devices, mobile applications, and cloud services. Context-aware behavior is decisive in industrial manufacturing settings to set up smart production systems and applications across robotic wireless sensor networks, in terms of operational tracking, zero-defect manufacturing, and data-driven maintenance optimization. Effective and robust data gathering is essential in configuring context-adaptive decision support connected systems that incessantly supervise production processes in flux. Context-awareness and robotic wireless sensor networks assist in integrating smart connected devices and can decrease manufacturing errors. Internet of Things-based real-time production logistics and deep learning-assisted smart process planning [65,66,67] facilitate continuous monitoring of smart shop floors. Effective upgrading of business operations and computational resource use typically depends on data intensive processes. Cloud computing, real-time sensor networks, and artificial intelligence data-driven Internet of Things systems are essential in enabling cyber-physical process monitoring systems: a massive volume of industrial device data are assimilated and product decision-making information systems are analyzed, providing the adjustability to customize scale to heterogeneous workloads to automate and enhance business operations and facilitating big data analytics, articulating predictable performance across industrial wireless networks of production facilities and manufacturing processes that are interconnected, thus leading to a cost-efficient supply chain. Data-driven software and intelligence are integrated into industrial manufacturing systems to cut down expenses and optimize the quality, performance, and adjustability of production.
The demand for increasingly customized, smart, and sustainable manufactured items, and the swift growth of cyber-physical system-based real-time monitoring [68,69,70,71], have resulted in the development of Internet of Things-based decision support systems. The manufacturing sector is driven by big data-driven decision-making processes that enable accelerated production, decreased expenses, and diminished downtime by harnessing artificial intelligence data-driven Internet of Things systems across industrial enterprises and integrated sensing and computing technologies. Embedded and coordinated value networks that harmonize and distribute manufacturing resources online, digitalization and assimilation of manufacturing resources on the Internet of Things-based real-time production logistics as dynamic, reliable, and on-demand micro-services, and data-driven and networked CPPS entities capable of instantaneous and self-governing decision-making facilitated by cognitive automation [72,73,74] provide customers with sustainable mass-personalized products and services, and further real-time adaptation to fluid alterations in user demand, shop floor environments, and supply/value networks. As the physical resources of data-driven production systems are formalized as smart factory components with heterogeneous features, the supplied operations or functions should be set up as conventional services to facilitate robust and on-demand production for value network assimilation and assistance. The demand for customized, networked, smart, and sustainable products and services supported by artificial intelligence-based decision-making algorithms are progressively restructuring the manufacturing sector. Digitalization and assimilation of manufacturing resources on the Internet of Things-based real-time production logistics as on-demand services further value network consolidation and cooperation throughout the industrial unit.
Deployment of artificial intelligence-based decision-making algorithms, deep learning-assisted smart process planning, real-time sensor networks, and cloud technologies [75,76,77] are instrumental in remote maintenance support. Leveraging massive volumes of data gathered on equipment and their processing through product decision-making information systems enable the adoption of groundbreaking techniques for condition-based maintenance. Artificial intelligence-based decision-making algorithms, robotic wireless sensor networks, and Internet of Things-based real-time production logistics are decisive in the development of smart CPPSs and Industry 4.0-based manufacturing systems. Industry 4.0-based manufacturing systems aim for breakthroughs, cognitive automation, increased feedback to customer demands, and artificial intelligence-based decision-making algorithms. By harnessing data-driven modeling, cyber-physical process monitoring systems will reshape manufacturing as intuitive and automated [78,79,80], leading to the decentralization of production processes. Real-time big data analytics, cognitive automation, Internet of Things-based decision support systems, and production process optimization are pivotal in configuring CPPS-enabled data-driven manufacturing. Smart networked factories will be driven by the integration of robotic wireless sensor networks and industrial big data analytics for cyber-physical process monitoring systems. Throughout the organized layers of CPPS-enabled smart networked factories, big data-driven manufacturing facilitates decentralized production through automation and interconnected industrial units. The subsystems throughout decentralized production collect and share data with heterogeneous industrial units to make coherent decisions. Leveraging descriptive, causal, predictive, and prescriptive analytics is reconfiguring manufacturing into value-based and big data-driven production facilities by use of designed and informed decision-making (Table 3).

4. Internet of Things Sensing Networks, Sustainable Product Lifecycle Management, and Real-Time Big Data Analytics in CPPSs

CPPSs improve the flexibility and output of smart manufacturing [81,82,83,84], adjusting the design and quality of products to fluid market demands and customized requirements. Cyber-physical system-based smart factories optimize the networking among equipment, sensors and big data-driven systems, and enhance the self-governance, soundness, agility, and responsiveness of sustainable smart manufacturing. Real-time wireless networks can collect heterogeneous information in the underlying applications and perform mining, inspection, and assessment of supply chain logistics data. The variable dynamics of industrial wireless sensor–actuator networks across CPPSs necessitates incessant amendments of the system states and continuing operations by sharing high-priority and event-driven data to preserve the stability of the system so that the shop floor does not shut down. Heterogeneous instantaneous transmission scheduling algorithms handle the distribution of the channel resources [85,86,87], but cyber and physical units have distinct demands to enhance the network performance. Industrial robot applications are developing swiftly as a result of cyber-physical system-based real-time monitoring. As demands with agile product iterations are progressively variable and purpose-built, the assembly operations of industrial robots confront fluctuating restructuring and redesign, ubiquitous sensing, and networking in real time. Industrial robot assembly process designing, coordination, and scheduling developed on instantaneous data collection and fusion is pivotal in groundbreaking plant communication and computing technologies (e.g., edge computing, wireless connected sensors, and actuator networks). The integration, networking, and interaction of connected devices with instantaneous data collection and fusion articulate the multi-agent pattern of industrial robot assembly process by use of smart planning and scheduling algorithms.
CPPSs can be thoroughly and steadily engineered during their lifecycle in smart manufacturing through Internet of Things sensing networks, cognitive automation, real-time process monitoring, and artificial intelligence-based decision-making algorithms [88,89,90,91,92,93], even though inconstant integration routines may lead to variable data patterns at heterogeneous levels of production processes. The shift from planning to the operational phase impacts the harnessing of physical tools on the industrial unit and brings about a relevant realignment of logistical requirements throughout the plants. Pervasive sensing technologies and wireless sensor networks are pivotal in CPPSs. In CPPSs, heterogeneous equipment, actuators, sensors and monitoring systems are networked to manufacture items efficiently. Monitored industrial wireless sensor–actuator networks are pivotal in CPPSs. Groundbreaking technologies furthering cyber-physical enterprise systems regarding real-time decision-making determined from streamlined data [94,95,96] necessitate networked sensor and operational systems. Networking, physical/virtual joint performance, interoperability, self-organization, and smart big data-driven decision making are key in furthering Industrial Internet of Things. The digital transformation aims to advance Internet of Things sensing networks, smart industrial value creation, deep learning-assisted smart process planning, and CPPSs architectures that can connect smart devices from heterogeneous data-driven technologies, virtualizing manufacturing enterprises’ assets and integrating them across production sectors and plants. Processing devices and systems are crucial in ensuring vertical and horizontal integration.
Adaptive production systems are crucial in sustainable manufacturing Internet of Things [97,98,99], deriving from the demand for robust characteristics of the system to react to disruption as product changes or alterations to operational parameters. Functional and behavioral modeling assists in the reshaping of Industry 4.0-based manufacturing systems. Sustainable cyber-physical production systems developed on functional and behavioral patterns can address the inconveniences of disruptive Industry 4.0-related production environments. Self-adaptive reconfigurable manufacturing systems are crucial in the operational modeling design, where the processes have to be assimilated into the system monitoring behavior. The capacity of sustainable cyber-physical production systems to reconfigure in conformity with variable demands [100,101,102] enables a rise in deployment and a decrease in expenses and alterations in time, leading to increased output across disruptive production environments. To thoroughly capitalize on Industry 4.0-based manufacturing systems, production, data-driven, and biological systems have to network. Sustainable Industry 4.0 aims to optimize the adjustability of manufacturing systems so that customized items can be made within a large-scale production regime. Manufacturing systems thus resemble natural organisms, both regarding their structural planning and the achievable computational and sensing capabilities. Unfolding digitalization expedites the reconfiguration and assimilation of physical manufacturing and computing systems into smart entities and their networking, constituting artificial intelligence data-driven Internet of Things systems. In sustainable manufacturing Internet of Things, the cross-linking of integrated systems sets up flexible and self-organizing CPPSs. As a result of growing cross-linking, swift technological breakthroughs, and multifunctionality, the intricacy and structural impenetrability of CPPSs are intensifying at a fast pace.
The convergence of standard automation systems within CPPSs, together with service-oriented designs and fog, edge, and cloud computing technologies [103,104,105], are developing sustainable manufacturing Internet of Things and cyber-physical process monitoring systems. For the purpose of ensuring robust manufacturing processes, any disruptions throughout the system have to be monitored by operational technology and big data services. The fluid assessment, integration, and positioning of services across CPPSs constitute elements of the process control [106,107,108] throughout the consolidated modeling and appraisal of operational phases. The reliability of manufacturing and service integration and positioning in dynamically variable system environments is impacted by engineering production processes. CPPSs develop on heterogeneous connected computers, devices, and applications having diverse conditions, performance, and latency, while necessitating data management in relation to deficiency detection and diagnosis through cyber-physical process monitoring systems at a complexity level unrelated to Internet of Things-based real-time production logistics in smart networked factories. Cyber-physical machine tools are smart, networked, broadly available, highly robust, and thoroughly autonomous devices.
Artificial intelligence data-driven Internet of Things systems necessitate high-performance operations and adjustable production systems [109,110,111,112] by use of flexible and real-time scheduling. Sustainable manufacturing Internet of Things enables robust and dynamic planning and monitoring of production systems by leveraging efficient and sound real-time data gathering, handling, and analysis from the industrial unit. Accumulated data can be deployed in a sound decision making system that encompasses a multi-criteria supervising algorithm and a condition-based assistance approach, endeavoring to optimize shop floor operations. The scheduling algorithms can become cognizant of the heterogeneous data coming from the industrial unit in relation to relevance and convenience of the resources when carrying out assignments [113,114,115], concerning machine tools status by the coherent integration between controlling and planning systems and adaptive and real-time scheduling algorithm. Instantaneous data collection and monitoring can be configured from heterogeneous sources, data analysis, assimilation of planning and supervision, in addition to streamlined, precise, and dynamic scheduling suited to managing real-time information from the industrial unit, furthering the digitalization of manufacturing enterprises efficiently. Robust, affordable, and reconfigurable controlling systems that gather and deliver meaningful data are pivotal in CPPSs for flexible time management and condition-based maintenance.
CPPSs autonomously identify and react to inconstant and unplanned situations on the shop floor (e.g., machine failure, a sudden deficiency in unprocessed materials, or last-minute request orders), enabling interoperable connections among distributed business applications [40,70,116,117], while rendering supervision of manufacturing processes with first-rate quality and adjustability, and reducing operational risk or unpredictability. Generic and cross-deployable data-driven patterns powered by deep learning-assisted smart process planning, real-time advanced analytics, and artificial intelligence-based decision-making algorithms can exemplarily trace the data derived from sustainable cyber-physical production systems and harness it for Internet of Things-based real-time production logistics, real-time optimization, or cyber-physical process monitoring systems. Accurate and reliable assessments of product quality through cyber-physical system-based real-time monitoring assist in optimizing manufacturing processes and big data-driven decision-making instantaneously. Advancing data-driven monitoring systems and leveraging them across a CPPS platform may result in large-scale supervision and an increase in efficiency [118,119,120] during the sustainable product lifecycle management in plants, consequently mitigating operational constraints. Industry 4.0 manufacturing and logistics systems require significantly custom-designed supply network monitoring, the setting up of resilient factories to manage risks, advancements in the administration of decision-support systems for the configuration, scheduling and supervision of resilient production networks, adaptable workstations, and shared operational control (Table 4).

5. Deep Learning-Assisted Smart Process Planning, Internet of Things-Based Real-Time Production Logistics, and Sustainable Industrial Big Data in CPPSs

CPPSs constitute cutting-edge technologies for the adoption of smart manufacturing [121,122,123,124] that are effective only when processing standards and application procedures for heterogeneous data—which can modify instantaneously due to the character of a factory—are carried out. Intelligent production systems gather unprocessed data from a shop floor in real time. The automation of manufacturing requires complete mapping, scaling, and standardization of the collected data of the industrial unit into operational processes across a CPPS environment. The increasing complexity of cyber-physical process monitoring systems necessitates convenient control designs that enable fluid adaptation throughout their runtime. CPPSs are adequate in managing complexity and flexibility, configuring the smart factory. Companies expand their product portfolio and try to decrease their manufacturing time to maximize earnings and market presence [12,125,126], indirectly exacerbating the intricacy of the operational processes. The manufacturing sector frequently builds upon cutting-edge monitoring systems to mitigate quality losses. Redesigning the production systems and optimizing their performance depend significantly on the harnessing of cutting-edge decision support tools so as to satisfy the inconstant demand of high-mix, low-volume manufacturing settings. Assessing the predictable values of the key performance measures is difficult because the intricate structure, performance, and input demand leads to a vastly massive variable area limiting the analysis. A groundbreaking undertaking for supplying simulation-based decision support for enhancing key performance indicators of high-mix, low-volume manufacturing systems would decrease the proportion of the input domain by leveraging unsupervised machine learning algorithms.
Smart manufacturing harnesses predictive production systems systematically [127,128,129,130,131,132]: cognitive networked assets can predict, identify cause, and redesign malfunctioning events automatically. CPPS-related data are inspected and networked between a physical industrial unit and the cyber computational space, integrating smart analytics to grasp undetectable issues for swift and precise decision-making. CPPSs can be harnessed to predictive production systems to stimulate resilience and coordination, increasing manufacturing productivity. The fusion between data technologies shapes the performance of cyber-physical automation systems. Variable manufacturing systems and product demands derive from inconstant customer behavior [133,134,135], which is an incessantly moving target impacting product development, and thus CPPSs leveraging condition monitoring, cognitive automation, and reconfigurability have to be designed and adopted. CPPSs are instrumental in the advancement of adjustable and reactive systems. A decentralized monitoring mode can significantly meet the system demands of a CPPS due to its upsides (e.g., flexibility, reconfigurability, swift approachability, and soundness), emerging as the essential control routine of CPPSs. Non-hierarchical manufacturing networks comprising self-governing companies acquire accurate big data-driven technologies or groundbreaking industrial platforms, leading to cyber-physical production structures with heterogeneous automated operations.
Cyber-physical machine tools can develop Industry 4.0-based equipment regarding intelligence and self-governance [136,137,138,139,140] by integrating physical devices and machining operations with computation and networking performance through functional modules such as real-time control, process monitoring and optimization, and manufacturing simulation. Developments in Industry 4.0-based manufacturing systems further the advancement of CPPSs. Industrial production systems and items are perpetually advancing as a result of swift technological breakthroughs and inconstant customer requirements, while a consolidated co-development of both is needed, as otherwise the lifecycles of manufactured items and production systems may be at variance. Sustainable Industry 4.0 wireless networks can shape effective and robust manufacturing [141,142,143] by automatically monitoring production equipment in a flexible fashion. For joint sharing and deployment of distributed and interconnected production resources, coherent planning and control at heterogeneous levels and locations are needed, advancing feature-based manufacturing for dynamic tool monitoring and resource-task matching throughout Industry 4.0-based manufacturing systems. As a result of the advancement of CPPSs, self-governing monitoring of manufacturing processes are indispensable in smart production processes. Control operations such as order release, systematization, and capacity monitoring can be integrated across subtasks of digital manufacturing supervision to conform to due dates.
CPPSs are redesigning hierarchical control arrangements [90,144,145,146,147,148] into distributed structures in which the components operate autonomously. Agent network operations can be optimized through knowledge assimilation and interaction in distributed monitoring for manufacturing environments. Resilient CPPSs can disregard or repair faults, or function by self-governing reconfiguration facilitated by surplus at state, behavioral, or operational levels. CPPSs can carry out multi-product and small-batch operations intelligently and autonomously by use of processing route generation algorithms, backpropagation neural networks, and scheduling rules. Smart connected devices have heterogeneous processing and manufacturing capabilities and optimization operation mechanisms. The production system has to inspect the tasks to integrate with the smart connected devices and perform them unsupervised and automatically [149,150,151], configuring a machining route across production system operations through intelligent decision-making. Manufacturing process monitoring systems have advanced as decentralized reconfigurable networked entities by use of cutting-edge intelligent machines. CPPSs integrate high power computation, shared interoperability, and advanced analytics. Biological collective systems have shaped the configuration of manufacturing systems because of their intrinsic features. Industry 4.0 can handle customized demands by setting up CPPSs for smart networked factories.
In smart industrial units, CPPSs control physical operations, configure a digital duplicate of the physical world, and decisions are decentralized [152,153]: the virtual world stores and processes networked data in real time. Physical, logical, and interconnection layers can integrate intelligent operations within manufacturing processes across the smart shop floor. Physical entities on the plant are self-governing intelligent logical units carrying out undertakings directed by distributed control functions in smart networked factories. Computing capacity and optimization operations are integrated in logical units to make decisions so as to smoothly react to constant incidents of unplanned disturbances on the plant. Physical entities can be coordinated and self-governing logical units can automatize manufacturing system processes. Tools for assessing and managing enhancements in the performance, the soundness, and the responsiveness of manufacturing systems are required [154,155,156], as a decrease in due-date soundness is typically associated with external causes and not with planning behavior. Negative dynamic behavior can happen if imminent declines in due-date reliability are not properly considered. Holding patterns and lead-time-related amendments impact the ensuing system behavior and operations. Autonomous data gathering and integrated patterns can diminish postponement in decision making and implementation of sustainable Industry 4.0 wireless networks.
CPPSs provide the technological basis for the digitalization and decentralization of manufacturing processes [157,158,159,160] and their integration across plant networks. Industry 4.0 offers increased adjustability, facilitating inexpensive product and service customization, and enabling lot-size one manufacturing, deep learning-assisted smart process planning, cognitive automation, and data-driven predictive maintenance. Planning an exemplary chain of manufacturing operations constitutes a determining competitive component for the engineering of CPPSs, considering a rise in customization with concomitantly increasing sales volumes, while plants have to handle the issue of the manufacturing of personalized items efficiently. Developed on the swift organization of industrial enterprises, constant digital coordination, and target cost analysis, intelligent engineering focuses on designing CPPSs that ensure the manufacturing of items in conformity with the distinct demands at the target cost, thus improving economic efficiency. The intelligent engineering of manufacturing comprises assessment of requirements for CPPSs, estimation and examination of the target cost, planning of CPPSs in keeping with the customer demands and target cost, setting up of a production digital twin, harnessing of an incessant digital organization strategy, and supervision and appraisal of received data. CPPSs ensure a thorough networking of the smart connected devices and resources integrated in manufacturing processes [161,162,163,164] and, consequently, enhanced availability of collected data. Decision support systems facilitate adequate processing and display of the gathered data, with production scheduling being the reason for the swift planning and monitoring of released orders in sustainable manufacturing Internet of Things. Decision support systems and CPPSs can be harnessed in production scheduling on a large scale, capturing real-time data from manufacturing processes. The demands in groundbreaking engineering of CPPSs are difficult to carry out because of massive system proportions, component diversity, integration of heterogeneous machines, and constant advancement. Formal and semi-formal languages, approaches, devices, and arrangements can configure replicable and sound structures for bringing about, clarifying, inspecting, checking, and maintaining CPPS-related requirements. Industrial control systems can carry out distributed, instantaneous system monitoring, and durable stability. Physical operations, together with control-related sensors, actuators, and processors, are networked and distributed across CPPSs. Computational devices can be deployed as monitoring and interaction technologies and as heterogeneous collaborative devices and modes of networking to configure crucial tools in operating, maintaining, and upgrading data-driven CPPSs (Table 5).

6. Discussion

The importance of artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, product decision-making information systems, and deep learning-assisted smart process management in cyber-physical production systems is extensively consistent with, and provides additional confirmation of, previous review articles, e.g., [40,44,68,91,93], clarifying that CPPSs are adequate in managing complexity and flexibility, configuring the smart factory. CPPSs inspect, supervise, and automate business operations, optimizing manufacturing and logistic processes across smart shop floor environments. Cloud computing, real-time process monitoring, and artificial intelligence data-driven Internet of Things systems are essential in enabling cyber-physical process monitoring systems. Artificial intelligence data-driven Internet of Things systems necessitate high-performance operations and adjustable production systems by use of flexible and real-time scheduling. Intelligent production systems gather unprocessed data from a shop floor in real time. Industry 4.0-based manufacturing systems require the networking of all devices integrated across the shop floor. The interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. Smart autonomous devices are pivotal in planning and control systems as Internet of Things elements of CPPSs.
The outcomes of our systematic review develop on empirical research [1,2,3,4,5,6,7,8,9,10,11,39,40,44,121,122,123,124,136,137,138,139,140,144,145,146,147,148,157,158,159,160] contending that sustainable cyber-physical production systems developed on functional and behavioral patterns can address the inconveniences of disruptive Industry 4.0-related production environments. Wireless sensor technology monitor manufacturing assets and networked production or logistics business operations in real time. Accurate and reliable assessments of product quality through cyber-physical system-based real-time monitoring assist in optimizing manufacturing processes and big data-driven decision-making instantaneously. CPPSs and sustainable manufacturing Internet of Things reconfigure how shop floor operations are designed and carried out. Cyber-physical system-based smart factories optimize the networking among equipment, sensors and big data-driven systems. Sustainable Industry 4.0 wireless networks can shape effective and robust manufacturing by automatically monitoring production equipment in a flexible fashion. Cyber-physical system-based manufacturing configures knowledge-intensive industrial autonomous settings where smart customized items are produced through deep learning-assisted smart process planning, real-time advanced analytics, and cognitive automation.
There has been a small but growing volume of studies [51,52,53,54,58,68,75,76,77,128,129,130,131,132,152,153] claiming that industrial enterprises advance as wireless sensor networks to constantly control the operations of their plants. Internet of Things-based real-time production logistics, product decision-making information systems, and deep learning-assisted smart process planning facilitate continuous monitoring of smart shop floors. As a result of the advancement of CPPSs, self-governing monitoring of manufacturing processes is indispensable in smart production processes. CPPSs are instrumental in instantaneous heterogeneous data collection, inspection, and distribution by use of interoperability support integrated in process monitoring systems. Autonomous data gathering and integrated patterns can diminish postponement in decision making and implementation of sustainable Industry 4.0 wireless networks.
As underlying mechanisms between cyber-physical production networks, deep learning-assisted smart process planning, product decision-making information systems, artificial intelligence-based decision-making algorithms, and cognitive automation in sustainable manufacturing Internet of Things are comprehended to a limited extent in the current literature, certain empirical studies [81,82,83,84,88,89,90,91,92,93,97,98,99,103,104,105,108,164] systematically indicate that smart connected devices have heterogeneous processing and manufacturing capabilities and optimization operation mechanisms. Manufacturing process monitoring systems have advanced as decentralized reconfigurable networked entities by use of cutting-edge intelligent machines. Sustainable manufacturing Internet of Things enables robust and dynamic planning and monitoring of production systems [165,166,167,168], by leveraging efficient and sound real-time data gathering, handling, and analysis from the industrial unit. Physical entities on the plant are self-governing intelligent logical units carrying out undertakings directed by distributed control functions. Industrial cyber-physical systems are pivotal in sustainable smart manufacturing, by integrating control engineering with artificial intelligence-based decision-making algorithms [169,170,171] to set up cognitive and self-configuring plants.

7. Synopsis of the Main Research Outcomes

CPPSs can be thoroughly and steadily engineered during their lifecycle in smart manufacturing through Internet of Things sensing networks, industrial big data analytics, real-time process monitoring, and artificial intelligence-based decision-making algorithms. CPPSs ensure thorough networking of the smart connected devices and resources integrated in manufacturing processes and, consequently, enhanced availability of collected data. Digital transformation aims to advance Internet of Things sensing networks, cognitive automation, deep learning-assisted smart process planning, and CPPS architectures that can connect smart devices from heterogeneous data-driven technologies in smart factory performance. Industry 4.0-based manufacturing systems aim for breakthroughs, cognitive automation, increased feedback to customer demands, and artificial intelligence-based decision-making algorithms. The demand for increasingly customized, smart, and sustainable manufactured items, and the swift growth of cyber-physical system-based real-time monitoring, have resulted in the development of Internet of Things-based decision support systems. Deployment of artificial intelligence-based decision-making algorithms, real-time sensor networks, and cloud technologies are instrumental in remote maintenance support. Real-time big data analytics, Internet of Things-based decision support systems, and production process optimization are pivotal in configuring CPPS-enabled data-driven manufacturing. Throughout the organized layers of CPPS-enabled smart networked factories, big data-driven manufacturing facilitates decentralized production through automation and interconnected industrial units. Generic and cross-deployable data-driven patterns powered by deep learning-assisted smart process planning, industrial big data analytics, and artificial intelligence-based decision-making algorithms can exemplarily trace the data derived from sustainable cyber-physical production systems and harness it for Internet of Things-based real-time production logistics, real-time optimization, or cyber-physical process monitoring systems.

8. Conclusions

Significant research has attempted to clarify in the recent past whether CPPSs are instrumental in instantaneous heterogeneous data collection, inspection, and distribution by use of interoperability support integrated in process monitoring systems. Intelligent production systems gather unprocessed data from a shop floor in real time. The interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. Our systematic literature review puts forward first-rate peer-reviewed evidence concerning how sustainable manufacturing Internet of Things enables robust and dynamic planning and monitoring of production systems by leveraging efficient and sound real-time data gathering, handling, and analysis from the industrial unit. Physical entities on the plant are self-governing intelligent logical units carrying out undertakings directed by distributed control functions. Accurate and reliable assessments of product quality through cyber-physical system-based real-time monitoring assist in optimizing manufacturing processes and big data-driven decision-making instantaneously. The findings derived from the above analyses indicate that Industry 4.0-based manufacturing systems require the networking of all devices integrated across the shop floor. Autonomous data gathering and integrated patterns can diminish postponement in decision-making and implementation of sustainable Industry 4.0 wireless networks. Wireless sensor technology monitor manufacturing assets and networked production or logistics business operations in real time. Internet of Things-based real-time production logistics and deep learning-assisted smart process planning facilitate continuous monitoring of smart shop floors.

9. Limitations, Implications, and Further Directions of Research

By analyzing only articles published in journals indexed in ProQuest, Scopus, and the Web of Science between 2017 and 2021, significant research on artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, cognitive automation, and deep learning-assisted smart process management in smart factory performance and cyber-physical production systems may have been excluded. Limitations of this research comprise particular types of published research (original empirical research and review articles) without considering others (editorial materials, conference proceedings articles, and books). The scope of this systematic review does not approach complex connections between product decision-making information systems, real-time advanced analytics, cyber-physical smart manufacturing, and robotic wireless sensor networks in sustainable Industry 4.0. Subsequent analyses should develop on real-time sensor networks so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks. Future research should consequently investigate how real-time big data analytics enable the advancement of Industry 4.0-based manufacturing systems by use of Internet of Things smart devices and deep learning-assisted smart process planning. Attention should be directed to how sustainable Industry 4.0 wireless networks articulate smart factory performance and Internet of Things-based decision support systems through cognitive automation and cyber-physical system-based real-time monitoring.

Author Contributions

Conceptualization, G.L. and M.A.; methodology, R.Ș. and M.C.; validation, M.I. and M.C.; investigation, C.U. and G.L.; resources, C.U. and M.A.; data curation, C.U. and G.L.; writing—original draft preparation, M.A. and M.I.; writing—review and editing, M.A. and R.Ș.; visualization, G.L. and R.Ș.; supervision, C.U. and M.C.; project administration, R.Ș. and M.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ma, J.; Wang, Q.; Jiang, Z.; Zhao, Z. A hybrid modeling methodology for cyber physical production systems: Framework and key techniques. Prod. Eng. Res. Dev. 2021, 15, 773–790. [Google Scholar] [CrossRef]
  2. Wu, X.; Goepp, V.; Siadat, A.; Vernadat, F. A method for supporting the transformation of an existing production system with its integrated Enterprise Information Systems (EISs) into a Cyber Physical Production System (CPPS). Comput. Ind. 2021, 131, 103483. [Google Scholar] [CrossRef]
  3. Yao, X.; Zhou, J.; Lin, Y.; Li, Y.; Yu, H.; Liu, Y. Smart manufacturing based on cyber-physical systems and beyond. J. Intell. Manuf. 2019, 30, 2805–2817. [Google Scholar] [CrossRef] [Green Version]
  4. Ansari, F.; Khobreh, M.; Seidenberg, U.; Sihn, W. A problem-solving ontology for human-centered cyber physical production systems. CIRP J. Manuf. Sci. Technol. 2018, 22, 91–106. [Google Scholar] [CrossRef]
  5. Engel, G.; Greiner, T.; Seifert, S. Ontology-assisted engineering of cyber–physical production systems in the field of process technology. IEEE Trans. Ind. Inform. 2018, 14, 2792–2802. [Google Scholar] [CrossRef]
  6. Otto, J.; Vogel-Heuser, B.; Niggemann, O. Automatic parameter estimation for reusable software components of modular and reconfigurable cyber-physical production systems in the domain of discrete manufacturing. IEEE Trans. Ind. Inform. 2018, 14, 275–282. [Google Scholar] [CrossRef]
  7. Yao, B.; Zhou, Z.; Wang, L.; Xu, W.; Yan, J.; Liu, Q. A function block based cyber-physical production system for physical human–robot interaction. J. Manuf. Syst. 2018, 48, 12–23. [Google Scholar] [CrossRef]
  8. Biró, M.; Mashkoor, A.; Sametinger, J. Safe and secure cyber-physical systems. J. Softw. Evol. Proc. 2021, 33, e2340. [Google Scholar] [CrossRef]
  9. Deng, C.; Guo, R.; Liu, C.; Zhong, R.Y.; Xu, X. Data cleansing for energy-saving: A case of cyber-physical machine tools health monitoring system. Int. J. Prod. Res. 2018, 56, 1000–1015. [Google Scholar] [CrossRef]
  10. Jiang, J.-R. An improved cyber-physical systems architecture for Industry 4.0 smart factories. Adv. Mech. Eng. 2018, 10, 1–15. [Google Scholar] [CrossRef] [Green Version]
  11. Leiden, A.; Herrmann, C.; Thiede, S. Cyber-physical production system approach for energy and resource efficient planning and operation of plating process chains. J. Clean. Prod. 2021, 280, 125160. [Google Scholar] [CrossRef]
  12. Keane, E.; Zvarikova, K.; Rowland, Z. Cognitive automation, big data-driven manufacturing, and sustainable industrial value creation in Internet of Things-based real-time production logistics. Econ. Manag. Financ. Mark. 2020, 15, 39–48. [Google Scholar] [CrossRef]
  13. Bell, E. Cognitive automation, business process optimization, and sustainable industrial value creation in artificial intelligence data-driven Internet of Things systems. J. Self Gov. Manag. Econ. 2020, 8, 9–15. [Google Scholar] [CrossRef]
  14. Walker, A. Internet of Things-enabled smart sustainable cities: Big data-based urban governance, wireless sensor networks, and automated algorithmic decision-making processes. Geopolit. Hist. Int. Relat. 2020, 12, 58–64. [Google Scholar] [CrossRef]
  15. Peters, E.; Kliestik, T.; Musa, H.; Durana, P. Product decision-making information systems, real-time big data analytics, and deep learning-enabled smart process planning in sustainable Industry 4.0. J. Self Gov. Manag. Econ. 2020, 8, 16–22. [Google Scholar] [CrossRef]
  16. Bennett, S.; Durana, P.; Konecny, V. Urban Internet of Things systems and interconnected sensor networks in sustainable smart city governance. Geopolit. Hist. Int. Relat. 2020, 12, 51–57. [Google Scholar] [CrossRef]
  17. Balica, R. Automated data analysis in organizations: Sensory algorithmic devices, intrusive workplace monitoring, and employee surveillance. Psychosociol. Issues Hum. Resour. Manag. 2019, 7, 61–66. [Google Scholar] [CrossRef]
  18. Clarke, G. Sensing, smart, and sustainable technologies in big data-driven manufacturing. J. Self Gov. Manag. Econ. 2020, 8, 23–29. [Google Scholar] [CrossRef]
  19. Taylor, E. Autonomous vehicle decision-making algorithms and data-driven mobilities in networked transport systems. Contemp. Read. Law Soc. Justice 2021, 13, 9–19. [Google Scholar] [CrossRef]
  20. Costea, E.-A. Machine learning-based natural language processing algorithms and electronic health records data. Linguist. Philos. Investig. 2020, 19, 93–99. [Google Scholar] [CrossRef]
  21. Chessell, D.; Neguriță, O. Smart industrial value creation, cyber-physical production networks, and real-time big data analytics in sustainable Internet of Things-based manufacturing systems. J. Self Gov. Manag. Econ. 2020, 8, 49–58. [Google Scholar] [CrossRef]
  22. Pera, A. Towards effective workforce management: Hiring algorithms, big data-driven accountability systems, and organizational performance. Psychosociol. Issues Hum. Resour. Manag. 2019, 7, 19–24. [Google Scholar] [CrossRef]
  23. Throne, O.; Lăzăroiu, G. Internet of Things-enabled sustainability, industrial big data analytics, and deep learning-assisted smart process planning in cyber-physical manufacturing systems. Econ. Manag. Financ. Mark. 2020, 15, 49–58. [Google Scholar] [CrossRef]
  24. Lyons, N.; Lăzăroiu, G. Addressing the COVID-19 crisis by harnessing Internet of Things sensors and machine learning algorithms in data-driven smart sustainable cities. Geopolit. Hist. Int. Relat. 2020, 12, 65–71. [Google Scholar] [CrossRef]
  25. Davies, S. Interconnected sensor networks and decision-making self-driving car control algorithms in smart sustainable urbanism. Contemp. Read. Law Soc. Justice 2020, 12, 88–96. [Google Scholar] [CrossRef]
  26. Bekken, G. The algorithmic governance of data driven-processing employment: Evidence-based management practices, artificial intelligence recruiting software, and automated hiring decisions. Psychosociol. Issues Hum. Resour. Manag. 2019, 7, 25–30. [Google Scholar] [CrossRef]
  27. Davis, R.; Vochozka, M.; Vrbka, J.; Neguriță, O. Industrial artificial intelligence, smart connected sensors, and big data-driven decision-making processes in Internet of Things-based real-time production logistics. Econ. Manag. Financ. Mark. 2020, 15, 9–15. [Google Scholar] [CrossRef]
  28. Kral, P.; Janoskova, K.; Podhorska, I.; Pera, A.; Neguriță, O. The automatability of male and female jobs: Technological unemployment, skill shift, and precarious work. J. Res. Gend. Stud. 2019, 9, 146–152. [Google Scholar] [CrossRef] [Green Version]
  29. Nica, E.; Miklencicova, R.; Kicova, E. Artificial intelligence-supported workplace decisions: Big data algorithmic analytics, sensory and tracking technologies, and metabolism monitors. Psychosociol. Issues Hum. Resour. Manag. 2019, 7, 31–36. [Google Scholar] [CrossRef] [Green Version]
  30. Davidson, R. Cyber-physical production networks, artificial intelligence-based decision-making algorithms, and big data-driven innovation in Industry 4.0-based manufacturing systems. Econ. Manag. Financ. Mark. 2020, 15, 16–22. [Google Scholar] [CrossRef]
  31. Kovacova, M.; Kliestikova, J.; Grupac, M.; Grecu, I.; Grecu, G. Automating gender roles at work: How digital disruption and artificial intelligence alter industry structures and sex-based divisions of labor. J. Res. Gend. Stud. 2019, 9, 153–159. [Google Scholar] [CrossRef] [Green Version]
  32. Noack, B. Big data analytics in human resource management: Automated decision-making processes, predictive hiring algorithms, and cutting-edge workplace surveillance technologies. Psychosociol. Issues Hum. Resour. Manag. 2019, 7, 37–42. [Google Scholar] [CrossRef]
  33. Kliestik, T.; Nica, E.; Musa, H.; Poliak, M.; Mihai, E.-A. Networked, smart, and responsive devices in Industry 4.0 manufacturing systems. Econ. Manag. Financ. Mark. 2020, 15, 23–29. [Google Scholar] [CrossRef]
  34. Ionescu, L. Digital data aggregation, analysis, and infrastructures in fintech operations. Rev. Contemp. Philos. 2020, 19, 92–98. [Google Scholar] [CrossRef]
  35. Meyers, T.D.; Vagner, L.; Janoskova, K.; Grecu, I.; Grecu, G. Big data-driven algorithmic decision-making in selecting and managing employees: Advanced predictive analytics, workforce metrics, and digital innovations for enhancing organizational human capital. Psychosociol. Issues Hum. Resour. Manag. 2019, 7, 49–54. [Google Scholar] [CrossRef]
  36. Smith, A. Cognitive decision-making algorithms, real-time sensor networks, and Internet of Things smart devices in cyber-physical manufacturing systems. Econ. Manag. Financ. Mark. 2020, 15, 30–36. [Google Scholar] [CrossRef]
  37. Popescu, G.H.; Zvarikova, K.; Machova, V.; Mihai, E.-A. Industrial big data, automated production systems, and Internet of Things sensing networks in cyber-physical system-based manufacturing. J. Self Gov. Manag. Econ. 2020, 8, 30–36. [Google Scholar] [CrossRef]
  38. Bennett, A. Autonomous vehicle driving algorithms and smart mobility technologies in big data-driven transportation planning and engineering. Contemp. Read. Law Soc. Justice 2021, 13, 20–29. [Google Scholar] [CrossRef]
  39. Islam, S.O.B.; Lughmani, W.A.; Qureshi, W.S.; Khalid, A.; Mariscal, M.A.; Garcia-Herrero, S. Exploiting visual cues for safe and flexible cyber-physical production systems. Adv. Mech. Eng. 2019, 11, 1–13. [Google Scholar] [CrossRef]
  40. Panetto, H.; Iung, B.; Ivanov, D.; Weichhart, G.; Wang, X. Challenges for the cyber-physical manufacturing enterprises of the future. Annu. Rev. Control 2019, 47, 200–213. [Google Scholar] [CrossRef]
  41. Valaskova, K.; Ward, P.; Svabova, L. Deep learning-assisted smart process planning, cognitive automation, and industrial big data analytics in sustainable cyber-physical production systems. J. Self Gov. Manag. Econ. 2021, 9, 9–20. [Google Scholar] [CrossRef]
  42. Lewis, E. Smart city software systems and Internet of Things sensors in sustainable urban governance networks. Geopolit. Hist. Int. Relat. 2021, 13, 9–19. [Google Scholar] [CrossRef]
  43. Konecny, V.; Barnett, C.; Poliak, M. Sensing and computing technologies, intelligent vehicular networks, and big data-driven algorithmic decision-making in smart sustainable urbanism. Contemp. Read. Law Soc. Justice 2021, 13, 30–39. [Google Scholar] [CrossRef]
  44. Rojas, R.A.; Rauch, E. From a literature review to a conceptual framework of enablers for smart manufacturing control. Int. J. Adv. Manuf. Technol. 2019, 104, 517–533. [Google Scholar] [CrossRef] [Green Version]
  45. Novak, A.; Bennett, D.; Kliestik, T. Product decision-making information systems, real-time sensor networks, and artificial intelligence-driven big data analytics in sustainable Industry 4.0. Econ. Manag. Financ. Mark. 2021, 16, 62–72. [Google Scholar] [CrossRef]
  46. Mircică, N. Cyber-physical systems for cognitive industrial Internet of Things: Sensory big data, smart mobile devices, and automated manufacturing processes. Anal. Metaphys. 2019, 18, 37–43. [Google Scholar] [CrossRef]
  47. Johnson, A. Medical wearables and biosensor technologies as tools of Internet of Things-based health monitoring systems. Am. J. Med. Res. 2020, 7, 7–13. [Google Scholar] [CrossRef]
  48. Brown, M. Artificial intelligence data-driven Internet of Things systems, real-time process monitoring, and sustainable industrial value creation in smart networked factories. J. Self Gov. Manag. Econ. 2021, 9, 21–31. [Google Scholar] [CrossRef]
  49. Cooper, H.; Poliak, M.; Konecny, V. Computationally networked urbanism and data-driven planning technologies in smart and environmentally sustainable cities. Geopolit. Hist. Int. Relat. 2021, 13, 20–30. [Google Scholar] [CrossRef]
  50. Gordon, A. Autonomous vehicle interaction control software and smart sustainable urban mobility behaviors in network connectivity systems. Contemp. Read. Law Soc. Justice 2021, 13, 40–49. [Google Scholar] [CrossRef]
  51. Neubauer, M.; Krenn, F.; Majoe, D.; Stary, C. Subject-orientation as design language for integration across organisational control layers. Int. J. Prod. Res. 2017, 55, 3644–3656. [Google Scholar] [CrossRef]
  52. O’Donovan, P.; Gallagher, C.; Leahy, K.; O’Sullivan, D.T.J. A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications. Comput. Ind. 2019, 110, 12–35. [Google Scholar] [CrossRef]
  53. Morgan, J.; O’Donnell, G. Multi-sensor process analysis and performance characterisation in CNC turning-a cyber physical system approach. Int. J. Adv. Manuf. Technol. 2017, 92, 855–868. [Google Scholar] [CrossRef]
  54. Rossit, D.; Tohmé, F. Scheduling research contributions to smart manufacturing. Manuf. Lett. 2018, 15, 111–114. [Google Scholar] [CrossRef]
  55. Hawkins, M. Cyber-physical production networks, Internet of Things-enabled sustainability, and smart factory performance in Industry 4.0-based manufacturing systems. Econ. Manag. Financ. Mark. 2021, 16, 73–83. [Google Scholar] [CrossRef]
  56. Ionescu, D. Deep learning algorithms and big health care data in clinical natural language processing. Linguist. Philos. Investig. 2020, 19, 86–92. [Google Scholar] [CrossRef]
  57. Williams, A.; Suler, P.; Vrbka, J. Business process optimization, cognitive decision-making algorithms, and artificial intelligence data-driven Internet of Things systems in sustainable smart manufacturing. J. Self Gov. Manag. Econ. 2020, 8, 39–48. [Google Scholar] [CrossRef]
  58. Preuveneers, D.; Ilie-Zudor, E. The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0. J. Ambient Intell. Smart Environ. 2017, 9, 287–298. [Google Scholar] [CrossRef] [Green Version]
  59. Edwards, C. Real-time advanced analytics, automated production systems, and smart industrial value creation in sustainable manufacturing Internet of Things. J. Self Gov. Manag. Econ. 2021, 9, 32–41. [Google Scholar] [CrossRef]
  60. Townsend, J. Interconnected sensor networks and machine learning-based analytics in data-driven smart sustainable cities. Geopolit. Hist. Int. Relat. 2021, 13, 31–41. [Google Scholar] [CrossRef]
  61. Mitchell, A. Autonomous vehicle algorithms, big geospatial data analytics, and interconnected sensor networks in urban transportation systems. Contemp. Read. Law Soc. Justice 2021, 13, 50–59. [Google Scholar] [CrossRef]
  62. Hamilton, S. Real-time big data analytics, sustainable Industry 4.0 wireless networks, and Internet of Things-based decision support systems in cyber-physical smart manufacturing. Econ. Manag. Financ. Mark. 2021, 16, 84–94. [Google Scholar] [CrossRef]
  63. Popescu Ljungholm, D. Governing self-driving cars: Do autonomous vehicles pose a significant regulatory problem? Rev. Contemp. Philos. 2019, 18, 119–125. [Google Scholar] [CrossRef]
  64. Brown, J.; Cug, J.; Kolencik, J. Internet of Things-based smart healthcare systems: Real-time patient-generated medical data from networked wearable devices. Am. J. Med. Res. 2020, 7, 21–26. [Google Scholar] [CrossRef]
  65. Suler, P.; Palmer, L.; Bilan, S. Internet of Things sensing networks, digitized mass production, and sustainable organizational performance in cyber-physical system-based smart factories. J. Self Gov. Manag. Econ. 2021, 9, 42–51. [Google Scholar] [CrossRef]
  66. Gibson, P. Internet of Things sensing infrastructures and urban big data analytics in smart sustainable city governance and management. Geopolit. Hist. Int. Relat. 2021, 13, 42–52. [Google Scholar] [CrossRef]
  67. Wells, R.; Suler, P.; Vochozka, M. Networked driverless technologies, autonomous vehicle algorithms, and transportation analytics in smart urban mobility systems. Contemp. Read. Law Soc. Justice 2021, 13, 60–70. [Google Scholar] [CrossRef]
  68. Moghaddam, M.; Cadavid, M.N.; Kenley, C.R.; Deshmukh, A.V. Reference architectures for smart manufacturing: A critical review. J. Manuf. Syst. 2018, 49, 215–225. [Google Scholar] [CrossRef]
  69. Dawson, A. Robotic wireless sensor networks, big data-driven decision-making processes, and cyber-physical system-based real-time monitoring in sustainable product lifecycle management. Econ. Manag. Financ. Mark. 2021, 16, 95–105. [Google Scholar] [CrossRef]
  70. Kovacova, M.; Kliestik, T.; Pera, A.; Grecu, I.; Grecu, G. Big data governance of automated algorithmic decision-making processes. Rev. Contemp. Philos. 2019, 18, 126–132. [Google Scholar] [CrossRef]
  71. Miller, E. Networked and integrated sustainable urban technologies in Internet of Things-enabled smart cities. Geopolit. Hist. Int. Relat. 2020, 12, 30–36. [Google Scholar] [CrossRef]
  72. Popescu, G.H.; Valaskova, K.; Majerova, J. Real-time sensor networks, advanced robotics, and product decision-making information systems in data-driven sustainable smart manufacturing. Econ. Manag. Financ. Mark. 2020, 15, 29–38. [Google Scholar] [CrossRef]
  73. Harris, B. Data-driven Internet of Things systems and urban sensing technologies in integrated smart city planning and management. Geopolit. Hist. Int. Relat. 2021, 13, 53–63. [Google Scholar] [CrossRef]
  74. Green, L.; Zhuravleva, N.A. Autonomous driving perception algorithms and urban mobility technologies in smart transportation systems. Contemp. Read. Law Soc. Justice 2021, 13, 71–80. [Google Scholar] [CrossRef]
  75. Jantunen, E.; Gorostegui, U.; Zurutuza, U.; Albano, M.; Ferreira, L.L.; Hegedűs, C.; Campos, J. Remote maintenance support with the aid of cyber-physical systems and cloud technology. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 2018, 232, 784–794. [Google Scholar] [CrossRef]
  76. Vogel-Heuser, B.; Wildermann, S.; Teich, J. Towards the co-evolution of industrial products and its production systems by combining models from development and hardware/software deployment in cyber-physical systems. Prod. Eng. Res. Dev. 2017, 11, 687–694. [Google Scholar] [CrossRef] [Green Version]
  77. Suvarna, M.; Yap, K.S.; Yang, W.; Li, J.; Ng, Y.T.; Wang, X. Cyber-physical production systems for data-driven, decentralized, and secure manufacturing—A perspective. Engineering 2021. [Google Scholar] [CrossRef]
  78. Shaw, S.; Rowland, Z.; Machova, V. Internet of Things smart devices, sustainable industrial big data, and artificial intelligence-based decision-making algorithms in cyber-physical system-based manufacturing. Econ. Manag. Financ. Mark. 2021, 16, 106–116. [Google Scholar] [CrossRef]
  79. Ionescu, D. Semantically enriched Internet of Things sensor data in smart networked environments. Anal. Metaphys. 2019, 18, 30–36. [Google Scholar] [CrossRef]
  80. Moore, C. Medical Internet of Things-based healthcare systems: Wearable sensor-based devices, patient-Generated big data, and real-time clinical monitoring. Am. J. Med. Res. 2020, 7, 41–47. [Google Scholar] [CrossRef] [Green Version]
  81. Sinha, D.; Roy, R. Scheduling status update for optimizing age of information in the context of industrial cyber-physical system. IEEE Access 2019, 7, 95677–95695. [Google Scholar] [CrossRef]
  82. Tan, Q.; Tong, Y.; Wu, S.; Li, D. Modeling, planning, and scheduling of shop-floor assembly process with dynamic cyber-physical interactions: A case study for CPS-based smart industrial robot production. Int. J. Adv. Manuf. Technol. 2019, 105, 3979–3989. [Google Scholar] [CrossRef]
  83. Yu, Z.; Zhou, L.; Ma, Z.; El-Meligy, M.A. Trustworthiness modeling and analysis of cyber-physical manufacturing systems. IEEE Access 2017, 5, 26076–26085. [Google Scholar] [CrossRef]
  84. Yu, Z.; Ouyang, J.; Li, S.; Peng, X. Formal modeling and control of cyber-physical manufacturing systems. Adv. Mech. Eng. 2017, 9, 1–12. [Google Scholar] [CrossRef]
  85. Tucker, G. Sustainable product lifecycle management, industrial big data, and Internet of Things sensing networks in cyber-physical system-based smart factories. J. Self Gov. Manag. Econ. 2021, 9, 9–19. [Google Scholar] [CrossRef]
  86. Wade, K.; Vrbka, J.; Zhuravleva, N.A.; Machova, V. Sustainable governance networks and urban Internet of Things systems in big data-driven smart cities. Geopolit. Hist. Int. Relat. 2021, 13, 64–74. [Google Scholar] [CrossRef]
  87. Welch, C. Real-world connected vehicle data, deep learning-based sensing technologies, and decision-making self-driving car control algorithms in autonomous mobility systems. Contemp. Read. Law Soc. Justice 2021, 13, 81–90. [Google Scholar] [CrossRef]
  88. Harrison, R.; Vera, D.A.; Ahmad, B. A connective framework to support the lifecycle of cyber–physical production systems. Proc. IEEE 2021, 109, 568–581. [Google Scholar] [CrossRef]
  89. Bordel, B.; Alcarria, R.; Robles, T.; Martín, D. Cyber–physical systems: Extending pervasive sensing from control theory to the Internet of Things. Pervasive Mob. Comput. 2017, 40, 156–184. [Google Scholar] [CrossRef]
  90. Tomiyama, T.; Moyen, F. Resilient architecture for cyber-physical production systems. CIRP Ann. 2018, 67, 161–164. [Google Scholar] [CrossRef]
  91. Weichhart, G.; Panetto, H.; Molina, A. Interoperability in the cyber-physical manufacturing enterprise. Annu. Rev. Control 2021, 51, 346–356. [Google Scholar] [CrossRef]
  92. Sinha, D.; Roy, R. Optimal scheduling for maximizing information freshness and system performance in industrial cyber–physical systems. Comput. Commun. 2021, 169, 33–47. [Google Scholar] [CrossRef]
  93. Pivoto, D.G.S.; de Almeida, L.F.F.; da Rosa Righi, R.; Rodrigues, J.J.P.C.; Baratella Lugli, A.; Alberti, A.M. Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: A literature review. J. Manuf. Syst. 2021, 58, 176–192. [Google Scholar] [CrossRef]
  94. Grant, E. Big data-driven innovation, deep learning-assisted smart process planning, and product decision-making information systems in sustainable Industry 4.0. Econ. Manag. Financ. Mark. 2021, 16, 9–19. [Google Scholar] [CrossRef]
  95. Graessley, S.; Suler, P.; Kliestik, T.; Kicova, E. Industrial big data analytics for cognitive Internet of Things: Wireless sensor networks, smart computing algorithms, and machine learning techniques. Anal. Metaphys. 2019, 18, 23–29. [Google Scholar] [CrossRef]
  96. Lewis, S. Wearable Internet of Things healthcare systems: Smart biomedical sensors, wireless connected devices, and real-time patient monitoring. Am. J. Med. Res. 2020, 7, 55–60. [Google Scholar] [CrossRef]
  97. Bergs, T.; Schwaneberg, U.; Barth, S.; Hermann, L.; Grunwald, T.; Mayer, S.; Biermann, F.; Sözer, N. Application cases of biological transformation in manufacturing technology. CIRP J. Manuf. Sci. Technol. 2020, 31, 68–77. [Google Scholar] [CrossRef]
  98. Sanderson, D.; Chaplin, J.C.; Ratchev, S. A function-behaviour-structure design methodology for adaptive production systems. Int. J. Adv. Manuf. Technol. 2019, 105, 3731–3742. [Google Scholar] [CrossRef] [Green Version]
  99. Berger, S.; Häckel, B.; Häfner, L. Organizing self-organizing systems: A terminology, taxonomy, and reference model for entities in cyber-physical production systems. Inf. Syst. Front. 2021, 23, 391–414. [Google Scholar] [CrossRef]
  100. Lăzăroiu, G.; Machová, V.; Kucera, J. Connected and autonomous vehicle mobility: Socially disruptive technologies, networked transport systems, and big data algorithmic analytics. Contemp. Read. Law Soc. Justice 2020, 12, 61–69. [Google Scholar] [CrossRef]
  101. Lowe, R. Networked and integrated sustainable urban technologies in Internet of Things-enabled smart city governance. Geopolit. Hist. Int. Relat. 2021, 13, 75–85. [Google Scholar] [CrossRef]
  102. Davidson, R. The algorithmic governance of connected autonomous vehicles: Data-driven decision support systems and smart sustainable urban mobility behaviors. Contemp. Read. Law Soc. Justice 2020, 12, 16–24. [Google Scholar] [CrossRef]
  103. Engelsberger, M.; Greiner, T. Dynamic reconfiguration of service-oriented resources in cyber–physical production systems by a process-independent approach with multiple criteria and multiple resource management operations. Future Gener. Comput. Syst. 2018, 88, 424–441. [Google Scholar] [CrossRef]
  104. He, Y.; Nie, B.; Zhang, J.; Kumar, P.M.; Muthu, B. Fault detection and diagnosis of cyber-physical system using the computer vision and image processing. Wirel. Pers. Commun. 2021, 1–20. [Google Scholar] [CrossRef]
  105. Liu, C.; Vengayil, H.; Lu, Y.; Xu, X. A cyber-physical machine tools platform using OPC UA and MTConnect. J. Manuf. Syst. 2019, 51, 61–74. [Google Scholar] [CrossRef]
  106. Durana, P.; Perkins, N.; Valaskova, K. Artificial intelligence data-driven Internet of Things systems, real-time advanced analytics, and cyber-physical production networks in sustainable smart manufacturing. Econ. Manag. Financ. Mark. 2021, 16, 20–30. [Google Scholar] [CrossRef]
  107. Ionescu, L. Big data, blockchain, and artificial intelligence in cloud-based accounting information systems. Anal. Metaphys. 2019, 18, 44–49. [Google Scholar] [CrossRef]
  108. Taylor, L. Healthcare body sensor networks and smart wearable devices in Internet of Medical Things. Am. J. Med. Res. 2020, 7, 67–73. [Google Scholar] [CrossRef]
  109. Mourtzis, D.; Vlachou, E. A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance. J. Manuf. Syst. 2018, 47, 179–198. [Google Scholar] [CrossRef]
  110. Watkins, D. Real-time big data analytics, smart industrial value creation, and robotic wireless sensor networks in Internet of Things-based decision support systems. Econ. Manag. Financ. Mark. 2021, 16, 31–41. [Google Scholar] [CrossRef]
  111. Cunningham, E. Artificial intelligence-based decision-making algorithms, sustainable organizational performance, and automated production systems in big data-driven smart urban economy. J. Self Gov. Manag. Econ. 2021, 9, 31–41. [Google Scholar] [CrossRef]
  112. Wright, S.; Birtus, M. Real-time medical data analytics in Internet of Things-based smart healthcare systems. Am. J. Med. Res. 2020, 7, 61–66. [Google Scholar] [CrossRef] [Green Version]
  113. Cohen, S. Interconnected sensor networks and digital urban governance in data-driven smart sustainable cities. Geopolit. Hist. Int. Relat. 2021, 13, 97–107. [Google Scholar] [CrossRef]
  114. Nelson, A. Smart transportation systems: Sustainable mobilities, autonomous vehicle decision-making algorithms, and networked driverless technologies. Contemp. Read. Law Soc. Justice 2020, 12, 25–33. [Google Scholar] [CrossRef]
  115. Mircică, N. Restoring public trust in digital platform operations: Machine learning algorithmic structuring of social media content. Rev. Contemp. Philos. 2020, 19, 85–91. [Google Scholar] [CrossRef]
  116. Stehel, V.; Bradley, C.; Suler, P.; Bilan, S. Cyber-physical system-based real-time monitoring, industrial big data analytics, and smart factory performance in sustainable manufacturing Internet of Things. Econ. Manag. Financ. Mark. 2021, 16, 42–51. [Google Scholar] [CrossRef]
  117. Riley, C.; Vrbka, J.; Rowland, Z. Internet of Things-enabled sustainability, big data-driven decision-making processes, and digitized mass production in Industry 4.0-based manufacturing systems. J. Self Gov. Manag. Econ. 2021, 9, 42–52. [Google Scholar] [CrossRef]
  118. Russell, H. Sustainable urban governance networks: Data-driven planning technologies and smart city software systems. Geopolit. Hist. Int. Relat. 2020, 12, 9–15. [Google Scholar] [CrossRef]
  119. Walker, A.; Rowland, Z.; Frajtova Michalikova, K.; Svabova, L. Urban mobility technologies, algorithm-driven sensing devices, and machine learning-based ethical judgments in a connected vehicle environment. Contemp. Read. Law Soc. Justice 2020, 12, 34–42. [Google Scholar] [CrossRef]
  120. Ionescu, L. Robotic process automation, deep learning, and natural language processing in algorithmic data-driven accounting information systems. Anal. Metaphys. 2020, 19, 59–65. [Google Scholar] [CrossRef]
  121. Kang, H.S.; Lee, J.Y.; Noh, S.D. A dynamic processing methodology of manufacturing data for the automated throughput analysis in cyber-physical production environment. Concurr. Eng. 2019, 27, 155–169. [Google Scholar] [CrossRef]
  122. Cruz Salazar, L.A.; Ryashentseva, D.; Lüder, A.; Vogel-Heuser, B. Cyber-physical production systems architecture based on multi-agent’s design pattern—comparison of selected approaches mapping four agent patterns. Int. J. Adv. Manuf. Technol. 2019, 105, 4005–4034. [Google Scholar] [CrossRef] [Green Version]
  123. Elhabashy, A.E.; Wells, L.J.; Camelio, J.A.; Woodall, W.H. A cyber-physical attack taxonomy for production systems: A quality control perspective. J. Intell. Manuf. 2019, 30, 2489–2504. [Google Scholar] [CrossRef]
  124. Gödri, I.; Kardos, C.; Pfeiffer, A.; Váncza, J. Data analytics-based decision support workflow for high-mix low-volume production systems. CIRP Ann. 2019, 68, 471–474. [Google Scholar] [CrossRef]
  125. Coatney, K.; Poliak, M. Cognitive decision-making algorithms, Internet of Things smart devices, and sustainable organizational performance in Industry 4.0-based manufacturing systems. J. Self Gov. Manag. Econ. 2020, 8, 9–18. [Google Scholar] [CrossRef]
  126. Scott, R.; Poliak, M.; Vrbka, J.; Nica, E. COVID-19 response and recovery in smart sustainable city governance and management: Data-driven Internet of Things systems and Machine learning-based analytics. Geopolit. Hist. Int. Relat. 2020, 12, 16–22. [Google Scholar] [CrossRef]
  127. Peters, E. Sustainable and smart urban transport systems: Sensing and computing technologies, intelligent vehicular networks, and data-driven automated decision-making. Contemp. Read. Law Soc. Justice 2020, 12, 43–51. [Google Scholar] [CrossRef]
  128. Lee, J.; Jin, C.; Bagheri, B. Cyber physical systems for predictive production systems. Prod. Eng. Res. Dev. 2017, 11, 155–165. [Google Scholar] [CrossRef]
  129. Schneider, G.F.; Wicaksono, H.; Ovtcharova, J. Virtual engineering of cyber-physical automation systems: The case of control logic. Adv. Eng. Inform. 2019, 39, 127–143. [Google Scholar] [CrossRef]
  130. Francalanza, E.; Borg, J.; Constantinescu, C. A knowledge-based tool for designing cyber physical production systems. Comput. Ind. 2017, 84, 39–58. [Google Scholar] [CrossRef]
  131. Jiang, Z.; Jin, Y.; Mingcheng, E.; Li, Q. Method of tasks and resources matching and analysis for cyber-physical production system. Adv. Mech. Eng. 2018, 10, 1–9. [Google Scholar] [CrossRef] [Green Version]
  132. Mladineo, M.; Veza, I.; Gjeldum, N. Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm. Int. J. Prod. Res. 2017, 55, 2506–2521. [Google Scholar] [CrossRef]
  133. Duft, G.; Durana, P. Artificial intelligence-based decision-making algorithms, automated production systems, and big data-driven innovation in sustainable Industry 4.0. Econ. Manag. Financ. Mark. 2020, 15, 9–18. [Google Scholar] [CrossRef]
  134. Bourke, E.; Kovacova, M.; Kliestikova, J.; Rowland, Z. Smart industrial Internet of Things devices, services, and applications: Ubiquitous sensing and sensory data, predictive analytics algorithms, and cognitive computing technologies. Anal. Metaphys. 2019, 18, 50–56. [Google Scholar] [CrossRef]
  135. Gray-Hawkins, M.; Lăzăroiu, G. Industrial artificial intelligence, sustainable product lifecycle management, and Internet of Things sensing networks in cyber-physical smart manufacturing systems. J. Self Gov. Manag. Econ. 2020, 8, 19–28. [Google Scholar] [CrossRef]
  136. Liu, C.; Cao, S.; Tse, W.; Xu, X. Augmented reality-assisted intelligent window for cyber-physical machine tools. J. Manuf. Syst. 2017, 44, 280–286. [Google Scholar] [CrossRef]
  137. Vogel-Heuser, B.; Trunzer, E.; Hujo, D.; Sollfrank, M. (Re)deployment of Smart Algorithms in Cyber–Physical Production Systems Using DSL4hDNCS. Proc. IEEE 2021, 109, 542–555. [Google Scholar] [CrossRef]
  138. Penas, O.; Plateaux, R.; Patalano, S.; Hammadi, M. Multi-scale approach from mechatronic to cyber-physical systems for the design of manufacturing systems. Comput. Ind. 2017, 86, 52–69. [Google Scholar] [CrossRef]
  139. Adamson, G.; Wang, L.; Moore, P. Feature-based control and information framework for adaptive and distributed manufacturing in cyber-physical systems. J. Manuf. Syst. 2017, 43, 305–315. [Google Scholar] [CrossRef]
  140. Grundstein, S.; Freitag, M.; Scholz-Reiter, B. A new method for autonomous control of complex job shops—Integrating order release, sequencing and capacity control to meet due dates. J. Manuf. Syst. 2017, 42, 11–28. [Google Scholar] [CrossRef]
  141. Hyers, D. Big data-driven decision-making processes, Industry 4.0 wireless networks, and digitized mass production in cyber-physical system-based smart factories. Econ. Manag. Financ. Mark. 2020, 15, 19–28. [Google Scholar] [CrossRef]
  142. Grayson, J. Big data analytics and sustainable urbanism in Internet of Things-enabled smart governance. Geopolit. Hist. Int. Relat. 2020, 12, 23–29. [Google Scholar] [CrossRef]
  143. Robinson, R. Connected and autonomous transport systems: Deep learning-based sensing technologies, data-driven mobilities, and intelligent vehicular networks. Contemp. Read. Law Soc. Justice 2020, 12, 52–60. [Google Scholar] [CrossRef]
  144. Vrabič, R.; Kozjek, D.; Malus, A.; Zaletelj, V.; Butala, P. Distributed control with rationally bounded agents in cyber-physical production systems. CIRP Ann. 2018, 67, 507–510. [Google Scholar] [CrossRef]
  145. Jiang, Z.; Jin, Y.; Mingcheng, E.; Li, Q. Distributed dynamic scheduling for cyber-physical production systems based on a multi-agent system. IEEE Access 2018, 6, 1855–1869. [Google Scholar] [CrossRef]
  146. Morgan, J.; O’Donnell, G.E. Cyber physical process monitoring systems. J. Intell. Manuf. 2018, 29, 1317–1328. [Google Scholar] [CrossRef]
  147. Dias-Ferreira, J.; Ribeiro, L.; Akillioglu, H.; Neves, P.; Onori, M. BIOSOARM: A bio-inspired self-organising architecture for manufacturing cyber-physical shopfloors. J. Intell. Manuf. 2018, 29, 1659–1682. [Google Scholar] [CrossRef] [Green Version]
  148. Wang, S.; Wan, J.; Imran, M.; Li, D.; Zhang, C. Cloud-based smart manufacturing for personalized candy packing application. J. Supercomput. 2018, 74, 4339–4357. [Google Scholar] [CrossRef]
  149. Popescu, G.H.; Petreanu, S.; Alexandru, B.; Corpodean, H. Internet of Things-based real-time production logistics, cyber-physical process monitoring systems, and industrial artificial intelligence in sustainable smart manufacturing. J. Self Gov. Manag. Econ. 2021, 9, 52–62. [Google Scholar] [CrossRef]
  150. Nica, E.; Janoskova, K.; Kovacova, M. Smart connected sensors, industrial big data, and real-time process monitoring in cyber-physical system-based manufacturing. J. Self Gov. Manag. Econ. 2020, 8, 29–38. [Google Scholar] [CrossRef]
  151. Miller, K. Internet of Things-enabled smart devices in medical practice: Healthcare big data, wearable biometric sensors, and real-time patient monitoring. Am. J. Med. Res. 2020, 7, 27–33. [Google Scholar] [CrossRef] [Green Version]
  152. Tang, D.; Zheng, K.; Zhang, H.; Zhang, Z.; Sang, Z.; Zhang, T.; Espinosa-Oviedo, J.A.; Vargas-Solar, G. Using autonomous intelligence to build a smart shop floor. Int. J. Adv. Manuf. Technol. 2018, 94, 1597–1606. [Google Scholar] [CrossRef]
  153. Duffie, N.; Bendul, J.; Knollmann, M. An analytical approach to improving due-date and lead-time dynamics in production systems. J. Manuf. Syst. 2017, 45, 273–285. [Google Scholar] [CrossRef]
  154. Harrower, K. Algorithmic decision-making in organizations: Network data mining, measuring and monitoring work performance, and managerial control. Psychosociol. Issues Hum. Resour. Manag. 2019, 7, 7–12. [Google Scholar] [CrossRef]
  155. Lăzăroiu, G.; Kliestik, T.; Novak, A. Internet of Things smart devices, industrial artificial intelligence, and real-time sensor networks in sustainable cyber-physical production systems. J. Self Gov. Manag. Econ. 2021, 9, 20–30. [Google Scholar] [CrossRef]
  156. Wingard, D. Data-driven automated decision-making in assessing employee performance and productivity: Designing and implementing workforce metrics and analytics. Psychosociol. Issues Hum. Resour. Manag. 2019, 7, 13–18. [Google Scholar] [CrossRef]
  157. Kannengiesser, U.; Frysak, J.; Stary, C.; Krenn, F.; Müller, H. Developing an engineering tool for cyber-physical production systems. Elektrotech. Inftech. 2021. [Google Scholar] [CrossRef]
  158. Andreev, V.N.; Charuyskaya, M.A.; Kryzhanovskaya, A.S.; Mursalov, I.D.; Mozharovskaia, A.A.; Chervenkova, S.G. Application of intelligent engineering in the planning of cyber-physical production systems. Int. J. Adv. Manuf. Technol. 2021, 115, 117–123. [Google Scholar] [CrossRef]
  159. Freier, P.; Schumann, M. Decision support systems in the context of cyber-physical systems: Influencing factors and challenges for the adoption in production scheduling. Australas. J. Inf. Syst. 2021, 25, 1–21. [Google Scholar] [CrossRef]
  160. Zahid, F.; Tanveer, A.; Kuo, M.M.Y.; Sinha, R. A systematic mapping of semi-formal and formal methods in requirements engineering of industrial cyber-physical systems. J. Intell. Manuf. 2021. [Google Scholar] [CrossRef]
  161. Williams, P. Medical big data, body sensor networks, and online patient engagement in artificial intelligence-enabled healthcare delivery. Am. J. Med. Res. 2020, 7, 14–20. [Google Scholar] [CrossRef]
  162. Davies, S.; Kovacova, M.; Valaskova, K. Urban big data and Internet of Things sensing infrastructures in smart and environmentally sustainable cities. Geopolit. Hist. Int. Relat. 2020, 12, 72–78. [Google Scholar] [CrossRef]
  163. Allen, M. Connected and networked driving: Smart mobility technologies, urban transportation systems, and big data-driven algorithmic decision-making. Contemp. Read. Law Soc. Justice 2020, 12, 79–87. [Google Scholar] [CrossRef]
  164. Dhiman, H.; Röcker, C. Middleware for providing activity-driven assistance in cyber-physical production systems. J. Comput. Des. Eng. 2021, 8, 428–451. [Google Scholar] [CrossRef]
  165. Pelău, C.; Dabija, D.C.; Ene, I. What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics on the acceptance of artificial intelligence in the service industry. Comput. Hum. Behav. 2021, 122, 106855. [Google Scholar] [CrossRef]
  166. Valaskova, K.; Durana, P.; Adamko, P. Changes in consumers’ purchase patterns as a consequence of the COVID-19 pandemic. Mathematics 2021, 9, 1788. [Google Scholar] [CrossRef]
  167. Kral, P.; Valjaskova, V.; Janoskova, K. Quantitative approach to project portfolio management: Proposal for Slovak companies. Oecon. Copernic. 2019, 10, 797–814. [Google Scholar] [CrossRef] [Green Version]
  168. Kliestik, T.; Belas, J.; Valaskova, K.; Nica, E.; Durana, P. Earnings management in V4 countries: The evidence of earnings smoothing and inflating. Econ. Res.-Ekonomska Istraživanja 2021, 34, 1452–1470. [Google Scholar] [CrossRef]
  169. Ginevicius, R.; Kliestik, T.; Stasiukynas, A.; Suhajda, K. The impact of national economic development on the shadow economy. J. Compet. 2020, 12, 39–55. [Google Scholar] [CrossRef]
  170. Poliak, M.; Svabova, L.; Konecny, V.; Zhuravleva, N.A.; Culik, K. New paradigms of quantification of economic efficiency in the transport sector. Oecon. Copernic. 2021, 12, 193–212. [Google Scholar] [CrossRef]
  171. Stanca, L.; Dabija, D.C.; Voj, E. Community of practice: Converting IT graduate students into specialists via professional knowledge sharing. Kybernetes 2021. [Google Scholar] [CrossRef]
Figure 1. PRISMA flow diagram describing the search results and screening.
Figure 1. PRISMA flow diagram describing the search results and screening.
Electronics 10 02497 g001
Table 1. Topics and types of scientific products identified and selected.
Table 1. Topics and types of scientific products identified and selected.
TopicIdentifiedSelected
Cyber-physical production systems9842
Cyber-physical manufacturing systems8230
Smart process manufacturing6319
Smart industrial manufacturing processes5816
Networked manufacturing systems5316
Industrial cyber-physical systems5015
Smart industrial production processes4614
Sustainable Internet of Things-based manufacturing systems3912
Type of Paper
Original research423159
Review295
Conference proceedings220
Book80
Editorial70
Source: Processed by the authors. Some topics overlap.
Table 2. General synopsis of evidence regarding focus topics and descriptive outcomes (research findings).
Table 2. General synopsis of evidence regarding focus topics and descriptive outcomes (research findings).
Intelligent plant modules and smart factory automation have advanced CPPSs that are pivotal in collision identification, impedance monitoring, and assimilating machine learning-based tasks. Wireless sensor technology monitor manufacturing assets and networked production or logistics business operations in real time. Cyber-physical system-based manufacturing configures knowledge-intensive industrial autonomous settings in which smart customized items are produced through deep learning-assisted smart process planning, real-time advanced analytics, and cognitive automation. Groundbreaking technologies furthering cyber-physical enterprise systems regarding real-time decision-making determined from streamlined data necessitate networked sensor and operational systems. The scheduling algorithms can become cognizant of the heterogeneous data coming from the industrial unit in relation to relevance and convenience of the resources when carrying out assignments.Bell, 2020; Brown et al., 2020; Cohen, 2021; Edwards, 2021; Graessley et al., 2019; Grant, 2021; Hamilton, 2021; Islam et al., 2019; Keane et al., 2020; Lewis, 2020; Ma et al., 2021; Mircică, 2019; Mitchell, 2021; Nelson, 2020; Panetto et al., 2019; Popescu Ljungholm, 2019; Preuveneers and Ilie-Zudor, 2017; Townsend, 2021; Walker, 2020; Wu et al., 2021; Yao et al., 2019
Adaptive production systems are crucial in sustainable manufacturing Internet of Things, deriving from the demand for robust characteristics of the system to react to disruption as product changes or alterations to operational parameters. CPPSs autonomously identify and react to inconstant and unplanned situations on the shop floor. Because of the growing volume of modular components and systems, interwoven and heterogeneous factory systems are required for big data-driven decision-making processes and collaborative control in sustainable manufacturing routines. Internet of Things-based real-time production logistics, robotic wireless sensor networks, and deep learning-assisted smart process planning facilitate continuous monitoring of smart shop floors.Ansari et al., 2018; Balica, 2019; Bennett et al., 2020; Berger et al., 2021; Bergs et al., 2020; Engel et al., 2018; Gibson, 2021; Konecny et al., 2021; Lewis, 2021; Otto et al., 2018; Panetto et al., 2019; Peters et al., 2020; Riley et al., 2021; Sanderson et al., 2019; Stehel et al., 2021; Suler et al., 2021; Suvarna et al., 2021; Valaskova et al., 2021; Wells et al., 2021; Yao et al., 2018
In CPPSs, smart connected devices team up automatically to constantly optimize manufacturing processes, manage disturbances, and adjust to variable conditions. The demand for increasingly customized, smart, and sustainable manufactured items and the swift growth of cyber-physical system-based real-time monitoring have resulted in the development of Internet of Things-based decision support systems. The capacity of sustainable cyber-physical production systems to reconfigure in conformity with variable demands enables a rise in deployment and a decrease in expenses and alterations in time. Advancing data-driven monitoring systems and leveraging them across a CPPS platform may result in large-scale supervision and an increase in efficiency during the sustainable product lifecycle management in plants.Biró et al., 2021; Clarke, 2020; Costea, 2020; Davidson, 2020; Dawson, 2021; Deng et al., 2018; Ionescu, 2019 a; Jiang, 2018; Johnson, 2020; Kovacova et al., 2019; Lăzăroiu et al., 2021; Leiden et al., 2021; Lowe, 2021; Miller, 2020; Mircică, 2020; Moghaddam et al., 2018; Novak et al., 2021; Rojas and Rauch, 2019; Russell, 2020; Taylor, 2021; Walker et al., 2020
CPPSs constitute cutting-edge technologies for the adoption of smart manufacturing that is effective only when processing standards and application procedures for the heterogeneous data, which can modify instantaneously due to the character of a factory, are carried out. CPPSs are redesigning hierarchical control arrangements into distributed structures in which the components operate autonomously. Data processing to collect significant information and physical value creation in the production operations can be attained through the assimilation of the enterprise resource coordination and manufacturing execution systems. Embedded and coordinated value networks provide customers with sustainable mass-personalized products and services, and further real-time adaptation to fluid alterations in user demand, shop floor environments, and supply/value networks.Brown, 2021; Chessell and Neguriță, 2020; Cooper et al., 2021; Cruz Salazar et al., 2019; Dias-Ferreira et al., 2018; Elhabashy et al., 2019; Gordon, 2021; Gödri et al., 2019; Green and Zhuravleva, 2021; Harris, 2021; Jiang et al., 2018; Kang et al., 2019; Morgan and O’Donnell, 2018; Pera, 2019; Popescu et al., 2021; Throne and Lăzăroiu, 2020; Tomiyama and Moyen, 2018; Vrabič et al., 2018; Wang et al., 2018
Companies expand their product portfolio and try to decrease their manufacturing time to maximize earnings and market presence, indirectly exacerbating the intricacy of the operational processes. The production system has to inspect the tasks to integrate with the smart connected devices and perform them unsupervised and automatically. CPPSs and sustainable manufacturing Internet of Things reconfigure how shop floor operations are designed and carried out. Deployment of artificial intelligence-based decision-making algorithms, deep learning-assisted smart process planning, real-time sensor networks, and cloud technologies are instrumental in remote maintenance support.Bekken, 2019; Coatney and Poliak, 2020; Davies, 2020; Jantunen et al., 2018; Lyons and Lăzăroiu, 2020; Miller, 2020; Morgan and O’Donnell, 2017; Neubauer et al., 2017; Nica et al., 2020; O’Donovan et al., 2019; Peters, 2020; Popescu et al., 2020; Rossit and Tohmé, 2018; Scott et al., 2020; Suvarna et al., 2021; Vogel-Heuser et al., 2021
Cloud computing and service-oriented designs can network and develop physical factory performance to the cyber world in terms of engineering. By harnessing data-driven modeling, cyber-physical process monitoring systems will reshape manufacturing as intuitive and automated. Smart manufacturing harnesses predictive production systems systematically. In smart industrial units, CPPSs control physical operations, configure a digital duplicate of the physical world, and decisions are decentralized.Davis et al., 2020; Duffie et al., 2017; Francalanza et al., 2017; Hawkins, 2021; Ionescu, 2020 a, b; Jiang et al., 2018; Kral et al., 2019; Lee et al., 2017; Mladineo et al., 2017; Moore, 2020; Nica et al., 2019; Schneider et al., 2019; Shaw et al., 2021; Tang et al., 2018; Williams, 2020
Variable manufacturing systems and product demands derive from inconstant customer behavior. Tools for assessing and managing enhancements in the performance, the soundness, and the responsiveness of manufacturing systems are required. CPPSs improve the flexibility and output of smart manufacturing, adjusting the design and quality of products to fluid market demands and customized requirements. The convergence of standard automation systems within CPPSs, together with service-oriented designs and fog, edge, and cloud computing technologies, are developing sustainable manufacturing Internet of Things and cyber-physical process monitoring systems.Bourke et al., 2019; Davidson, 2020; Duft and Durana, 2020; Engelsberger and Greiner, 2018; Gray-Hawkins and Lăzăroiu, 2020; Harrower, 2019; He et al., 2021; Kovacova et al., 2019; Lăzăroiu et al., 2020; Liu et al., 2019; Noack, 2019; Sinha and Roy, 2019; Tan et al., 2019; Wingard, 2019; Yu et al., 2017 a, b
Cyber-physical machine tools can develop Industry 4.0-based equipment regarding intelligence and self-governance, by integrating physical devices and machining operations with computation and networking performance. CPPSs provide the technological basis for the digitalization and decentralization of manufacturing processes, and their integration across plant networks. Heterogeneous instantaneous transmission scheduling algorithms handle the distribution of the channel resources, but cyber and physical units have distinct demands to enhance the quality of network performance. The fluid assessment, integration, and positioning of services across CPPSs constitute elements of the process control throughout the consolidated modeling and appraisal of operational phases.Adamson et al., 2017; Andreev et al., 2021; Durana et al., 2021; Freier and Schumann, 2021; Grundstein et al., 2017; Ionescu, 2019 b; Ionescu, 2020 c; Kannengiesser et al., 2021; Kliestik et al., 2020; Liu et al., 2017; Meyers et al., 2019; Penas et al., 2017; Taylor, 2020; Tucker, 2021; Vogel-Heuser et al., 2017; Wade et al., 2021; Welch, 2021, Zahid et al., 2021
CPPSs can be thoroughly and steadily engineered and during their lifecycle in smart manufacturing through Internet of Things sensing networks, real-time process monitoring, and artificial intelligence-based decision-making algorithms. Artificial intelligence data-driven Internet of Things systems necessitate high-performance operations and adjustable production systems by use of flexible and real-time scheduling. Sustainable Industry 4.0 wireless networks can shape effective and robust manufacturing by automatically monitoring production equipment in a flexible fashion. CPPSs ensure a thorough networking of the smart connected devices and resources integrated in manufacturing processes and, consequently, enhanced availability of collected data. Computational devices can be deployed as monitoring and interaction technologies and as heterogeneous collaborative devices and modes of networking to configure crucial tools in operating, maintaining, and upgrading data-driven CPPSs.Allen, 2020; Bennett, 2021; Bordel et al., 2017; Cunningham, 2021; Davies et al., 2020; Grayson, 2020; Harrison et al., 2021; Hyers, 2020; Mourtzis and Vlachou, 2018; Pivoto et al., 2021; Popescu et al., 2020; Robinson, 2020; Sinha and Roy, 2021; Tomiyama and Moyen, 2018; Smith, 2020; Watkins, 2021; Weichhart et al., 2021; Williams et al., 2020; Wright and Birtus, 2020 Dhiman and Röcker, 2021
Source: Processed by the authors.
Table 3. Synopsis of evidence regarding focus topics and descriptive outcomes (research findings).
Table 3. Synopsis of evidence regarding focus topics and descriptive outcomes (research findings).
Intelligent plant modules and smart factory automation have advanced CPPSs that are pivotal in collision identification, impedance monitoring, and assimilating machine learning-based tasks. Wireless sensor technology monitor manufacturing assets and networked production or logistics business operations in real time. Cyber-physical system-based manufacturing configures knowledge-intensive industrial autonomous settings where smart customized items are produced through deep learning-assisted smart process planning, real-time advanced analytics, and cognitive automation.Brown et al., 2020; Edwards, 2021; Hamilton, 2021; Islam et al., 2019; Mitchell, 2021; Panetto et al., 2019; Popescu Ljungholm, 2019; Preuveneers and Ilie-Zudor, 2017; Townsend, 2021
Because of the growing volume of modular components and systems, interwoven and heterogeneous factory systems are required for big data-driven decision-making processes and collaborative control in sustainable manufacturing routines. Internet of Things-based real-time production logistics and deep learning-assisted smart process planning facilitate continuous monitoring of smart shop floors.Gibson, 2021; Konecny et al., 2021; Lewis, 2021; Suler et al., 2021; Valaskova et al., 2021; Wells et al., 2021
In CPPSs, smart connected devices team up automatically to constantly optimize manufacturing processes, manage disturbances, and adjust to variable conditions, articulating the relevance of networking and control systems. The demand for increasingly customized, smart, and sustainable manufactured items and the swift growth of cyber-physical system-based real-time monitoring have resulted in the development of Internet of Things-based decision support systems.Dawson, 2021; Johnson, 2020; Kovacova et al., 2019; Miller, 2020; Mircică, 2020; Moghaddam et al., 2018; Novak et al., 2021; Rojas and Rauch, 2019
Data processing to collect significant information and physical value creation in production operations can be attained through the assimilation of the enterprise resource coordination and manufacturing execution systems.Brown, 2021; Cooper et al., 2021; Gordon, 2021; Green and Zhuravleva, 2021; Harris, 2021; Popescu et al., 2021
CPPSs and sustainable manufacturing Internet of Things reconfigure how shop floor operations are designed and carried out. Deployment of artificial intelligence-based decision-making algorithms, deep learning-assisted smart process planning, real-time sensor networks, and cloud technologies are instrumental in remote maintenance support.Jantunen et al., 2018; Morgan and O’Donnell, 2017; Neubauer et al., 2017; O’Donovan et al., 2019; Rossit and Tohmé, 2018; Suvarna et al., 2021; Vogel-Heuser et al., 2021
Cloud computing and service-oriented designs can network and develop physical factory performance to the cyber world in terms of engineering, monitoring, and Internet of Things sensing networks for increased reliability and resilience. By harnessing data-driven modeling, cyber-physical process monitoring systems will reshape manufacturing as intuitive and automated.Hawkins, 2021; Ionescu, 2020 a, b; Moore, 2020; Shaw et al., 2021; Williams, 2020
Table 4. Synopsis of evidence regarding focus topics and descriptive outcomes (research findings).
Table 4. Synopsis of evidence regarding focus topics and descriptive outcomes (research findings).
CPPSs improve the flexibility and output of smart manufacturing, adjusting the design and quality of products to fluid market demands and customized requirements. The convergence of standard automation systems within CPPSs, together with service-oriented designs and fog, edge, and cloud computing technologies, are developing sustainable manufacturing Internet of Things and cyber-physical process monitoring systems.Engelsberger and Greiner, 2018; He et al., 2021; Liu et al., 2019; Sinha and Roy, 2019, Tan et al., 2019, Yu et al., 2017 a, b
Heterogeneous instantaneous transmission scheduling algorithms handle the distribution of the channel resources, but cyber and physical units have distinct demands to enhance the network performance. The fluid assessment, integration, and positioning of services across CPPSs constitute elements of the process control throughout the consolidated modeling and appraisal of operational phases.Durana et al., 2021; Ionescu, 2019 a; Taylor, 2020; Tucker, 2021; Wade et al., 2021; Welch, 2021
CPPSs can be thoroughly and steadily engineered during their lifecycle in smart manufacturing through Internet of Things sensing networks, automated production systems, real-time process monitoring, and artificial intelligence-based decision-making algorithms. Artificial intelligence data-driven Internet of Things systems necessitate high-performance operations and adjustable production systems by use of flexible and real-time scheduling.Bordel et al., 2017; Cunningham, 2021; Harrison et al., 2021; Mourtzis and Vlachou, 2018; Pivoto et al., 2021; Sinha and Roy, 2021; Tomiyama and Moyen, 2018, Watkins, 2021; Weichhart et al., 2021; Wright and Birtus, 2020
Groundbreaking technologies furthering cyber-physical enterprise systems regarding real-time decision-making determined from streamlined data necessitate networked sensor and operational systems. The scheduling algorithms can become cognizant of the heterogeneous data coming from the industrial unit in relation to relevance and convenience of the resources when carrying out assignments.Cohen, 2021; Grant, 2021; Graessley et al., 2019; Lewis, 2020; Mircică, 2019; Nelson, 2020
Adaptive production systems are crucial in sustainable manufacturing Internet of Things, deriving from the demand for robust characteristics of the system to react to disruption as product changes or alterations to operational parameters. CPPSs autonomously identify and react to inconstant and unplanned situations on the shop floor, enabling interoperable connections among distributed business applications, while rendering supervision of manufacturing processes with first-rate quality and adjustability, and reducing operational risk or unpredictability.Berger et al., 2021; Bergs et al., 2020; Panetto et al., 2019; Riley et al., 2021; Sanderson et al., 2019; Stehel et al., 2021; Suvarna et al., 2021
The capacity of sustainable cyber-physical production systems to reconfigure in conformity with variable demands enables a rise in deployment and a decrease in expenses and alterations in time. Advancing data-driven monitoring systems and leveraging them across a CPPS platform may result in large-scale supervision and an increase in efficiency during sustainable product lifecycle management in plants.Davidson, 2020; Ionescu, 2019 b; Lăzăroiu et al., 2021; Lowe, 2021; Russell, 2020; Walker et al., 2020
Table 5. Synopsis of evidence regarding focus topics and descriptive outcomes (research findings).
Table 5. Synopsis of evidence regarding focus topics and descriptive outcomes (research findings).
CPPSs constitute cutting-edge technologies for the adoption of smart manufacturing that is effective only when processing standards and application procedures for the heterogeneous data that can modify instantaneously due to the character of a factory are carried out. CPPSs are redesigning hierarchical control arrangements into distributed structures in which the components operate autonomously.Cruz Salazar et al., 2019; Dias-Ferreira et al., 2018; Elhabashy et al., 2019; Gödri et al., 2019; Jiang et al., 2018; Kang et al., 2019; Morgan and O’Donnell, 2018; Tomiyama and Moyen, 2018; Vrabič et al., 2018; Wang et al., 2018
Companies expand their product portfolio and try to decrease their manufacturing time to maximize earnings and market presence, indirectly exacerbating the intricacy of the operational processes. The production system has to inspect the tasks to integrate with the smart connected devices and perform them unsupervised and automatically.Coatney and Poliak, 2020; Miller, 2020; Nica et al., 2020; Peters, 2020; Popescu et al., 2020; Scott et al., 2020
Smart manufacturing harnesses predictive production systems systematically: cognitive networked assets can predict, identify cause, and redesign malfunctioning events automatically. In smart industrial units, CPPSs control physical operations, configure a digital duplicate of the physical world, and decisions are decentralized: the virtual world stores and processes networked data in real time.Duffie et al., 2017; Francalanza et al., 2017; Jiang et al., 2018; Lee et al., 2017; Mladineo et al., 2017; Schneider et al., 2019; Tang et al., 2018
Tools for assessing and managing enhancements in the performance, the soundness, and the responsiveness of manufacturing systems are required, as a decrease in due-date soundness is typically associated with external causes and not with planning behavior.Bourke et al., 2019; Duft and Durana, 2020; Gray-Hawkins and Lăzăroiu, 2020; Harrower, 2019; Lăzăroiu et al., 2020; Wingard, 2019
Cyber-physical machine tools can develop Industry 4.0-based equipment regarding intelligence and self-governance by integrating physical devices and machining operations with computation and networking performance through functional modules. CPPSs provide the technological basis for the digitalization and decentralization of manufacturing processes, and their integration across plant networks.Adamson et al., 2017; Andreev et al., 2021; Freier and Schumann, 2021; Grundstein et al., 2017; Kannengiesser et al., 2021; Liu et al., 2017; Penas et al., 2017; Vogel-Heuser et al., 2017; Zahid et al., 2021
Sustainable Industry 4.0 wireless networks can shape effective and robust manufacturing by automatically monitoring production equipment in a flexible fashion. CPPSs ensure thorough networking of the smart connected devices and resources integrated in manufacturing processes and, consequently, an enhanced availability of collected data. Computational devices can be deployed as monitoring and interaction technologies and as heterogeneous collaborative devices and modes of networking to configure crucial tools in operating, maintaining, and upgrading data-driven CPPSs.Allen, 2020; Davies et al., 2020; Dhiman and Röcker, 2021; Grayson, 2020; Hyers, 2020; Robinson, 2020; Williams et al., 2020
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Andronie, M.; Lăzăroiu, G.; Iatagan, M.; Uță, C.; Ștefănescu, R.; Cocoșatu, M. Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems. Electronics 2021, 10, 2497. https://doi.org/10.3390/electronics10202497

AMA Style

Andronie M, Lăzăroiu G, Iatagan M, Uță C, Ștefănescu R, Cocoșatu M. Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems. Electronics. 2021; 10(20):2497. https://doi.org/10.3390/electronics10202497

Chicago/Turabian Style

Andronie, Mihai, George Lăzăroiu, Mariana Iatagan, Cristian Uță, Roxana Ștefănescu, and Mădălina Cocoșatu. 2021. "Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems" Electronics 10, no. 20: 2497. https://doi.org/10.3390/electronics10202497

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