Smart Production Systems Research Articles

Advanced technological adaptations can improve the energy-cum-carbon-efficiency of diverse rice production systems

Worldwide, there is an urgent need to develop energy-cum-carbon smart and cost-effective rice production systems for farmer's adoption. Data were collected from 280 farmer's fields representing the South Asia rice production system. Out of these 75 fields following transplanted rice (TPR), 55 fields of wet direct seeded rice (WDSR), 60 fields of drill sown direct seeded rice in line (DSR L), 60 fields of traditional direct seeded rice (DSR) and 30 fields of DSR + beushning (DSR + B). Results show that grain and straw yields in the TPR were 6056 and 7752 kg ha−1, respectively; however, they were neither profitable, energy efficient, or eco-friendly. At the same time, the grain and straw yields in DSR L were recorded by 5832 and 7757 kg ha−1, respectively. It was profitable with the highest net returns (1111.5 US$ ha−1), energy use efficiency (12.77), energy productivity (0.41 kg MJ−1), energy profitability (11.77 US$ MJ−1), energy output efficiency (1314.3 MJ day−1) environment friendly in terms of carbon efficiency 7.20, carbon sustainability index (6.20) and had most diminutive carbon footprint (0.14 kg CO2 eq kg−1 grain) with a comparable carbon credit. DSR L is productive, economically viable, energy efficient, and environmentally safer among rice production systems.

Poka Yoke in Smart Production Systems with Pick-to-Light Implementation to Increase Efficiency

Product assembly is often the last step in the manufacturing process. This task is usually performed by an assembly worker who needs to have practical experience and expertise. For complex products, the assembly may require a long time to study assembly plans. This paper presents a custom-designed Pick-to-Light system using Poka Yoke principles to make this activity easier. The created modular system with two-stage verification serves to guide the assembler precisely. It shows him on the display which parts he should use in a strictly defined assembly step. Our proposal aims to shorten assembly time and reduce the number of errors, which was supported by a case study in a small company with 30 employees. After analysing the data, we can declare that the proposed system significantly reduces the time required for assembly from 7 to 35% and reduces the error rate by 35%. The solution is scalable and flexible, as it can be easily adapted to display assembly steps for a different product.

Self-decision mechanisms of smart production systems based on improved uncertainty theory and user-CFA

Smart production systems (SPSs) are complex smart network systems composed of multiple smart objects that require a self-decision-making mechanism for quick and accurate execution and decision making of dynamic smart production tasks. However, there is currently no uniform self-decision mechanism to handle the dynamic uncertainty of multi-smart object behaviors and ensure SPSs stability. To address this problem, this paper proposes a comprehensive self-decision method for SPSs that considers users’ preferences. A common technological framework for SPSs, a self-decision feature tree, and a self-decision mechanism with three modules are proposed in this paper to define conditions of dynamic smart production scheme (DSPs), optimize DSPs, and recommend DSPs. Autonomic computing methods and improved uncertainty theory are used to achieve real-time optimization and satisfy multi-objective performance requirements. Additionally, a collaborative filtering algorithm integrated with basic user preferences (user-CFA) is proposed to enable autonomous scheme recommendations. Finally, the effectiveness and autonomous operation of self-decision method is demonstrated through a case study on multi-Automated Guided Vehicles (AGVs), where we compare it with conventional methods. The results confirm the advantages of our proposed self-decision method, which contributes to the development and improvement of SPSs.

A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities

Industry 4.0 smart production systems comprise industrial systems and subsystems that need to be integrated in such a way that they are able to support high modularity and reconfigurability of all system components. In today’s industrial production, manufacturing execution systems (MESs) and supervisory control and data acquisition (SCADA) systems are typically in charge of orchestrating and monitoring automated production processes. This article explicates an MES architecture that is capable of autonomously composing, verifying, interpreting, and executing production plans using digital twins and symbolic planning methods. To support more efficient production, the proposed solution assumes that the manufacturing process can be started with an initial production plan that may be relatively inefficient but quickly found by an AI. While executing this initial plan, the AI searches for more efficient alternatives and forwards better solutions to the proposed MES, which is able to seamlessly switch between the currently executed plan and the new plan, even during production. Further, this on-the-fly replanning capability is also applicable when newly identified production circumstances/objectives appear, such as a malfunctioning robot, material shortage, or a last-minute change to a customizable product. Another feature of the proposed MES solution is its distributed operation with multiple instances. Each instance can interpret its part of the production plan, dedicated to a location within the entire production site. All of these MES instances are continuously synchronized, and the actual global or partial (i.e., from the instance perspective) progress of the production is handled in real-time within one common digital twin. This article presents three main contributions: (i) an execution system that is capable of switching seamlessly between an original and a subsequently introduced alternative production plan, (ii) on-the-fly AI-powered planning and replanning of industrial production integrated into a digital twin, and (iii) a distributed MES, which allows for running multiple instances that may depend on topology or specific conditions of a real production plant. All of these outcomes are demonstrated and validated on a use-case utilizing an Industry 4.0 testbed, which is equipped with an automated transport system and several industrial robots. While our solution is tested on a lab-sized production system, the technological base is prepared to be scaled up to larger systems.

Development of a simulative approach in order to estimate the impact of smart services on a production system

The increasing digitalisation and development of the fourth industrial revolution have created new opportunities in the B2B manufacturing industry. These opportunities come in the form of smart services, which are made possible by the interconnectedness of machines and products and the collection and analysis of data. However, there is uncertainty among potential users about the benefits of these smart services for their production systems. This paper aims to address this uncertainty by developing a simulation model that quantifies the impact of a smart service on a production system, using monetary benefit as a key performance indicator. To do so, the influence of a smart service on a production system is analysed and a generic production system model is developed. The generic model can then be used to analyse different smart service configurations and production systems to examine the effect of the smart service. This is demonstrated through the application of the simulation method to a use case studying the benefit of intelligent quality control and predictive maintenance.

Success Factors of the Adoption of Smart Factory Transformation: An Examination of Korean Manufacturing SMEs

A smart factory is a fully automated production system enabled with novel digital technologies. Numerous studies consider its emergence as the arrival of a new wave of production innovation. However, research is scant on why its adoption rate has lagged behind the expectations of investors and policymakers. Thus, this study examined the effect of top management support for information systems, the existing production systems, the perceived usefulness of smart factories, and outsourcing experiences on firms’ intention to adopt smart factories. Using the data of 1,067 Korean manufacturing small and medium-sized enterprises and structural equation modeling, this study finds that the performance of the existing production systems significantly increases the benefits expected from smart production systems, thus strengthening firms’ intention to adopt smart factories. It also finds that the top management’s support for information systems does not have a significant impact on the benefits expected from smart production systems. Furthermore, the overall mechanism of smart factories’ adoption is strengthened when firms develop their production systems in-house. The results of this study provide useful insights for practitioners seeking to transform traditional production systems into smart factories. They also provide a strategic guideline regarding outsourcing experiences.

Smart Factories: New Production Spaces in Digital Transition

Digital developments have affected humanity in industry and many areas in recent years. The new revolution, defined as Industry 4.0, is planned to ensure remote and digital machine–human collaboration for a new working life. Thanks to internet technologies, communication can be achieved in a digital network with the machines and robots used in production and the employees who manage and supervise them. In this new order, the possibility of people participating in the system remotely, leaving physical production to robots, and using smart production systems leads to factories turning into smart factories. While smart factories require a digital environment between humans and machines, they also bring humans and machines together for different functions. As a result of the digital revolution, the transformation of factories into smart factories and innovations in the production space initiates a new discussion in industrial architecture. This study focuses on the spatial effects of the smart production model of the new factory revolution. The research aims to understand how digital production processes in smart factories change factory designs. For this reason, smart factory definitions were first researched in the literature, and future factory propositions were examined. Then, in this context, smart production spaces designed and built to produce new technologies since the day the new revolution was introduced were analysed. This study aims to present a projection for new production spaces with the morphological analyses performed. As a result, this study will form the basis for future studies as an architectural criticism of the transition process.

Возможные последствия внедрения умных производственных систем «Умная фабрика» в период интеллектуализации среды обитания

Возможные последствия внедрения умных производственных систем «Умная фабрика» в период интеллектуализации среды обитания

Endüstri 4.0 Sürecinde İş Sağlığı ve Güvenliği Uygulamaları

Significant advances are experienced in the area of occupational health and safety (OHS) practices as a result of technological developments and emerging new technics in the Industry 4.0 Process. Innovative production methods and factors such as advances in software and programming, simulation and augmented reality, internet of things, smart production systems, Big Data, cyber-physical systems, which are all along the fundamental elements of the Industry 4.0 process, bring new problems in the area of occupational health and safety. Based on this foresight, the latest developments, problems, challenges, and solutions in the field of OHS are being reviewed and evaluated in this study by utilising the previous literature.

Digital twin and IOT technology for secure manufacturing systems

The Digital Twin (DT) method offers new concepts for choosing the context of information technologies and smart production systems. This research focuses on low-price, extremely effective defect diagnosis methods, low-efficiency, high-cost devices to obtain timely feedback and accurate fault detection results, and secure manufacturing systems. The data structure, control plane, and output units are the three components of the manufacturing system that creates a data link between the virtual model using Micro-Electro-Mechanical (MEM) devices and the Zigbee wireless transmission system in the database layer. This study acquired DT information from the control plane using the Internet of Things (IoT) through sensors for secure manufacturing information. It separates and calls the pertinent data by the attribute processor and transfers it to the outcome units. To produce the classification and outcomes of work build features information, the evaluation method analysis the output nodes that split the test set and learning group using a dynamic database. The hybrid IoT with DT technology to examine the consequences of defects detected efficiently predicted and secures the manufacturing system.

Comparing Modern Manufacturing Tools and Their Effect on Zero-Defect Manufacturing Strategies

The aim of most manufacturing and production factories can be defined as achieving smart and sustainable long-term production systems, which means moving towards strategies defined by Zero-Defect Manufacturing strategies with many areas of improvement, such as: lowering overall cost and energy consumption, amount of scrapped output and wasting raw material and improving overall lead times, production status overview, and planning abilities. These facts were the motivations behind the writing of this paper. The authors considered if the classical architecture of Zero-Defect Manufacturing can be improved by additional tools conventionally used in modern manufacturing. The authors have selected Advanced Planning and Scheduling software tools. To prove that different scheduling methods can have a serious impact on overall production results, we prepared a simple case base comparing different scheduling rules. The theoretical basis for writing this manuscript was prepared by studying classical ZDM methodology and defining the industry gap. The methodology is based on the Zero-Defect Manufacturing architecture, which is essential for high-level implementation in industrial practice. Adding new tools, such as Scheduling and the Industrial Internet of Things, to the classic ZDM architecture improves overall methodology. The impact of different scheduling strategies, which is also described in this study, depends on industry and working conditions. The scheduling rules were compared by several key performance indicators, such as lead time and the number of late/unfinished orders. The study realized, with the practical accent shown this challenge, further research in connection with digitalization.

The Advantages and Barries in Implementing of Industry 4.0 and Key Features of Industry 4.0

Since the publication of the term "Industry 4.0" in 2011, the digital transformation required by Industry 4.0 has immediately attracted the attention of industrialists and governments around the world. Majority of the countries around the world are dealing with the difficulty of producing more goods from limited and consumed natural resources to meet the ever-increasing consumer demand worldwide with the first industrial revolution in the 18th century due to environmental and vital issues. Therefore, the sustainability impacts of Industry 4.0 and the way it contributes to sustainable economic, environmental and social development are getting more and more attention. Nowadays, Industry 4.0 is about digitalization in all industrial and consumer markets, from smart production systems to all distribution channels. Industry 4.0 digital transformation involves the digitization and integration of the entire value chain of the product life cycle. Industry 4.0 is a technological concept that contributes to the sustainability of businesses in today's conditions. Industry 4.0 changes the organization, business models, products, supply chain and strategies of companies. Industry 4.0 enables businesses to be more agile and flexible by integrating people, machines and data. Nowadays, countries such as Germany, the United States of America, India, China, Japan, the United Kingdom and Brazil have been developing policies for the implementation of Industry 4.0. However, the adequacy and implementation of Industry 4.0 technologies can be difficult for both industry representatives and countries. In this research, the advantages and barriers in implementing of Industry 4.0 were expressed. In addition, Industry 4.0 and its main features have been explained.

Additive manufacturing: expanding 3D printing horizon in industry 4.0

Additive Manufacturing (AM) technology enables the production of personalized goods at reduced development costs, shorter lead times, lower energy consumption during manufacturing, and decreased material waste. AM will be consolidated as a leading technology in numerous sectors in the near future due to the maturity of the technology, the wide range of possibilities afforded by 3D printing, and the institutional push. One of the most important aspects of Industry 4.0 is 3D printing. It may be used to fabricate complicated parts and allows companies to cut inventory, develop on-demand items, create smaller localized manufacturing conditions, and even shorten supply chains. AM is expected to increase rapidly in the future because of its above stated remarkable “performance record.“ According to a report published the AM market is predicted to produce US$2 trillion worth of components and end products by year 2030. Hence integration of smart technology and production systems or indirectly one can say that AM is promoting Industry 4.0 and it plays a pivotal role in solving some of the 4th industrial revolution’s most important needs. AM is a future paradigm for futuristic production systems, and Industry 4.0 will leverage its potential to reach essential goals. AM will be found now days in a variety of industrial applications including aerospace and health care to consumer goods. This review article discusses about brief AM technology, history, its industrial applications, challenges, and future prospective. Finally, case studies using AM has been considered.

Digitalization Potential in Production Sphere

Global virtualization of production, new challenges and goals of society informatization made today’s production companies change their own production processes in order to retain competitiveness on market. Standard approaches to production management now cannot guarantee stable profitability of business. The concept of smartproduction and wise factories targets at integration of people, information and value chains in one network. It can provide a new impulse to labour productivity, predetermine new opportunities for improving the work efficiency. Today’s foreign and home literature shows academic interest in new production technologies. In spite of various trends and approaches to the subject, principle priorities of advanced smart production processes have not been revealed adequately, which proves the necessity to continue research in this field. The article substantiates the demand for production process digitalization at Russian enterprises. Possibilities of using artificial intellect and introducing smart-production in industry are studied. Priorities of the advanced Russian production companies in the field of digitalization were highlighted: wider use of cloud technologies; predictive analysis; IaaS-services, i. e. virtual centers of data processing and systems of data storage; centralized dispatcher control and data collection (SCADA) and others. The author characterizes parameters of monitoring development of smart production systems on the territory of the Russian Federation, which has been carried out by Rosstat since 2020. The forecast data on assessing the required resources in respect to support tools on behalf of state were shown, aimed at developing technologies of smart production. Target indicators of developing smart technologies were identified within the frames of the federal project ‘Digital Technologies’. The author systematized science-intensive technological innovations, non-standard solutions and advanced tools of managing production systems, specified target indicators of principle technological trends in smart-production development and substantiated lines in the development of smart-potential of Russian industry.

Distributing decision-making authority in manufacturing – review and roadmap for the factory of the future

The question of the benefits of autonomous control is more important than ever: production managers, governments and society hope that the vision of smart and digital production systems with high flexibility and low costs may save the value adding and therefore welfare in the high wage, industrialised countries. At the same time, the discussion on the social implications of autonomous objects and decentralised control approaches is growing. Looking back on the history of production research and practice, we find that there has been a constant ply among scholars and production managers between the advantages of the two concepts of centralised and decentralised control approaches. In this article, we study the concept of autonomy in production planning and control, enabled by cyber-physical systems and the distribution of decision-making authority. Based on a profound structured literature review, we analyse the perception of autonomy, the technological requirements and the increasing complexities of modern smart manufacturing. Moreover, we find that recently several research streams suggest the advantages and benefits of autonomous control concepts compared to traditional centralised approaches based on qualitative analysis and identify a distinct lack of quantitative results.

Integrating process management and event processing in smart factories: A systems architecture and use cases

The developments of new concepts for an increased digitization of manufacturing industries in the context of Industry 4.0 have brought about novel system architectures and frameworks for smart production systems. These range from generic frameworks for Industry 4.0 to domain-specific architectures for Industrial Internet of Things (IIoT). While most of the approaches include a service-based architecture for selective integration with enterprise systems, a close two-way integration of the production control systems and IIoT sensors and actuators with Process-Aware Information Systems (PAIS) on the management level for automation and mining of production processes is rarely discussed. This fusion of Business Process Management (BPM) with IIoT can be mutually beneficial for both research areas, but is still in its infancy. We propose a systems architecture for IIoT that shows how to integrate the low-level hardware components–sensors and actuators–of a smart factory with BPM systems. We discuss the software components and their interactions to address challenges of device encapsulation, integration of sensor events, and interaction with existing BPM systems. This integration is demonstrated within several use cases regarding process modeling, automation and mining for a smart factory model, showing benefits of using BPM technologies to analyze, control, and adapt discrete production processes in IIoT.

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Smart production systems conforming the Industry 4.0 vision are based on subsystems that are integrated in a way that supports high flexibility and re-configurability. Specific components and devices, such as industrial and mobile robots or transport systems, now pose full-blown systems, and the entire Industry 4.0 production system constitutes a system-of-systems. Testing, fine-tuning, and production planning are important tasks in the entire engineering production system life-cycle. All these steps can be significantly supported and improved by digital twins, which are digitalized replicas of physical systems that are synchronized with the real systems at runtime. However, the design and implementation of digital twins for such integrated, yet partly stand-alone, industrial sub-systems can represent challenging and significantly time-consuming engineering tasks. In this article, the problem of the digital twin design for discrete-event production systems is addressed. The article also proposes to utilize a formal description of production resources and related production operations that the resources can perform. An executable version of such formalization can be automatically derived into a form of a digital twin. Such a derived digital twin can be enhanced with operation duration times that are obtained with process mining methods, leading to more realistic simulations for the entire production system. The proposed solution was successfully tested and validated in the Industry 4.0 Testbed, equipped with four robots and a transport system, which is utilized as a use-case in this article.

Interface and interaction design principles for Mixed Reality applications: the case of operator training in wire harness activities

Operator 4.0 has to deal with a vast amount of product variants and production data especially within the mass customization paradigm, high mental demanding tasks, and smart production systems. Technologies capable of supporting his training and his work become fundamental, such as the extended reality (XR). Its increasing use in industrial applications, however, opens up new challenges related to interface and interaction design, which can determine the success of both the use and development experience. The lack of guidelines for designing interfaces for mixed reality (MR) applications is what this paper aims to address. Design requirements for MR interfaces are presented and applied in the context of operator training in wire harness activities. Different interaction modes and user interfaces have been developed to evaluate the most suitable and user-friendly one for the operator. A pilot test was conducted to assess the applications’ usability and potentialities with satisfactory results.

An Innovative Agile Model of Smart Lean–Green Approach for Sustainability Enhancement in Industry 4.0

Industry 4.0 emphasizes developing an innovative approach to eliminating the problems caused by environmental and shop floor waste, which is accomplished by a suitable process optimization approach. The process optimization approach is used to maximize productivity within limited constraints by observing end-to-end management systems. The present research work developed an innovative agile model using the lean, smart, and green approach to improve operational performance within limited constraints in Industry 4.0. The proposed model was developed by thoroughly reviewing research articles conducted over the past decades on process optimization approaches that include lean manufacturing, smart manufacturing, kaizen, and lean six sigma. The model was validated through two real production case studies in the mining machinery and automobile industries. The present article concluded that overall operational performance was enhanced in both case studies by improvement in different factors, including working environment, worker efficiency, environmental evolution, logistics management, and resources utilization. The authors of the present article strongly believe that the proposed innovative agile model would help people in industry make aesthetic and smart sustainable production systems in Industry 4.0 within limited constraints.

Assessing the implementation feasibility of intelligent production systems based on cloud computing, industrial internet of things and business social networks

PurposeThe increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.Design/methodology/approachIntelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R2, T-Value and GOF.FindingsThe study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.Research limitations/implicationsStudy limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.Originality/valueThe research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.