Search

Nowadays many processes in the food industry are monitored in an automatic way, with the purpose of minimising the need for workforce and of ensuring the proper control of the quality and safety of the foodstuff. All the sensors share data with a centralised management unit, where often a Manufacturing Execution System collects and evaluates them. As reported in recent research, however, a further step that can be undertaken, exploiting Industry 4.0 enabling technologies, is the implementation of digital twin approaches, with the additional aim to prevent possible issues during production. In line with these considerations, this work aims at showing two different digital twin models intended for improving the control of as many real food systems. Liquid and powder fluids are taken as examples for highlighting the differences in the optimization of the two food processes, as well as for fully exploring the potential of the digital twin approach. Finally, based on the real data taken from two pilot plants, a framework for the selection of the best digital twin tool in the food sector is delineated. [ABSTRACT FROM AUTHOR]

This research focuses on the implementation of Operational Excellence (OE) in the pharmaceutical industry, especially in companies that transformed from PT Askes (Persero) to the Health Insurance Organizing Agency (BPJS). With the entry of the COVID-19 pandemic in early 2020, the need for industry readiness in the face of unexpected changes was further exposed. One approach adopted is Lean Six Sigma (LSS) which is supported by the Manufacturing Execution System (MES) to improve the efficiency and quality of the production process. This research takes a case study on product A with a focus on improvements to hardness issues. Through the Define, Measure, Analyze, Improve, and Control (DMAIC) stages, traditional methods and advanced analysis based on Machine Learning (ML) algorithms are used to improve production processes. The results showed success in achieving significant improvements in the productivity index and quality of product A production processes, making a valuable contribution in the context of Operational Excellence research in the pharmaceutical industry. The LSS method, which has been modified by integrating with ML algorithms and MES, provides productivity aspects consisting of LT, PCE and TD, as well as quality aspects consisting of better Pp, Ppk and sigma levels. The modified LSS method also provides the potential for COGM savings of 2,66 billion Rupiah and reduces the risk of production process failure to less than 200 batches for every 1 million production batches. Based on this, it can be concluded that the development of the Lean Six Sigma method which has been integrated with ML and MES algorithms has the positive potential to increase OE at ABC Farma. [ABSTRACT FROM AUTHOR]

The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation. [ABSTRACT FROM AUTHOR]

Background: In supply chain management, consumers are increasingly sensitive to the delivery time and delivery reliability. Within the textile industry, which is characterized by elongated delivery times, the effective management of delivery timelines is imperative for the timely and trustworthy arrival of products on a global scale. Conventional methods of estimating delivery timelines, which are founded upon historical data, possess certain limitations in their ability to handle the intricacies inherent in contemporary textile manufacturing environments. The paper introduces an innovative deep learning model designed for predicting lead time delivery, with the anticipation that this predictive capability will produce a host of advantages. These benefits encompass heightened operational efficiency and elevated levels of customer satisfaction. Methods: To deal with these difficulties, this paper suggests employing deep learning approaches to predict the lead time in the textile industry. The utilization of historical production data obtained from manufacturing execution systems is leveraged for the purpose of training the models. This paper appraises the most advanced approaches in lead-time prediction, delineates a methodology for anticipating textile manufacturing outcomes, and examines the practicality of these methods through experimentation using real-world data. Results: The conducted research compares three advanced models of deep learning with a classical deep learning model. The three models under consideration are Eagle Strategy Slime Mould Algorithm - General Regression Neural Network (ESSMA-GRNN), Eagle Strategy Slime Mould Algorithm - Convolutional Neural Networks (ESSMA-CNN), and Eagle Strategy Slime Mould Algorithm - Generative Adversarial Networks (ESSMA-GAN), in the specific context of predicting lead-time in textile supply chain. The results demonstrate that the ESSMA-GRNN model exhibits high performance in terms of key evaluation measures, such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared, and Explained Variance, when compared to ESSMA-CNN and ESSMA-GAN. Improving the lead time prediction afforded decision-makers the opportunity to overcome delays and establish effective strategies for minimizing lead time. Conclusions: The study highlights the limitations of conventional lead-time estimation approaches and supports the implementation of sophisticated deep learning methods in the textile supply chain. This paper provides practical perspectives for production planners, underscoring the significance of utilizing advanced techniques to surmount the intricacies of the supply chain. The study concludes by proposing future research directions for lead-time prediction and optimization in the textile sector. [ABSTRACT FROM AUTHOR]

Considering the pivotal role of ferroalloys in the steel industry and the escalating global emphasis on sustainability (e.g., zero emissions and carbon neutrality), the demand for ferroalloys is anticipated to increase. However, the electric arc furnace (EAF) of ferroalloy plants generates substantial amounts of nitrogen oxides (NOx) because of the high-temperature combustion processes. Despite the substantial contributions of many studies on NOx prediction from various industrial facilities, there is a lack of studies considering the environmental condition of the EAF in ferroalloy plants. Therefore, this study presents a deep learning model for predicting NOx emissions from ferroalloy plants and further can provide guidelines for predicting NOx in industrial sites equipped with electric furnaces. In this study, we collected various historical data from the manufacturing execution system of electric furnaces and exhaust gas systems to develop a prediction model. Additionally, an interpretable artificial intelligence method was employed to track the effects of each variable on the NOx emissions. The proposed prediction model can provide decision support to reduce NOx emissions. Furthermore, the interpretation of the model contributes to a better understanding of the factors influencing NOx emissions and the development of effective strategies for emission reduction in ferroalloys EAF plants. [ABSTRACT FROM AUTHOR]

The automation and control of industrial machines and processes is a task traditionally performed by PLCs and SCADA systems. However, with the advent of Industry 4.0 paradigm, which merges OT and IT domains, this task can also be performed inside the IT domain. This paper proposes an edge architecture where a flexible manufacturing line is led by a state machine programmed and running on the IT domain inside the Manufacturing Execution System. Each industrial asset has been modelled as a black-box, where security and hard real-time functionalities are programmed and controlled inside each black-box by traditional OT tools. Functionalities and variables required to synchronize each black-box, have been modelled as inputs and outputs. These input and output variables and methods are available using Industry 4.0 industrial communication protocols, such as OPC UA or MQTT. Then, a state machine programmed on a high-level IT language model inside the MES, automates the manufacturing line reacting to the inputs and outputs of each black-box. This architecture has been successfully validated automating a manufacturing line composed by a conveyor, a robot and a vision based quality control system. [ABSTRACT FROM AUTHOR]

Smart manufacturing is an important research field that is associated with production planning and scheduling, the Internet of Things and artificial intelligence technologies. Production lines use advanced planning and scheduling systems for production operations, time forecasting and planning; integrated manufacturing execution systems are used to collect real-time production information via the Internet of Things to strengthen scheduling control; and artificial intelligence machine learning technology is used to perform predictive maintenance to achieve high-accuracy planning and scheduling. Advanced planning and scheduling systems use genetic algorithms for planning with the aim of increasing speed and accuracy, and the integration of real-time production information from manufacturing execution systems and dynamic adjustments to shift planning are important issues in smart manufacturing. A traditional cyber-physical system integrates historical and real-time production information and carries out a machine learning analysis to improve the production scheduling efficiency, but the prediction of production times for new product orders is a topic that needs further research. This paper proposes new methods of dynamic productivity prediction and new production feature selection, with the aim of improving the performance of advanced planning and scheduling systems. A genetic ant colony algorithm is used to predict dynamic productivity based on real-time production information, to reduce the error between production time plans and actual operations. Historical production information is analysed, and the best correlation coefficient is used in new production feature selection, in order to reduce the discrepancy between production productivity forecasts and actual results. Our proposed dynamic productivity prediction method can reduce the error by at least 1.5% compared with other schemes in the literature, while the proposed production feature selection method can reduce the error by 0.08%. [ABSTRACT FROM AUTHOR]

Cognitive ergonomics and the mental health of production workers have attracted increasing interest in industrial companies. However, there is still not much research available as it is regarding physical ergonomics and muscular load. This paper designs an experiment to analyze the cognitive ergonomics and mental stress of shop floor production workers interacting with different user interfaces of a Manufacturing Execution System (MES) that is adjustable for analyzing the influence of other assistive systems, too. This approach is going to be designed with the Design of Experiments (DoE) method. Therefore, the respective goals and factors are going to be determined. The environment will be the laboratories of the University of Applied Sciences Amberg-Weiden and its Campus for Digitalization in Amberg. In detail, there will be a sample assembly process from the automotive supplier industry for demonstration purposes. At this laboratory, the MES software from the European benchmark SAP is installed, and the respective standard Production Operator Desk is going to be used with slight adaptions. In order to make the cognitive ergonomics measurable, different approaches are going to be used. For instance, body temperature, heart rate and skin conductance as well as subjective methods of self-assessment are planned. The result of this paper is a ready-to-run experiment with sample data for each classification of participants. Further, possible limitations and adjustments are going to be discussed. Finally, an approach to validating the expected results is going to be shown and future intentions are going to be discussed. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST) analysis to quantify the impacts of the imperative Industry 4.0 technologies for manufacturing firms located in Fujian Province, China, namely, Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM), on the sustainable development performance of firms. The findings of the study indicate that these technologies greatly improve the effectiveness of the utilization of resources, reduce the costs of operations, and reduce the impact on the environment. In addition, they have a favorable influence on social considerations, such as preserving the well-being of employees and the outcome of training programs. This research work has convincingly provided an underlying strategic adoption of these technologies for sustainability production by raising important insights that could be valuable for industry managers and policymakers, especially those seeking sustainability at the global level.

In today's volatile, uncertain, and complex business environments, manufacturing companies must not only adapt to market demands but also minimize the time between problem occurrence and resolution. The implementation of lean manufacturing systems has been crucial in this regard. However, traditional approaches have shown notable inefficiencies that can be effectively addressed through digitalization. By embracing digital solutions, manufacturing companies can ensure efficient continuous improvement, driving performance to higher levels. This study aims to find a digital solution for a specific company that faces daily challenges associated with low visibility into production. An investigation revealed that the Lean tools used by the company were outdated, directly affecting the generated information and consequently, decision-making. The integration of a Manufacturing Execution System into the factory's dynamics was the solution found. In this context, a step-by-step methodology is proposed to guide the implementation. As a result, a prototype of the system was designed. The validation of the system by end-users demonstrates the success of the proposed methodology. [ABSTRACT FROM AUTHOR]

Manufacturing processes of the future will rely on standards for asset interoperability and service orchestration. The Asset Administration Shell (AAS) facilitates information exchange among Industry 4.0 assets, while standardized Business Processes enable workflow execution in manufacturing systems. Combining these technologies provides agility and scalability to manufacturing systems by incorporating asset services within business processes. Service orchestration involves coordinating multiple services, which must be dynamic during runtime to manage unforeseen situations that may arise during the manufacturing process. Context information plays a crucial role in identifying such scenarios and selecting the most suitable devices/services in response, and the Semantic Web accurately represents this information. This paper proposes a context-aware approach for service orchestration using industrial asset services. Our contributions include (1) a component for Context-Aware Service Re-Selection. (2) a domain-specific ontology (DeviceServiceOnt) for Semantic Web-based context representation. And, (3) validation of our proposal in a manufacturing setting where robots are responsible for dispatching and distributing materials within a warehouse. Opportunities for future work are also highlighted, with a primary focus on enhancing workflow dynamicity with context-aware capabilities. [ABSTRACT FROM AUTHOR]

The complexity in the collaboration between humans and robots in smart manufacturing remains a significant challenge. This paper introduces an LLM-based manufacturing execution system enhancing Human-Robot Collaboration (HRC) in smart manufacturing. By leveraging Large Language Models (LLMs), the system provides a natural language interface for operators, integrates with Digital Twins for real-time data, and employs behavior-based control for robots. This integration facilitates intuitive interactions and rapid system programming, addressing communication complexities in HRC. The effectiveness of this approach is validated through two HRC assembly case studies, demonstrating significant improvements in collaboration and efficiency. [ABSTRACT FROM AUTHOR]

The article describes the project of the JSC Research Institute "Submicron" of development an informational analytical platform for operating the technological route. A database containing a list of serial numbers and product names, all operations along the technological route, users who have access to information about production processes has been created. The advantages of implementing this management information system at the enterprise are explained. The possibilities of tracking the history—dates and times of all operations of the technological route with indication of exact department and responsible person at the current time are shown. [ABSTRACT FROM AUTHOR]

Due to various interference factors, a pre-planned assembly scheme and its cycle time can be disturbed, resulting in the failure of product delivery on schedule. However, when assembly data of the production line can be obtained in real time, the balance of the assembly line could be dynamically adjusted in case of experiencing serious interferences to optimize its cycle time in time and improve its production efficiency. Therefore, this paper proposes a dynamic rebalancing framework by integrating real time manufacturing data into a novel serial two-stage adaptive alternate genetic fireworks algorithm for solving a stochastic type-II simple assembly line balancing problem (SSALBP-II). MES (manufacturing execution system) is used to obtain some real time data such as the resource status, operation information and task information and to judge abnormal phenomenon and the overdue delivery caused by interferences. On this basis, the stochastic type-II simple assembly line balance model is constructed, with a new serial two-stage adaptive alternate genetic fireworks algorithm (STAGFA). This new algorithm can incorporate both genetic algorithm and fireworks algorithm to solve the model according to the transformation of population diversity discrimination index. Through the comparison between STAGFA and other algorithms such as fireworks algorithm, genetic algorithm, other improved intelligent algorithms, it is proved that the STAGFA is effective and superior in solving the assembly line (re)balance problem. Then, the rebalanced scheme verified by simulation is dispatched to control the physical assembly line and realize dynamic rebalancing cycle time effectively. [ABSTRACT FROM AUTHOR]

The interconnection of medical information networks results in extremely complex network architectures, and the current management level of multi-source heterogeneous data generated by such networks is difficult to meet the requirements of informatization. Therefore, an improved information integration management system is proposed. The article adopted the wild horse optimizer (WHO) algorithm to improve the recurrent neural network (RNN) and designed a multi-source heterogeneous data fusion model. Based on this, a network heterogeneous information integration management system was established with the manufacturing execution system (MES) as the core. By synchronously integrating and processing the data output from the multisource fusion model, all data was transmitted to the system database for storage, and the integrated management of multi-source heterogeneous data was completed through the designed functional modules. The experimental results show that the data fusion integrity of the network heterogeneous information integration management system with WHO-RNN multi-source fusion algorithm as the core is maintained at a high level of 0.700~0.800, and as the data volume increases, the ultimate update level (UUL) index gradually approaches 80%. The system has great computational efficiency and recognition accuracy for multi-source heterogeneous data, and strong engineering practical application performance. [ABSTRACT FROM AUTHOR]

In the context of bottleneck detection, most data-driven approaches employ data from diverse production variables (machine processing times, machine state tags, input timestamps, etc.) for a detailed analysis of bottlenecks. However, for manufacturing companies initiating their digitalization process (i.e. requiring the smallest hardware investment), a bottom-top approach is still missing. In this work, a data-driven model based on minimal information (MI) retrieved from a manufacturing execution system is proposed for bottleneck detection. We consider MI timestamps when each product exits each station and show that this is the most elementary information from production-line operations, enough to autonomously generate an abstract manufacturing layout, and to detect and predict bottlenecks. A general abstract model of a production line is proposed, named queue directed graph (QDG). Incorporating the MI, the QDG model is able to represent a job-shop with a discrete production environment and to calculate production metrics. This work has been employed in the production system of a Bosch factory, in Portugal, using their manufacturing data sets for validation. Different variants of two well-known bottleneck detection methods were implemented and adapted to Bosch's use case: the Active Period Method and the Average Active Period Method. [ABSTRACT FROM AUTHOR]

In the field of industry, especially in the production areas, it is particularly important that the monitoring of assembly efficiency takes place in real-time mode, and that the related data-based estimation also works quickly and reliably. The Manufacturing Execution System (MES), Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems used by companies provide excellent support in data recording, processes, and storing. For Overall Equipment Effectiveness (OEE) data showing the efficiency of assembly lines, there is a regular need to determine expected values. This paper focuses on OEE values prediction with Multiple Linear Regression (MLR) as supervised machine learning. Many factors affecting OEE (e.g., downtimes, cycle time) are examined and analyzed in order to make a more accurate estimation. Based on real industrial data, we used four different methods to perform prediction with various machine learning algorithms, these were the cumulative, fix rolling horizon, optimal rolling horizon and combined techniques. Each method is evaluated based on similar mathematical formulas. [ABSTRACT FROM AUTHOR]

The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and the failure to detect quality issues promptly in accordance with manufacturing settings, which may trigger the propagation of downstream defects. Currently, most inspection quality methods consist of direct measurements followed by manual judgment. The integration of deep learning methods provides a feasible way in which to identify defects on time, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model, built around discrete manufacturing characteristics, and uses fuzzy theory to evaluate the quality levels of production stages. Furthermore, a multivariate long- and short-term memory sequence model is proposed in order to explore the qualitative information from time domain features. The data regarding the produced water dispensers are validated using three evaluation indices, namely, RMSE, MAE, and MAPE. The results indicate that the multivariate long- and short-term memory model exhibits stronger prediction performance. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through the extensive introduction of automatons, computers and Internet of Things (IoT) components. All this seeks to streamline, improve, and increase production at the lowest possible cost and avoid any failure in the creation of the product, following a strategy called "Zero Defect Manufacturing". Machine Learning Operations (MLOps) provide a ML-based solution to this challenge, promoting the automation of all product-relevant steps, from development to deployment. When integrating different machine learning models within manufacturing operations, it is necessary to understand what functionality is needed and what is expected. This article presents a maturity model that can help companies identify and map their current level of implementation of machine learning models. [ABSTRACT FROM AUTHOR]

Summary: The rapidly growing information technologies such as cloud computing, edge computing, artificial intelligence offer cutting‐edge technical means for virtual reality of industrial manufacturing process. An efficient and accurate method for situation awareness (SA) to monitor the process of marginal layer plays an essential role to enhance the safety of industrial equipment. In this study, a manufacturing life cycle is described and characterized as operation and remote controlling of industrial machineries. The edge servers are first employed to track physical devices. Then a novel unified adaptive deep classification framework for SA with semi‐supervised classification method is developed to find unknown patterns in a huge amount of industrial data. Furthermore, a robust model based on the selection of adaptive local neighbor is proposed to find out an optimal weight and high similarity of neighbors. The combination of the proposed framework and model can realize real‐time SA of modern manufacturing the execution system. Plentiful experiments have been conducted to demonstrate the high accuracy and reliability of the proposed framework. [ABSTRACT FROM AUTHOR]

Industry 4.0 has driven the need for Information Technology (IT) & Operational Technology (OT) convergence to modernize OT by leveraging IT. The challenges for manufacturing operations are utilizing and converting years of scattered data into valuable information integrated into the company's digital transformation strategy. This paper aims to provide a systematic literature review of current evidence in digital transformation for acquiring automation & control technologies data in manufacturing operations, such as Programmable Logic Controller (PLC), Supervisory Control and Data Acquisition (SCADA), Distributed Control System (DCS), and Manufacturing Execution System (MES), for analytics purposes and identify the current trends and best practices in this area. The findings cover current information on influential researchers, published journals, research trends, industries, industry types, types of analytics applications, methods, and frameworks for acquiring automation & control technologies data from the legacy OT infrastructure. [ABSTRACT FROM AUTHOR]

The digital twin (DT) concept plays a crucial role in Industry 4.0 and the digitalization of manufacturing processes. A DT is a virtual representation of a physical object, system, or process, designed to accurately reflect its real-world counterpart. In manufacturing, existing process data are often incomplete and do not qualify as a DT. However, with the help of specialized communication frameworks and cheaper, easier-to-use sensors, it is possible to integrate the existing manufacturing execution system (MES) and enterprise resource planning (ERP) data with the missing data gathered from the shop floor to create a comprehensive DT. In this paper, we present a digital shop floor decision support system (DSS) for non-linear aluminum manufacturing production. The system is split into five main components: digitization of shop floor orders; merging and sorting of MES, ERP, and shop floor data; custom and genetic optimization algorithms for the aging furnace production step; layout construction mechanism for optimal placement and stacking of orders in the furnace; and a user-friendly graphical user interface (GUI). The system's performance was evaluated through three tests. The first test measured the efficiency of digitization, the second aimed to quantify time saved in finding packets in the hall, and the last test measured the impact of the optimizer on furnace productivity. The results revealed a 23.5% improvement in furnace capacity, but limitations were identified due to usability and human intervention. [ABSTRACT FROM AUTHOR]

Purpose: The ever-evolving market dynamics and the trend toward increased product individualization demand a high degree of adaptability in production management. This calls for novel approaches, such as Industry 4.0 (I4.0), which integrates the Quality 4.0 (Q4.0) model as well. Methodology/Approach: I4.0 builds upon the foundation of digital manufacturing and entails three key dimensions: (i) The employment of advanced digital technologies to enable digital production, (ii) The development of smart products, which incorporate novel production methods and innovative features, and, (iii) The adoption of an intelligent supply chain, designed to facilitate the procurement of raw materials and the delivery of finished goods. In our research studies, we focus specifically on the first and third approaches. Findings: A bilateral flow of information in collaborative manufacturing is facilitated through digital platforms that span the entirety of the production process, from the inception of cutting-edge product designs to the final delivery to end-customers, integrating quality management (QM) as well. Research Limitation/Implication: The current study outlines a QM model developed for a digital factory in Serbia, utilizing the Q4.0 model, with particular emphasis on the workshop section, integrated within the Enterprise Resource Planning/Manufacturing Execution Systems module. Originality/Value of paper: The manuscript serves as a notable illustration of best practices for digital manufacturing, and the implementation of a digital QM model within the metalworking industry. [ABSTRACT FROM AUTHOR]

During the last decade, a lot of procedure models for the introduction of industry 4.0 (I40) or industrial internet of things (IIoT) solutions have been proposed, especially in the German literature. Since they target all kinds of I40 projects they necessarily must be somewhat generic and are missing important details especially for the "implementation phase". However, even in specific models for introducing manufacturing execution systems (MES) there are a lot of vague hints instead of concrete advice of how to do an effective and efficient implementation. Although it is certainly right that soft factors like finding the right partners and doing a good change management that involves end users early in the process are at least as critical as technical aspects, the latter should not be neglected. In this paper, some desirable features of MES are discussed and how they positively influence the efforts necessary for introducing such a system in a small to medium sized enterprise (SME). [ABSTRACT FROM AUTHOR]

Purpose: The ever-evolving market dynamics and the trend toward increased product individualization demand a high degree of adaptability in production management. This calls for novel approaches, such as Industry 4.0 (I4.0), which integrates the Quality 4.0 (Q4.0) model as well. Methodology/Approach: I4.0 builds upon the foundation of digital manufacturing and entails three key dimensions: (i) The employment of advanced digital technologies to enable digital production, (ii) The development of smart products, which incorporate novel production methods and innovative features, and, (iii) The adoption of an intelligent supply chain, designed to facilitate the procurement of raw materials and the delivery of finished goods. In our research studies, we focus specifically on the first and third approaches. Findings: A bilateral flow of information in collaborative manufacturing is facilitated through digital platforms that span the entirety of the production process, from the inception of cutting-edge product designs to the final delivery to end-customers, integrating quality management (QM) as well. Research Limitation/Implication: The current study outlines a QM model developed for a digital factory in Serbia, utilizing the Q4.0 model, with particular emphasis on the workshop section, integrated within the Enterprise Resource Planning/Manufacturing Execution Systems module. Originality/Value of paper: The manuscript serves as a notable illustration of best practices for digital manufacturing, and the implementation of a digital QM model within the metalworking industry.

Dedicated fieldbuses were developed to provide temporal determinisms for industrial distributed real-time systems. In the early stages, communication systems were dedicated to a single protocol and generally supported a single service. Industrial Ethernet, which is used today, supports many concurrent services, but usually only one real-time protocol at a time. However, shop-floor communication must support a range of different traffic from messages with strict real-time requirements such as time-driven messages with process data and event-driven security messages to diagnostic messages that have more relaxed temporal requirements. Thus, it is necessary to combine different real-time protocols into one communication network. This raises many challenges, especially when the goal is to use wireless communication. There is no research work on that area and this paper attempts to fill in that gap. It is a result of some experiments that were conducted while connecting a Collaborative Robot CoBotAGV with a production station for which two real-time protocols, Profinet and OPC UA, had to be combined into one wireless network interface. The first protocol was for the exchange of processing data, while the latter integrated the vehicle with Manufacturing Execution System (MES) and Transport Management System (TMS). The paper presents the real-time capabilities of such a combination—an achievable communication cycle and jitter. [ABSTRACT FROM AUTHOR]

This paper presents results from an empirical study that investigates the effect of human-centered performance management concerning operational performance and work motivation of shop floor employees. The approach is based on gamified information provisioning. To date, the concept of gamification has been applied in numerous fields, yet hardly any related work provides empirical findings for the production environment. The approach is realized as an MES (Manufacturing Execution System) application and integrated into a business game simulation. The study design builds on an application scenario in manual assembly. Three treatment groups were defined for investigation. The developed business game was conducted among 150 trainees in technical vocational schools in Germany. The obtained data is evaluated concerning operational performance and motivation using descriptive statistics. Quantitative and qualitative observations show that the provision of gamified metrics-based information proves to be a motivation driver. [ABSTRACT FROM AUTHOR]

The object of the study is Manufacturing Execution System. The relevance of the study is conditioned by the fact that the metallurgical industry has been demonstrating a high level of volatility in the global market over the past few years. The issue of the effectiveness of the metallurgical enterprise is directly related to ensuring environmental safety. The purpose of the study is to consider how the Manufacturing Execution System (MES) operates in the metallurgical industry and highlight its features, offering recommendations aimed at improving operational efficiency with the introduction of MES systems at enterprises of the metallurgical industry of the Republic of Kazakhstan. The following methods were used in the study: analysis, synthesis, comparison, graphical representation of data. Using the example of the Magnitogorsk Iron and Steel Works, the study examined the relationship between MES and APCS (Automated Process Control System), highlighted the requirements for the transition from individual management of particular cases of technological rules and restrictions to the digitization of general algorithms. It was defined that the advantages of a production system include its fast payback. Also, it was determined that the MES system allows automating production operations and information support, carrying out operational planning, accounting for production and quality of metal products, tracking the history of each product, managing equipment, and analyzing performance. In addition, the recommendations that can be used as a basis for creating an enterprise development program, increasing the level of productivity, therefore, reducing the cost of the enterprise’s products, were developed [ABSTRACT FROM AUTHOR]

Small- and medium-sized manufacturing companies must adapt their production processes more quickly. The speed with which enterprises can apply a change in the context of data integration and historicization affects their business. This article presents the possibilities of implementing the integration of control processes using modern technologies that will enable the adaptation of production lines. Integration using an object-oriented approach is suitable for complex tasks. Another approach is data integration using the entity referred to as tagging (TAG). Tagging is essential to apply for fast adaptation and modification of the production process. The advantage is identification, easier modification, and generation of data structures where basic entities include attributes, topics, personalization, locale, and APIs. This research proposes a model for integrating manufacturing enterprise data from heterogeneous levels of management. As a result, the model and the design procedure for data integrating production lines can efficiently adapt production changes. [ABSTRACT FROM AUTHOR]

The importance of predictive maintenance (PdM) programs has been recognized across many industries. Seamless integration of the PdM program into today's manufacturing execution systems requires a scalable and generic system design and a set of key performance indicators (KPIs) to make condition monitoring and PdM activities more effective. In this study, a new PdM system and its implementation are presented. KPIs and metrics are proposed and implemented during the design to enhance the system and the PdM performance monitoring needs. The proposed system has been tested in two independent use cases (autonomous transfer vehicle and electric motor) for condition monitoring applications to detect incipient equipment faults or operational anomalies. Machine learning-based data augmentation tools and models are introduced and automated with state-of-the-art AutoML and workflow automation technologies to increase the system's data collection and data-driven fault classification performance. [ABSTRACT FROM AUTHOR]

This paper investigates the mechanisms for integrating an analytic artefact with Manufacturing Execution System (MES), by constructing and deploying an artefact within two manufacturing sites. The findings indicate that analytic artefacts, unlike EIS or IT integrations, can be constructed based on a business problem and do not have to address specific factors such as IT-systems, process, people, or tasks. As a consequence, an analytic artefact can be built centrally and deployed across heterogeneous manufacturing sites, if they share a common data model. [ABSTRACT FROM AUTHOR]

Recently, manufacturing companies have been improving quality and productivity, reducing costs, and producing customized products according to Industry 4.0. The global value chain (GVC) is also being reorganized and manufacturing companies are recovering the connectivity of value chains based on, e.g., the regional value chain (RVC) and reshoring. With the advent of Industry 4.0, many manufacturing companies are introducing smart factories. A new type of manufacturing execution system (MES), a core system of smart factories, is necessary, owing to the new technologies and the increase in collaboration between companies. Here, we present the framework, development, and application processes of a "cloud-based collaborative MES System" to support the value chain of "order-design-production-delivery" for the manufacture of personalized sportswear products in the fashion industry in Korea. To this end, first, nine future MES deployment directions and frameworks are presented. Second, we present the UML modeling, conceptual framework, and functional framework for MES system development, considering six future MES establishment directions such as cloud and collaboration. Third, the application and effect of the designed and developed cloud-based collaborative MES system are analyzed for design, fabric, printing, and sewing companies that play a role in each stage of the sportswear value chain. [ABSTRACT FROM AUTHOR]

Based on the work breakdown structure theory, the lean shipbuilding method guided by intermediate products is proposed in this article. The hull segment task package is divided according to the content of the shipbuilding operation. Considering the constraints of resources and personnel at the production site, the scheduling strategy of the task package is formulated. Moreover, a virtual flow production system of the shipbuilding workshop is established. On this basis, a lean shipbuilding manufacturing execution system for small and medium-sized shipbuilding enterprises is developed to achieve lean production in a shipbuilding workshop. The application results indicate that implementation of the lean shipbuilding method and manufacturing execution system can significantly reduce the ship production cycle and the number of operators, as well as improve the production efficiency. [ABSTRACT FROM AUTHOR]

The next-generation intelligent smart factory system that is proposed in this paper could improve product quality and realize flexible, efficient, and sustainable product manufacturing by comprehensively improving production and management innovation via its digital network and intelligent methods that reflect the characteristics of its printed circuit board (PCB) manufacturing design and on-site implementation. Intelligent manufacturing systems are complex systems that are composed of humans, cyber systems, and physical systems and aim to achieve specific manufacturing goals at an optimized level. Advanced manufacturing technology and next-generation artificial intelligence (AI) are deeply integrated into next-generation intelligent manufacturing (NGIM). Currently, the majority of PCB manufacturers are firms that specialize in processing orders from leading semiconductor and related product manufacturers, such as Samsung Electronics, TSMC, Samsung Electro-Mechanics, and LG Electronics. These top companies have been responsible for all product innovation, intelligent services, and system integration, with PCB manufacturers primarily playing a role in intelligent production and system integration. In this study, the main implementation areas were divided into manufacturing execution system (MES) implementation (which could operate the system using system integration), data gathering, the Industrial Internet of Things (IIoT) for production line connection, AI and real-time monitoring, and system implementation that could visualize the collected data. Finally, the prospects of the design and on-site implementation of the next-generation intelligent smart factory system that detects and controls the occurrence of quality and facility abnormalities are presented, based on the implementation system. [ABSTRACT FROM AUTHOR]

Featured Application: Digital risk analysis of collaborative robotics applications, featuring connections with manufacturing execution systems to support change management. Building on the idea of Industry 4.0, new models of the highly connected factory that leverage factory-generated data to introduce cost-effective automation and involve the human worker for creating higher added value are possible. Within this context, collaborative robots are becoming more common in industry. However, promises regarding flexibility cannot be satisfied due to the challenging process of ensuring human safety. This is because current regulations and standards require updates to the risk assessment for every change to the robotic application, including the parts involved, the robotic components, and the type of interaction within the workspace. This work presents a novel risk analysis software tool that was developed to support change management for adaptive collaborative robotic systems in the connected factory model. The main innovation of this work is the tool's ability to automatically identify where changes have been made to components or processes within a specific application through its integration with a connected factory architecture. This allows a safety expert to easily see where updates to the risk assessment are required, helping them to maintain conformity with the CE marking process despite frequent changes. To evaluate the benefits of this tool, a user study was performed with an exemplary use-case from the SHOP4CF project. The results show that this newly developed technology for risk assessment has better usability and lower omission errors when compared to existing methods. Therefore, this study underlines the need for tools that can help safety engineers cope with changes in flexible robotics applications and reduce omission errors. [ABSTRACT FROM AUTHOR]