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Modern manufacturing methods demand highly flexible and reusable assembly systems. Such flexibility and reusability can be attained using systems that are modular. These modular systems have the ability to react faster to market forces and are adaptable beyond the limits of the so-called flexibility corridor. Also, placing the constraints of flexibility and reusability on software paves the way for design and usage of generic systems that have a relatively low initial cost. However, these advantages are often overshadowed by the complexity of these software systems. Especially, one faces many difficulties in software modularizing the assembly system so that each module can be integrated seamlessly into the entire system. Defining an interface in the development phase is practically impossible given that systems evolve a lot before reaching production phase. [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]

This paper explores the importance of smart manufacturing in the context of Industry 4.0, highlighting the crucial role of Manufacturing Execution Systems (MESs) in facilitating Industry 4.0, particularly in data capture and process management. It is worth noting that Small and Medium Enterprises (SMEs) face several obstacles, unlike large companies that have the resources to adopt these principles. This text explores the challenges that SMEs encounter when adopting Industry 4.0, considering budget constraints and technology transfer difficulties. The potential benefits of such projects are often difficult to measure during the initial stages, but they can facilitate the digital transformation of small businesses. To support this thesis, this paper presents an example of MES implementation in a manufacturing SME, showcasing the creation of a comprehensive data monitoring and industrial performance assessment system. This paper aims to introduce a systematic approach for integrating a Key Performance Indicator (KPI) framework using MESs within an SME. This paper highlights the importance of transitioning from big data to smart data to achieve outcomes in terms of operational efficiency, cost analysis, workload management, resource utilisation, knowledge dissemination, and enhanced operator engagement. [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]

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]

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]

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.

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]

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.

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]

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]

The Industrial Internet of Things (IIoT) has evolved industrial operations to be more efficient and reliable. The fastener forming process (FFP) used to rely on the judgments of in-process inspectors and experienced operators. Since no critical information was transmitted between machines, the machining status during production was unable to reveal. Now, with sensors installed in the machines of FFP, manufacturing data can be collected, analyzed, and responded to the machines. This IIoT-embedded FFP can not only carry out real-time data, pre-processing, and feature engineering but also enable optimally selecting classification algorithms for preventive maintenance. Consequently, the FFP establishes machine-health indicators for the production site and the heading process recognizes the abnormal situations between punches. The operator-delayed response leads to a great amount of loss that can be avoided. Besides, with the integration of enterprise resource planning (ERP) and manufacturing execution system (MES), this IIoT-embedded FFP converts receiving customer orders into work orders and coordinates operations and sales within enterprises. This system integration catches the status of various production indicators and further advances accurate materials preparation and inventory costs control. Overall, this IIoT-embedded FFP system becomes no necessity for increasing inventory and reserving backlog funds to cope with the impact of a vast number of scrap products. Finally, other types of machining processes or systems can adopt this systematic approach in the future. [ABSTRACT FROM AUTHOR]

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. [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]

In past decades, manufacturing companies have paid considerable attention to using their available resources in the most efficient way to satisfy customer demands. This endeavor is supported by many Industry 4.0 methods. One of these is called MES (Manufacturing Execution System), which is applied for monitoring and controlling manufacturing by recording and processing production-related data. This article presents a possible method of implementation of a risk-adjusted production schedule in a data-rich environment. The framework is based on production datasets of multiple workshops, which is followed by statistical analysis, and its results are used in stochastic network models. The outcome of the simulation is implemented in a production scheduling model to determine how to assign the production among workshops. After collecting the necessary data, the reliability indicator-based stochastic critical path method was applied in the case study. Two cases were presented based on the importance of inventory cost and two different scheduling results were created and presented. With the objective of the least inventory cost, the production was postponed to the latest time possible, which means that workshops had more time to finish their previous work on the first day due to the small production quantity. When the cost was not relevant, the production started on the first day of each workshop, and the production was completed before the deadline. These are optimal solutions, but alternative solutions can also be performed by the decision maker based on the results. The use of the modified stochastic critical path method and its analysis shed light on the deficiency of the production, which is a merit in the continuous improvement process and the estimation of the total project time. [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 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.

As a software delivery model, Software as a Service (SaaS) has attracted considerable attention from software providers and users. Most traditional companies are shifting their businesses to an SaaS model. SaaS development is a very complicated process and its success depends on architectural design and development. A Manufacturing Execution System (MES) was used at the expense of licensing fees for features not used in the On-Premise environment, although the features used vary depending on the manufacturing environment. In an SaaS environment, MES is applied with a function-specific container approach through a Microservice Architecture (MSA) to select and employ only the necessary functions. Furthermore, as the number of customers of virtualized applications increases in SaaS-based services, complexity and operating costs increase; thus, Multi-tenancy Architecture (MTA) technology, which serves all customers through a single instance of the application is crucial. Thus, in this study, we investigate the MTA approach and propose a suitable MTA for the manufacturing execution system. Real-time response is crucial to achieving a cyber-physical system of digital manufacturing in SaaS-based MES. Furthermore, SaaS-based big data analytics and decision-making cannot meet the needs of numerous applications in real-time sensitive workplaces. In this study, we propose an SaaS-based MSA/MTA model for real-time control of Internet of Things (IoT) Edge in digital manufacturing (SaaMES), an architecture of SaaS-based MES with MSA and MTA to meet vulnerable workplaces and real-time responses in Cloud environments. The analysis is used by applying the Autoencoder and Generic Adversarial Networks analysis model to IoT Edge for the connection between the Cloud environment and work site to enable real-time response and decision-making through communication using OPC-UA and small-scale analysis. [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]

Small and Medium-sized Enterprises (SMEs) hold a significant proportion in the economy and more than half of employment worldwide. Nowadays, many SMEs are embarking on a digital transformation journey by embracing and deploying the Fourth Industrial Revolution (a.k.a. Industry 4.0) technologies with a vision of the "Factory of the Future". Factory of the future aims to enhance production by making improvements in three dimensions: plant structure, plant digitization, and plant processes, which cut across many key function areas of Manufacturing Execution Systems (MES) such as management of product definitions, resource scheduling, order execution and dispatch, production analysis, product quality, and materials track and trace. The technological shift with the Factory of the Future, e.g., the adoption of information, communication, and networking technologies in the form of the cyber-physical production system (CPPS) in order to manage interoperability at the factory's functional levels in real-time, demands a fresh look at the functional aspects of MES as well as the interplays between MES and an enterprise resource planning (ERP) system and a process control system (e.g., SCADA). The Factory of the Future may lead to hierarchical changes in MES byways of redesigning the value chain for dynamic production and mass personalization scenarios. Deployment of the responsive and adaptive value chain in MES is still state-of-art-issue. The authors will address dynamic interoperability between modular functionalities for end-to-end integration and consider microservices scenarios to form a service-oriented architecture that supports the part of MES in the responsive and adaptive industrial value chain. Primary modules of the manufacturing execution system have been identified from ISA-95 standards and demonstration of end-to-end integration via OPC-UA server/client and PubSub mechanism contributing to the execution of business and manufacturing functions. Each element or activity runs in its own process and provides a microservice; each microservice communicate with other services and form a service-oriented architecture for MES. Finally, a universal framework solution has been proposed for dynamic interoperability, which intends to upgrade the value chain and networking of a conventional industry towards a factory of the future. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit 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.)

Machine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for monitoring crucial changes that occur in the production system. In this paper, reinforcement learning (RL)-based dynamic scheduling and a selection mechanism for multiple dynamic scheduling rules (MDSRs) are proposed to support the operating characteristics of a flexible manufacturing system (FMS) and semiconductor wafer fabrication (FAB). The proposed RL-based dynamic scheduling MDSR selection mechanism consisted of initial MDSR KB generation and revision phases. According to various performance criteria, the presented approach yields a system performance that is superior to those of the fixed-decision scheduling approach, the machine learning classification approach, and the classical MDSR selection mechanism.

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.

The pressing process is a compression process in which a product is made by applying force to a heated or unheated material to transform it into the desired shape. Due to the characteristics of press equipment that produces products through continuous compression for a short time, product defects occur continuously, and systems for solving these problems are being developed using various technologies. This paper proposes a real-time defect detection system based on an artificial intelligence algorithm that detects defects. By attaching various sensors to the press device, the relationship between equipment status and defects is defined and collected based on a big data platform. By developing an artificial intelligence algorithm based on the collected data and implementing the developed algorithm using an embedded board, we will show the practicality of the system by applying it to the actual field. [ABSTRACT FROM AUTHOR]

Digital transformation and artificial intelligence are creating an opportunity for innovation across all levels of industry and are transforming the world of work by enabling factories to embrace cutting edge Information Technologies (ITs) into their manufacturing processes. Manufacturing Execution Systems (MESs) are abandoning their traditional role of legacy executing middle-ware for embracing the much wider vision of functional interoperability enablers among autonomous, distributed, and collaborative Cyber-Physical Production System (CPPS). In this paper, we propose a basic methodology for universally modeling, digitalizing, and integrating services offered by a variety of isolated workcells into a single, standardized, and augmented production system. The result is a reliable, reconfigurable, and interoperable manufacturing architecture, which privileges Open Platform Communications Unified Architecture (OPC UA) and its rich possibilities for information modeling at a higher level of the common service interoperability, along with Message Queuing Telemetry Transport (MQTT) lightweight protocols at lower levels of data exchange. The proposed MES architecture has been demonstrated and validated in several use-cases at a research manufacturing laboratory of excellence for industrial testbeds. [ABSTRACT FROM AUTHOR]

According to connotation of intelligent coal preparation plant, framework of intelligent coal preparation plant was introduced from aspects of Internet of things layer, control layer, management layer and decision-making layer. Key technologies adopted in construction of intelligent coal preparation plant were expounded emphatically, including intellectualization of dense medium separation process, flotation process, slime water health guarantee system and coal preparation manufacturing execution system, which laid technical foundation for development of coal preparation plant from automation and informatization to intelligence.

Precautions of semi-worsted intelligent manufacturing system construction were discussed. In response to characteristics of semi-worsted production, difficulties and needs, intelligent manufacturing was introduced to semi-worsted production. Through intelligent organization production and adjustment technology, utilization of equipment and bottleneck resources were improved, production adaptability was enhanced and product traceability was realized. It was pointed out that by constructing semi-worsted intelligent production management system, it could provide more real and comprehensive data reference to decision maker. It is considered that introduction of intelligent production management system plays a vital role in the development of semi-worsted and the entire textile industry. [ABSTRACT FROM AUTHOR]

Manufacturing execution system is a factory information system that handles production-related quality data as well as executes production plans of process unit for all resources in the production process on site. As the 4th industrial revolution, which maximizes an automation and connectivity with artificial intelligence, has become a hot topic, manufacturers are showing interest in building a smart factories, but enormous construction costs and unstandardized production processes are obstacles to smart factory construction. Therefore, this paper designs and implements a manufacturing execution system for building a smart factory in a deterioration factory. we propose a Web-based manufacturing execution system aiming at a smart factory at the basic level for steel wire processing. The proposed system will smoothly support interworking with the existing ERP system using REST APIs, and will consider extensibility so that it can be used in various devices and browsers. We will show practicality by implementing the proposed WoT-based manufacturing execution system. [ABSTRACT FROM AUTHOR]

Lean production principles have greatly contributed to the efficient and customer-oriented mass production of goods and services. A core element of lean production is the focus on cycle times and designing production controls and buffers around any bottlenecks in the system. Hence, a production line organized by lean principles will operate in a static or at least quasi-static way. While the individualization of products is an interesting business approach, it can influence cycle times and in-time production. This work demonstrates how performance losses induced by highly variable cycle times can be recovered using a digital twin. The unit under analysis is an industrial joiner’s workshop. Due to the high variance in cycle time, the joinery fails its production target, even if all machines are below 80% usage. Using a discrete event simulation of the production line, different production strategies can be evaluated efficiently and systematically. It is successfully shown that the performance losses due to the highly variable cycle times can be compensated using a digital twin in combination with optimization strategies. This is achieved by operating the system in a non-static mode, exploiting the flexibilities within the systems. [ABSTRACT FROM AUTHOR]

Copyright of Mining & Metallurgy (10057854) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group 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.)

Open pit mines experience several challenges and constraints in the management of mining operations and processes. The heterogeneity of the infrastructures and the complexity of the physical and managerial systems highlight these various challenges. The creation of a generic system that can drive but at the same time models in a dynamic logic all these constraints and elements assume a major complexity. In addition, the modelling allows the implementation of real-time decision-making components and increases the responsiveness of the system. This paper presents six modelling methodologies of the production system, namely Open System Architecture for Computer Integrated Manufacturing, GRAI Integrated Methodology, Purdue Enterprise Reference Architecture, Generalized Enterprise Reference Architecture and Methodology, Zachman and Model Based System Engineering. Based on the multi-criteria analysis, we have shown that the Modelling Based System Engineering is the most adapted methodology in the case of open pit mines but this method knows a lack of agility in the system. Our system will use a methodology based on Model Based System Engineering and will consider the agility of the system.

With the development trend of intelligent,networked,integrated in the automation field of mineral processing industry,the longitudinal seamless fusion and integration from the Internet of things to the Internet of the full interconnection manufacturing network of mineral processing were constructed based on the actual demand of the enterprise management and control,data acquisition,transmission,storage and analysis framework were designed for industrial big data generated from mineral processing,the intelligent manufacturing execution system based on the Internet of things and the industrial cloud was developed.The new functions of integrated process production index optimization and decision making,equipment safety video surveillance and management,personnel vehicle location and tracking,energy management,mobile and remote monitoring,and so on were discussed.The manufacturing execution system based on the Internet of things and the industrial cloud was implemented and used in an iron and steel group.The effectiveness of the MES is verified.