Management Of Manufacturing Systems Research Articles

Green cell scheduling and performance management of manufacturing system based on heat loss optimization

Heat loss is an important loss in the manufacturing process, which has a significant impact on the overall system performance and resource utilization efficiency. Therefore, optimizing heat loss can not only improve manufacturing efficiency, but also promote the green transformation of enterprises. The purpose of this paper is to explore the green unit scheduling and performance management strategy of manufacturing system based on heat loss optimization, so as to improve the energy efficiency and overall performance of manufacturing system. A new scheduling model is proposed, which combines heat loss analysis with green management strategy. By using multi-objective optimization algorithm, the scheduling of manufacturing cells is optimized, focusing on the minimization of heat loss and the maximization of production efficiency. The thermal energy loss characteristics of each process and the operating state of the equipment are considered in the model, and the energy efficiency performance of different scheduling schemes is verified by simulation experiments. The experimental results show that the optimized scheduling scheme significantly reduces the thermal energy loss of the manufacturing system, significantly improves the overall production efficiency, and effectively improves the green performance indicators, including the reduction of energy consumption and waste emission. Through the scheduling and management strategy based on the optimization of heat loss, the green production goal of the manufacturing system is realized. The research of this paper provides a new idea and method for manufacturing enterprises to realize sustainable development while pursuing economic benefit.

30th international conference on flexible automation and intelligent manufacturing (FAIM2021) special issue: modern manufacturing systems management

30th international conference on flexible automation and intelligent manufacturing (FAIM2021) special issue: modern manufacturing systems management

DESIGN OF MANUFACTURING MANAGEMENT SYSTEM: PRODUCT SALES MANAGEMENT SYSTEM

The advent of Industry 4.0 has fuelled the demand for Manufacturing Management Systems (MMS) in the manufacturing industry. This article presents the design and development of a comprehensive Product Sales Management System (PSMS) tailored for small and medium-sized manufacturing companies, focusing on the dairy product industry. Employing the Rapid Application Development (RAD) approach, the development process prioritised user-friendly interfaces and rapid prototyping. Through meticulous requirements gathering, key features were identified, including product information management, customer information management, sales order management, report generation, product advertisement management, and an analytic dashboard. Unified Modeling Language (UML) diagrams, such as use case, activity, and class diagrams, were utilised to visualise the system's functionalities and ensure alignment with requirements. Iterative development and user feedback enhanced the system's usability, resulting in a user-friendly interface for streamlined product sales handling. The proposed PSMS offers efficient management, enhanced communication, and revenue generation for manufacturing companies and tiny and medium-sized enterprises. Furthermore, the study contributes to an understanding of the system requirements and user interface of a PSMS, providing a valuable reference model for developers and researchers in the manufacturing industry. The proposed PSMS represents a significant step towards efficient product sales management, streamlining operations, and boosting manufacturers' profitability. In future work, the PSMS's user interface will be optimised for better mobile responsiveness, and its functionality will be extended to support collaborative sales management and provide manufacturers with advanced capabilities to enhance their sales processes and decision-making.

Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

Lean manufacturing and quality management: Ways to improve efficiency

The reasons for the growing interest in lean manufacturing and quality management systems in engineering production systems are considered. It is shown that these systems currently work without proper efficiency. The essence of lean manufacturing and quality management concepts is considered. The main errors in their understanding and application are analyzed. It is established that it is expedient to implement these systems on the basis of research of processes of the organization's activity. The hierarchy of organizational and technological systems is considered and the main tasks solved within the lean manufacturing and quality management systems are presented. The tools of lean manufacturing and quality management are offered, which it is expedient to use in providing highly efficient machine-building production. Suggestions for restructuring of engineering departments of machinebuilding enterprises are given.

Game Theory Based Dynamic Event-Driven Service Scheduling in Cloud Manufacturing

Due to the individualized consumer needs, cloud manufacturing (CMfg) has been widely used in the optimization of available manufacturing resource allocation to enhance resource utilization and reduce energy consumption. However, efficient scheduling of tasks and subtasks under dynamic CMfg environments to these re-sources are challenging problems. This paper proposes a game theory based on task scheduling and model selection for effectively exploiting distributed manufacturing resources in CMfg, and the Nash equilibrium (NE) in this game theory is implemented by a double ant colony optimization (DACO) algorithm. Through this model, services provided by different providers can handle a batch of tasks in real-time. Besides, to satisfy different service providers and demanders, the proposed approach considers multiple task attributes simultaneously, including completion time, cost, service quality, service composition capability, service availability, energy consumption, service sustainability, service maintainability, and service trust. Simulation results demonstrate that the proposed method is not only effective for the relevant optimization objective but also can achieve great performance under real-time CMfg environments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To provide the best production guides, the efficiency of configuration optimization of manufacturing resources is critical to the control and management of smart manufacturing systems. This paper investigates the dynamic scheduling problem for manufacturing services in CMfg. Previous task scheduling approaches fail to evaluate multiple factors together, like completion time, cost, and energy consumption. Also, the traditional scheduling method cannot respond to requests caused by service state changes in an efficient way. Therefore, in this paper, a game theory model that consists of a static scheduling sub-game and a dynamic selection sub-game is presented. This model is achieved by adopting a proposed double ant colony optimization algorithm that solves constrained non-linear programming. Simulation experiments shown in this paper prove that the proposed method outperforms existing scheduling methods in multiple aspects, including completion time and energy consumption. Also, this method can be readily implemented and incorporated into real production environments. Future work can improve the proposed method by analyzing the uncertainty during scheduling tasks and sharing the logistics resources on the same routes.

Simulation-Based Management Method for Circular Manufacturing Using Response Surfaces

To establish a circular manufacturing system, continuous management of the resource flow and the condition of individual items including products, components, and the materials in the system is indispensable. This study proposes a life cycle simulation (LCS) based management method to support optimal decision-making under conditions that include various uncertainties, such as consumer behavior and market trends. LCS is a simulation method that simulates dynamic material flow in the entire life cycle system and enables the evaluation of environmental and economic efficiencies. Since the simulation model consists of many parameters, exhaustive simulations with all parameter combinations are impossible in a practical timeframe. Therefore, the method extracts parameters that have a significant impact on eco-efficiency through sensitivity analysis using LCS. Then, a response surface of multi-level resolution is created in a multidimensional space composed of the parameters. The response surface is used to quickly identify optimal parameter values under uncertain conditions for effective management. As a case study, the proposed method is applied to the management of a circular manufacturing system for lithium-ion batteries of automotive to verify the effectiveness of the management on eco-efficiency.

Towards a Modular Approach for Workforce Qualification in Reconfigurable Manufacturing Systems

The perpetual changes affecting manufacturing systems caused by highly fluctuating demand and customer requirements make quality management of manufacturing systems more complex. In this context, the implementation of Reconfigurable Manufacturing Systems (RMS) offers decision-makers an efficient solution to cope with these changes. However, these systems require a large number of modifications and changes handled by workforces who must have an adequate level of qualification and skills. The aim of this paper is to propose a workforce qualification framework in RMS to ensure the skills needed for performing the activities. First, new framework is proposed to define the main phases of workforce qualification in a system requiring several changes. Second, new approach is proposed for workforce skills management and the building of training modules linked to the proposed qualification phases, using a modular design. This work provides decision-makers with a solution for the qualification and management of workforce skills in RMS.

Binary decision diagram-based reliability modeling of phased-mission manufacturing system processing multi-type products

ABSTRACT The development of science and technology drives phased-mission manufacturing systems to be increasingly complex, which poses significant challenges to system reliability analysis. Although it has been considered that machine degradation is the key factor affecting system reliability, very little has been done to explore the effect of multi-type products on machine degradation, where the dependency of phases processing multi-type products further increases the difficulty in reliability modeling. Focusing on the phased-mission manufacturing system processing multi-type products, this paper presents a novel machine reliability model to investigate the mixed degradation process caused by multi-type products, which characterizes the dynamics of machine reliability. Further, given the proposed dynamic machine reliability model dealing with phase dependency, a binary-decision-diagram-model-based algorithm is proposed to analyze the reliability of phased-mission manufacturing systems when processing multi-type products. Finally, the experimental result verifies the effectiveness of the proposed method, and the accuracy is validated by the traditional Monte Carlo method. Also, the proposed method shows great potential to provide decision support for the operation management of phased-mission manufacturing systems.

JAPON YÖNETİM SİSTEMİ OMIKOSHI YÖNETİMİ İLKELERİNİN GELENEKSEL BATILI YÖNETİM YAKLAŞIMINA EKLENMESİ: UYGUN OLUR MU?

This conceptual research study focuses on to provide a framework on whether Japanese omikoshi management techniques can be applied in traditional Western management style or not and to suggest its potential effects. The Industrial Revolution began in the 18th century in Great Britain, and it spread rapidly to other countries in Europe and the U.S and then to other parts of the world. Though some Eastern countries, including Japan, began undergoing industrial revolutions and process of economic transformation later than the West, it’s obvious that Japanese companies have dramatically brought their competitive challenge to the early-industrialized Western states within the concept of organizational management and superior manufacturing management systems since 1960s. In sum, this study gives an insight into the main principles in the Western management and Japanese management techniques by comparing their major notions and argues omikoshi management techniques and its prospective outcomes when it is applied to the today’s Western management style.

Process and Product Change Management as a Predictor and Innovative Solution for Company Performance: A Case Study on the Optimization Process in the Automotive Industry

Change and innovation are increasingly exerting a significant influence on the daily activities of companies. To ensure optimal control, innovative solutions are employed that are encapsulated in the concept of change management. In the engineering change sector, the proposed approach involves developing solutions and making continuous adjustments to the manufacturing process to enhance productivity and to meet customer needs. Within the automotive industry, companies utilize innovations and process change management to continuously improve and strengthen their position in the market, such as KPI/KPRS and PCI. To achieve this, the present study gathers real digital data from the Romanian branches of two renowned automotive companies. The data regarding change requests include 215 registrations for the first company and 734 registrations for the second company. By employing complex statistical methods such as ANOVA, Student’s t-test, the Mann–Whitney test, and a regression model, the primary objective of this study is to model and to identify the best predictor of change request status. Additionally, this study aims to explore how this change process influences the economic performances of the companies and the performance indicators of change management in manufacturing processes. The findings indicate that, both in the organizations in general and within the automotive industry, when products experience high demand in the market, the number of change requests increases. This highlights the importance of internal optimization of the automation system. Moreover, the study results underscore the crucial role of an effective smart manufacturing and optimal change management system to uphold and to enhance the economic performance of automotive companies.

Digitalization platform for data-driven quality management in multi-stage manufacturing systems

Digital transformation is driving the current technological trends in manufacturing. An integral constituent is a communication between machines, between machines and humans, or between machines and products. This extensive communication involves large volumes of data. Many manufacturers apply data analytics (e.g., for quality management or improvement purposes) to translate the data into a business value. However, isolated, rigid, and area-specific IT solutions often carry this out. Today’s complex manufacturing requires quality management approaches that constitute a holistic view of and understanding of process–product interactions along the process chain instead of focusing solely on single processes. A novel platform approach to support quality management in manufacturing systems is proposed in this paper to overcome this deficit. It integrates state-of-the-art concepts of IT with modeling approaches for planning and operation of quality management. A conceptual framework and the technical architecture for implementing a digitalization platform are presented in this regard. Moreover, the approach is validated and implemented within a web application based on a use case of data-driven quality management in electronics production.

Evaluation research on the independent controllability of high-end equipment manufacturing industry based on dual incentive control line: an empirical analysis from Jiangsu Province

PurposeIn order to analyze the level of independent controllability and its evolution of high-end equipment manufacturing industry from Jiangsu Province, this article introduces the dual-excitation control line method to construct a comprehensive evaluation model for independent controllability.Design/methodology/approachThrough the collection of information of high-end equipment manufacturing industry's independent and controllable capabilities on different indicators, the three aspects of advancement, autonomy and controllability, an empirical evaluation of 10 enterprises in the high-end equipment cluster in Jiangsu Province was conducted in terms of advancement, autonomy and controllability.FindingsIt effectively reveals the area and evolution characteristics of the “reward” and “punishment” of different indicators of each representative enterprise and reflects the development status and different characteristics of each representative enterprise on the three indicators. The research results provide decision-making guidance for enterprises in the management and control of advanced manufacturing systems with independent and controllable capabilities.Originality/valueExisting research focuses on the evaluation of enterprises' independent controllability only on a single angle or index. This paper maps the dynamic evaluation problem of multiple time-point data to the evaluation problem of single time-point multi-index data and investigates the fluctuation of the performance of the same enterprise under different indexes, so as to comprehensively evaluate the independent controllable level of high-end equipment manufacturing industry and analyze the reasons. Further, this paper first establishes an evaluation index system of independent controllable level of high-end equipment manufacturing industry and quantitatively measures the advanced, independent, controllable and other aspects of typical enterprises in this industry by constructing a double incentive control line evaluation model.

МЕТОДИКА РЕАЛИЗАЦИИ ПРОЕКТОВ ПО УЛУЧШЕНИЯМ В СИСТЕМЕ МЕНЕДЖМЕНТА БЕРЕЖЛИВОГО ПРОИЗВОДСТВА ОРГАНИЗАЦИИ ОБОРОННО-ПРОМЫШЛЕННОГО КОМПЛЕКСА

A method to implement improvement projects in the lean manufacturing management system (LMMS) of a military-industrial complex enterprise (MIC) is proposed, which is developed on the basis of the scientific and methodological apparatus of the modern theory of lean manufacturing (LM), as well as on the principles of scientific labor organization and project management. The algorithm of implementing the successful life cycle of such projects in the military-industrial complex enterprise is considered. A comprehensive system to evaluate the proposed improvement projects is presented, which allows determining the priority of the project in improvement activities. The best cases of implementing improvement projects are considered by the example of JSC "Obukhov Plant".

Research and Development of Manufacturing Management System

MES system is an integrated information system integrating multiple functional modules, which is used to manage and monitor the production process and improve the production efficiency and quality. With the rapid development of the manufacturing industry, the manufacturing management system (MES) has become a key technology to improve the production efficiency and product quality of the manufacturing enterprises. In this paper, we deeply study and discuss the key technology and application of MES system. Through the design of the physical layer and software layer architecture of the MES system, including the overall system structure, database, functional module, system integration, etc., the development of the manufacturing management system (MES) is realized.

Capabilities and skills for manufacturing planning in an automotive use case scenario

Abstract The necessity for increased flexibility in production and handling of high-variant product families is a strong trend in Industry 4.0 scenarios. A promising approach to achieve this is to replace the rigid programming of the manufacturing management systems by a declarative description of machines and their functionality. This allows for a dynamic allocation of tasks to resources. This paper reports the ongoing work on applying the concept of capabilities and skills to an industrial application example in order to investigate its potential benefits. To apply the abstract concept of capabilities and skills to the production scenario it is implemented using specific technologies. These include Semantic Web ontologies, constraint solving methods and OPC UA for skill invocation and communication.

Identification of Key Brittleness Factors for the Lean–Green Manufacturing System in a Manufacturing Company in the Context of Industry 4.0, Based on the DEMATEL-ISM-MICMAC Method

In the context of Industry 4.0, the lean–green manufacturing system has brought many advantages and challenges to industrial participants. Security is one of the main challenges encountered in the new industrial environment, because smart factory applications can easily expose the vulnerability of manufacturing and threaten the operational security of the whole system. It is difficult to address the problem of the brittleness factor in manufacturing systems. Therefore, building on vulnerability theory, this study proposes a vulnerability index system for lean–green manufacturing systems in a manufacturing company in the context of Industry 4.0. The index has four dimensions: human factors, equipment factors, environmental factors, and other factors. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach was used to calculate the degree of influence, the degree of being influenced, and the centrality and causes of the factors. The causal relationships and key influences between the factors were identified. Then, the dependence and hierarchy of each of the key influencing factors were analyzed using the Matrix-Based Cross-Impact Multiplication Applied to Classification (MICMAC) and Interpretative Structural Model (ISM) methods, and a hierarchical structural model of the factors was constructed. Finally, an intelligent manufacturing system that produces a micro-acoustic material and device was used as an example to verify the accuracy of the proposed method. The results show that the method not only identifies the key brittleness factors in a lean–green manufacturing system but can also provide a guarantee for the safe operation of a manufacturing system. This study provides theoretical guidance for the effective management of intelligent manufacturing systems; moreover, it lays a foundation and provides a new methodology for assessing the vulnerability of manufacturing systems.

Safety Poka Yoke in Zero-Defect Manufacturing Based on Digital Twins

In this article, the proposed work aims to further optimize the fault diagnosis effect of manufacturing equipment, explore the application of digital twins technology in intelligent manufacturing. The equipment failures in Poka Yoke technology are adopted, and a fault identification and chopping algorithm is designed based on the active learning—deep neural network (AL-DNN) and domain adversarial neural networks (DANN). In addition, a digital twins workshop management and control system is designed for intelligent manufacturing management. The experimental exploration reveals that the accuracy of the AL-DNN algorithm is as high as 99.248%, which is more in line with practical applications. The DANN algorithm can realize fault identification and diagnosis under different working conditions. Compared with other deep learning algorithms, the accuracy of the DANN can be increased by up to 20.256%, showing higher accuracy in contrast to the traditional algorithm, so the effect is more stable. In addition, the digital twins manufacturing management system designed shows good performances, which can intuitively display the specific conditions of workshop and realize basic operating functions. The concept of digital twins is innovatively introduced into equipment fault diagnosis and trend prediction, which can provide scientific and effective reference data for subsequent research on intelligent manufacturing.

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

The gearbox is widely applied as the mechanical transmission system of intelligent manufacturing systems, such as machine tools and robotics. The harsh working environments make the gear surface prone to wear. The progression of surface wear can bring severe failures to the gear tooth, including gear tooth root crack, surface spalling of gear tooth, and tooth breaking, all of which could damage the whole transmission system. Hence, it is essential to monitor and evaluate the gear surface wear propagation. The gear wear has been proven highly relevant with the vibration second-order cyclostationary (CS2) characteristics. Therefore, this paper develops a novel cyclostationarity-based framework to monitor and evaluate gear wear propagation. More specifically, the squared envelope (SE) of the residual signal, removing deterministic components, is utilized to identify the gear wear distribution and its propagation trends, validated using the measured gear surface morphology. Moreover, a new CS2-based indicator is proposed to assess the severity of gear surface wear, achieving a high correlation with measured surface roughness: is more than 0.9. The developed cyclostationarity-based framework can comprehensively evaluate the degradation status of the gear system caused by surface wear, significantly benefiting the health management of the gear transmission system, which is of great practical value for the health management of intelligent manufacturing systems. A series of endurance tests are conducted to verify the effectiveness and superiority of the developed framework for gear wear monitoring compared with the conventional indicators.

Proposal for an expert system to aid decision-making in the design and management of flexible manufacturing systems

Purpose: The objective of this article is to introduce a concept for facilitating decision-making processes in the design and management of flexible manufacturing systems. This concept aims to minimize the risk of either overestimating or underestimating the flexibility and efficiency of production potential. Design/methodology/approach: The proposal involves the utilization of a hybrid expert system, incorporating a combination of rules, scenarios, and semantic schemes to construct a comprehensive knowledge base. This system also integrates fuzzy logic, artificial neural networks, and agent systems with expert systems. Findings: The proposed concept of a hybrid expert system allows for the management of flexible manufacturing systems across various levels, including strategic, operational, and technological aspects of production organization and management. Practical implications: The article has broad applications as it is specifically tailored to address the intricacies of flexible manufacturing systems and expert systems for facilitating decision-making processes within the production context. Originality/value: Each management level possesses its distinct characteristics, methods, and tools that can be seamlessly integrated through the use of hybrid expert systems. Category of the paper: Research paper, conceptual paper. Keywords: Expert systems, flexible manufacturing system, Industry 5.0, production management and engineering.