Implementation Of Manufacturing Systems Research Articles

Multi-objective optimization for layout planning of matrix manufacturing system

Abstract The automotive industry is experiencing rapid changes due to the rise of the Industry 4.0 manufacturing paradigm, which requires strategic implementation of advanced manufacturing systems to meet diverse customer needs. The Matrix Manufacturing System, characterized by modular facilities and autonomous mobile robots, offers greater flexibility compared to traditional dedicated production systems. This paper conducts a multi-objective optimization of facility layout planning within the matrix manufacturing system to enhance efficiency and responsiveness to market volatility. To solve the optimization problem, three heuristic algorithms—Simulated Annealing, Particle Swarm Optimization, and Non-dominated Sorting Genetic Algorithm-II are employed and their performance is compared. For the comparative analysis, frequency maps are used, visualizing the optimization processes and outcomes between metaheuristic algorithms. The framework with methodologies presented in this report is expected to improve productivity and flexibility of a matrix manufacturing system in the automotive industry.

Hybrid manufacturing thermal environment cycle and industrial economic transformation based on artificial intelligence

With the continuous development of global manufacturing, hybrid manufacturing has become an important way to improve production efficiency and respond to market demand. However, traditional manufacturing enterprises face many challenges in thermal environment management and energy consumption, and urgently need to achieve transformation and upgrading through artificial intelligence technology. This article aims to explore the thermal environment cycle of hybrid manufacturing based on artificial intelligence and its impact on industrial economic transformation, in order to provide theoretical support and practical guidance for manufacturing enterprises in intelligent manufacturing and energy management. We have researched and designed an intelligent manufacturing system, clarified the design principles and requirements of the system, constructed the system architecture, and designed the functional modules in detail. By collecting and processing data, using intelligent scheduling prediction technology to simulate the thermal environment of the manufacturing workshop, and then simulate the energy consumption situation. Finally, a manufacturing workshop scheduling model was constructed, process optimization strategies were proposed, and the transformation path of industrial economy was explored. Research has shown that through the implementation of intelligent manufacturing systems, the energy consumption of manufacturing workshops is significantly reduced, the thermal environment is effectively improved, and the application of process optimization and scheduling models greatly improves production efficiency. The exploration of the path of industrial economic transformation provides new ideas for the sustainable development of enterprises in the new situation.

Towards circular manufacturing systems implementation: An integrated analysis framework for circular supply chains

The transition to circular manufacturing systems (CMS) is crucial for achieving sustainable growth, addressing the environmental concerns and resource scarcity challenges. Shifting towards CMS requires a systemic approach that integrates value proposition models, product design, and supply chains (SCs). Circular supply chains (CSCs) emerge as a core pillar of CMS, incorporating value delivery, use, recovery, and reuse. CSCs are inherently more complex and dynamic than linear SCs requiring a holistic analysis approach to capture their complex and dynamic attributes. This research proposes an integrated analysis framework combining qualitative and quantitative approaches to explore the complexities and dynamics of CSCs and assess their economic, environmental, and technical performance. Through the lens of two different CMS implementation case studies, one in automotive parts remanufacturing and one in white goods manufacturing, this research illustrates the framework's applicability. In the automotive case, centralizing core management activities was found to improve economic performance by 50-54 %. However, the introduction of regional logistics hubs, while economically efficient, led to a 20 % increase in CO2-equivalent emissions. On the other hand, the white goods case study highlighted the trade-offs in centralizing end-of-life recovery facilities, where financial savings of up to 60 % were offset by increased transportation costs and increased CO2 emissions. The analysis of CSCs in these two distinct manufacturing sectors underscores the relevance and flexibility of the proposed framework, providing decision-makers with a tool to examine how different CSCs configurations and strategies impact overall performance. This guidance is crucial for developing optimal CSCs design and implementation strategies.

Evaluating environmental sustainability factors in implementation of reconfigurable manufacturing systems (RMS) using DEMATEL approach

Evaluating environmental sustainability factors in implementation of reconfigurable manufacturing systems (RMS) using DEMATEL approach

Impact Assessment of Smart Manufacturing System Implementation in Small and Medium Enterprises: Moderating Role of Enabling Technology and Government Support

Impact Assessment of Smart Manufacturing System Implementation in Small and Medium Enterprises: Moderating Role of Enabling Technology and Government Support

A Smart Manufacturing Process for Textile Industry Automation under Uncertainties

Most textile manufacturing companies in the world heavily rely on manual labor, particularly in the fabric inspection section, especially for cotton fabric. Establishing smart manufacturing systems like industrial automation in the textile industry for cotton fabric inspection is important for error-free inspection. The proposed make-to-order (MTO) inventory model focuses on the strategic development of a supply chain network under fuzzy uncertainty. The distinctiveness of this research lies in integrating a methodology that involves human and machine interaction, along with allocating resources to investment in smart manufacturing. This article presents a case study of the Jagatjit Cotton Textiles (JCT) manufacturing company in Punjab, India, as an example to validate the model and check the performance of SMT in the fabric inspection process in cotton TC mills. This paper contributes by developing four distinct textile supply chain models with industrial automation under triangular and trapezoidal fuzzy demand. A numerical analysis is conducted to verify the effectiveness of installing automated fabric inspection machines in the cotton plant. This article proposes an iterative solution algorithm (KDPMG) to obtain the global optimum for the proposed model. A comparative study of the proposed algorithm, KDPMG, and the genetic algorithm (GA) is presented in this study to verify the credibility of the obtained results. It is observed that KDPMG provides more appropriate solutions to the problem compared to the GA. Moreover, the computational time of KDPMG is significantly less than that of the GA. The rigorous analysis reveals that maximum profit can be achieved under trapezoidal fuzzy demand with fully automated fabric inspection technology. Using a triangular fuzzy demand pattern, the model with fully automated smart manufacturing achieves an 8.62% higher profit compared to a traditional system. Similarly, in the case of a trapezoidal fuzzy demand pattern, the adoption of automation in cotton plants can achieve an 8.69% higher profit. Hence, the implementation of smart manufacturing systems in the mending section of the cotton textile industry proves to be more profitable compared to the traditional inspection process.

Methodology for Implementing a Manufacturing Execution System in the Machinery and Equipment Industry

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.

Design and implementation of intelligent manufacturing system based on sensor networks and cloud computing technology

Design and implementation of intelligent manufacturing system based on sensor networks and cloud computing technology

Readiness assessment for smart manufacturing system implementation: multiple case of Indian small and medium enterprises

ABSTRACT In recent times, a paradigm shift is observed in the manufacturing domain through the inception of the smart manufacturing (SM) system. Small and medium enterprises (SMEs) in developing countries are striving hard to catch up with this transition. Thus, a need arises to assess their preparedness and subsequently develop an appropriate mechanism for transition. In this context, readiness assessment is a suitable approach that clearly depicts the quantification and qualification of readiness stages through various dimensions. To this effect, this study proposes a generic readiness assessment model with 5 stages, 6 building blocks, and 22 dimensions. Furthermore, the dimensions are ranked using ‘Stepwise Weight Assessment Ratio Analysis (SWARA)’ to understand their influence on overall readiness attainment. The practical viability of the proposed readiness assessment model is tested on five Indian SMEs. Based on the readiness level of each organization, recommendations are made to further improve their preparedness for transformation. Other SMEs can utilize this tool as a self-assessment methodology to check their status and subsequently strategize their policies for future transformation.

A multi-objective joint optimisation method for simultaneous part family formation and configuration design in delayed reconfigurable manufacturing system (D-RMS)

ABSTRACT In the era of Industry 4.0, the demand fluctuation has become fiercer due to the characteristics of diversification, customisation, and uncertainty. Reconfigurability of manufacturing systems has been proven to be a useful and necessary feature when it comes to handling demand uncertainty. This feature can be achieved through the implementation of reconfigurable manufacturing system (RMS) and delayed reconfigurable manufacturing system (D-RMS). D-RMS is a subclass of RMS that focuses primarily on improving the convertibility of the manufacturing system. The two main phases involved in implementing D-RMS are part family formation and configuration design. Therefore, we proposed a multi-objective joint optimisation method of part family formation and configuration design according to the philosophy of D-RMS. Firstly, we develop a multi-objective joint optimisation model that takes into account investment cost, reconfiguration cost, similarity coefficient, and delayed reconfiguration to optimise the part family and configuration of D-RMS simultaneously. Three types of machine tools namely dedicated machine tools, flexible machine tools, and reconfigurable machine tools are considered in the optimisation model. Secondly, the non-dominated sorting genetic algorithm-III (NSGA-III) is adopted to solve the proposed multi-objective integer programming problem. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed multi-objective joint optimisation method.

Implementation of flexible manufacturing systems in Africa: Multiple case studies in the Gambia and Ghana

In comparison to Europe, Asia, and America, the African manufacturing sector performs poorly. This is largely attributable in part to inadequate use of advanced manufacturing concepts and technologies, as well as the insufficiently skilled workforce. African manufacturing companies must adopt and implement advanced manufacturing technologies and concepts such as flexible manufacturing systems in order to boost growth and accelerate development in the sector. In this study, we conducted multi-case studies on the implementation of flexible manufacturing systems in the manufacturing sectors of two West African countries (The Gambia and Ghana). Six manufacturing companies from The Gambia and eight from Ghana were chosen. Many of the companies involved in the study are small businesses that specialize in mechanical parts/system production, welding, and fabrication. The findings revealed that the use of advanced manufacturing technologies and concepts, as well as the adoption of flexible manufacturing systems, is extremely low. The results also show that the major obstacles to the adoption and implementation of advanced manufacturing technologies are, cost and, lack of qualified personnel. Companies have agreed to adopt and implement advanced manufacturing technologies and concepts if the opportunity arises. They also require more information on some cutting-edge technologies before deciding whether to adopt and implement them. Some of the other major challenges faced by African manufacturing companies include, high energy costs and unstable power supply, poor transportation network, and, lack of adequate finance.

An integrated approach to modelling of barriers in implementation of cellular manufacturing systems in production industries

An integrated approach to modelling of barriers in implementation of cellular manufacturing systems in production industries

Investigating the determinants of continuance intention on cloud ERP systems adoption

With the rapid developments in cloud technology, the cloud Enterprise Resource Planning (ERP) system is indispensable when investigating the adoption and implementation of manufacturing systems among industries. The cloud ERP system can support manufacturing systems through various cloud services. The study was based on the well-known Information Systems Success Model (ISSM) to explore the predictive power of these theory-based variables. Our research provides an integrated managerial model to predict the continuance intention of adopting the cloud ERP system to enable effective manufacturing. The paper employed Structural Equation Model (SEM) to verify and confirm the hypotheses from an online survey in Taiwan. The empirical results have confirmed that (1) self-efficacy and convenience positively predict perceived ease of use; (2) information quality, system quality, service quality, perceived price, and perceived ease of use positively predict satisfaction; perceived security has no significant effect on satisfaction; (3) perceived ease of use, perceived security and satisfaction positively predict continuance intention. This study’s analysis results thus provide a suitable integrated framework to predict continuance intention. Finally, our research provides practical and managerial recommendations for manufacturing industries of adopting the cloud ERP system.

The Implementation of Lean Manufacturing System in Production Process Theoretical Analysis and Empirical Investigations. An Empirical Study on the Manufacturing Companies in Poland

Purpose: The purpose of this paper is to investigate the implementation of Lean Manufacturing System (LMS) in the manufacturing companies of Poland. Design/methodology/approach: Using a survey questionnaire, data was collected against the most common lean practices and tools which formed the Lean House (LH) presented by Liker (2004), from 40 manufacturing companies acting in Poland. Lean practices and tools were clustered into five groups according to its position in the LH structure: The roof: the goal of lean manufacturing system, the lift pillar: Just-in-Time principle (JIT), the right pillar: Jidoka tool, the center: People who identify wastes and solve problems to insure continuous improvement, and the foundation: Lean Tools: 5S, Standardization, Visual Management and Production levelling. The responding companies were categorized according to their respective field of production and the company size (big, middle and small). 181 manufacturing companies acting in the manufacturing field of Poland were contacted, 40 companies participated and 3 of them were excluded due to lack of implementation. The results identified by statistical analysis to find out the Average and the Percentage of implementing each and all Lean Manufacturing practices and tools. Findings: The manufacturing companies in Poland have implemented various lean practices and tools to a certain extent. However, the results suggest that the adoption of lean manufacturing as a fully integrated, systematic, and cultural approach may not be highly developed enough. Hence, reaping the maximum benefits of the LMS in those companies may be doubted. Research limitations/implications: Since the sample size is not very large, the results need to be considered with caution as being limited to the participated companies. Practical implications: The paper gives an indication about the status of the implementation of LMS in the Polish manufacturing companies. It also includes a description for better understanding of Lean concept and practices through a specific literature review. Originality/value: The implementation of LMS was researched widely in different countries all over the world and some of them are EU countries. Despite that, there is a lack of similar studies in the manufacturing companies in the 10 countries which have joined the EU in the year 2014, and specifically in Poland. Moreover, this paper also illustrates a better understanding of the Lean concept through a literature review of selected recognized articles dealt with this concept.

A maturity model for the autonomy of manufacturing systems

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model.

Analysis of supporting factors of the successful implementation of green manufacturing for Indonesian Manufacturing SMEs

Many manufacturing SMEs experience difficulties in finding an effective framework for building green manufacturing system for production and operations activities. The study purpose to identify the elements support the successful implementation of green manufacturing systems in SMEs, develop a conceptual hierarchical model of elements based on the ISM approach, and identify and analyze the dominant elements. This study applies a survey method to obtain data that is relevant to the research objectives. The respondents involved were 32 managers/owners of manufacturing SMEs located in Tegal Regency, Central Java Province of Indonesia. The study results show the existence of 39 supporting elements for the successful implementation of green manufacturing in SMEs. Factor analysis shows that these elements can be simplified into 12 common elements. MICMAC analysis shows that five elements can be categorized as dependent elements; lower production costs, environmental knowledge, Improved waste management, investing in innovation and technology, and new market opportunities; seven elements can be categorized as independent elements, namely green supply chain, government promotion and regulation, pressure from the market, organizational culture, involvement of various business stakeholders, top management commitment, and environmental specialist assistance. ISM analysis shows there are four elements that can be categorized as dominant elements; namely green supply chain, involvement of various business stakeholders, top management commitment, and government promotion and regulation.

A principal component analysis for barriers to cellular manufacturing system implementation in manufacturing industries

A principal component analysis for barriers to cellular manufacturing system implementation in manufacturing industries

Total Productive Maintenance, Affective Commitment and Employee Retention in Apparel Production

The success of the implementation of advanced manufacturing systems, such as total productive maintenance (TPM), depends on employee commitment and retention. This study investigated total productive maintenance (TPM) practices implemented by apparel production firms, and the effect of these practices on affective commitment (AC), and employee retention (ER) of operators on the production floor. The study adopted the survey research methodology. In total, 411 operators from apparel production firms responded to the study. The responses were analyzed using statistical methods. Four hundred and eleven responses were received for the survey and data were analyzed using statistical methods. The findings suggest that TPM practices significantly positively affect AC. Affective commitment mediates between TPM and ER. The success and sustainability of TPM implementations depend on the firm’s ability to retain trained operators and keep their AC high. The study has several implications for theory and practice.

Remaining useful life prediction of manufacturing system based on fuzzy Quality State Task Network

Accurate prediction of operational state and remaining useful life (RUL) is the premise for PHM (Prognostic and Health Management) implementation and predictive maintenance decision of intelligent manufacturing system. The quality state of the produced WIP (work-in-process) is also a core indicator of the RUL of the running manufacturing system, which can have an impact on the degradation of the downstream machine. However, few studies have fused the quality data of WIP(work-in-process) into the RUL prediction considering the fuzzy operational mechanism of the manufacturing system. Therefore, an RUL prediction method of intelligent manufacturing systems based on the fuzzy Quality State Task Network (QSTN) model is proposed. First, the principles of the RUL prediction of manufacturing system is expounded by taking fuzzy mission reliability as the indicator of system health state. Second, a fuzzy QSTN model that can simultaneously characterize the performance degradation of manufacturing equipment and the quality degradation of WIP is established. Third, a dynamic mission reliability and remaining useful life prediction method of the manufacturing system based on dynamic Bayesian networks is proposed. Finally, the effectiveness of the method is verified by taking the continuous stamping process of a bogie manufacturing system as an example.

Factors Affecting Successful Implementation of Smart Manufacturing Systems

Although the introduction and utilization of smart manufacturing systems within the industry is accelerating, it is difficult to quantitatively analyze the effects of smart manufacturing systems. It is also difficult to grasp the factors that influence the success of system implementation. In this study, we measured the factors affecting the successful implementation of various types of smart manufacturing systems by confirming the factors affecting user satisfaction of smart manufacturing system. Based on the 282 samples, this study empirically tests a structural model using PLS 3.0 for the explanation of the successful implementation of smart manufacturing system. As a result of this study, it is expected to find that which factors affect to the user satisfaction of the system among the sub-factors representing aspects of information, systemic, organizational, and user background.