Implementation Of Manufacturing Systems Research Articles

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.

Towards circular manufacturing systems implementation: A complex adaptive systems perspective using modelling and simulation as a quantitative analysis tool

A transition towards circular manufacturing systems (CMS) has brought awareness of untapped economic and environmental benefits for the manufacturing industry. Conventional manufacturing systems already present a high level of complexity in terms of physical flows of materials and products as well as information and financial flows linked to them. Closing the loop of materials and products through multiple lifecycles, as proposed in CMS, increases this complexity manifold. To support practitioners in implementing CMS through enhanced decision-making, this research studies CMS from a complex adaptive systems (CAS) perspective and proposes to exploit methods and tools used in the study of CAS to characterise, model and analyse CMS. By viewing CMS as CAS composed of autonomous, interacting agents, this research proposes a multi-method model architecture for modelling and simulating CMS. The different CMS stakeholders are modelled individually as autonomous agents by integrating agent-based, discrete-event, and/or system dynamics modules within each agent to capture their diverse and heterogeneous nature. The applicability of the proposed multi-method approach is illustrated through a case study of a white goods manufacturing company implementing CMS in practice. This case study shows the relevance and feasibility of the proposed multi-method approach as a decision support tool for the systemic exploration and quantification of CMS. It also shows how a transition towards CMS necessitates a lifecycle approach in terms of costs, revenues and environmental impacts to identify hotspots and, therefore, design circular systems that are viable in both economic and environmental terms. In fact, the analyses of the simulation results indicate how decisions in terms of business models, product design, and supply chain might affect the CMS performance of the case company. For instance, implementing a service-based model led to a high number of usecycles (on average six usecycles per washing machine), which, in turn, led to high lifecycle costs and emissions due to more frequent transportation and recovery operations. Similarly, the deployment of long-lasting washing machines, which is a core principle of CMS, led to high manufacturing costs. Due to the high initial costs and a time mismatch between revenues and costs in the service-based model, it required a longer time for the company to reach the break-even point (approximately 23 months). Overall, the case study shows that multi-method simulation modelling can provide decision-making support for a successful implementation of CMS.

Knowledge-based program generation approach for robotic manufacturing systems

In recent decades, robotic manufacturing systems have been considered as effective solutions for providing more productive manufacturing processes, but with less cost and risk. However, the programming for robotic manufacturing systems is a time-consuming task, and hinders the implementation of robotic manufacturing systems in today's industry. This paper proposes a knowledge-based program-generation approach for robotic manufacturing systems. The proposed approach provides effective support for the standardization of the rules and knowledge related to manufacturing programs that have proven successful in previous manufacturing cases; this can not only increase the programming efficiency, but can also improve the manufacturing stability and production quality. First, an ontological knowledge model is developed to provide an explicit semantic description of the relevant concepts for the robotic manufacturing system, basic instruction units for the program, and product models of the workpieces. Second, a rule-based reasoning mechanism is established to infer the implicit relationships between the basic instruction units of the manufacturing program. Finally, based on the semantic descriptions and reasoning mechanism of the proposed knowledge model, the basic instruction units of the manufacturing program are instantiated based on data extracted from the product models and integrated according to the relationships inferred by the reasoning mechanism, thereby generating the robotic manufacturing program.

Intelligent Supply Chain Management Modules Enabling Advanced Manufacturing for the Electric-Mechanical Equipment Industry

Electric-mechanical equipment manufacturing industries focus on the implementation of intelligent manufacturing systems in order to enhance customer services for highly customized machines with high-profit margins such as electric power transformers. Intelligent manufacturing consists in using supply chain data that are integrated for smart decision making during the production life cycle. This research, in cooperation with a large electric power transformer manufacturer, provides an overview of critical intelligent manufacturing (IM) technologies. An ontology schema forms the terminology relationships needed to build two intelligent supply chain management (SCM) modules for the IM system demonstration. The two core modules proposed in this research are the intelligent supplier selection and component ordering module and the product quality prediction module. The intelligent supplier selection and component ordering module dispatches orders that match the best options of suppliers based on combined analytic hierarchy process (AHP) analysis and multiobjective integer optimization. In the case study, the intelligent supplier selection and component ordering module demonstrates several acceptable Pareto solutions based on strict constraints, which is a very challenging task for decision makers without assistance. The second module is the product quality prediction module which uses multivariate regression and ARIMA to predict the quality of the finished products. Results show that the R square values are very close to 1. The module shortens the time for the company to accurately judge whether the two semifinished iron cores for the product meet the quality requirements. The component supplier selection module and the finished product quality prediction module developed in this research can be extended to other IM systems for general high-end equipment manufacturers using mass customization.

A Principal Component Analysis (PCA) for Barriers to Cellular Manufacturing System Implementation in Manufacturing Industries

A Principal Component Analysis (PCA) for Barriers to Cellular Manufacturing System Implementation in Manufacturing Industries

An extended technology-organization-environment framework to investigate smart manufacturing system implementation in small and medium enterprises

Manufacturing sector is undergoing through revolution with inception of Industry 4.0 (I4.0). However, this revolution in form of Smart Manufacturing (SM) is mostly limited to multi-nationals and large enterprises. Small and Medium Enterprises (SMEs) have a unique importance in boosting social-economic aspects of any nation’s growth. Thus, there is strong need for implementation of SM system to empower SMEs. To this effect, the present study investigates 31 Critical Success Factors (CSFs) for SM system implementation in SMEs. The work is benefited from six cases of Indian SMEs, surveyed by interviewing experts in the area of SM. The identified CSFs are categorized using an extended Technology-Organization-Environment (TOE) framework. The identified CSFs are modeled using an integrated TISM - Fuzzy DEMATEL (“Total Interpretive Structural Modelling - Fuzzy Decision Making Trial and Evaluation Laboratory”) approach which provides hierarchy and shows the intensity of interrelationship among the factors. Fuzzy MICMAC analysis is also applied to understand the dependency/driving power and mutual influences among the identified CSFs. Theoretical contribution, justifications and managerial implications of this research are discussed elaborately that can provide valuable insights to researchers, academicians and decision makers of the related organizations.