Performance Of Production Line Research Articles

Modeling of Automobile Assembly Line Performance Using ARENA Simulation Software

Due to the intense competition in today's business environment, companies must continuously analyze and enhance their existing manufacturing systems. Discrete event simulation, which is especially helpful for simulating queuing systems, involves describing the system as it evolves. The building and study of a simulation model of an existing production line are the main topics of this paper. The findings demonstrate that the current system's throughput is low due to bottlenecks, extended processing times at workstations, and inefficient resource utilization, all contribute to productivity losses in automotive assembly lines. This paper aims to evaluate the efficiency of the production line performance of automobile assembly lines using Arena modeling and simulation. Data provided by the company's management is utilized to calculate the processing time and standard time for each step in the production line. Additional information is gathered through direct observation of the assembly line. A car assembly manufacturing line was selected as a case study, and Arena 16.0 software was employed for basic modeling and analysis to achieve these objectives.

A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).11 https://w3id.org/rgom However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data22 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football,.33 https://zenodo.org/record/7779522 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc.

Work Standardization in Lean Manufacturing for Improvement of Production Line Performance in SME

Lean manufacturing is a management philosophy and production system that provides all humanly conceived soft technology to remove waste in any production line. One of the requirements of lean is work standardization. Work standards impose a 'rule of law' in a production line by restricting operators to how long they should work on a single unit. The standards consist of the standard workflow, standard work time, and standard quantity that must be adhered to to meet the customers' demands. The main objectives of this study were to improve the performance of a production line, say SW660 x 600, in terms of weekly output, lead time, and work-in-process (WIP). By applying certain lean elements to the production line, this work indeed improved the performance of the production line. In this work, 5S, PDCA cycle, SPC, and VSM tools were used together to solve the production line problems systematically. It was noticed that the low weekly output and long cycle time at each workstation resulted in delays in the delivery of products to customers. The study managed to increase the weekly output from 61.75 per week to 128.75, an improvement of more than 108.50 percent. Next, in terms of lead time, with the help of a simulation exercise, this work also managed to reduce the lead time from 5 to 1 hour, a reduction of 80 percent. Lastly, the WIP also recorded a reduction from an average of 17 to 9 units weekly, which was an improvement of 47 percent on the weekly WIP. Therefore, this work showed that with the introduction of work standards on the production line, the organization could meet the customers' demands and deliver on time as promised. The study can also be used in other production lines of this and other organizations to help improve their production line performance in the long run.

A data-driven approach toward a machine- and system-level performance monitoring digital twin for production lines

Efficient performance monitoring in production systems holds paramount importance as it enables organizations to optimize their manufacturing processes, enhance productivity, and maintain a competitive edge in the market. Typically, machine and system level performance monitoring systems are investigated independently, whereas an integrated approach that considers both levels can offer valuable insights and benefits. This paper introduces a data-driven approach for evaluating and improving the performance of production lines by monitoring the performance of both individual machines and their interactions as a system. The approach begins with a rigorous methodology for classifying machine states recorded by the Manufacturing Execution System (MES) into finer-grained substates, enabling a comprehensive analysis of machine cycle time variability. Subsequently, these substates are leveraged as a foundation for constructing performance monitoring models at both the machine and system levels, employing probabilistic automata for the machine level and logistic regression for the system level. The system-level performance monitoring model is constructed to predict a Flow metric that enables the prediction of abnormal behaviors and deviations from production targets. This data-driven approach serves as a foundational ingredient of a system-level digital twin, designed to provide production lines with insights that enable proactive implementation of measures aimed at optimizing overall manufacturing efficiency. Through an industrial test case from the automotive industry, the results demonstrate the capability of performance monitoring, capturing errors within confidence intervals, and establishing predictive cause-and-effect relationships between machines within the production system.

Introduction of Collaborative Robotics in the Production of Automotive Parts: A Case Study

Incorporating collaborative applications constitutes a challenging and increasingly intricate objective within the context of small and medium-sized enterprises (SMEs). This challenge stems from the shortage of highly specialized personnel in these types of companies when it comes to adopting cutting-edge technologies. The lack of innovation in production processes, however, increases the risk that SMEs will not be able to adapt to rapid changes in the market and the growing needs of consumers, who today are evolving at an unprecedented pace. The importance of adopting collaborative applications can be found in their capacity to harmonize human adaptability with the precision of robotic technology. This synergy contributes to the establishment of a safer work environment while guaranteeing effective and efficient performance. These features not only lead to improved production line performance compared to traditional manual or stationary operations, but also highlight new perspectives in the design, production, and customization of new products. This, in turn, helps companies strengthen their competitiveness in the global market. In this scenario, the primary challenge centers around effectively putting these solutions into practice. Our research aims to highlight how significant benefits can be achieved, both in terms of performance improvements and economically, through the analysis of a simple yet illuminating case study.

Line Balancing Analysis in Ribbed Smoked Sheet Production Using Heuristics Methods (Study at PT. Wabin Jayatama, Serang, Banten, Indonesia)

Production line balancing is done to increase line efficiency. Product flow between workstations is stated to be better when the percentage of line efficiency is higher. One of the factors that can hinder the smooth running of a line is a bottleneck. The occurrence of a bottleneck at the Ribbed Smoked Sheet production station, namely the milling and sorting station, at PT. Wabin Jayatama caused the production line to become unbalanced. The output that will be generated from this research is the efficiency level of the actual production line and the proposed design of a production line with more optimal efficiency, which will later be used as a consideration for recommendations to improve the performance of the company's production line. This research was carried out using the Kilbridge & Wester Heuristic method and the Ranked Positional Weights Heuristic method. The results of the line balance analysis showed that the production line had a higher line efficiency (LE) of 50.05%, an increase by 25.62% to 75.67%. The initial line balance delay (BD) of 49.55% can be reduced by 25.22% to 24.33%. Then the Smoothest Index (SI) of 447.74 can decrease by 360.51 to 87.23.

The Impact of Increasing Sigma Value on the Performance of the Weapon Production Line at PT. X

PT X is one of the companies that produces the Main Armament System (Alutsista) used by the Indonesian National Army. In an effort to meet the production needs of Assault rifle weapons, PT. X must meet the quality standards set by the Ministry of Defense to support the operational tasks of the Indonesian National Army. This study aims to improve the performance of the weapon production line by measuring and evaluating the amount of sigma value in long-barreled weapons. The method used in this research is DMAIC (Define, Measure, Analyze, Improve, and Control), which is a structured approach to quality improvement. This method is used to address problems that occur on the weapon production line and analyze improvements that can be made to improve production performance. The steps recommended in this study include using the Critical to Quality method to identify the causes of problems that occur in weapon production. Furthermore, the Cause and Effect method is used to rank the most significant causes of problems based on the scores obtained. The 5W+1H method is also used to formulate appropriate improvement proposals based on the ranking results from the Cause and Effect Matrix. Thus, it is expected that an effective improvement proposal can be found to improve the performance of PT. X weapon production line.

Towards the Augmentation of Digital Twin Performance.

Digital Twin (DT) aims to provide industrial companies with an interface to visualize, analyze, and simulate the production process, improving overall performance. This paper proposes to extend existing DT by adding a complementary methodology to make it suitable for process supervision. To implement our methodology, we introduce a novel framework that identifies, collects, and analyses data from the production system, enhancing DT functionalities. In our case study, we implemented Key Performance Indicators (KPIs) in the immersive environment to monitor physical processes through cyber representation. First, a review of the Digital Twin (DT) allows us to understand the status of the existing methodologies as well as the problem of data contextualization in recent years. Based on this review, performance data in Cyber-Physical Systems (CPS) are identified, localized, and processed to generate indicators for monitoring machine and production line performance through DT. Finally, a discussion reveals the difficulties of integration and the possibilities to respond to other major industrial challenges, like predictive maintenance.

Implementation of ergonomics and lean manufacturing principles to improve work environment and performance of soccer ball production line

Value addition and performance improvement are important to deal with increasing local and global market competitiveness. Interventions and automation based on Lean and ergonomics principles have strong influence on performance improvement of the production systems. This study focuses on implementation of lean manufacturing and ergonomics principles to improve work environment and performance of the soccer manufacturing production line. Work visualization and value stream mapping (VSM) techniques were employed to identify the opportunities for improvements. Time of value and non-value-added activities is recorded using time study method. Working postures and associated risk factors are evaluated using Rapid Entire Body Assessment (REBA) method. Lean manufacturing principles are employed to minimize wastes and non-value added activities. Similarly, improvements in work environment are made through ergonomics interventions. Various models comprising different levels of lean and ergonomics interventions were evaluated through simulation before actual implementation. The results of the finalized model in terms of decrease in cycle time (8.64 to 7.018 seconds), increase in daily production (20505 to 24176 soccer balls), and improved work environment are evident of performance improvements in all aspects. The integration of lean and ergonomics principles leads to improve the productivity and working environment for workers in soccer ball manufacturing industry.

Heuristic Guided Hierarchical Reinforcement Learning Approach For The Economic Improvement Of Production Lines

The increasing variety of products poses a challenge for efficient manufacturing on production lines due to resulting small batch sizes and frequent product changes, which lower the average overall effectiveness. Especially for industries such as the Fast Moving Consumer Goods (FMCG) industry that manufacture at high speed on production lines, it is mandatory to increase the performance of production lines in an economical way. Due to the complexity of such production lines, identifying efficient actions and combining them into economic improvement trajectories is challenging. There are numerous approaches based on different concepts, such as simulation-based heuristics, to solve these challenges. In other application areas, reinforcement learning has shown remarkable success in recent years, and first reinforcement learning-based approaches to this specific problem can be found. However, these approaches mostly focus on details instead of providing a holistic view of the possibilities to improve a production line or are limited in their practical application due to lack of integration of existing expert knowledge or limited quality of results. For this reason, this paper proposes a hierarchical reinforcement learning approach that combines discrete event simulation with a heuristically driven multi-agent system. Thus, the selection of the improvement strategy of the production line is performed by one agent, and the dedicated improvement of specific parameters is performed by specialized subordinate other agents. Through this hierarchical multi-agent system, on the one hand, the learning rate can be increased. On the other hand, by guiding the agents through a heuristic based on expert knowledge, the learning quality is increased.

An integrated lean production-sustainability framework for evaluation and improvement of the performance of pharmaceutical factory

Given their direct impact on public health, evaluating and improving the performance of pharmaceutical companies are one of the main challenges for the managers of this industry. Focusing on environmental, social, and economic considerations, sustainability can play an effective role along with Lean production in improving the performance of organizations. The present study introduces an innovative lean-production-sustainability framework for performance evaluation of a pharmaceutical factory. Then, the significance of each sub-indicator is calculated based on the best-worst method (BWM). The required data were collected using a standard questionnaire, and the data envelopment analysis (DEA) was employed to assess the performance of the production line of the case-study company in Tehran. Using sensitivity analysis to understand the positive and/or negative impact of each sub-indicator on the production line performance leads to the generation of appropriate improvement measures. The results of sensitivity analysis demonstrated that the sub-indicators of Investment and reduction of operation and maintenance costs, training, development, working conditions, customer orientation, and standard product are the weaknesses of the studied company, and the sub-indicators of waste reduction and pull system are its strengths. Then, with the assistance of the expert committee and based on existing threats and opportunities in the pharmaceutical industry, the results of the conducted Strength-Weakness-Opportunity-Threat (SWOT) analysis provided appropriate strategies for improving the performance of the production unit. To the best of our knowledge, this research is the first to quantitatively and qualitatively use these indicators in order to evaluate a pharmaceutical company. The proposed framework enables the decision-makers to consistently evaluate their company's performance based on the two indicators of sustainability and lean production and implement improvement measures according to the present situation of the company.

Silicon solar cell production line and key performance indicators: A case of study at front size serigraphy stage

Photovoltaic industry has devices power improvement as a main target. This implies that technological advances are continuously implemented in production lines and their power improvements have to be monitored with the suitable key performance indicators. In this work, front size serigraphy design has been selected as process improvement and laminated unit power and cell to module ratio has been defined as the main key performance indicator. Real size silicon PV cells with three different front finger morphologies have been produced in industrial production lines by the use of two front size serigraphy designs. The modification of the finger dimensions (wide/height) from (183.0 μm/31.6 μm) to (184.0 μm/37.6 μm) and (140.0 μm/40.8 μm) leads to a redistribution of the majority produced cell power range from [4.10–4.15) W to [4.10–4.15) W and [4.20–4.25) W respectively. Concerning the cell production, it has successfully been monitored by the laminated unit power indicator along a month when shows an increment from 3.95 W to 4.20 W. Concerning module level, cell to module ratio per process cell range is selected as suitable indicator and monitoring during a year. In the specific case of [4.30–4.35) W cell range, cell to module ratio decrease from 7.7 % to 6.5 %

An Agent-Based Approach for Make-To-Order Master Production Scheduling

In recent decades, manufacturers’ intense competitiveness to suit consumer expectations has compelled them to abandon the conventional workflow in favour of a more flexible one. This new trend increased the importance of master production schedule and make-to-order (MTO) strategy concepts. The former improves overall planning and controls complexity. The latter enables the production businesses to reinforce their flexibility and produce customized products. In a production setting, fluctuating resource capacity restricts production line performance, and ignoring this fact renders planning inapplicable. The current research work addresses the MPS problem in the context of the MTO production environment. The objective is to resolve Rough-Cut Capacity Planning by considering resource capacity fluctuation to schedule the customer’s order with the minimum cost imposed by the company and customer side. Consequently, this study is an initial attempt to propose a mathematical programming approach, which provides the optimum result for small and medium-size problems. Regarding the combinatorial intrinsic of this kind of problem, the mathematical programming approach can no longer reach the optimum solution for a large-scale problem. To overcome this, an innovative agent-based heuristic has been proposed. Computational experiments on variously sized problems confirm the efficiency of the agent-based approach.

Optimizing U-Shaped Production Line Balancing Problem with Exchangeable Task Locations and Walking Times

This present study proposes to solve the U-shaped production line balancing problem with two constraints. First, following the precedence relationships between any pair of tasks, the locations of tasks are exchangeable. Second, all tasks are designed to be performed in fixed locations. When two or more tasks are assigned to the same operator, the walking time between the locations has to be considered when estimating the cycle time. The proposed problem is first formulated using an integer programming formulation, which is then solved by commercial software, LINGO, to minimize the cycle time and performance of the U-shaped production line. The empirical results show that U-shaped production lines outperform traditional straight production lines. In addition, the U-shaped production line balancing problem with consideration of walking time provides an opportunity to further reduce cycle time.

Digital Twin-Based Integrated Assessment of Flexible and Reconfigurable Automotive Part Production Lines

The manufacturing industry has witnessed rapid changes, including unpredictable product demand, diverse customer requirements, and increased pressure to launch new products. To deal with such changes, the reconfigurable manufacturing system has been proposed as one of the advanced manufacturing systems that is close to the realisation of smart manufacturing since it is able to reconfigure its hardware, software, and system structures in a much quicker manner. Conventional simulation technologies lack convergence with physical manufacturing systems, and reconfigurable manufacturing lines require the manual construction of production line models for each reconfiguration. This study presents a digital twin-based integrated reconfiguration assessment application that synchronises with real-time manufacturing data and provides accurate, automated simulation functionality to build and analyse a manufacturing system. The paper discusses the architectural design and implementation of the application, an information model, and an assessment model that enable quantitatively assessment on reconfigurations of manufacturing systems from various aspects. The effectiveness of the proposed application is verified via application to an automotive parts production line to assess the reconfiguration indicators of the manufacturing system under different scenarios. The results reveal that the proposed application provides faster and more accurate reconfiguration assessments compared to existing methods. The findings of this study are expected to facilitate accurate and consistent decision making for evaluating the various indicators of production line performance.

A Multiobjective Crystal Optimization-based association rule mining enhanced with TOPSIS for predictive maintenance analysis

To obtain computer procedures that intelligently guide the search process by efficiently exploring the search space corresponds to an optimization problem that is solved using meta-heuristics. However, there are many optimization problems whose objective is to extract a single efficient solution that reflects the quality of the system performance. Hence, we also encounter multi-objective problems. This paper studies a multi-objective optimization problem that addresses the complexity of problems with synchronous indices. In this paper, association rule mining (ARM) is treated as an optimization problem. In recent literature, most of the proposed methods of ARM generate a large number of redundant and irrelevant rules. Therefore, we propose an improved multi-objective crystal structure algorithm using the TOPSIS approach. The experimental study is performed in two steps. First, we use a classical benchmark suite, namely WFG, to analyze the effectiveness of the proposed approach in generating solutions close to the Pareto fronts. In a second step, to confirm the performance of our algorithm, we apply it on the database of the Bosch Production line performance, and we conduct a comparative evaluation towards recent algorithms. The obtained results demonstrate the efficiency of the proposed algorithm in terms of number of rules, average support, average confidence, average conviction, average certain factor.

A remote MQTT-based data monitoring system for energy efficiency in industrial environments

The concept of the new industry seeks not only to improve production processes, but also to bring solutions to environmental problems, in addition to reducing resource consumption, while maintaining high yields. This constant search for process optimization has been the main agent in the development of new technologies aimed at improving the performance of industrial production lines. Thus, this article proposes to raise some important concepts of Industry 4.0, and present the development of a remote IoT-based system that, through MQTT and Modbus protocols, will be responsible for monitoring the entire electrical network of an industrial plant, sending its data to the cloud, where it can be monitored and analyzed by the industry management sector or even by an artificial intelligence system, in a simple and effective way, in real time and from anywhere, in order to assist in decision-making focused on energy efficiency.

Dynamic performance prediction in flexible production lines with two geometric machines

Due to the intelligent manufacturing strategies deployed and being implemented in recent years, flexible production is widely used on the factory floor at present. While most of the existing investigations are concerned with the steady-state of systems, very few results have been published on systems' dynamic behaviour. However, since flexible production is sometimes carried out according to customised orders, most of which are in relatively medium or even small sizes, part or all the production of such systems are in the dynamic process. As a result, the dynamic characteristics of production systems are of significant importance. Meanwhile, dynamic performance is regarded as an important direction in the research of manufacturing systems. In this work, for serial flexible production lines with two geometric machines and one finite buffer, we study the problems of dynamic performance evaluation, system behavioural properties, and energy-efficient operation control. Specifically, the model of production lines under consideration is formulated. Then, an analytical method to assess the dynamic performance of such flexible production lines is proposed. Moreover, the behavioural properties of the systems are also investigated. Finally, we study the problem of control parameters selection for the system under operation control.

Improving Production Performance Through Multi-Plant Cross Learning

Abstract The advancement in Web-/Internet-based technologies and applications in manufacturing sector has increased the tilization of cyber workspace to enable more efficient and effective ways of doing manufacturing from distributed locations. This work introduces a novel continuous improvement framework to enhance the performance of production lines through multi-plant comparison and learning among identical or similar production lines in different locations by leveraging the information stored on factory cloud. In this work, production data from multiple identical production lines are collected and analyzed to learn the “best” feasible action on critical machines, which offers a new way to optimize the management of product lines. Machine learning and system model are used to find the relationships between the performance index and the available data. A real case study based on multiple similar automotive plants is provided to demonstrate the method and the increases of throughput are predicted.

An inspection policy for shredder equipment used in steel production lines considering buffer level and operating time

On the basis of scrap metal recycling, the shredding process transforms a large variety of objects composed predominantly of steel in crunched scrap. The main equipment used in this process is called shredder: a device consisting of several hammers attached in a horizontal rotor. Due to high efforts involved in the transformation process, the maintenance is an essential aspect to ensure the continuity of the shredder operation. A non-efficient maintenance policy can have negative consequences for the shredder performance, whose failure can stop all downstream processes for which it provides the basic input (crunched scrap). In order to improve the performance of the steel production line, we are introducing an opportunistic inspection policy that makes use of the crushed scrap buffer level. The policy guides the execution of an inspection after a time T of continuous operation, when the system fails, when the buffer is full or empty, or when the opportunity criterion (operation time ≥ t AND buffer level ≥ h) is verified, whichever occurs first. At an inspection, the shredder is stopped, the hammers are inspected, and the failed and defective ones are replaced by new units. A discrete-event simulation model was developed and applied to the context of a real steel production line of a company. The optimal opportunistic inspection policy was obtained, and its performance was compared with that of the policy currently adopted by the company. The results showed the possibility of obtaining significant economic savings. A sensitivity analysis showed a cost reduction of around 95 %.