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This study explores productivity optimization in the electronics industry using a simulation-based modeling approach. With growing demand and competition, electronics manufacturers face the challenge of balancing production speed, quality, and cost. Simulation-based modeling provides a solution by enabling a virtual replication of manufacturing processes, allowing for scenario testing without disrupting actual operations. By implementing this approach, critical factors such as resource allocation, machine utilization, and process flow were examined, identifying bottlenecks and inefficiencies. This study specifically used discrete-event simulation to model production lines and test optimization strategies under various conditions, including shifts in demand, machine maintenance schedules, and workforce availability. Results indicated a measurable improvement in production throughput and utilization rates, while delay times were significantly reduced. By adopting a simulation-based approach, the electronics industry can enhance decision-making accuracy, minimize downtime, and increase overall productivity. This method not only provides real-time insights for ongoing operations but also offers a cost-effective tool for long-term strategic planning and continuous process improvement.

Many industrial production lines today are initially constructed and outfitted with machinery that, despite its inherent capabilities, struggles to satisfy increasing demand over time. The goal of this research is to find solutions to bottlenecks and improve the efficiency of the production line to satisfy rising productivity demands. The study primarily looks at an existing bottle production line in the edible oil industry, where the product is witnessing a boom in demand. Data on processing times at various workstations and their capacities were thoroughly collected and analyzed. Simulation emerged as the preferred tool for investigating and addressing the production line's intrinsic difficulties. Using Microsoft Excel for statistical analysis, an existing simulation model was created using Flexsim simulation, a process-oriented simulation software, to detect bottlenecks in the production line using the Processor block in the simulation. As a result, a modified model was offered to reduce waiting times by 12%, increase productivity by 6%, and increase overall profitability while efficiently dealing with rising demand across all seasons. As a result, this research represents a complete effort to identify operational challenges and present viable solutions that correspond with the industry's changing objectives. The study aims to contribute to optimizing production lines by utilizing contemporary simulation tools and statistical analysis, guaranteeing that they stay adaptive and responsive in the face of rising productivity demands. [ABSTRACT FROM AUTHOR]

Production lines face numerous challenges to meet market demands, including constant changes in products that require continuous adjustments. Efficient and rapid reconfiguration and adaptation of production processes are crucial. In cases of inadequate adaptation, bottlenecks can arise due to human errors or incorrect configurations, often introducing complexity in pinpointing the root cause and resulting in financial losses. Furthermore, improper machine maintenance contributes to this situation as well. This article seeks to establish a framework grounded in the contemporary smart factory, the IIoT, the Industry 4.0 paradigm, and Big Data. The proposed system places emphasis on leveraging real-time data analysis for predicting risks, while concurrently conducting a thorough analysis of historical data to monitor trends and enhance bottleneck identification. The defined architecture operates across multiple levels, acquiring real-time information and generating historical data for training and continuous optimization. Predictive results contribute to decision-making and assist in mitigating bottlenecks in manufacturing lines. [ABSTRACT FROM AUTHOR]

Production lines face numerous challenges to meet market demands, including constant changes in products that require continuous adjustments. Efficient and rapid reconfiguration and adaptation of production processes are crucial. In cases of inadequate adaptation, bottlenecks can arise due to human errors or incorrect configurations, often introducing complexity in pinpointing the root cause and resulting in financial losses. Furthermore, improper machine maintenance contributes to this situation as well. This article seeks to establish a framework grounded in the contemporary smart factory, the IIoT, the Industry 4.0 paradigm, and Big Data. The proposed system places emphasis on leveraging real-time data analysis for predicting risks, while concurrently conducting a thorough analysis of historical data to monitor trends and enhance bottleneck identification. The defined architecture operates across multiple levels, acquiring real-time information and generating historical data for training and continuous optimization. Predictive results contribute to decision-making and assist in mitigating bottlenecks in manufacturing lines.

Industrial loads play an important role in the success of demand response programs (DRPs). However, these programs may compromise the consumers' convenience, which can overshadow their real-world practicality. In response, this article provides a two-level decision-making tree approach to effectively determine the participation abilities of different industrial processes in DRPs considering various features and abilities of these customers. The level I of this framework introduces several classifying variables by which a basic criterion is extracted to classify different industrial processes applying the analytic hierarchy process (AHP). A participation factor is then introduced in level II of the suggested decision tree to estimate the participation level of different classes attained in level I. Finally, a desirability coefficient is formulated, offering the system operators an efficient indicator to verify the attractiveness of different incentive-based programs in the viewpoint of industrial customers. Implementing the presented framework on industrial customers of a region in Iran, it is shown that applying this method lends the decision-makers a hand in practically and effectively introducing DRPs for industrial customers. [ABSTRACT FROM AUTHOR]

It is possible that workers’ error, variation in processing time, lack of parts and machine failure will affect the delivery time of each production process. Consecutive delay in a process can lead to the postponement of production. In this paper, we propose an optimal assignment model in reset limited-cycle multiple periods to study the optimal assignment property with three work capacity levels. First, we present the reset limitedcycle model. Then, we consider an assignment with the minimized total expected cost within a certain range remove analytically determined using these three groups of workers (i.e., referred to in this paper as "local optimal assignment"). After that, we propose the theorems of the optimal assignment property. Finally, we conduct numerical experiments and discuss the optimal assignment rules withing all ranges. [ABSTRACT FROM AUTHOR]

The problem of production flow and evaluation of productivity in the manufacturing line is analysed. Machines can be operated by humans or by robots. Since breakdowns and human factors affect the destabilization of the production processes, robots are preferred. The main problem is a proper methodology—how can we determine the real difference in work efficiency between human and robot at the design stage? Therefore, an analysis of the productivity and reliability of the machining line operated by human operators or industrial robots is presented. Some design variants and simulation models in FlexSim have been developed, taking into consideration the availability and reliability of the machines, operators and robots. Traditional productivity metrics, such as the throughput and utilization rate, are not very helpful for identifying the underlying problems and opportunities for productivity improvement in a manufacturing system, therefore we apply the OEE (overall equipment effectiveness) metric to present how the availability and reliability parameters influence the performance of the workstation, in the short and long terms. The implementation results of a real robotic line from industry are presented with the use of the overall factory efficiency (OFE) metric. The analysis may help factories achieve the level of world class manufacturing. [ABSTRACT FROM AUTHOR]

The problem of production flow and evaluation of productivity in the manufacturing line is analysed. Machines can be operated by humans or by robots. Since breakdowns and human factors affect the destabilization of the production processes, robots are preferred. The main problem is a proper methodology—how can we determine the real difference in work efficiency between human and robot at the design stage? Therefore, an analysis of the productivity and reliability of the machining line operated by human operators or industrial robots is presented. Some design variants and simulation models in FlexSim have been developed, taking into consideration the availability and reliability of the machines, operators and robots. Traditional productivity metrics, such as the throughput and utilization rate, are not very helpful for identifying the underlying problems and opportunities for productivity improvement in a manufacturing system, therefore we apply the OEE (overall equipment effectiveness) metric to present how the availability and reliability parameters influence the performance of the workstation, in the short and long terms. The implementation results of a real robotic line from industry are presented with the use of the overall factory efficiency (OFE) metric. The analysis may help factories achieve the level of world class manufacturing.

The manufacturing line is a fundamental element in short filament fibre production, in which the gearbox is the key mechanical part. Any faults in the gearbox will greatly affect the quality of the short filament fibres. However, due to the harsh working environment, the gearbox is vulnerable to failure. Due to the complexity of the manufacturing line, effective and efficient feature extraction of gear faults is still a challenge. To this end, a new fault diagnosis method based on image recognition is proposed in this paper for gear fault detection in fibre manufacturing lines. In this method, wavelet packet bispectrum analysis (WPBA) is proposed to process the gear vibration signals. The bispectrum texture is obtained and then analysed by an image fusion algorithm for texture feature extraction. The grey-level co-occurrence matrix is used in the image fusion and the extracted texture features are four parameters of the grey-level co-occurrence matrix. Finally, a support vector machine (5VM) is adapted to recognise the gear fault type and location. Experimental data acquired from a real-world manufacturing line of short filament fibres are used to evaluate the performance of the proposed image-based gear fault detection method. The analysis results demonstrate that the newly proposed method is capable of accurate gear fault detection in fibre manufacturing lines. [ABSTRACT FROM AUTHOR]

Designing of manufacturing working places supposes a draft of optimal number of machines to provide a planned daily production, or to create stand-by machines that could substitute a presupposed downtime in production caused by failures. In manufacturing systems demanding implementation of setting, maintenance and repairs of smaller range on manufacturing machines there is a need to determine an appropriate number of personnel carrying out maintenance. We use the most often the deterministic or stochastic methods of operational analysis to solve the mentioned problem. However they do not describe a time flow of failure rise eventually additional random effects in providing availability of complex systems in a real operation. Drafts of logistic and maintenance systems and modeling of availability of technical systems use more often connection of knowledge about operational reliability, economic and simulation modeling and they replace traditional approaches of process examination. [ABSTRACT FROM AUTHOR]

The paper presents the implementation of the RTTRR small-sized robot in a process of microprocessor packing, as a final stage in a modern manufacturing line. After a brief presentation of the RTTRR small-sized robot, the work cycle and the work phases are described. The work cycle is split into ten phases, each of them being described in terms of generalized coordinate’s variation with respect to time. By dividing each work phase into minimum three path segments, the method of path planning (4-3- 4) can be applied, with the aim of determining the interpolation polynomials of the generalized coordinates, velocities and accelerations with respect to time. [ABSTRACT FROM AUTHOR]

Manufacturing line efficiency is an important issue directly influencing the profitability and competitiveness on the market. Comparison shows efficiency advantages for the cell-based manual assembly line in comparison with sequential line. Downsides of the cell-based line configuration are increased capital cost, larger footprint and increased complexity of the manufacturing task on each station. [ABSTRACT FROM PUBLISHER]

Fuzzy risk analysis is widely used in risk assessment of components by linguistic terms. Fuzzy numbers are used to quantify the associated uncertainty. This study employs fuzzy risk analysis to evaluate processes for implementing statistical process control (SPC) in a specified manufacturing system. To reach this goal, fuzzy risk analysis has been applied based on both ranking and similarity of generalized trapezoidal fuzzy numbers in a stepwise procedure. Therefore, a new approach has been introduced for fuzzy risk analysis of processes to overcome the shortcomings of previous fuzzy risk analysis approaches. As a result, fuzzy risk analysis is used as a decision making technique to select critical processes under uncertainty. Also, the application of the proposed SPC implementation algorithm is illustrated in the manufacturing line of a car battery factory. [ABSTRACT FROM AUTHOR]

The classical assignment problem has been widely applied in span manufacturing, personnel scheduling and transportation, to name just a few areas. Generally, the problem seeks to minimize the cost of assigning a given set of jobs (tasks) to a group of machines (worker). Computationally, the assignment problem is considered "easy" because it can be solved in polynomial time using several efficient algorithms. In this paper, we use a limited-cycle model with multiple periods where the risk itself is also affected greatly by the risks which existed in the earlier periods, and we focus on the property of the optimal assignment with minimized total expected risk (cost) in the limited-cycle model. First, the reset model, a simple model of the limited-cycle model, is presented. Then, the optimal assignment problem under the reset model is defined. After that, the property of the optimal assignment with two special workers is proposed. Finally, the property is proved using a mathematical approach and investigated via numerical experiments with a program in which an algorithm based on the branch and bound method is used [ABSTRACT FROM AUTHOR]

manufacturing process for the wedge-shaped folded core of the M-crimp type is considered. Also presented is the parametrical simulation model of the cutting head; the model was constructed by using the Solid Works 2009 CADS software and is meant for automatic change of core geometry in cases when one or several initial parameters are varied. [ABSTRACT FROM AUTHOR]

In a social system or production line, the restrictions of the cost and the due-time exist in each period. Generally, whether these restrictions are satisfied is dependent not only on the risks of this period, but also on the risks generated beforehand. We consider controlling the production process by switching the processing rate to a faster one at a given period. This paper deals with the optimal switching period to minimize the total expected cost of the production process. We first propose the optimal switching period model, and then the mathematic formulation of the total expectation is presented. Finally, the policy of optimal switching period is investigated in detail by numerical experiments. [ABSTRACT FROM AUTHOR]

Practical studies on the method of contamination control for yield enhancement in the cellular phone modules production line were carried out. A contamination control method was proposed, consisting of data collection, data analysis, improvement action, verification, and implementation of control. The partition check method and the composition analysis for data collection and data analysis were respectively used in the cellular phone modules manufacturing process, and these methods were evaluated by the variation of yield loss before and after implementing the actions for improvement. In the partition check method, the critical process step was selected, and yield loss reduction through improvement actions was observed, whereas in the composition analysis, critical sources were selected, and yield loss reduction through improvement actions was investigated. From the results, it is concluded that the partition check and the composition analysis are effective solutions for contamination control in cleanroom production lines. [ABSTRACT FROM PUBLISHER]