A Simulation Model for Machine Efficiency Improvement Using Reliability Centered Maintenance: Case Study of Semiconductor Factory

Supsomboon, Srisawat; Hongthanapach, Kanthapong

doi:10.1155/2014/956182

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…For decades, modelling and simulation have been employed by companies worldwide to improve the design, evaluation, and development of the operation of complicated systems [11,[14][15]. Many industries and businesses require effective production planning to enhance their products to keep their businesses in the global competitive.…”

Section: Introductionmentioning

confidence: 99%

Improving Productivity Employ Simulation Model: A Case Study of a Steel Pipe Manufacturing Company

Alden¹

2022

AJES

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Productivity improvement in the manufacturing industry of piping is a key challenge facing manufacturers in today's competitive markets. Improving productivity in the pipe manufacturing companies by implementing manufacturing principles that utilize simulation modeling was the purpose of this study. To improve productivity, an approach that focuses on the workstations and workforces process was suggested. The suggested approach's goal was to increase productivity by providing customer prerequisites and leaving some products for other customers in the store. Based on the data has been gathered from the company of steel pipes, Bansal Ispat Tubes Private Limited in India, a simulation model was utilized to enhance its performance of operational. The investigation methodology consists of a simulation model, acceptable distribution, and data investigation. By simulating individual workstations and evaluating all relevant processes according to the data collected, the simulation model was built. Actual employment data were gathered from the line of manufacturing and supervisory workers, with observations carried out throughout the process of manufacturing. The used method involves videotaping of the process and interviewing workers using a video-camera. The superior continuous distributions were picked to fulfill a convenient statistical model. The results could be helps to ameliorate the manufacturing industry productivity. Furthermore, the outcomes could assist to solve the problems of scheduling in pipe manufacturing "simulating and modeling" which reveals active ways in enhancing pipe manufacturing productivity. Consequently, the findings might support well competition among companies.

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Section: Introductionmentioning

confidence: 99%

Improving Productivity Employ Simulation Model: A Case Study of a Steel Pipe Manufacturing Company

Alden¹

2022

AJES

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“…The failure mode prioritization method approach, according to the reviewed literature, can be generally placed into one of five main categories, namely: (1) multi-criteria decision making (MCDM) [7][8][9][10][11][12]; (2) mathematical programming (MP) [13]; (3) artificial intelligence (AI) [14][15][16][17][18][19][20][21]; (4) hybrid approaches [22,23]; (5) cost-based models as an additional factor in decision making [24,25]. From the literature, the input parameters for the proposed models have used the conventional approach of FMEA analysis, taking into account the uncertainty of the assessment of risk variables by experts [15][16][17]25,26].…”

Section: Introductionmentioning

confidence: 99%

New FMEA Risks Ranking Approach Utilizing Four Fuzzy Logic Systems

Ivančan¹,

Lisjak²

2021

Machines

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Process equipment and plant maintenance problems are complex in the oil refinery business, since effective maintenance needs to ensure the reliability and availability of the plant. Failure Mode and Effects Analysis (FMEA) is a risk assessment tool that aims to determine possible failure modes, and to reduce the ratio of unknown failure modes, by identifying business-critical systems and the risks of their failures. For the identified failure modes, FMEA determines risk mitigation action(s). The goal is to prevent failure and keep assets and plants running at peak performance by providing fully integrated operations, maintenance, turnarounds, modifications, and asset integrity solutions, during all phases of the asset life cycle. This research was based on FMEA use/application in refineries’ units, and proposes the new fuzzy FMEA risk quantification approach method: “four fuzzy logic system”. The model included a pre-assessment, by sets of fuzzy logic systems, that examined the input parameters that affected the variables of severity, occurrence, and detectability. The proposed model prioritized risks better and addressed the drawbacks of the conventional FMEA method.

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Reliability Centered Maintenance to Determine Priority of Machine Damage Mode

Rizkya

Siregar

et al. 2019

E3S Web Conf.

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High machine downtime results in the production process being disrupted. The average engine breakdown in the sterilizer production is 35.66 hours/month. The corrective maintenance method has not been able to guarantee a smooth production process. This study will plan preventive maintenance activities in order to increase the reliability of production machinery and also maintain the smooth production process. The study was carried out by identifying the damage level of a sterilizer machine with the FMEA method. Based on the result of the FMEA method, maintenance planning of the production machine using the Reliability Centered Maintenance (RCM). FMEA indicates that there are 5 highest risk priority number values, namely the IGBT, Mosfet, bearing bushings, rool former and v-block components. For the maintenance planning using the RCM method, the results obtained were the Total Minimum Downtime (TMD) criteria indicates that the optimum turnover interval of Roll Former, Mosfet, V-Block, IGBT, and Bearing Bushing components is 24 days, 23 days, 25 days, 26 days, and 22 days. The recommendation of the RCM maintenance method has the potential to have a positive impact, namely a decrease in the average critical component downtime of 37,103 % and also an increase in reliability.

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A Simulation Model for Machine Efficiency Improvement Using Reliability Centered Maintenance: Case Study of Semiconductor Factory

Cited by 4 publications

References 3 publications

Improving Productivity Employ Simulation Model: A Case Study of a Steel Pipe Manufacturing Company

Improving Productivity Employ Simulation Model: A Case Study of a Steel Pipe Manufacturing Company

New FMEA Risks Ranking Approach Utilizing Four Fuzzy Logic Systems

Reliability Centered Maintenance to Determine Priority of Machine Damage Mode

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