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Quality Control Analysis of Porcelain Products Using Overall Equipment Effectiveness and Statistical Quality Control Methods

Nurprihatin Filscha, Rembulan Glisina Dwinoor, Andry Johanes Fernandes, Immanuella Sarah, Widiwati Ivana Tita Bella

Management and Production Engineering Review

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2023

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Vol. 14, No. 3

134--147

EN

This study uses statistical quality control (SQC) and overall equipment effectiveness (OEE) to examine quality at a porcelain production firm. The study is motivated by the most frequently broken machines in 2019, is the Jigger 01 machine. This paper aims to evaluate the machine’s effectiveness using the OEE method. The OEE determines the scope of the problem to be solved using the SQC method. The average OEE value in 2019 was 70%. Based on the SQC method, the product defect produced is still under control. However, the average defect is still above the company’s tolerance limit of 10%. Consequently, this study offers enhancements utilizing the Failure Mode Effect Analysis (FMEA) technique. The results indicate that human resources and machines caused defective products. This paper contributes to providing several improvements that the company can apply to maximize its quality control analysis. After implementing the improvement, the OEE value increases to 74%.

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Minimizing Product Defects Based on Labor Performance using Linear Regression and Six Sigma Approach

Nurprihatin Filscha, Ayu Yayang Nadistya, Rembulan Glisina Dwinoor, Andry Johanes Fernandes, Lestari Tika Endah

Management and Production Engineering Review

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2023

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Vol. 14, No. 2

88--98

EN

The objective of this research is to minimize product defects based on labor performance and prove the hypothesis on how labor performance affects the quality of a product through a scientific calculation using Overall Labor Effectiveness (OLE). The primary data is obtained by interviewing the supervisor and labor directly. For secondary data is obtained from the company, such as labor working time, machine scheduled downtime, total production, and defective products. The approach to extract the data is using OLE and the continued regression method. Furthermore, it proceeds to Six Sigma using the DMAIC approach since the results show a significant correlation. The result from Failure Mode and Effects Analysis (FMEA) shows four of six potential failures caused by product defects are coming from labor. To prevent failure mode, it is recommended to have the regular machine checked by labor, check the temperature of the machine, and provide Standard Operating Procedures.

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Integration of Overall Equipment Effectiveness and Six Sigma Approach to Minimize Product Defect and Machine Downtime

Nurprihatin Filscha, Rembulan Glisina Dwinoor, Andry Johanes Fernandes, Lubis Maulidina, Widiwati Ivana Tita Bella, Vaezi Ali

Management and Production Engineering Review

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2023

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Vol. 14, No. 4

71--91

EN

This study was conducted in a company that produces palm oil-based products such as cooking oil and margarine. The study aimed to encounter defects in packaging pouches. This study integrated the overall equipment effectiveness (OEE) with the six sigma DMAIC method. The OEE was performed to measure the efficiency of the machine. Three factors were measured in OEE: availability, performance, and quality. These factors were calculated and compared to the OEE world-class value. Then, the Multiple Linear Regression was performed using SPSS to determine the correlation between measurement variables toward the OEE value. Lastly, the six sigma method was implemented through the DMAIC approach to find the solution and improve the packaging quality. Supposing the recommendations are implemented, the OEE is expected to increase from 82% to 85%, with availability ratio, performance ratio, and quality ratio at, 99%, 86%, and 99.8%, respectively.

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Developing a Decision Support System for Supply Chain Component

Andry Johanes Fernandes, Nurprihatin Filscha, Liliana Lydia

Management and Production Engineering Review

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2023

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Vol. 14, No. 2

124--133

EN

Increased competition has led businesses to compete with each other in streamlining supply chain processes, especially in the manufacturing sector. Supply Chain Management (SCM) determines the success of industrial business processes because it regulates product flow regarding integration, performance, and information. However, several problems have emerged in the supply chain process, such as a lack of coordination in the production queue, difficulties in forecasting trending products, and suboptimal production capacity. To address these issues, the role of information technology is crucial for implementing a Decision Support System (DSS). This study aims to develop a DSS to improve the supply chain processes. The research method used is Extreme Programming (XP) with a qualitative approach through a questionnaire. The research process involves collecting data, defining boundaries and problems, and designing, coding, and testing the system. As a final step, evaluation is carried out by distributing surveys to obtain valid satisfaction results. This research produces a DSS that has applicability in marketing, accounting, and production processes. The application of DSS in the furniture manufacturing industry can help manage the movement of resources, optimize strategic networks, and assist decision-making in the supply chain process.

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Supply Chain Disruptions Mitigation Plan Using Six Sigma Method for Sustainable Technology Infrastructure

Andry Johanes Fernandes, Nurprihatin Filscha, Liliana Lydia

Management and Production Engineering Review

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2022

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Vol. 13, No. 4

88--97

EN

Supply Chain Management (SCM) is a very important part of the industrial world, especially in the manufacturing sector. The development of the business world affects the complexity of the supply chain due to the lack of logistics infrastructure, quality of materials and components, and much more. Supply chain disruption risk mapping needs to be done due to high uncertainty, which is overcome by implementing a decision support system. Based on the background of the problem, supply chain disruption mapping uses the help of the Six Sigma method, which consists of 5 stages: Define, Measure, Analyze, Improve, and Control (DMAIC). The measurement of disturbance also uses the Failure Mode and Effect Analysis (FMEA) approach to prioritize risk. Risks that have a high assessment and cause failure need to be prioritized for improvement. This study aims to map supply chain disruptions in the current manufacturing industry based on the barriers, resistances, and causes detected for making a decision support system prototype. By implementing a decision support system in the supply chain process, it is hoped that the manufacturing industry can minimize potential losses from existing risks.