Tan Chan Sin
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Preferred name
Tan Chan Sin
Official Name
Tan, Chan Sin
Alternative Name
Chan Sin, Tan
Tan, C. S.
Sin, Tan Chan
Sin, T. C.
Sin, T. Chan
Main Affiliation
Scopus Author ID
56237650800

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Defect Factor Analysis Using Statistical Process Control Analysis: A Case Study in Spices Defected Packaging Production

2021-01-01 , Idris N.I. , Tan Chan Sin , Safwati Ibrahim , Mohammad Fadzli Ramli , Rosmaini Ahmad

The problems faced by food and beverages in Malaysia are still the emergence of defect products in every production and it escaped from quality control checked thus it reaches the consumer. This study aims to analyze the variability of defects in the production line in XX Food Industries Sdn. Bhd., using p-chart control and fishbone diagram. This company produce a food spices products whether it is well within the control or not and looking for the factors that causing to the defect products. Data collection and information obtained from interviews and documentation for the number of products and production defects for three months in XX Food Industries. The methods used to analyze products quality control by using p-chart, one of the statistical process control and fishbone diagram to determine the cause of defects products. The defects that usually occur in the production line are types of packaging defects such as leaking sachets, eyemark on the sachet sealing, and less product fill in the sachet that cause lightweight sachet. Results showed that products quality control in XX Food Industries Sdn. Bhd. is still controlled by the largest type of defects that are leaking with defects percentage 46.3 and with 36.28% percentage of defects caused by eyemark. Then, followed by defect caused by lightweight is 17.5%. Factors causing defects products are human, machine, work methods, and materials. In this case unskilled worker and the material of the plastic wrapping for the spices packet are primary cause of defects products. This study is useful in providing useful information in identifying causes for rejection or defect analysis. This research also helpful in proposing optimal solution to be implemented for productivity and quality improvement.

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Product defect prediction model in food manufacturing production line using multiple regression analysis (MLR)

2021-07-21 , Illa I.N. , Tan Chan Sin , Fadzli R. , Safwati Ibrahim , Rosmaini Ahmad , Mohd Fathullah Ghazli@Ghazali

This paper aims to develop an improved general mathematical model by focusing on human factors variables that related to the product defect in the manufacturing production line. This is because many studies found that almost 40% of total defects resulted from the operator error and the defects are usually not obvious and neglected. The objective to have defect prediction mathematical model to satisfy as early quality indicator of the manufacturing flow production line and assist the quality control team in manufacturing industries. Thus, the human factor variables will be investigate thoroughly and final model can be used to predict product defect on the line to improve product quality. Product defects quantity are identified and analyzed to determine the potential predictors for developing the mathematical model. A case study is offered that illustrates in a spice packaging semi-automated production line the effect that complexity variables have on assembly quality. By using Minitab, Multiple Regression analysis is conducted to model the relationship between the input variables towards response variables. From the analysis, the predicted data showed reasonable correlation with the observed data improved with adjusted R-Sq from 2.6% to 7.9%. Hence, the regression equation obtain is selected to be the prediction mathematical model for defects based on human factor input variables.

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