Rosmaini Ahmad
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Preferred name
Rosmaini Ahmad
Official Name
Rosmaini, Ahmad
Alternative Name
Ahmad, Rosmaini
Ahmad, Rasyidah
Ahmad, R.
Main Affiliation
Scopus Author ID
14021065800
Researcher ID
EJM-9902-2022

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  • Publication
    Applying lean six sigma approach: A study in recipe recovery and backup process environment
    ( 2021-07-21)
    Kiang B.K.
    ;
    Jusoh M.S.
    ;
    Sri Sarah Maznah Mohd Salleh
    ;
    Rosmaini Ahmad
    ;
    Din M.S.H.
    Lean and Six Sigma are recent developments in continuous improvement methodology that have been popularized by several companies. The success and complementary nature of these methodologies has led to their combination into a single methodology, commonly called Lean Six Sigma. It is a key driver in enhancing the continuous improvement opportunity in various industries. Thus, this study aims to explore the Lean Six Sigma approach implementation in small, medium and large scale firms in detail, and apply the DMAIC concept in the recipe recovery and backup project. DMAIC stands for Define, Measure, Analyze, Improve and Control. Besides, the knowledge of the study will not limit to Lean Six Sigma analysis and literature analysis in the past. Thus, the findings will assist the subsequence case studies and idea to further improve on other industry process. Besides, it will seize great improvement opportunity in improving the efficiency of the existing process in any fields. Therefore, an analysis was constructed in assisting scholars and practitioners to better understand the concept and the influence of this approach. It helps to eliminate the existing impact of high configuration time and establish an excellent operational process in manufacturing. By applying the Lean Six Sigma methodology, it is targeted to achieve MTTR < 30 minutes after the implementation of centralized recipe backup solution. It is recommendation to use Lean Six Sigma methodology in various industries for continuous improvement such as in cost and quality.
  • Publication
    A Case Study of Coffee Sachets Production Defect Analysis Using Pareto Analysis, P-Control Chart and Ishikawa Diagram
    ( 2021-01-01)
    Idris N.I.
    ;
    Sin T.C.
    ;
    Safwati Ibrahim
    ;
    FadzliRamli M.
    ;
    Rosmaini Ahmad
    Nowadays, food and beverages companies in Malaysia are struggle to survive with their rival, hence improving quality and increase productivity are vital. This paper propose the method of analysis a coffee sachet production defectives by using statistical process control (SPC) tools and also to identify each types of defects with their root cause. This paper are using methodology of physical observation through examination of automated production flow line, then Ishikawa cause-and-effect diagram are created. The company valid information are obtained from the professional such as production managers, quality control executives and line supervisors, also staffs and operators that direct or indirectly involves the production line activities through interview and distributed feedback form. After that, a Pareto diagram analysis is done hence creating a control chart (p-chart) to illustrate the result analysis. The result shows there are high number of product defectives according to each type and waste production occur. The problem found was underweight, leaking, overweight, empty, unsealed and height out of the standard. The major causes of defectives coffee sachet and root causes of each defect types are specified which are human, machine, work methods, and materials. While the main root cause of underweight and leaking defect are caused by unskilled worker and improper adjustment at the machine each time before running the production.
      30  38
  • Publication
    On time delivery improvement - Implementing six sigma DMAIC method
    ( 2021-07-21)
    Salvaragh I.R.
    ;
    Muhammad Shahar Jusoh
    ;
    Sri Sarah Maznah Mohd Salleh
    ;
    Rosmaini Ahmad
    ;
    Din M.S.H.
    The likelihood of meeting on time delivery expectation for electronic manufacturer is demonstrated as the productivity's result. It depicts that adequate item is accessible for delivery, permitting the variability in yield, and the likelihood that the item will be sent in the submitted time frame, taking into consideration process duration change. Numerous service firms use delivery time assurances to go after clients in the commercial focus. This study reports the application of a 6σ project on the improvements of Company X products on time delivery. The methodology adopted in this study is the Define Measure Analyse Improve Control (DMAIC) technique of 6σ. This method is adopted as Six Sigma gives a viable instrument to concentrate on customer prerequisites, through improvements of process quality. Six Sigma projects are performed with the goal of improving on time delivery, in- process quality and product quality. This study describes the application of the Six Sigma methodology, focusing the five phases - Define, Measure, Analyze, Improve and Control, by taking the example of a project, and demonstrates the benefits attained.
      34  7
  • Publication
    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.
      28  46
  • Publication
    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.
      2  32