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
    Industry 4.0 challenges and benefits in Malaysia's manufacturing firms: A review
    ( 2024-03-07)
    Arumugam T.
    ;
    Tan Chan Sin
    ;
    Mohammad Fadzli Ramli
    ;
    Ahmad Humaizi Hilmi
    ;
    Rosmaini Ahmad
    ;
    Azmi H.
    Malaysia is now ready to adopt Industry 4.0 as a vital tool for transitioning to a new industry. The major focus of this article is on Industry 4.0's challenges and benefits. Inadequate time and effort, higher investment, lack of expertise and knowledge, and lack of internet coverage and IT facilities are some of the challenges associated with adopting Industry 4.0. Many other benefits of Industry 4.0 implementation include higher productivity, increased competitiveness, increased opportunities for collaborative working and knowledge sharing, cost reduction, flexibility and agility, and improved customer experience. As a result, this article contributes to a more realistic knowledge of Industry 4.0. As a final note, this paper offers some directions for firms wishing to implement Industry 4.0 with great success.
      31  1
  • 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
  • Publication
    A review of industry 4.0 development progress in Indonesia
    ( 2024-03-07)
    Mursadi R.A.
    ;
    Tan Chan Sin
    ;
    Ramli M.F.
    ;
    Ahmad Humaizi Hilmi
    ;
    Rosmaini Ahmad
    ;
    Azmi H.
    Rapid advancements in digital technology have resulted in a new industrial revolution phenomenon known as Industry 4.0. This revolution introduces modern technologies that enable the connectivity of all components within industries. This paper reviews the current status and progress in Indonesia heading to Industry 4.0. The Indonesia' government has been launched 'Making Indonesia 4.0's' roadmap in 2018 including the five priority industrial sectors namely food and beverages, chemicals, textile, automotive, and electronics and ten national priorities. Indonesia which is currently only in the stage of entering the industrial era 4.0 with few of challenges and opportunities by following the roadmap of "Making Indonesia 4.0".
      28  6
  • Publication
    Implementation of kanban-based fifo system to minimize lead time at automated optical inspection operation - A case study in semiconductor industry
    ( 2021-01-01)
    Krom P.
    ;
    Rosmaini Ahmad
    ;
    Shaliza Azreen Mustafa
    ;
    Tan Chan Sin
    This paper presents an improvement project in a semiconductor industry to minimize product lead time in automated optical inspection (AOI) operation. The Lean Thinking (LT) approach is applied to drive this improvement project, which it performed based on three main stages; observation, improvement and validation. Initial observation study found a significant cause of this problem (lead time). There have improper record and control mechanisms of receiving the product lots from production department, picking the products lots for inspection and restoring the products lots after inspection. Therefore, this not only cause the high and inconsistent lead time of products lot to be inspect (due to random rack location searching during picking and restoring processes) also the right sequence of product lots from one type to another that went through the AOI operation cannot systematically managed and controlled. In improvement stage, a production control system, namely Kanban-based First-In-First-Out (FIFO) system is proposed to solve the stated problem. Generally, Kanban technique applied in this research project is to systematically guide the inspection operator for performing AOI operation based on FIFO rule. There are two keys rules behind Kanban technique application; First is to record the sequence of the product lots that been received from production department and obtained the rack location for their temporary store. Second is to provide input information for the inspection operator to perform the activities picking and restoring in the right sequence of product lot direct to the rack location that has been stored before. Another technical mechanism included in Kanban-based FIFO system is the application of supermarket buffer system to minimize the wafer lot searching activity. In validation stage, the proposed solution has been tested within the period of one month. Results show that the searching times for picking and restoring the product lots has reduced to 77.0% and overall product lead time due inspection process has reduced 54.2%. The mechanism of Kanban-based FIFO system is then proposed to the top management of the case study company to be embedded with Internet of Thing (IoT) technology to support Industrial 4.0 evolution.
      22  1