Shaliza Azreen Mustafa
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Preferred name
Shaliza Azreen Mustafa
Official Name
Shaliza Azreen, Mustafa
Alternative Name
Mustafa, Shaliza Azreen
Mustafa, S.A
Main Affiliation
Scopus Author ID
55655687300
Researcher ID
FQJ-1111-2022

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  • Publication
    DMAIC steps application to improve ergonomics problem: a case study in coffee manufacturing industry
    ( 2022)
    Nurul Najwa Mansor
    ;
    Shaliza Azreen Mustafa
    ;
    R Badlishah Ahmad
    ;
    Mohd Asyraf Che Doi
    ;
    Tan Chan Sin
    ;
    Mohd Asri Jusoh
    The health issue related to Musculoskeletal Disorders (MSDs) faced by manufacturing industries’ workers affects the work quality and productivity, commonly due to the tasks performed with unsuitable posture. This study was carried out to assess the level of Ergonomics Risk Factors (ERFs) among shop-floor workers in a coffee manufacturing company, located in northern peninsular of Malaysia. The Six Sigma approach was applied based on Define, Measure, Analyze, Improve and Control (DMAIC) steps. Observation, interviews and self-report surveys were conducted in the Define step to identify the potential tasks that lead to the high level of ergonomics risk. In the Measure step, the Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA) tools were then used to assess the level of ERF on twenty-three tasks that practice at eight workstations. In the Analyze step, assessment results were evaluated, thus identifying that the lifting task (mixing workstation) has the highest ergonomics risk level (score level 11) and follows by root cause identification. Then, the Improve step focuses on a simple invention proposal, where the optimal solution that fulfills the principles of proper height working procedure and reducing excessive force is applied. The proposed solution shows a significant improvement in ergonomics risk level based on the score value of 3. Lastly, the Control step focuses on sustaining the improved version performance by revising the working procedure according to the solution principles proposed in the previous step.
  • Publication
    Application of fuzzy failure mode, effect and criticality analysis (fuzzy FMECA) with extended rule of criticality ranking assessment: a case study in ghee and soap industry
    (AIP Publishing, 2023)
    Adel Ali Azawqari
    ;
    Rosmaini Ahmad
    ;
    Badiea Aswaidy M
    ;
    Shaliza Azreen Mustafa
    This paper presents the application of fuzzy FMECA for item criticality ranking assessment. A case study from Yemen's ghee and soap industry was used, which focuses on a critical production system of seaming process. The proposed fuzzy FMECA framework is divided into three main phases. The first phase is the FMECA procedure that is commonly applied with criticality analysis is performed based on risk priority number (RPN) calculation of severity (S), occurrence (O) and detection (D) parameters measures. The second phase is the estimation of RPN based on the fuzzy approach, which is carried out based on Gaussian membership function application. The third phase is the final criticality ranking assessment process to prioritize the production components under study. In this phase, an extended rule is introduced to avoid/minimize overlapping values in criticality ranking determination. A case study on a critical production system that consists of overall 32 components was used for framework validation. The MATLAB fuzzy logic toolbox was used to assist the fuzzification process towards RPN numbers estimation. The overall results of FMECA with comparison values of typical RPN and fuzzy RPN are presented. The discussion on the implications and benefits of the extended rule application towards final criticality ranking determination is then given.
      2  7
  • 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
  • Publication
    Ergonomics assessment of production’s workstations – a case study in the food manufacturing industry
    (AIP Publishing, 2023)
    N. N. N. Yusuf
    ;
    Shaliza Azreen Mustafa
    ;
    R Badlishah Ahmad
    Musculoskeletal disorders (MSD) is the common injury among workers in the manufacturing industry. Industrial workers tend to experience MSD due to unsuitable occupation they performed. This study aims to assess the level of ergonomics risk factor (ERF) in production's workstations using an ergonomics assessment tools. The study was conducted at a Small and Medium Enterprise (SME) a food manufacturing company located in Kangar, Perlis, Malaysia. Initial assessment based on field observations, interviews and a self-report survey was first carried out to identify the current practice of working environment in production activities of the company. The result of the initial assessment reveals that 100% of 8 respondents have experience pain/discomfort at the back followed by waist/buttock with 87.5%, then 37.5% at shoulder, while lower neck and knee share the same percentage of 25% and the least percentage of 12.5% at upper neck and foot. This indicates that the existing design of the workplace does not provide comfort, satisfaction, health and safety from high risks of MSD. Then, the Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA) tools were applied to further assessment their ERF on seven workstations. Results found that the filling (Workstation 4) is the highest risk condition due to the highest score of REBA and this workstation requires necessary "NOW"action. Thus, some related recommendations for improvement were then given for future works planning include redesign filling workstation, reducing distances between workstations, height adjustable worktable and ergonomics training.
      17  12