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  1. Mohamed Shamaun Yushak, Nooh Abu Bakar, Khairur Rijal Jamaludin, Rozzeta Dolah
    MyJurnal
    A Shipyard in Malaysia has been trying to change, but facing employee Resistance to
    Change (RTC). Resistance is attributed to the poor coupling of tasks to its technical
    core, creating bad habits leading to thoughtlessness and neglect. Lewin’s Field Theory
    and Festinger’s Theory of Cognitive Dissonance was used to understand and identify
    the underlying behaviour of the employees. Lean principles were used as an in-depth
    intervention to understand how context provoked or shaped reactions. A Dual
    Imperative Action Research (AR) with the author as a participant researcher was
    conducted not only to create knowledge but also, change. To position the Shipyard in
    its historical context, face-to-face interview was conducted with managers to get thick
    description of the RTC and archaic documentations was reviewed. A survey using
    tested questionnaire was conducted to gauge the employees RTC disposition.
    Resistance is due to incoherency of a person’s belief to establish standards, giving rise
    to cognitive dissonance. These dissonances hidden as non-conscious behaviour, social
    habits or norms, lead the organisation to deterioration. Lean intervention reduce
    dissonance, creating psychological flow in the workforce and momentum for change.
    Thereby, the Shipyard managed to recover the delay of a ship undergoing a ship-life
    extension program and avoided liquidated damages amounting to RM63 million. The
    Shipyard also managed to reduce its average delay for ship repair from 17 to 6 months.
    The knowledge on how the researcher can gain utility from RTC and mediate through
    the application of Lean principles would be of considerable benefit to ‘change
    managers’.
  2. Sri Yulis M. Amin, Norhamidi Muhamad, Khairur Rijal Jamaludin, Fayyaz A, Heng SY
    Sains Malaysiana, 2014;43:123-128.
    Feedstock preparation, as well as its characterization, is crucial in the production of highly sintered parts with minimal defect. The hard metal powder - particularly, cemented carbide (wc-co) used in this study was investigated both physically and thermally to determine its properties before the mixing and injection molding stage. Several analyses were conducted, such as scanning electron microscopy, energy dispersive X-ray diffraction, pycnometer density, critical powder volume percentage (cPvP), as well as thermal tests, such as thermogravimetric analysis and differential scanning calorimetry. On the basis of the CPVP value, the feedstock, consisting of wc-co powder, was mixed with 60% palm stearin and 40% polyethylene at an optimal powder loading, within 2 to 5% lower than the CPVP value. The CPVP spotted value was 65%. The feedstock optimal value at 61% showed good rheological properties (pseudoplastic behavior) with an n value lower than 1, considerably low activation energy and high moldability index. These preliminary properties of the feedstock serve as a benchmark in designing the schedule for the next whole steps (i.e. injection, debinding and sintering processes).
  3. Khairur Rijal Jamaludin, Nolia Harudin, Faizir Ramlie, Mohd Nabil Muhtazaruddin, Che Munira Che Razali, Wan Zuki Azman Wan Muhamad
    MATEMATIKA, 2020;36(1):69-84.
    MyJurnal
    Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of determination (R2) value for the model creation, the optimization process in reducing the number of variables would be much reliable and accurate. Comparing between T-Method+OA and T-Method+BPSO in four different case study, it shows that T-Method+BPSO performing better with greater R2 and means relative error (MRE) value compared to T-Method+OA.
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