Preliminary investigation into the influence of green lean six sigma enablers on wastewater treatment operations

DOI Web Site 参考文献30件
  • MOHAMAD Effendi
    Faculty of Industrial and Manufacturing Technology and Engineering Universiti Teknikal Malaysia Melaka
  • ISHAK Anuar
    Faculty of Industrial and Manufacturing Technology and Engineering Universiti Teknikal Malaysia Melaka Department of Environment Malaysia Federal Government Administrative Centre
  • AREP Hambali
    Faculty of Industrial and Manufacturing Technology and Engineering Universiti Teknikal Malaysia Melaka
  • ITO Teruaki
    Faculty of Computer Science and Systems Engineering Okayama Prefectural University
  • RAHMAN Muhamad Arfauz A
    School of Mechanical and Aerospace Engineering Queen's University Belfast
  • JALIL Mohd Faizal Ab
    Department of Environment Malaysia Federal Government Administrative Centre

説明

<p>Manufacturing industries have increasingly recognized the value of Lean Six Sigma (LSS) not only for improving productivity but also for enhancing sustainability. The combination of green concept with LSS has also gained popularity by reducing waste, cost, and emissions. Prompting the exploration of enablers supporting its adoption in environmental services. This study aims to investigate the relationship between Green LSS (GLSS) enablers and operational benefits (OBs) in wastewater treatment plants (WWTPs) in Malaysia. The study examines five independent variables (IVs): strategic-based enablers (S), environmental-based enablers (Env), culture-based enablers (C), resource-based enablers (R), and linkage-based enablers (L), in relation to the dependent variable (DV) of OB. Data was collected from 65 certified competent personnel working in WWTPs and analysed using validity, reliability, factor analysis, and multiple linear regression. The results indicate that the IVs significantly predict OB when the p-value is below the 5% threshold. This suggests that the factors examined have a significant impact on WWTP operational benefits. Furthermore, the R2 value of 0.390 indicates that the model explains 39% of the variance in OB. Specifically, the variables S and C significantly support the hypotheses, while Env, R, and L do not significantly influence OB. These findings provide valuable insights for the wastewater service sector in improving their understanding and implementation of GLSS to enhance operational performance in a developing country, Malaysia.</p>

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