Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    The Application of Machine Learning to Consolidate Critical Success Factors of Lean Six Sigma
    (IEEE, 17/08/2021) Perera AD; Jayamaha NP; Grigg NP; Tunnicliffe M; Singh A
    Lean six sigma (LSS) is a quality improvement phenomenon that has captured the attention of the industry. Aiming at a capability level of 3.4 defects per million opportunities (Six Sigma) and efficient (lean) processes, LSS has been shown to improve business efficiency and customer satisfaction by blending the best methods from Lean and Six Sigma (SS). Many businesses have attempted to implement LSS, but not everyone has succeeded in improving the business processes to achieve expected outcomes. Hence, understanding the cause and effect relationships of the enablers of LSS, while deriving deeper insights from the functioning of the LSS strategy will be of great value for effective execution of LSS. However, there is little research on the causal mechanisms that explain how expected outcomes are caused through LSS enablers, highlighting the need for comprehensive research on this topic. LSS literature is overwhelmed by the diverse range of Critical Success Factors (CSFs) prescribed by a plethora of conceptual papers, and very few attempts have been made to harness these CSFs to a coherent theory on LSS. We fill this gap through a novel method using artificial intelligence, more specifically Natural Language Processing (NLP), with particular emphasis on cross-domain knowledge utilization to develop a parsimonious set of constructs that explain the LSS phenomenon. This model is then reconciled against published models on SS to develop a final testable model that explains how LSS elements cause quality performance, customer satisfaction, and business performance.
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    Gender Diversity Population Simulations in an Extended Game of Life Context
    (IEEE, 20/06/2019) Mathrani A; Scogings C; Mathrani S
    Cellular automata studies have been instrumental in computational and biological studies for simulating life contours based on simple rule-based strategies. Game of Life (GoL) presented us with one of the earliest automata studies that led the way in exemplifying non-linear spatial representations, such as large-scale population evolution scenarios depicting species dominance, species equilibrium, and species extinction. However, the GoL was driven by interactions among vegetative entities comprising live and die states only. This paper extends GoL to gendered-GoL (g-GoL) in which male phenotypes and female phenotypes interact in an extended world to procreate. Using the g-GoL, we have demonstrated many evolution contours by applying gender-based dependence rules. Evolution scenarios have been simulated with skewed gender ratios that favor the birth of male offspring. Preference for a male child is common in certain cultures; therefore, empirical data realized with skewed gender settings in g-GoL can reveal the long-term impact of non-egalitarian gender societal structures. Our model provides a tool for the study of emergent life contours and brings awareness on current gender imbalances to strengthen multi-disciplinary research inquiry in the areas of social practices, mathematical modeling, and use of computational technologies.
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    Blood donation, being Asian, and a history of iron deficiency are stronger predictors of iron deficiency than dietary patterns in premenopausal women
    (Hindawi Publishing Corporation, 2014) Beck KL; Conlon CA; Kruger R; Heath A-LM; Matthys C; Coad J; Jones B; Stonehouse W
    This study investigated dietary patterns and nondietary determinants of suboptimal iron status (serum ferritin < 20 μg/L) in 375 premenopausal women. Using multiple logistic regression analysis, determinants were blood donation in the past year [OR: 6.00 (95% CI: 2.81, 12.82); P < 0.001], being Asian [OR: 4.84 (95% CI: 2.29, 10.20); P < 0.001], previous iron deficiency [OR: 2.19 (95% CI: 1.16, 4.13); P = 0.016], a "milk and yoghurt" dietary pattern [one SD higher score, OR: 1.44 (95% CI: 1.08, 1.93); P = 0.012], and longer duration of menstruation [days, OR: 1.38 (95% CI: 1.12, 1.68); P = 0.002]. A one SD change in the factor score above the mean for a "meat and vegetable" dietary pattern reduced the odds of suboptimal iron status by 79.0% [OR: 0.21 (95% CI: 0.08, 0.50); P = 0.001] in women with children. Blood donation, Asian ethnicity, and previous iron deficiency were the strongest predictors, substantially increasing the odds of suboptimal iron status. Following a "milk and yoghurt" dietary pattern and a longer duration of menstruation moderately increased the odds of suboptimal iron status, while a "meat and vegetable" dietary pattern reduced the odds of suboptimal iron status in women with children.