Browsing by Author "Grigg NP"

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    A survey to better understand the performance measurement dimensions for Australasian nonprofit healthcare organisations: data summary report
    (Massey University, 2016-10) Soysa IB; Jayamaha NP; Grigg NP
    This report has been especially prepared for those who responded to our survey, which was designed to test the performance measurement system that we developed for Australasian healthcare nonprofit organisations (NPOs). The performance measurement (PM) system itself was developed through extensive case studies involving nine Australasian NPOs (six from New Zealand and three from Australia). Figure 1 shows our PM Framework. The report provides key findings from a survey recently conducted by us to test the PM framework that we developed through case studies (some details of the case studies have been described). Our performance framework was found to be reliably generalisable across Australasian NPOs in the healthcare sector. The framework is therefore useful for performance monitoring and improvement of healthcare NPOs in the region. An online questionnaire was used to collect the data from senior managers belonging to healthcare NPOs across Australia and New Zealand. Out of the 1550 senior managers invited to participate in the survey, 232 responded, resulting in a response rate of 15%, which is considered satisfactory for this type of a survey. We found that the most survey participants were familiar with PM systems. The study validated the nine PM domains (categories) in our framework, namely Mission; Strategy; Organisational Infrastructure; People; Financial Health; Process; Client Satisfaction; People Satisfaction and Donor Satisfaction. The survey showed that out of the 41 survey questions (items) allotted to the nine PM domains, five are incompatible with the PM framework (they do not relate to any of the nine PM dimensions of our model); these have been removed from the final analysis. We found that out of the 36 valid survey items considered, organisations performed exceptionally well in 6 items (Q3, Q5, Q20, Q1, Q17 and Q27), reasonably well in 27 items, and moderately well in 3 items (Q34, Q7, and Q41); see Table 2 for definitions as well as results. The study confirmed that the organisation has to be driven by their directors and the senior leadership in order to achieve better performance. This includes understanding and developing the people within the organisation. We found that the processes put in place by the organisations to achieve stakeholder satisfaction can be divided into three types: continuous improvement; designing of safe, efficient and effective processes; and designing of the infrastructure, technology and material to create the necessary support processes. We also found that the three key stakeholders of NPOs — clients (or customers), employees, and donors — carry approximately equal weight in achieving the mission. Each of these stakeholder groups has their own set of expectations and these expectations belong to three themes: delivering high quality services and support to the community; valuing skilled workers and recognising people (volunteers included); and commitment to social responsibility. The complementary Microsoft Excel spreadsheet that we have provided helps organisations to conduct self-assessments on organisational performance. This in turn helps an organisation to identify: (a) best practices for process improvements, (b) trends in performance management practices, benchmark practices and (c) relationships between performance and stakeholders and organisational performance.
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    Creating engineers - climbing the educational stair-case
    (Massey University, 2015-11-18) Frater TG; Grigg NP
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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.