The papers below cover the philosophies, tools, techniques, new products and processes and…

The papers below cover the philosophies, tools, techniques, new products and processes and quality programs for effective implementation of Total Quality Management strategies. Students are required to do a critical review of these papers (papers are attached).


  1. LEAN SIX SIGMA AND QUALITY FRAMEWORKS IN HIGHER EDUCATION – A REVIEW OF LITERATURE (JanelleMargaretDavidson FacultyofBusiness,UniversityofWollongong,Wollongong,Australia OrianaMilaniPrice SchoolofManagement,OperationsandMarketing, UniversityofWollongong,Wollongong,Australia,and MatthewPepper FacultyofBusiness,SchoolofManagement,OperationsandMarketing, UniversityofWollongong,Wollongong,Australia)
  2. Six-sigma Quality Management of Additive Manufacturing. (Hui Yang1, Prahalad Rao2, Timothy Simpson1,4, Yan Lu3, Paul Witherell3, Abdalla R. Nassar4, Edward Reutzel4, and Soundar Kumara1)



Purpose – This paper aims to present a review of literature that considers the use of quality frameworks in higher education (HE). Quality frameworks provide a minimum standard of teaching and learning of students. This systematic literature review identifies the tools and techniques to continuously improve the systems and processes that underpin teaching and learning are missing. With this in mind, the authors present a focus on Lean Six Sigma (LSS) as an improvement methodology adopted by the HE sector and present the factors that drive or hinder the implementation of LSS in higher education institutions (HEIs). Design/methodology/approach – A review of the literature and thematic analysis has been undertaken relating to the application of quality frameworks and methodologies within the literature set. Findings – The findings show that quality frameworks to be lacking insofar as their focus on compliance is no incentive for continuous improvement. This finding is not unique to the HEI sector and similar challenges exist in other sectors. A further finding identifies the need for academic professional practice to go beyond quality assurance to attend to the transformation of students. Together these present an apparent disconnect between continuous improvement methodology and HE quality frameworks. Research limitations/implications – A literature review does have limitations insofar as some literature may have been missed because of different key terms. A further consideration being literature from 2019 not available at the time the review was conducted. Practical implications – It represents the state of play in regard to the use of quality frameworks operating in HE and business schools. Insight is offered into how the use of continuous improvement methods can deliver quality in HE to benefit the sector, students and others. An agenda for future research is offered. Originality/value – The discussion is valuable as it seeks to improve understanding of the relationships between methodologies with adopted quality frameworks in the HEI sector. A contribution is made in the use of force field analysis to represent the critical success factors and barriers of LSS in HEI.

Six-sigma Quality Management of Additive Manufacturing


Quality is a key determinant in deploying new processes, products or services, and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of Six Sigma has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics and optimization. Six sigma entails a data-driven DMAIC methodology of five steps – Define, Measure, Analyze, Improve, and Control. Notwithstanding the sustained successes of Six-Sigma knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics and beyond, there is a dearth of concentrated application of Six-Sigma quality management approaches in the context of AM. In this paper, we propose to design, develop, and implement the new DMAIC methodology for Six-Sigma quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-akind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, environment, to post-build inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems, and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE) and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multi-disciplinary research efforts to accelerate the application of Six-Sigma quality management in AM.

NB: The full papers is attached


  1. Discuss five specific problems the papers (2 papers) seek to address and explain why these problems are relevant to the area of study.
  2. Examine two strengths and three weaknesses of the research methodologies (Research approach, design, sampling technique, data analysis tools etc. i) used by both papers relative to the case study organisation in TQM and Lean Six sigma.
  1. Evaluate five gaps identified in the literatures reviewed and justify how these gaps were addressed relative to improving the products and processes of the case study organisations.
  1. Discuss five key findings of any of the papers, support these findings with current literatures and justify how these findings can improve TQM and Lean Six sigma practices.

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