Impacting Big Data analytics in higher education through Six Sigma techniques

Abstract

Purpose The purpose of this paper is to develop a framework for utilizing Six Sigma (SS) principles and Big Data analytics at a US public university for the improvement of student success. This research utilizes findings from the Gallup index to identify performance factors of higher education. The goal is to offer a reimagined SS DMAIC methodology that incorporates Big Data principles. Design/methodology/approach The authors utilize a conceptual research design methodology based upon theory building consisting of discovery, description, explanation of the disciplines of SS and Big Data. Findings The authors have found that the interdisciplinary approach to SS and Big Data may be grounded in a framework that reimagines the define, measure, analyze, improve and control (DMAIC) methodology that incorporates Big Data principles. The authors offer propositions of SS DMAIC to be theory tested in subsequent study and offer the practitioner managing the performance of higher education institutions (HEIs) indicators and examples for managing the student success mission of the organization. Research limitations/implications The study is limited to conceptual research design with regard to the SS and Big Data interdisciplinary research. For performance management, this study is limited to HEIs and non-FERPA student data. Implications of this study include a detailed framework for conducting SS Big Data projects. Practical implications Devising a more effective management approach for higher education needs to be based upon student success and performance indicators that accurately measure and support the higher education mission. A proactive approach should utilize the data rich environment being generated. The individual that is most successful in engaging and managing this effort will have the knowledge and skills that are found in both SS and Big Data. Social implications HEIs have historically been significant contributors to the development of meritocracy in democratic societies. Due to a variety of factors, HEIs, especially publicly funded institutions, have been under stress due to a reduction of public funding, resulting in more limited access to the public in which they serve. Originality/value This paper examines Big Data and SS in interdisciplinary effort, an important contribution to SS but lacking a conceptual foundation in the literature. Higher education, as an industry, lacks penetration and adoption of continuous improvement efforts, despite being under tremendous cost pressures and ripe for disruption.

Revisión de la implementación de Lean Six Sigma en Instituciones de Educación Superior

“…Resulta interesante nombrar el proyecto que se ejecutó en una IES no especificada para reducir la deserción estudiantil utilizando la metodología Lean Six Sigma con la estructura DMAIC, se demostró que resulta viable para este tipo de problemas aplicar esta metodología, sin embargo, al ser un primer proyecto de este tipo se presentan barreras importantes por superar [51]. [53], [54], [18], [55], [48], [20], [56], [38], [13], [57], [58], [25], [28], [14], [40], [59], [47], [50], [60], [61], [62] 21 2 Mejorar el sistema de calidad y acreditación de las IES [54], [9], [48], [63], [64], [56], [38], [10], [57], [26], [59], [52], [47], [65], [61], [62] 16…”

“…Se constataron casos que para responder a esta situación se implementó Lean Manufacturing, Six Sigma o LSS [26], [53], [55]; el resultado de esta decisión constituye un esfuerzo concertado de las instituciones académicas para reducir sus costos. Los esfuerzos de reducción de costos han tenido un efecto ostensible en las universidades públicas que han reducido las plantas de personal en todos los niveles [54].…”

Role of Human Knowledge and Communication on Operational Benefits Gained from Six Sigma

“…It is important for companies to find a balance between these two conceptions [20], otherwise SS may become a significant obstacle for those who are not experts in statistics [65]. In this sense, other studies, having detected an imbalance between the different ways SS can be approached, also report that, in such cases, SS projects are more often abandoned [66,67] and that is why this is an opportunity for techniques such as big data or novel techniques to help analyze information obtained from production process, because it allows finding trends, as it is applied in education [68], and SS is a philosophy based in education and training.…”

Frameworks Para a Integração Entre a Manufatura Enxuta E a Indústria 4.0

“…Big data É um termo que pode ser descrito através de três características: volume, velocidade e variedade; no seu coração estão a análise e o refinamento de dados, que levam à tomada de decisão eficaz. Essa tecnologia coleta dados brutos e os transforma em informações (LAUX et al, 2017;LEEet al, 2016). Pereira et al, 2019Li, 2019Kipper et al, 2019Pagliosa et al, 2019Rosin et al, 2019Kamble et al,2019Pekarčíková et al, 2019Rossini et al, 2019Tortorella et al,2019aVarela et al, 2019Tortorella et al, 2019bAntony et al, 2018Arcidiacono e Pieroni,2018Buer et al, 2018aBuer et al, 2018bSony,2018 Tortorella e Fettermann, 2018 Chen e Lin,2017 Dombrowski et al,2017Karre et al, 2017Laux et al, 2017Oliveira et al,2017Sahin et al, 2017Bell e Bell, 2016Lee et al,2016Sanders et al,2016 Machine learning É uma ferramenta que prevê dados futuros com base em dados anteriores, é usada para melhorar decisões de negócios, aumentar a produtividade, detectar doenças e muitas outras coisas (HARRINGTON, 2012).…”

“…Última, a falta de comprovação de que haverá retorno sobre o investimento. Laux et al (2017) mencionam, mais uma vez, a necessidade de trabalhadores qualificados e que estes são difíceis de serem encontrados. Como desafio, os autores mencionam que existem anormalidades na produção na conexão entre IoT e o jidoka, estas devem ser mais bem estudadas.…”