Drivers influencing lean practices in street food vending process

S, Keerthana; Suresh, M.

doi:10.1109/iccic.2016.7919655

Cited by 14 publications

(6 citation statements)

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“…The enablers can be explained as below: Autonomous enablers (Zone-I): Enablers that have weak dependence power and driving power are known as autonomous enablers (Amrita and Suresh, 2016). In this study, there is no autonomous enabler. Dependent enablers (Zone-II): Enablers that have high dependence and low driving power are known as dependence enablers (Keerthana and Suresh, 2016; Renganath and Suresh, 2016). In this study, Educators' beliefs and skills (E2), Generation-Z learners (E3) are the dependent enablers.…”

Section: Resultsmentioning

confidence: 99%

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Enablers of technology agility in higher education

Menon

Suresh

2022

IJILT

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PurposeThe purpose of this study is two-fold. First, to identify and encapsulate the enablers that can facilitate technology integration in higher education and second, to understand and analyze the interplay between technology agility enablers.Design/methodology/approachThe study used the Total Interpretive Structural Modeling (TISM) approach to construct a theoretical model of the technology agility enablers in higher education and MICMAC analysis for ranking and segregating the enablers based on their dependence power into four categories: Autonomous, Dependent, Linkage and Independent.FindingsThe study helped identify eight technology agility enablers, with the Covid-19 pandemic as the most significant enabler. The Covid-19 pandemic has catalyzed the diffusion of technology across the education sector in India, including tertiary higher education. The study revealed government initiatives and institutional commitment as other enablers that can promote technology agility in higher education.Practical implicationsThe results of this study would assist the policymakers and management of universities and colleges in understanding the important enablers that can facilitate technology integration in higher education.Originality/valueResearch in the past on technology adoption in higher education has looked into each enabler in isolation. This research provides a comprehensive view of the enablers and has attempted to establish a multidirectional interplay between the enablers.

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Section: Resultsmentioning

confidence: 99%

“…Dependent enablers (Zone-II): Enablers that have high dependence and low driving power are known as dependence enablers (Keerthana and Suresh, 2016; Renganath and Suresh, 2016). In this study, Educators' beliefs and skills (E2), Generation-Z learners (E3) are the dependent enablers.…”

Section: Resultsmentioning

confidence: 99%

Enablers of technology agility in higher education

Menon

Suresh

2022

IJILT

Self Cite

View full text Add to dashboard Cite

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“…These enablers are explained as below: Autonomous enablers (Zone-I): Weak dependence and driving power characterize autonomous enablers (Amrita and Suresh, 2016). In this study, government initiative is the autonomous enabler. Dependent enablers (Zone-II): Enablers that have high dependence but less driving power are known as dependence enablers (Keerthana and Suresh, 2016; Renganath and Suresh, 2016). In this study, partnering champion ( E 1), institution's willingness to change ( E 3) and educators' capacity building ( E 7) are the dependent factors.…”

Section: Resultsmentioning

confidence: 99%

“…Dependent enablers (Zone-II): Enablers that have high dependence but less driving power are known as dependence enablers (Keerthana and Suresh, 2016; Renganath and Suresh, 2016). In this study, partnering champion ( E 1), institution's willingness to change ( E 3) and educators' capacity building ( E 7) are the dependent factors.…”

Section: Resultsmentioning

confidence: 99%

Enablers facilitating industry-academia, transnational education and university-community partnering agility in higher education

Menon

Suresh

Raman

2021

HESWBL

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PurposeThe study has a two-fold purpose: first, to identify the enablers of partnering agility in higher education, and, second, to analyze the interplay between the enablers.Design/methodology/approachTotal interpretive structural modelling (TISM) was used to construct a theoretical model of partnering agility enablers, and cross-impact matrix multiplication applied to classification (MICMAC) analysis was used to rank and segregate the enablers into independent, autonomous, dependent and linkage zones on the basis of their driving and dependence power.FindingsThe study helped in identifying eight enablers that can be instrumental in driving partnering agility in higher education. According to the TISM model, clarity on roles and responsibilities of partners was found to be the most crucial and vital enabler followed by resource sharing.Practical implicationsThe conceptual model provides a new direction on how to develop and strengthen higher education partnerships. The model has prioritized all the crucial enablers that the management can work around in order to drive partnering agility in higher education institutions.Originality/valueStudies in the past have majorly focused on academia–industry partnerships. This research has tried to provide a comprehensive view of the enablers and the multidirectional interplay between the enablers that can facilitate partnerships between academia and industry, Indian and international universities, and academia and community.

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“…I 4.0 help firms to identify the problem in realistic approach known as digital lean. By reducing the waste through eliminating non-value added activities using sensors, robots, data analytics and automation [1], [2], [8]. I 4.0 prefer predictive manufacturing along with the help of advance prediction tool.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Challenges in implementing industry revolution 4.0 in INDIAN manufacturing SMES: insights from five case studies

Hemamala¹,

N²

2018

IJET

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Purpose-This research examines Small and Medium manufacturing Enterprises (SME’s) awareness, current capability, willingness and ability to identify the challenges involved in implementing Industry 4.0(I 4.0) at their premises.Design/methodology/approach-A set of questionnaire was framed to collect qualitative and quantitative data from five manufacturing SME’s and they were analyzed to gain insight.Findings –3 out of 5 manufacturing SME’s are aware, capable, willing and have ability to identify the challenges for implementing Industry 4.0 at their premises. The study also found that implementation of I 4.0 depends on size of the firm. Medium size manufacturing firms had started investing in Information Technology but small scale industries is still struggling to figure out their long term benefit.Practical implications – The advancement and the integration of the technologies such as Cyber Physical system, Internet Of Things, Artificial intelligence, Big data, Cloud computing and 3D printing provides greater flexibility to the manufacturing firms. In today’s global competition with a huge demand for personalized products at low price with best quality, innovation and capability to full filling batch size of one is becoming important. Hence, to meet the market demand many large-scale industries started investing in advanced technology where SME has yet to pay attention.Originality/value –Paper indicates the Indian manufacturing SME’s preparedness for Industry 4.0. It contains five cases capturing the current manufacturing practices followed in the SME’s and their capability towards implementing Industry I 4.0 in Indian environment.

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Drivers influencing lean practices in street food vending process

Cited by 14 publications

References 0 publications

Enablers of technology agility in higher education

Enablers of technology agility in higher education

Enablers facilitating industry-academia, transnational education and university-community partnering agility in higher education

Challenges in implementing industry revolution 4.0 in INDIAN manufacturing SMES: insights from five case studies

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