Roadmap for business analytics implementation using DIPPS model for sustainable business excellence: case studies from the multiple fields

Santosh B. Rane and Nandkumar Mishra

International Journal of Business Excellence, 2018, vol. 15, issue 3, 308-334

Abstract: Today the organisations are increasingly adopting analytics for breakthrough business results. There are enough talks about potentials of business analytics, software solutions on analytics and data visualisations. However, very few success stories are published and easily available in public domain. The focus is required to translate potentials of analytics into the breakthrough business results. This paper proposes discover-innovate-predict-perform-sustain (DIPPS) model for the business excellence through analytics. It also presents success stories of big data analytics and industrial internet of the things (IIoT) from the multiple fields for the breakthrough business results. The hypothesis testing indicates that the organisations deploying the DIPPS model have higher success rate and the probability of achieving their analytics goals.

Keywords: artificial neural network; ANN; business analytics; business excellence; Delphi method; discover- innovate- predict- perform- sustain model; DIPPS; hypothesis testing; F-test and t-test; industrial internet of the things; IIoT; logistic regression model; multiple regression model. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.inderscience.com/link.php?id=92574 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbexc:v:15:y:2018:i:3:p:308-334

Access Statistics for this article

More articles in International Journal of Business Excellence from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().