A logistic regression modelling approach to business opportunity assessment
by Cathy Lawson, Douglas C. Montgomery
International Journal of Six Sigma and Competitive Advantage (IJSSCA), Vol. 3, No. 2, 2007

Abstract: Significant opportunities for improvement exist in the optimisation of business processes. The use of statistically-based methods to characterise business process performance is one way to realise those improvements. Business processes can be difficult to characterise due to variables that are difficult to define, measure and analyse. Outputs in the business process may be qualitative or binary in nature. The cause and effect relationships among inputs, actions and outputs may be difficult to observe and monitor. Sources of variation exist throughout the process, but may be difficult to identify and control. Consequently, statistically-based characterisation methods that have been proven successful for manufacturing processes may not be directly applicable to business processes. Because many of the variables in these business processes are subjective or qualitative in nature, categorical data analysis techniques may be useful. This paper illustrates the use of logistic regression to establish statistically significant relationships between the input and output variables of one complex business process.

Online publication date: Tue, 04-Sep-2007

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Six Sigma and Competitive Advantage (IJSSCA):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com