Demystifying data analysis: an alternative approach for managers of manufacturing SMEs

Nicholas O'Regan (Bristol Business School, University of the West of England, Bristol, UK)
Martin Sims (Business School, University of Hertfordshire, Hatfield, UK)

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Publication date: 31 July 2007



Effective decision making is a crucial activity for manufacturing firms of all sizes. To this end, statistical techniques, such as variance theory, cognitive maps, heuristics and process theory, are widely used. However, such techniques rarely help chief executives to understand the dynamics of competitive behaviour, and often fail to bridge the gap between theory and practice. Indeed, from a small‐ and medium‐sized (SME) firm perspective, such techniques are rarely used owing to inadequate resources and/or skills. The paper seeks to address these issues.


This paper proposes and tests a new approach to multivariate analysis based on the conditional formatting of spreadsheets. The analysis was confirmed using conventional statistical methods in order to validate the proposed methodology.


The results are depicted as a visual picture of the attribute(s) under consideration and can be visually analysed.


Such an approach can be used to complement and enhance current research techniques as well as facilitating data analysis.



O'Regan, N. and Sims, M. (2007), "Demystifying data analysis: an alternative approach for managers of manufacturing SMEs", Journal of Manufacturing Technology Management, Vol. 18 No. 6, pp. 701-713.

Download as .RIS



Emerald Group Publishing Limited

Copyright © 2007, Emerald Group Publishing Limited

To read the full version of this content please select one of the options below

You may be able to access this content by logging in via Shibboleth, Open Athens or with your Emerald account.
To rent this content from Deepdyve, please click the button.
If you think you should have access to this content, click the button to contact our support team.