Assessing manufacturing strategy definitions utilising text-mining

Sourabh Kulkarni, Priyanka Verma and R. Mukundan

International Journal of Production Research, 2019, vol. 57, issue 14, 4519-4546

Abstract: The variations in Manufacturing Strategy (MS) definitions create confusion and lead to lack of shared understanding between academic researchers and practitioners on its scope. The purpose of this study is to provide an empirical analysis of the paradox in the difference between academic and industry definitions of MS. Natural Language Processing (NLP) based text mining is used to extract primary elements from the various academic, and industry definitions of MS. Co-word and Principal Component Analysis (PCA) provide empirical support for the grouping into nine primary elements. We posit from the terms evolution analysis that there is a stasis currently faced in academic literature towards MS definition while the industry with its emphasis on ‘context’ has been dynamic. We believe that the proposed approach and results of the present empirical analysis can contribute to overcoming the current challenges to MS design and deployment – imprecise definition leading to its inadequate operationalisation.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2018.1512764 (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:taf:tprsxx:v:57:y:2019:i:14:p:4519-4546

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2018.1512764

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().