Main Article Content
Material procurement in a large enterprise depends on typical factors like Type of Material, the Departmental Hierarchy, the location
where material is used, dealing officer, material group etc. Minimizing the material procurement Leadtime at different stages is a business
requirement. The influencing factors on Leadtime can be grouped according to business criteria and same can be analyzed for specific trends &
patterns. This paper examines the Data Mining techniques applied to uncover natural groupings among leading attributes of Leadtime like
Material groups, Purchase groups and Dealing officers. Performance criteria of Data Mining algorithms are measured by accuracy,
comprehensibility and interestingness. The analysis is carried out with an objective to improve predictive accuracy of different categories of
Leadtime. Our study confirms that regression modeling gives better predictive accuracy when outliers in data are less significant and scales up
well to match new dimensional attributes on model.
Keywords: Regression, Classification, APD, ARM, Purchase Order, Purchase Request, Prediction, BIW
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.