Data mining techniques applied to a manufacturing SME

Packianather, Michael S. ORCID: https://orcid.org/0000-0002-9436-8206, Davies, Alan, Harraden, Sam, Soman, Sajith and White, John 2017. Data mining techniques applied to a manufacturing SME. Procedia CIRP 62 , pp. 123-128. 10.1016/j.procir.2016.06.120

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Abstract

This paper examines how data mining, an aspect of analytical science, can be applied to assist a Small to Medium Enterprise (SME) industry using unsupervised learning techniques, association rules and time-series analysis. Whilst recent developments have meant it is now possible for SME to compile large amounts of commercial data, this information is rarely utilised effectively. The study builds on a number of standard data mining techniques to produce a tailored set of analyses that provide maximum benefit to the company. Self-Organising Maps were utilised to visualise the core characteristics of the firm's customers. The study outlines a new technique to determine associations between customer variables using the arules package available within RStudios. Finally, time-series forecasting was conducted highlighting the seasonal variations and trends for potential growth in the coming year.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	2212-8271
Date of First Compliant Deposit:	13 July 2018
Date of Acceptance:	10 June 2016
Last Modified:	03 May 2023 09:37
URI:	https://orca.cardiff.ac.uk/id/eprint/112453

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