SENRA Academic Publishers, British Columbia Vol. 8, No. 1, pp. 2751-2760, February 2014 Online ISSN: 1920-3853; Print ISSN: 1715-9997 QUALITY IMPROVEMENT OF FOUNDRY OPERATION IN NIGERIA USING SIX SIGMA TECHNIQUE *Abidakun OA, Leramo RO, Ohunakin OS, Babarinde TO and Ekundayo-Osunkoya AO Department of Mechanical Engineering, Covenant University, Ota, Nigeria ABSTRACT In this paper Six Sigma DMAIC analysis was applied in an aluminium mill in order to identify sources and causes of waste with the intention of providing veritable solutions. The foundry section was the segment under scrutiny. Re-work or defects in this firm was found to be on the average of about 37.05% of total production for the twenty-three months under study (January 2009- December 2010). Defect reduction was therefore chosen as the Critical-to-Quality (CTQ) factor. The sigma level of 1.87 in the firm indicated the existence of opportunities for improvement. Analysis was carried out using SPSS, SPC for Excel to perform regression analysis, process capability analysis, generate descriptive statistics, histograms and run charts. The results of these analyses identified three major defects and some of their behaviours. Based on the analysis, solutions were proffered in the Improve and Control phases of this project. Implementation of the proffered solutions resulted in noticeable improvement and led to the firm operating with near- perfect processes thus proving the applicability of Six Sigma. Keywords: Six sigma DMAIC, critical-to-quality, composition error, profile error, trimming error. INTRODUCTION Generally, it is typical of manufacturing processes to produce up to 69.1% as waste since production processes normally function at 1-2 sigma (Kaushik et al., 2008). It could be worse in many production firms where little attention is paid to quality control and improvement. This lack of quality may be in terms of customers’ dissatisfaction, delay in delivery, defective products and services or waste of resources. Manufacturing firms of the current age are faced with stringent economic conditions, stifling competition and increasing customer awareness among other factors. All these places high demand on manufacturers to constantly produce high quality products in the best way possible. Also, manufacturing industry occupies the central stage in nations’ development and in the world economy. The well-being of a nation is thus determined by its capability to convert raw materials to desirable finished goods. One of such processes involved in manufacturing is the foundry operation. Foundry operation which involves the melting of billets and/or scrap metals is a highly energy and labour intensive operation (Su and Chou, 2008). Waste generation and lack of quality is however a serious issue militating against the efficient performance in foundry operation. Since profit making remains a major objective of every business, value addition and quality improvement have to be given due attention in order to save money and increase revenue. Achieving this objective requires the implementation of such techniques as Six Sigma. Presently, many foundries are interested in implementing Six Sigma to improve the quality of their products (Su and Chou, 2008). Six Sigma is the application of scientific method to the design and operation of management systems and business processes which enable employees to deliver the greatest value to customers and owners (Pyzdek, 2003; Pantano et al., 2006). It is a disciplined, systematic, data- driven approach to process improvement that targets the near-elimination of defects from every product, process and transaction (Evans and Lindsay, 2005; Aggogeri and Gentili 2008; Kaushik et al., 2008). Although, it involves measuring and analyzing an organization’s business processes, Six Sigma is not merely a quality initiative; it is a business initiative (Pande and Holpp, 2002; Lee- Mortimer, 2006). The effectiveness of Six Sigma in improving quality and reducing waste has been proved in various sectors by both scholars and practitioners (Treichler et al., 2002; Goffnett, 2004; Banuelas et al., 2005; Kwak and Anbari, 2006; Aksoy and Orbak, 2009; Ung et al., 2007; Gijo and Scaria, 2010; Falcón et al., 2012). It has, as a numeric goal; the reduction of errors in output to an outrageous but possible and much desired 3.4 parts per million (Antony and Banuelas, 2002). It also has a business goal of improving customers’ satisfaction, reducing cycle time and defects (Antony and Banuelas, 2002; Rajagopalan et al., 2004; Evans and Lindsay, 2005; Parast, 2011). A process functioning at 6 sigma level is expected to produce satisfactory outputs 99.99966% of the time (Antony and Banuelas, 2002). The main benefit of a Six *Corresponding author email: olayinka.ohunakin@covenantuniversity.edu.ng