Prioritizing tactical quality improvement An action research study Douglas N. Hales College of Business Administration, University of Rhode Island, Kingston, Rhode Island, USA Samia M. Siha Michael J. Coles College of Business, Kennesaw State University, Kennesaw, Georgia, USA V. Sridharan College of Business and Behavioral Science, Clemson University, Clemson, South Carolina, USA, and Judith I. McKnew Department of Mathematics, Clemson University, Clemson, South Carolina, USA Abstract Purpose – The purpose of this paper is to test a method for improving decision-making at a tactical level – i.e. at the shop floor level. This is done by testing the use of the binary sorting algorithm (BSA) to improve decisions concerning quality improvement efforts among machine operators in a plastics manufacturer. Design/methodology/approach – The method used was the “action-research” approach since the researchers were actively involved in the training, implementation, and use of the BSA. Findings – It was found that using the BSA to prioritize quality improvement efforts by machine operators reduced the “scrap-from-line” rate in a plastics manufacturer. Research limitations/implications – The study should be replicated in different companies and industries using multiple methods because action-research is limited in its generalizability. Also, since the researchers are actively involved in the process, our observations could not be considered objective. Originality/value – To the authors’ knowledge, this is the first application of the BSA to improve decision-making at a tactical shop-floor level in a manufacturing company. Keywords Quality improvement, Action research, Shopfloor, Operations and production management, Plastics industry Paper type Research paper Introduction Since, the early 1900s researchers have studied and identified variation in manufacturing processes that reduce product quality and increase the overall cost of quality. Subsequently, several tools were developed to assist managers and workers in reducing variation and improving various processes such as statistical process control (SPC), Pareto analysis, cause-and-effect diagrams, and the Taguchi loss function The current issue and full text archive of this journal is available at www.emeraldinsight.com/0144-3577.htm IJOPM 26,8 866 International Journal of Operations & Production Management Vol. 26 No. 8, 2006 pp. 866-881 q Emerald Group Publishing Limited 0144-3577 DOI 10.1108/01443570610678648