Learning Improvement of DEA Technique in Decision Making for Manufacturing Applications Using DEA Excel-Solver Atefeh Amindoust 1, a , Shamsuddin Ahmed 1, b and Ali Saghafinia 2, c 1 Engineering Design & Manufacture Department, University of Malaya Kuala Lumpur, 50603, Malaysia 2 Electrical Engineering Department, Islamic Azad University, Majlesi Branch, Esfahan, Iran 2 Electrical Engineering Department, University of Malaya Kuala Lumpur, 50603, Malaysia a Atefeh_Amindoust@yahoo.com, b Ahmed@um.edu.my, c Saghafi_Ali@yahoo.com Keywords: DEA; Decision making; DEA Excel-Solver; Manufacturing application. Abstract. DEA (Data Envelopment Analysis) is the optimization method of mathematical programming to measure the relative efficiencies of decision making units (DMUs). Due to its wide applicability, the DEA has been studied extensively for the last 30 years to solve decision making problems. Since, there are a lot of selection decisions in manufacturing, DEA as an appropriate tool to be necessary-especially for engineers- to improve learning for decision making. In this paper, the DEA method is applied in decision making process through DEA Excel-Solver software and the required processes are explained step by step to help academics and practitioners to get appropriate results in making decision. Introduction Industrial engineering is a branch of engineering dealing with the optimization of complex processes or systems. The various topics concerning industrial engineers include management science, financial engineering, engineering management, supply chain management, process engineering, operations research, systems engineering, ergonomics / safety engineering, cost and value engineering, quality engineering, facilities planning, and the engineering design process [1, 2]. Operations research (OR) is a discipline which deals with the application of advanced analytical methods to help make better decisions [3]. It is often considered to be a sub-field of mathematics [4]. The OR encompasses a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency, such as simulation, mathematical optimization, queuing theory and other stochastic-process models, Markov decision processes, econometric methods, data envelopment analysis, neural networks, expert systems, decision analysis, and the analytic hierarchy process [5]. Nearly all of these techniques involve the construction of mathematical models that attempt to describe the system. Because of the computational and statistical nature of most of these fields, OR also has strong ties to computer science and analytics. However, the OR course will touch on more complex models. The main emphasis will be on solution techniques and on analysis of the underlying mathematical structure of these models. As a supporting theme, the course will also emphasize the use of mathematical solvers [6]. In all of the aforementioned scenarios, decision making issue may be one of the main components and the engineering side tends to emphasize extensive mathematical proficiency and usage of quantitative methods to make decisions. Data envelopment analysis (DEA) is a nonparametric method in operations research for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision making units (or DMUs). Due to the wide applications of DEA in deferent area [7-10], software is no longer an impediment to the incorporation of this technique into decision-support systems. DEA as an educational tool to be necessary-especially for students- to improve learning for Advanced Materials Research Vol. 903 (2014) pp 425-430 © (2014) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMR.903.425 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP, www.ttp.net. (ID: 60.53.109.177, Department of Electrical Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia, Kuala Lumpur, Malaysia- 20/02/14,15:40:51)