An Integrated Fuzzy Multi-Criteria Decision-Making Approach for Six Sigma Project Selection

Abstract

Selecting an appropriate six sigma project is one of the most important problems for a manufacturing company because it impacts the manufacturing costs. Besides, an appropriate choice of Six Sigma projects is very important as it helps to improve quality, reduce variation, and eliminate waste of a manufacturing system. Therefore, the main purpose of this study is to provide a good insight into the use of an integrated decision-making methodology in the evaluation of Six Sigma projects. For this purpose, we integrate three multi-criteria decision-making (MCDM) methods by applying a modified Delphi method, Analytical Hierarchy Process (AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) methodologies. After the evaluation criteria of Six Sigma projects are determined by a Modified Delphi method, the weights of criteria are calculated by applying the AHP method. The FTOPSIS method is then employed to achieve the final ranking results. A real case application along with a sensitivity analysis is presented to show the effectiveness of the proposed methodology. The research question for this methodology is how we can develop an integrated MCDM model to select six sigma projects. six sigma has become one of the most important subjects in business strategy. Thus, six sigma has received considerable attention from researchers. Three research areas for six sigma have attracted interest among the researchers: First, the critical success factors for six sigma implementation, second, the basic concepts, tools, and applications of six sigma approach and finally the selection of six sigma projects for business improvement. This paper focuses on the last area.Six sigma is regarded as a project-based methodology that can help a company achieve expected benefits through continuous project improvement (Su & Chou, 2008). It is originated at Motorola in the early 1980s, and then many companies such as GE

Prioritising solutions for Lean Six Sigma adoption barriers through fuzzy AHP-modified TOPSIS framework

“…The solutions necessary to tackle these barriers are not prioritised and linked. This logically AHP-modified TOPSIS framework creates need for developing and using a hybrid MCDM framework to meet this research challenge; and Researchers (Percin and Kahraman, 2010;Shishebori et al, 2014) developed MCDM-based frameworks mainly for barriers and its prioritisation. It is noticed that even while prioritising barriers also, researchers do not consider human judgment error-related possibilities, which may arise while developing these frameworks.…”

“…The use of TOPSIS in the framework improves its utility. To handle large number of alternatives with varied intensities over each attribute, it preferred to choose TOPSIS as it tackles the order preference effectively while changing attribute weights (Percin and Kahraman, 2010;Prakash and Barua, 2015). This means that the developed framework can be easily applied across manufacturing and non-manufacturing domains by modifying the inputs, i.e.…”

Applying a Hybrid MCDM Model for Six Sigma Project Selection

“…Recently, Büyüközkan andÖztürkcan [13] developed a novel approach based on decision-making trial and evaluation laboratory (DEMATEL) technique and an analytic network process (ANP) to help logistics companies to identify and prioritize Six Sigma projects. Perçin and Kahraman [14] integrated three methods including a modified Delphi method, an AHP, and a fuzzy technique for the selection of Six Sigma projects. Tkáç and Lyócsa [15] proposed a new model based on a real options approach for evaluating Six Sigma projects, which involves the stochastic nature of project outcomes, costs, and uncertainty regarding payoffs and managerial options.…”