Six Sigma is considered as a logical business strategy that attempts to identify and eliminate the defects or failures for improving the quality of product and processes. A decision on project selection in Six Sigma is always very critical; it plays a key role in successful implementation of Six Sigma. Selection of a right Six Sigma project is essentially important for an automotive company because it greatly influences the manufacturing costs. This paper discusses an approach for right Six Sigma project selection at an automotive industry using fuzzy logic based TOPSIS method. The fuzzy TOPSIS is a well recognized tool to undertake the fuzziness of the data involved in choosing the right preferences. In this context, evaluation criteria have been designed for selection of best alternative. The weights of evaluation criteria are calculated by using the MDL (modified digital logic) method and final ranking is calculated through priority index obtained by using fuzzy TOPSIS method. In the selected case study, this approach has rightly helped to identify the right project for implementing Six Sigma for achieving improvement in productivity.

Six Sigma is a strategic approach of significant value in achieving overall excellence. It helps to accomplish the organizations strategic aim through the effectual use of project controlled methodology. As Six Sigma is a project controlled approach, it is necessary to prioritize projects which give utmost economic benefits to the firm. In real practice, Six Sigma projects selection is very tough assignment because poor project selection also happens even in the well-managed organizations and this can weaken the success and trustworthiness of the Six Sigma practice. The present study aims to develop a project selection approach based on a combination of fuzzy and MADM technique to help organizations determine proper Six Sigma projects and identify the priority of these projects mainly in automotive companies. VIKOR and TOPSIS methods have been used to select the proper Six Sigma project composed with fuzzy logic. In this context, seven critical parameters have been considered for selection of finest alternative. The weights of evaluation criteria are obtained using the MDL (modified digital logic) method and final ranking is calculated through primacy index obtained by using fuzzy based VIKOR and TOPSIS methodology. A factual case study from automotive industry is used to investigate the efficacy of the planned approach.

Capacity waste management is highly essential because under utilization of capacity is often referred to as a major reason for lower productivity among industries around the world. For better estimation of capacity and its utilization and then for its improved management; newer techniques are being devised in industrial sector. The current case of capacity waste problem has been taken up as a Six Sigma project, where we try to analyze critical factors responsible for the capacity waste. Decisions on critical factor selection in analysis phase of Six Sigma are always very crucial. The paper discusses an approach for selection of capacity waste factors at an automotive industry using fuzzy logic based AHP method. The fuzzy AHP is a well recognized tool to undertake the fuzziness of the data involved in choosing the preferences of the different decision variables engaged in the process of capacity waste factors selection. In this context, we have explored six crucial parameters for selection of capacity waste factors. Final ranking is calculated through priority vector thus obtained and it is seen that conveyor malfunction is found to be the key factor for capacity waste among all alternatives at the selected site.

The companies in present challenging world are trying hard to improve their productivity and capacity utilization levels without actually increasing the sale price of their products. In India, in today combative world, the majority of automotive manufacturing industries are scuffling hard with their low levels of productivity. Possibly there are different reasons for this and capacity waste or under-utilization of productive capacity in industries appears to be one of the prime reasons. As corrective action, the present study makes an effort to check the efficacy of Six Sigma approach to improve capacity waste management. With a case study, through successful execution of Define phase, the study confirms the perception of Six Sigma for capacity waste management in Indian automotive industry.