Industries, nowadays, are concerned about energy consumption and ever narrowing rules of emissions by the governments. Therefore, a race to clean; green and less energy consuming manufacturing is going on throughout the world. But in authors’ perspective, the major part of energy exploitation lies in the production of a rejected product. Therefore alongside the use of energy saving processes and machinery, industry should primarily look for rejection reduction. This, apart from energy saving and profitability, will add to the moral responsibility of every person toward nature. Here in this paper, authors describe a case study in which the increased rejection rate of a part of cycle chain assembly is controlled by the application of Six Sigma. Six Sigma, from many years has proved to be an ultimate solution when it comes to the application part in manufacturing industries. It’s very generic and easily applicable methodology has drawn tremendous positive results throughout the world. A financial gain of INR 0.267 million was yielded by implying six-sigma approach. In a move toward energy saving, the money saved by the project was used for green manufacturing to promote energy conservation.

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.

This paper presents a methodology to improve the strength or the Modulus of Rupture (MOR) of fibre cement. The Six Sigma approach with the DMAIC steps was applied to a case study company. This research started from defining problem, setting the project objective and the project scope. Next, the measurement system was analyzed and the process map was set up. The potential factors of the problem was then determined. Due to there were many factors that affect the MOR, the Cause and Effect Matrix and the Failure Mode and Effect Analysis technique were then used to reduce the number of factors to be studied further. Next, three process factors, which were the pulp slurry freeness, the film-layer thickness, and the pressure step, were optimized using the results from the Box-Behnken experimental design. Other 13 remaining factors were improved by creating or revising the standard work instructions and training the operators. After that, the statistical process control and the control plan were set up to control the production processes. After improvement, the process capability index (Ppk) significantly increased from 0.26 to 1.35.

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.

Six-Sigma approach is a systematic and scientific operations management methodology aimed at achieving major enhancements in production process through the elimination of waste. In this case, a casting unit manufacturing cylinder blocks has been selected which was experiencing a rejection rate as high as 30% due to formation of blowholes, other than the surface. The main objective of this paper is to identify various causes of occurrence of blow-holes and recommend corresponding remedies to counter these defects by systematic imple-mentation of DMAIC cycle. Both preventive and corrective actions have been recommended to reduce the blowholes and overall improvement is validated through two-proportion test. Results of investigation demonstrated the net significant reduction of blowholes defect from 28,111 to 9,708 parts per million, which results in the net annual savings of INR 12,56,640.

Six Sigma is a philosophy of unremitting improvement and excellence in all aspects. The concept is a satisfactory modification process tool through customers, continuous improvement and stakeholder participation. Six Sigma is considered as statistical analysis, assessment scales and customer-oriented production accomplishments and it leads to defect production reduction. This paper recommends an approach to select Six Sigma projects using fuzzy multiple attribute decision making techniques composed with another concoction tool. Through insightful quarrying of literature, rudimentary criteria for selecting Six Sigma projects were revealed. The fundamental criteria were identified consuming the fuzzy hypothesis test. Having identified the most indispensable criteria, the weight of criteria were determined. Appling FANP techniques. Having calculated the weights pertinent to criteria through three methods, SAW, TOPSIS, and Fuzzy VIKOR, Six Sigma projects were introduced and prioritized. Applying the three methods engendered various results, which required the application of an amalgamation technique, entitled as Borda and it helped to clarify the final project rate.

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.

Outsourcing is a common practice among the organizations and it is a major element in business strategy. It has recognized as an area of strategic importance. This article represents the application of the DMAIC Six Sigma approach to improve outsourcing management process with an emphasis to reduce delay of delivery time for a manufacturing company. The paper identifies each stage of the DMAIC cycle and the Six Sigma tools that were applied. The improvement project has been implemented in seven months.

Six Sigma is known as the best quality management system, which has been applied in different places with distinct tasks. Any implementation of Six Sigma project normally can succeed only when human resource management is applied within the organization, properly. In this paper, we illustrate the intense of relationships between the human resources management indices and Six Sigma project implementation indices for some selected Iranian manufacturing firms. By using the fuzzy cognitive maps (FCM), we are able to show the strength of relationships among various indices. This study helps quality and human resource managers learn how to manage the critical indices to achieve their determined goals and to increase Six Sigma project performance throw human resource management perspective.