The performance of a product is generally characterized by more than one response variable. Hence the management often faces the problem of simultaneous optimization of many response variables. This study was undertaken to simultaneously optimize the surface hardness and case depth of carbonitrided bushes. Even though lots of literature has been published on various methodologies for tackling the multi-response optimization problem, the simultaneous optimization of heat treated properties of carbonitrided bushes are not reported yet. In this research the effect of four factors and two interactions on surface hardness and case depth of carbontirded bushes were studied using design of experiments. Based on the experimental results, the expected values of the heat treated properties of the bushes were estimated for all possible combination of factors. Then the best combination which, simultaneously optimized the response variables, was arrived at using desirability function. The study showed that the optimum combination obtained through desirability function approach not only minimized the variation around the targets of surface hardness and case depth but also was superior to the ones obtained by optimizing the response variables separately. Moreover this study provides a useful and effective approach to design the production process to manufacture bushes with customer specified surface hardness and case depth targets.
Purpose -In the service sector, reduction of cycle time is one of the key issues. Lean concepts and techniques are applied to increase the speed of operations. In many cases, improvement projects need to leverage a combination of Lean and Six-Sigma approaches and tools. The purpose of this paper is to present a Lean Six-Sigma case study for reducing cycle time in a BPO operation and to demonstrate application of Lean Six-Sigma methodology in BPO and ITeS industries. Design/methodology/approach -This paper presents an application of Lean Six-Sigma methodology for cycle time reduction in BPO organizations. Findings -Lean Six-Sigma is found to work very well in BPO industries for reducing process cycle time by carrying out process changes. "Improve result by improving the process" -this motto of Six-Sigma is very well demonstrated by this approach of Lean Six-Sigma for BPO organizations. Originality/value -This paper utilizes fundamental Lean and Six-Sigma approaches and presents applications. The main idea behind this paper is to utilize Lean concepts/techniques to speed up Six-Sigma projects. Apart from the paper's value for managers, it can also help researchers to extend this for other areas of business processes.
Purpose – The purpose of this paper is to develop a methodology to reduce the field failures of splined shafts. The paper also demonstrates the application of Mahalanobis-Taguchi system (MTS) for identifying the optimum hardness profile to avoid failures. Design/methodology/approach – Through the usage profile analysis and comparison between the failed and good shafts, the major reason for shaft failure was identified as hardness variation. Then MTS approach was used to identify the optimum hardness profile for the shafts. An experiment was designed with power, feed and the gap between inductor and quench ring representing the heat transfer rate, heat removal rate and the time between heat transfer and removal of induction hardening process as factors. Based on experimental results, the optimum combination factors that would reduce the variation around the optimum hardness profile were identified. Findings – The study showed that the shaft failures can be reduced by optimizing the hardness profile of the shafts rather than warning customers on overloading, changing the raw material or investing on machining operation to achieve better shaft finish. The study suggested heat transfer rate, heat removal rate and the time between heat transfer and removal had significant impact on the shaft's hardness profile. The study resulted in reducing the field failures from 0.32 to 0.029 percent. Practical implications – This study provides valuable information on how to identify optimum hardness profile using MTS methodology to reduce shaft failures and how to minimize the variation around the optimum hardness profile using design of experiments. Originality/value – To the best of author's knowledge, no study has been conducted to identify optimum hardness profile using MTS methodology. The study also provides an approach to minimize the variation around a non-linear hardness profile using design of experiments.
Purpose This paper is a case study on the successful application of Six Sigma methodology in the information technology industry. The purpose of this paper is to improve the resolution time performance of an application support process. Design/methodology/approach Through brainstorming, the potential factors influencing the resolution time are identified. From the potential factors, the important factors, namely, day-wise ticket volume, team’s software engineering skill and domain expertise are shortlisted using test of hypothesis, correlation, etc. Then a model is developed using principal component regression, linking the critical to quality characteristic with the root causes or important factors. Finally, a solution methodology is developed using the model to obtain the team composition and size with optimum software skill and domain expertise to resolve the tickets within the required time. Findings The implementation of the solution resulted in improving the process performance significantly. The process performance index increased from 0.00 to 1.2 and parts per million reduced from 501366.31 to 153. 33. Practical implications The software engineers can use the similar approach to improve the performance of core software activities such as coding, testing and bug fixing. The approach can also be used for improving the performance of other skill-based operations such as error reduction in medical diagnostics. Originality/value This is one of the rare Six Sigma case studies on improving skill-based processes such as software development. The study also demonstrates the usefulness of the Six Sigma methodology for solving dynamic problems whose solution needs to be continuously adjusted with the changes in the input or process conditions.
Purpose The purpose of this paper is to develop an integrated engineering process control (EPC)–statistical process control (SPC) methodology for simultaneously monitoring and controlling autocorrelated multiple responses, namely, brightness and viscosity of the pulp bleaching process. Design/methodology/approach The pulp bleaching is a process of separating cellulose from impurities present in cooked wood chips through chemical treatment. More chemical dosage or process adjustments may result in better brightness but adversely affect viscosity. Hence, the optimum chemical dosage that would simultaneously minimize the deviation of pulp brightness and viscosity from their respective targets needs to be determined. Since the responses are autocorrelated, dynamic regression is used to model the responses. Then, the optimum chemical dosage that would simultaneously optimize the pulp brightness and viscosity is determined by fuzzy optimization methodology. Findings The suggested methodology is validated in 12 cases. The validation results showed that the optimum dosage simultaneously minimized the variation in brightness and viscosity around their respective targets. Moreover, suggested solution has been found to be superior to the one obtained by optimizing the responses independently. Practical implications This study provides valuable information on how to identify the optimum process adjustments to simultaneously ensure autocorrelated multiple responses on or close to their respective targets. Originality/value To the best of the authors’ knowledge, this paper is the first to provide application of the integrated EPC–SPC methodology for simultaneously monitoring multiple responses. The study also demonstrates the application of dynamic regression to model autocorrelated responses.
The powder coating is an economic, technologically superior and environment friendly painting technique compared with other conventional painting methods. However large variation in coating thickness can reduce the attractiveness of powder coated products. The coating thickness variation can also adversely affect the surface appearance and corrosion resistivity of the product. This can eventually lead to customer dissatisfaction and loss of market share. In this paper, the author discusses a dual response surface optimization methodology to minimize the thickness variation around the target value of powder coated industrial enclosures. The industrial enclosures are cabinets used for mounting the electrical and electronic equipment. The proposed methodology consists of establishing the relationship between the coating thickness & the powder coating process parameters and developing models for the mean and variance of coating thickness. Then the powder coating process is optimized by minimizing the standard deviation of coating thickness subject to the constraint that the thickness mean would be very close to the target. The study resulted in achieving a coating thickness mean of 80.0199 microns for industrial enclosures, which is very close to the target value of 80 microns. A comparison of the results of the proposed approach with that of existing methodologies showed that the suggested method is equally good or even better than the existing methodologies. The result of the study is also validated with a new batch of industrial enclosures.
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