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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.

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.

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.

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.

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.

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.

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.

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.

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.

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 purpose of this paper is to develop a control chart pattern recognition methodology for monitoring the weekly customer complaints of outsourced information technology-enabled service (ITeS) processes.

A two-step methodology is used to classify the processes as having natural or unnatural variation based on past 20 weeks' customer complaints. The step one is to simulate data on various control chart patterns namely natural variation, upward shift, upward trend, etc. Then a deep learning neural network model consisting of two dense layers is developed to classify the patterns as of natural or unnatural variation.

The validation of the methodology on telecom vertical processes has correctly detected unnatural variations in two terminated processes. The implementation of the methodology on banking and financial vertical processes has detected unnatural variation in one of the processes. This helped the company management to take remedial actions, renegotiate the deal and get it renewed for another period.

This study provides valuable information on controlling information technology-enabled processes using pattern recognition methodology. The methodology gives a lot of flexibility to managers to monitor multiple processes collectively and avoids the manual plotting and interpretation of control charts.

The application of control chart pattern recognition methodology for monitoring service industry processes are rare. This is an application of the methodology for controlling information technology-enabled processes. This study also demonstrates the usefulness of deep learning techniques for process control.

The purpose of this paper is to demonstrate the application of the control chart procedure to monitor the characteristics whose profile over time resembles a set of connected line segments.

Fit a regression spline model by taking the subgroup average of the characteristic as response variable and time as the explanatory variable. Then monitor the response variable using the regression spline control chart with the fitted model as center line and upper and lower control limits at three standard deviation units of the response variable above and below the center line.

The proposed chart is successfully deployed to monitor the daily response time profile of a client server of an application support process. The chart ensured the stability of the process as well as detected the assignable cause leading to the slowing down of the server performance.

The methodology can be used to monitor any characteristics whose performance profile over time resembles a set of connected line segments. Some of the examples are the consumption profile of utility providers like power distribution companies, usage profiles of telecom networks, loading profile of airline check-in process, e-commerce websites, etc.

To the best of the author’s knowledge, construction of control charts using regression spline is new. The usage of the control chart to monitor the performance characteristics which exhibits a nonlinear profile over time is also rare.

This study aims to investigate a reliability-level demand-oriented pharmaceutical supply chain design with maximal anticipated demand coverage. Different hospitals with the particular reliability value associated with the various pharmaceutical items (PIs) are considered. An inter-connected multi-period supply chain comprising manufacturers, distribution centers, hospitals and patients is assumed for the smooth flow of health-care items, enhancing supply chain reliability. A reliability index for PIs is depicted to highlight product preference and facilitate hospitals’ service levels for patients.

A mixed-integer multi-objective programming problem that maximizes maximal demand coverage minimizes the total economic costs and pharmaceutical delivery time is depicted under intuitionistic fuzzy uncertainty. Further, a novel interactive neutrosophic programming approach is developed to solve the proposed pharmaceutical supply chain management (PSCM) model. Each objective’s marginal evaluation is elicited by various sorts of membership functions such as linear, exponential and hyperbolic types of membership functions and depicted the truth, indeterminacy and falsity membership degrees under a neutrosophic environment.

The proposed PSCM model is implemented on a real case study and solved using an interactive neutrosophic programming approach that reveals the proposed methods’ validity and applicability. An ample opportunity to generate the compromise solution is suggested by tuning various parameters. The outcomes are evaluated with practical managerial implications based on the significant findings. Finally, conclusions and future research scope are addressed based on the proposed work.

The propounded study has some limitations that can be addressed in future research. The discussed PSCM model can be merged with and extended by considering environmental factors such as the health-care waste management system, which is not included in this study. Uncertainty among parameters due to randomness can be incorporated and can be tackled with historical data. Besides, proposed interactive neutrosophic programming approach (INPA), various metaheuristic approaches may be applied to solve the proposed PSCM model as a future research scope.

The strategy advised is to provide an opportunity to create supply chains and manufacturing within India by helping existing manufacturers to expand, identifying new manufacturers, hand-holding and facilitating, teams of officers, engineers and scientists deployed and import only if necessary to meet timelines. Thus, any pharmaceutical company or organization can adopt the production and distribution management initiatives amongst hospitals to strengthen and enable the pharmaceutical company while fighting fatal diseases during emergencies. Finally, managers or policy-makers can take advantage of the current study and extract fruitful pieces of information and knowledge regarding the optimal production and distribution strategies while making decisions.

This research work manifests the demand-oriented extension of the integrated PSCM design with maximum expected coverage, where different hospitals with pre-determined reliability values for various PIs are taken into consideration. The practical managerial implications are explored that immensely support the managers or practitioners to adopt the production and distribution policies for the PIs to ensure the sustainability in supply chain design.

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.

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.

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.

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.

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.

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.

This paper presents an application of Lean Six‐Sigma methodology for cycle time reduction in BPO organizations.

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.

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.

The purpose of this study is to demonstrate the variation between the set torque and the actual torque at which the actuator trips can be minimized using Taguchi's robust engineering methodology. The paper also aims to demonstrate the application of feature selection approach for the identification of insignificant effects in unreplicated fractional factorial experiments.

The methodology used was design of experiments with the set torque as the signal factor and the tripping torque as response variable. The compounded noise factor was identified based on the type of operations and load variation, which are not under the manufacturer's control. The effect of five control factors (with two levels each) and two interactions were studied. The experiments were designed using L8 orthogonal array.

The result showed that the factors spring height, spring thickness, star washer position and the interaction between drive shaft length and spring height play a significant role in actuator performance. The implementation of the optimum combination of factors resulted in improving the overall capability indices, Cp from 0.52 to 2.12 and Cpk from 0.4 to 1.67.

This study provides valuable information to actuator manufacturers on optimizing actuator performance.

To the best of the author's knowledge, no study has been conducted using Taguchi's robust engineering methodology to optimize actuator performance. In addition, no attempt has been made in the past to identify the insignificant factors and interactions using feature selection approach for unreplicated fractional factorial experiments.

This paper aims to examine the pedagogical strategies and challenges of selected multigrade (MG) schoolteachers in Albay, Philippines.

This study utilized a case study design using a validated semi-structured instrument with in-depth interviews to ten MG teachers, observation and document analysis. Qualitative data analyses such as content and thematic analyses were used in analyzing the data.

The results show that MG teachers use a variety of overlapping and supplementary instructional strategies such as (1) teaching one topic to all grades and at varying levels of difficulty using differentiated activity, (2) “jump-jump strategy”/teaching one grade while others work independently and (3) peer teaching or tutoring. However, there are several pedagogical challenges that MG teachers are faced with: (1) conflict between the medium of instructions and grade grouping, (2) problems with lesson planning, (3) unfocused instruction, (4) insufficient learners' materials, (5) absence of training prior to MG teaching and (6) the negative perception of the effectiveness of MG instructions.

The research is limited within the context of the participants and focuses on the pedagogical strategies and challenges in the implementation of MG teaching.

The results of this study can serve as a reference on how to support and strengthen MG instruction.

This study provides a research-based overview of MG schools from the grassroots level and adds to the limited studies of MG education in the Philippines.

Research is needed regarding how to influence young adults’ patterns of cell phone use while driving, amid social pressures to stay connected to their peers. Such insight could form the basis of a social marketing campaign. This study aims to explore the potential of inoculation and narrative messages as strategies to protect (i.e. generate resistance against) negative attitudes toward texting and driving.

Using a three-phase experiment, the investigation explored the impact of different communication message strategies (i.e. inoculation, narrative, control) aimed at reducing texting while driving.

Results indicated that, for college students exposed to messages in support of texting and driving, inoculation messages were superior to both narrative and control messages. These findings can guide the development of strategic social marketing interventions.

Social marketing scholars and practitioners should consider weaving inoculation messages throughout social marketing campaigns focused on this important issue.

To the authors’ knowledge, this is the first study to evaluate and compare inoculation and narrative strategies in the context of texting and driving.