Six Sigma for Organizational Excellence

Six Sigma is a disciplined problem-solving method involving people management and stakeholder participation. The approach relies heavily on advanced statistical methods that complement the process and product knowledge to reduce variation in processes. Six Sigma as a quality improvement tool advocates the practice of measuring variability of a process, which may then be controlled by continuous improvement. We have critically examined various quality perceptions and definitions. The ultimate objective of Six Sigma philosophy is to deliver the expected customer satisfaction in a given time frame without harming the organizational environment. Many implementation tools including DMAIC, DMADV, DFSS, and kaizen are discussed in details. Apart from the above tools, some of the tools used in DMADV that are not covered in DMAIC are as follows: multi-generational project plans (MGPP), analytical hierarchy process (AHP), Kano analysis, KANSEI Engineering, TRIZ, Pugh analysis, Taguchi optimization, Capability Maturity Model Integration (CMMI), value stream mapping (VSM), and simulation methods. The chapter is organized as per the tasks and deliverables of Six Sigma. A brief discussion about the belt systems is also presented in this chapter.

A project is a temporary endeavor to achieve some specific objectives in a defined time, and it varies in size and duration. A project involves many processes, and each such process progresses with specific objectives. A project management is therefore a dynamic process that utilizes the appropriate resources of the organization in a controlled and structured manner to achieve some clearly defined objectives, identifies as strategic needs conducted within a defined set of constraints. The necessity of carrying out SWOT analysis, the importance of brainstorming, the necessity of planning a project, etc. are visualized through a Six Sigma project. Various phases of project management and the activities involved in each are critically examined in this chapter. The importance of stakeholder participation, customer-centric product realization, process-based measurement, model-based project, etc. is thoroughly studied for better understanding of a Six Sigma project. We have also studied the project risk assessment and provided risk-ranking matrix. The two critical evaluation of a project, namely critical path method and project (or program) evaluation and review technique (PERT) are also explained through an example.

This chapter typically addresses the issues of process as the core of any project management. The project components such as inputs, process, and outputs are clearly stated technically as well as managerial way. These identifications further give impetus to the development of Six Sigma process, as the core objective of a Six Sigma process is to enable companies to monitor and respond to the feedback from its processes, suppliers, employees, customers, and competitors to achieve higher levels of strength and performance. We have also centered our attention to process mapping and addressed the issue of eliminating variation in the process. The importance of critical success factors, documentation of process activities, process characterization, process capability, process performance, process improvement, and process Control is clearly presented from a Six Sigma project perspective.

Variation is inherent in any process-based project. Hence, the ultimate aim of a Six Sigma project is to eliminate variation and waste through continuous improvement. The main two sources of variation are the assignable causes of variation and the random or common causes of variation. The assignable causes of variations are the result of physical in nature, can result through man, machine, materials, management, methods, procedures, etc., and are generally able to control and eliminate. The random causes of variation are generally an effect of environment and situation specific, and therefore, eliminating complete variation from a process is impossible. The chapter also discusses the necessity of measuring variation and the importance of having a good measurement system in place. The importance of normal distribution in statistical study is emphasized from the process variation point of view. Various measures of variation are also studied to support the understanding of the basics of a processed data.

In the previous chapter, we have seen the importance of a process and variation associated with a process. The necessity of estimating standard deviation is always an important aspect in statistical study including variation estimation, sample size determination; control charts preparations, and sigma-level estimations. The importance of sigma estimation is the central part of Six Sigma study where process improvements are usually talked in terms of sigma level and its estimation. Various methods such as sample range and sample standard deviations have been used to estimate standard deviation. The obvious limitation of all these methods is their natural sensitivity regarding normality assumption of sample observations. In this chapter, we provide some robust estimates which are computationally simple and can be implemented as per the requirement in practical problems.

One of the questions most frequently asked of a statistician is: How big should the sample be? Managers are anxious to obtain an answer to this fundamental question during the planning phase of the survey since it impacts directly on operational considerations such as the number of interviewers required. There is no magical solution and no perfect recipe for determining sample size. It is rather a process of compromise in which the precision requirements of the estimates are weighed against various operational constraints such as available budget, resources, and time. In this chapter, we revisit to the estimate of sample size for various project characteristics. Tables and examples for each are supported numerically.

This chapter begins with the first phase of Six Sigma project. The project charter, problem statement, and goal statement are some of the essential characteristics of a Six Sigma project. The definition of SIPOC model and preparing high-level process mapping are presented here. The team roles and responsibilities, the process of managing a team, and various planning tools are discussed in length. The importance of Gantt charts, affinity diagram, process map, and flowchart are also mentioned in this chapter. One of the important techniques for documenting overall design logic is quality function deployment (QFD), which is a method for translating customer requirements into an appropriate company program and technical requirements at each phase of the product realization cycle. This and other requirements that impact customer satisfaction such as Kano model, Hoshin Kanri process, Modular Function Deployment are also presented in detail for a better understanding of project.

This chapter introduces the critical-to-quality parameters that have the biggest impact on the customer and the business directly. It also facilitates in understanding the long-term variabilities in each of the quality characteristics associated with a Six Sigma project. Process mapping, VA/NVA assessment, data collection plan, gage R&R, check sheets are some of the techniques through which a good measurement system can be achieved. A detailed study of gage R&R for attributes and variables are presented with examples for understanding the reliability and predictability of the process. Both voice of process (VOP) and voice of customer (VOC) are critically examined for identification of value-added and non-value-added activities. A detailed discussion on data collection plan and its importance in explorative data analysis is discussed in detail here. The importance of descriptive statistics, probability statistics, and confirmatory analysis is detailed with illustrations and graphical studies in this chapter. We have also focused our attention to the 7-QC tools, considered to be the pillar of any statistical decision making in business and trade. This chapter ends with the discussion on capability studies and performance evaluation of a process.

The essence of any Six Sigma project is the analysis part of measured data. Most of the analytical tools are presented and illustrated in this chapter. This includes the discussion on parametric and nonparametric inferences. The parameter estimation and confidential estimation are carried out for various process characteristics. Similar attempt is made on hypothesis testing of parameters and goodness-of-fit of the models. The technique used to test hypothesis for a multitude of parameters relating to population means known as the analysis of variance is also studied in detail. Further, the modeling relationship between variables is studied through correlation and regression analysis, which includes both linear and nonlinear models. Some of the management tools such as root cause analysis, fault tree analysis (FTA), and 5-Why’s techniques essential for deciding the critical-to-quality parameters are also a part of this chapter.

Data stand at the forefront of any statistical decision making. Improving the quality of data is as good as improving overall quality of the process. This is ascertained through a clearly stated operating procedure of the process and knowledge of key performance indicators that work for the process. The techniques of making data error free, systematizing the information flow and in the process, reducing wastes, etc., will be the primary objective of data quality. Many valuable tools for improving the data quality are presented in this chapter. The practical importance of designed experiments and robust designs are thoroughly examined here. The three principles of experimentation, namely replication, randomization, blocking, or local control, help the experimenter to decide the appropriate data that need to be collected, and the design to be used for finding the optimum level of the process. The necessity of complete, partial fractional designs, and robust design are also stressed here. It is expected that for all inferential studies, the data should follow a normal distribution. In the absence of this, the inferences drawn from the data may not be reliable. This problem is explained through the use of normalization, standardization, and stabilization techniques for a processed data. Another common problem seen in statistical research and management is to suggest a best model for a given data. Among the choice of many competing models, how to decide the best is even more crucial for researchers. This is addressed through three methods in this chapter, and they include a parametric, nonparametric, and the simulation techniques.

The statistical process-control techniques are generally employed with considerable effectiveness for monitoring quality and surveillance of the process. Hence, examining various dimensions of a process is important. The dimensions of quality perceived in any process are performance, reliability, durability, serviceability, aesthetics, features, perceived quality, and conformance to standards. The statistical process control (SPC) then works on these dimensions either individually or combined. The process control can be very well monitored using control chart techniques. The control chart technique is explained in detail for both variable and attributes characteristics in this chapter. The chapter also includes the continuous monitoring of process using moving range chart and cumulative sum control charts. Apart from this, a detailed discussion on nonparametric control chart and its application in industrial applications that do not adhere to any parametric assumptions are also studied in detail. The importance of mistake proofing, standardization, and process dashboards are also given with a focus on standard operating procedures and work instructions.

The sigma level estimation sets the base for improvement for any process. After setting the goal, the Six Sigma process performance is evaluated in terms of its sigma level, and for the evaluation of sigma level, we need to have the knowledge of the specification limits and target of the process. The process performance is also seen in terms of its long-term and short-term performance. All this concepts are discussed in this chapter. The sigma level is usually calculated on the basis of process capability, which generally depends on the type of data under investigation. For attribute data system, the capability is calculated in terms of defects per million opportunities often called parts per million, whereas for continuous data system, the capability is defined in terms of defects under the curve and outside of the specification limits. An important performance dimension not captured by defects or sigma level is the cost of impact of defects, often called cost of poor quality (CoPQ). The CoPQ quantifies the money lost as the result of defects and problems. It is essentially the cost of the defect or problem that has been identified in the process. It also refers to the overall cost of whatever defects are present in the process. Hence, various types of costs associated with quality are taken up for discussion in this chapter.

The necessity of overall total quality management (TQM) is the essence of this chapter. Quality management is all about the systematic way of guaranteeing that organized activities happen the way they are planned. Hence, many old and new philosophies of quality management are discussed here. Among all, the Deming’s plan–do–check–act, which describes the basic logic of data-based project management and Crosby’s principle of “doing it right the first time,” is still relevant in organizations and institutions. Further, the quality trilogy philosophy of Juran and robust design of Taguchi are extensively used by managers globally for quality improvement. The cause-and-effect diagram introduced by Ishikawa is also used sporadically by all quality practitioners. This is all apart from the various systems used nationally and internationally. Many such quality improvement tools are included in this chapter.

Six Sigma is a flexible methodology that can be applied successfully to the out of production processes. Even though originated in manufacturing sector, this method has been successfully applied to other organizational areas such as accounting and finance, sales and marketing, information systems, and human resource management. In this era of economic slowdown, organizations expect high return on investment (ROI) from every business function. Productivity of sales and marketing processes has always remains questionable. In order to justify significance of sales and marketing process compared to other business process to generate significant ROI, structured approach is required that alters the way of looking toward sales and marketing from merely creative field to the structured process-based approach. Application of Six Sigma to sales and marketing provides channel to the creativity that results in high ROI. Different tools and techniques based on application of Six Sigma in marketing process have been discussed in this article with their key performance indicators (KPI) and performance evaluation matrices.

This article studies various data-based characteristics essential for creating a platform for greening the business and its processes. The Green Six Sigma (GSS) can be defined as the qualitative and quantitative assessment of the direct and eventual environmental effects of all processes and products of an organization. The activities involve the systematic usage of infrastructure and manpower, optimum use of technology, and accountability of sustainable business practices. Various green quality tools, measures, and indicators are discussed. The leading indicators are the indicator that precedes the occurrence of something. It is used to signal the upcoming occurrence of an event, whereas an indicator that follows the occurrence of something is called the lagging indicators. Some quality guidelines in the context of sustainable business practices are also discussed in this article.

This chapter is devoted to the discussion of the pros and cons of Six Sigma. In various sections, we have detailed the advantages and disadvantages, limitations, and pros and cons of Six Sigma philosophy. Enough care is taken to explain the concepts from a practitioners’ and customers’ point of view. A separate session on “future of Six Sigma” is also included in this chapter.

There are three case studies presented in this chapter. Two of the case studies are based on manufacturing industry and other last one based on a service based industry, where we have considered a case for improving customer service. All DMAIC-related process steps are clearly stated and implemented.