Kevin Linderman

Six Sigma: a goal-theoretic perspective

Abstract Six Sigma is a phenomenon that is gaining wide acceptance in industry, but lacks a theoretical underpinning and a basis for research other than “best practice” studies. Rigorous academic research of Six Sigma requires the formulation and identification of useful theories related to the phenomenon. Accordingly, this paper develops an understanding of the Six Sigma phenomena from a goal theoretic perspective. After reviewing the goal theory literature, these concepts, when applied to Six Sigma, suggest some propositions for future research. This paper can help serve as a foundation for developing scientific knowledge about Six Sigma.

The Role of Culture as Driver of Quality Management and Performance: Infrastructure Versus Core Quality Practices*

Increasingly, scholars and practitioners recognize the importance of understanding organizational culture when implementing operations management practices. This study investigates the relationships among organizational culture, infrastructure and core quality management practices, and manufacturing performance using two alternative models. Understanding these relationships is important because culture can provide insight into the context dependence of quality management practices and shed light on the mixed results of past studies concerning the link between quality management and performance. Analysis of manufacturing plants from six countries indicates that organizational culture has a stronger influence on infrastructure quality management practices than on core quality management practices, regardless of whether the plants are located in Eastern or Western countries. In addition, infrastructure quality management practices have a significant effect on manufacturing performance. These results contribute to the quality management literature by emphasizing the importance of accounting for culture when making decisions to implement quality management practices to achieve a performance advantage. Finally, we also contribute to the literature on the culture–performance linkage by finding support for a direct link between culture and manufacturing performance.

In pursuit of implementation patterns: the context of Lean and Six Sigma

Combining Lean practices with Six Sigma has gained immense popularity in recent years. Whether a combined Lean-Six Sigma approach is the latest management fad, or leads to significant performance benefits that exceed isolated implementation is not yet apparent. Using implementation and performance data from a sample of 2511 plants, the research study attempts to uncover associative and predictive pattern of implementation between 15 Lean practices and the Six Sigma program. Our results indicate two major findings. First, implementation of any practice from a broader set of Lean practices improves the likelihood of implementing Six Sigma. Additionally, practices commonly bundled under quality management predict and distinguish the group of plants implementing Six Sigma extensively from non-implementers. Second, the regression results indicate a significant difference in the performance levels of the Six Sigma implementers group compared with the non-implementer group. These preliminary results are a first step towards separating fact from fiction.

Method and Psychological Effects on Learning Behaviors and Knowledge Creation in Quality Improvement Projects

This study investigates two mechanisms of knowledge creation---one that is method driven and the other that is psychologically driven. Results show that the two mechanisms have different effects on the learning behaviors and knowledge created in Six Sigma projects. The method mechanism directly influences learning behaviors, while the psychological mechanism directly affects knowledge creation. The effects of both mechanisms on knowledge creation are complementary, yet independent. Findings suggest that the value of adhering to a method may lie in modifying the learning behaviors that subsequently create knowledge. When a firm adopts a quality program such as Six Sigma, the method and the degree of its adherence can shape how the firm innovates and creates knowledge.

An integrated systems approach to process control and maintenance

Abstract Organizational leaders increasingly recognize process management as an essential element in organizational performance. Two key tools for process management––Statistical Process Control and Maintenance Management––can create profound economic benefits, particularly when they are coordinated. This paper demonstrates the value of integrating Statistical Process Control and maintenance by jointly optimizing their policies to minimize the total costs associated with quality, maintenance, and inspection. While maintenance is often scheduled periodically, this analysis encourages “adaptive” maintenance where the maintenance schedule accelerates when the process becomes unstable. This paper presents a number of models to demonstrate the economic behavior and value of coordinating process control and maintenance. Finally, a sensitivity analysis is conducted to develop insights into the economic and process variables that influence the integration efforts.

Economic and economic statistical designs for MEWMA control charts

This paper proposes methods for determining the optimal choice of design parameters (including control limits, sample size, exponential weights for past observations, and sampling interval) for the multivariate exponentially weighted moving average (MEWMA) control chart. Extending the Lorenzen-Vance flexible cost model (Lorenzen and Vance (1986)) to develop economic designs for MEWMA control chart parameters, we then add statistical constraints to obtain economic statistical designs. The choice of parameters is dependent on the average run length (ARL) when the process is in control and out of control. Evaluating the ARL values for the MEWMA chart through simulation, we determine optimal chart parameters given cost information. Results are presented on model sensitivity (in terms of expected cost and out-of-control ARL) to misspecification of the size of the shift in the process mean vector. We also consider the impact of perturbing the sampling interval on expected cost.

A Knowledge Framework Underlying Process Management

Organizations are increasingly implementing process-improvement techniques like Six Sigma, total quality management, lean, and business process re-engineering to improve organizational performance. These techniques are part of a process management system that includes the organizational infrastructure to support the improvement techniques. The knowledge-based view of a firm argues that organizational knowledge is the source of competitive advantage. To the extent that the process management system enables knowledge creation it should be a source of competitive advantage. This study investigates the underlying framework and factors of a process management system that lead to organizational knowledge creation. Prior studies have considered knowledge creation in process improvement, but none have considered the role of the process management system. Specifically, the study uses the case study method to investigate multiple levels (organization level and project level) of two firms using Six Sigma as their chosen process management system. Analysis of the cases reveals that the leadership creates a supportive infrastructure enabling process-improvement techniques to effectively create organizational knowledge. Interestingly, focusing on decision-making tools and methods may not be effective without developing a supportive infrastructure. The proposed framework provides a basis for organizational leaders to think about how to design and implement a process management system to better enable knowledge creation in organizations. Subject Areas: Case study, Knowledge creation, Process Management System, and Six Sigma.

Robust economic control chart design

Designing a control chart involves making fundamental decisions about the control chart parameters. Traditionally, practitioners select design parameters using ad hoc procedures. Academic research has been challenging this tradition by introducing a more rigorous criterion for selecting the design parameters based on economic criteria. However, there has been limited success in implementing economic designs. This research develops the concept of Robust Economic Design of control charts where multiple economic and process scenarios are considered in control chart design. By developing a robust optimization technique for economic control chart design, we hope to promote a better understanding of industrial implementation of economic designs of control charts. The effectiveness of this technique is illustrated through examples and a sensitivity analysis.

Process management, innovation and efficiency performance: The moderating effect of competitive intensity

Purpose – Much of the practitioner literature touts the universal benefits of process management and its impact on operational performance. However, in academic literature, empirical evidence is mixed. The purpose of this study is to investigate the role of the competitive intensity on the effectiveness of process management. Design/methodology/approach – Survey data from manufacturing plants were collected from through a global research project. Regression analysis was used to test hypotheses. Findings – The influence of process design on efficiency and innovation performance is not dependent on competitive intensity; however, the impact of process improvement and process control on efficiency and innovation performance is in some instances moderated by competitive intensity. Research limitations/implications – The inclusion of competitive intensity as a contingency variable helps to explain the contextual impact of process management on efficiency and innovation. Practical implications – Process managem...

Customizing Quality Management Practices: A Conceptual and Measurement Framework

Quality management (QM) has often been promoted as a universal remedy, where organizations adopt these practices to enhance performance. However, implementation of QM has led to mixed results with some high-profile failures. Some suggest that customizing QM practices to fit the organizations situational context can help avoid implementation failure and improve performance. However, research has not fully investigated how organizations should go about customizing quality practices. This article addresses this question by conceptualizing two fundamental yet different aspects of QM practices that have different learning objectives: quality exploitation (QEI) and quality exploration (QER). Drawing on experts and empirical data, we develop a reliable and valid set of measures for QEI and QER. Furthermore, the analysis shows the performance differences in the two sets of QM practices across different contextual settings. Specifically, the empirical results show that the benefits of different QM orientations depend on the level of competition and rate of product change. This research challenges prior conceptualizations of QM, and suggests a practical framework to guide decision makers in customizing QM practices.