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Articles 601 - 630 of 846

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Barriers And Best Practices For Material Management In The Healthcare Sector, Carlos Callender Jan 2007

Barriers And Best Practices For Material Management In The Healthcare Sector, Carlos Callender

Masters Theses

"For many years, the primary focus of the healthcare sector has been to provide patients with the best quality of care. Recently, with the escalating cost of supplies and the severe competition among healthcare providers, the pressure on material managers to operate more cost-efficiently without compromising the high patient care standards has significantly increased. While other sectors have experienced success through the deployment of supply chain management practices, the healthcare sector has not seen major improvements in this area. However, in spite of the uniqueness and complexity of the healthcare supply chain, opportunities for improvements are plentiful. Thus, this thesis …

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Integrated Product And Its Extended Enterprise Network Design Using Lean Principles, Abhijit K. Choudhury Jan 2007

Integrated Product And Its Extended Enterprise Network Design Using Lean Principles, Abhijit K. Choudhury

Masters Theses

"Recently, many system integration companies have begun to intensely focus on suppliers' involvement in the product realization process to enhance their competitiveness. The product architecture has a huge impact on the efficiency of the integrator's supplier network. One of the ways to enhance efficiency is to implement the lean principles while creating a collaborative design/manufacturing/supply chain environment. In this study, two distinct stages of the product realization process, namely, design stage and production stage are considered for determining the design and production supplier networks"--Abstract, page iv.

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The Development Of Hybrid Intelligent Systems For Technical Analysis Based Equivolume Charting, Thira Chavarnakul Jan 2007

The Development Of Hybrid Intelligent Systems For Technical Analysis Based Equivolume Charting, Thira Chavarnakul

Doctoral Dissertations

"This dissertation proposes the development of a hybrid intelligent system applied to technical analysis based equivolume charting for stock trading. A Neuro-Fuzzy based Genetic Algorithms (NF-GA) system of the Volume Adjusted Moving Average (VAMA) membership functions is introduced to evaluate the effectiveness of using a hybrid intelligent system that integrates neural networks, fuzzy logic, and genetic algorithms techniques for increasing the efficiency of technical analysis based equivolume charting for trading stocks"--Introduction, page 1.

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Security Architecture Methodology For Large Net-Centric Systems, Njideka Adaku Umeh Jan 2007

Security Architecture Methodology For Large Net-Centric Systems, Njideka Adaku Umeh

Masters Theses

"This thesis describes an over-arching security architecture methodology for large network enabled systems that can be scaled down for smaller network centric operations such as present at the University of Missouri-Rolla. By leveraging the five elements of security policy & standards, security risk management, security auditing, security federation and security management, of the proposed security architecture and addressing the specific needs of UMR, the methodology was used to determine places of improvement for UMR"--Abstract, page iii.

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Route Aware Predictive Congestion Control Protocol For Wireless Sensor Networks, Carl Larsen, Maciej Jan Zawodniok, Jagannathan Sarangapani Jan 2007

Route Aware Predictive Congestion Control Protocol For Wireless Sensor Networks, Carl Larsen, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Congestion in wireless sensor networks (WSN) may lead to packet losses or delayed delivery of important information rendering the WSN-based monitoring or control system useless. In this paper a routing-aware predictive congestion control (RPCC) yet decentralized scheme for WSN is presented that uses a combination of a hop by hop congestion control mechanism to maintain desired level of buffer occupancy, and a dynamic routing scheme that works in concert with the congestion control mechanism to forward the packets through less congested nodes. The proposed adaptive approach restricts the incoming traffic thus preventing buffer overflow while maintaining the rate through an …

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Near Optimal Neural Network-Based Output Feedback Control Of Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani Jan 2007

Near Optimal Neural Network-Based Output Feedback Control Of Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel online reinforcement learning neural network (NN)-based optimal output feedback controller, referred to as adaptive critic controller, is proposed for affine nonlinear discrete-time systems, to deliver a desired tracking performance. The adaptive critic design consist of three entities, an observer to estimate the system states, an action network that produces optimal control input and a critic that evaluates the performance of the action network. The critic is termed adaptive as it adapts itself to output the optimal cost-to-go function which is based on the standard Bellman equation. By using the Lyapunov approach, the uniformly ultimate boundedness …

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Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Jan 2007

Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate …

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Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Jan 2007

Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. …

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Online Reinforcement Learning Control Of Unknown Nonaffine Nonlinear Discrete Time Systems, Qinmin Yang, Jagannathan Sarangapani Jan 2007

Online Reinforcement Learning Control Of Unknown Nonaffine Nonlinear Discrete Time Systems, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel neural network (NN) based online reinforcement learning controller is designed for nonaffine nonlinear discrete-time systems with bounded disturbances. The nonaffine systems are represented by nonlinear auto regressive moving average with exogenous input (NARMAX) model with unknown nonlinear functions. An equivalent affine-like representation for the tracking error dynamics is developed first from the original nonaffine system. Subsequently, a reinforcement learning-based neural network (NN) controller is proposed for the affine-like nonlinear error dynamic system. The control scheme consists of two NNs. One NN is designated as the critic, which approximates a predefined long-term cost function, whereas an …

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Reinforcement Learning Based Output-Feedback Controller For Complex Nonlinear Discrete-Time Systems, Peter Shih, Jagannathan Sarangapani Jan 2007

Reinforcement Learning Based Output-Feedback Controller For Complex Nonlinear Discrete-Time Systems, Peter Shih, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex feedback nonlinear discrete-time systems in the presence of bounded and unknown disturbances. This nonlinear discrete-time system consists of a second order system in nonstrict form and an affine nonlinear discrete-time system tightly coupled together. Two adaptive critic NN controllers are designed - primary one for the nonstrict system and the secondary one for the affine system. A Lyapunov function shows the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates …

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Spatial Diversity In Signal Strength Based Wlan Location Determination Systems, Anil Ramachandran, Jagannathan Sarangapani Jan 2007

Spatial Diversity In Signal Strength Based Wlan Location Determination Systems, Anil Ramachandran, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Literature indicates that spatial diversity can be utilized to compensate channel uncertainties such as multipath fading. Therefore, in this paper, spatial diversity is exploited for locating stationary and mobile objects in the indoor environment. First, space diversity technique is introduced for small scale motion and temporal variation compensation of received signal strength and it is demonstrated analytically that it enhances location accuracy. Small scale motion refers to movements of the transmitter and/or the receiver of the order of sub-wavelengths while temporal effects refer to environmental variations with time. A novel metric is introduced for selection combining in order to improve …

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An Online Approximator-Based Fault Detection Framework For Nonlinear Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani Jan 2007

An Online Approximator-Based Fault Detection Framework For Nonlinear Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a fault detection scheme is developed for nonlinear discrete time systems. The changes in the system dynamics due to incipient failures are modeled as a nonlinear function of state and input variables while the time profile of the failures is assumed to be exponentially developing. The fault is detected by monitoring the system and is approximated by using online approximators. A stable adaptation law in discrete-time is developed in order to characterize the faults. The robustness of the diagnosis scheme is shown by extensive mathematical analysis and simulation results.

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Asymptotic Stability Of Nonholonomic Mobile Robot Formations Using Multilayer Neural Networks, Jagannathan Sarangapani, Travis Alan Dierks Jan 2007

Asymptotic Stability Of Nonholonomic Mobile Robot Formations Using Multilayer Neural Networks, Jagannathan Sarangapani, Travis Alan Dierks

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A multilayer neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as …

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Adaptive Power Control Protocol With Hardware Implementation For Wireless Sensor And Rfid Reader Networks, Kainan Cha, Jagannathan Sarangapani, David Pommerenke Jan 2007

Adaptive Power Control Protocol With Hardware Implementation For Wireless Sensor And Rfid Reader Networks, Kainan Cha, Jagannathan Sarangapani, David Pommerenke

Electrical and Computer Engineering Faculty Research & Creative Works

The development and deployment of radio frequency identification (RFID) systems render a novel distributed sensor network which enhances visibility into manufacturing processes. In RFID systems, the detection range and read rates will suffer from interference among high-power reading devices. This problem grows severely and degrades system performance in dense RFID networks. Consequently, medium access protocols (MAC) protocols are needed for such networks to assess and provide access to the channel so that tags can be read accurately. In this paper, we investigate a suite of feasible power control schemes to ensure overall coverage area of the system while maintaining a …

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Online Reinforcement Learning Neural Network Controller Design For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani Jan 2007

Online Reinforcement Learning Neural Network Controller Design For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel reinforcement learning neural network (NN)-based controller, referred to adaptive critic controller, is proposed for affine nonlinear discrete-time systems with applications to nanomanipulation. In the online NN reinforcement learning method, one NN is designated as the critic NN, which approximates the long-term cost function by assuming that the states of the nonlinear systems is available for measurement. An action NN is employed to derive an optimal control signal to track a desired system trajectory while minimizing the cost function. Online updating weight tuning schemes for these two NNs are also derived. By using the Lyapunov approach, …

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Performance Prediction Based Resource Selection In Grid Environments, Peggy Lindner, Edgar Gabriel, Michael M. Resch Jan 2007

Performance Prediction Based Resource Selection In Grid Environments, Peggy Lindner, Edgar Gabriel, Michael M. Resch

Engineering Management and Systems Engineering Faculty Research & Creative Works

Deploying Grid technologies by distributing an application over several machines has been widely used for scientific simulations, which have large requirements for computational resources. The Grid Configuration Manager (GCM) is a tool developed to ease the management of scientific applications in distributed environments and to hide some of the complexities of Grids from the end-user. In this paper we present an extension to the Grid Configuration Manager in order to incorporate a performance-based resource brokering mechanism. Given a pool of machines and a trace file containing information about the runtime characteristics of the according application, GCM is able to select …

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Use Of Analytical Hierarchy Process In University Strategy Planning, Mihir Gokhale Jan 2007

Use Of Analytical Hierarchy Process In University Strategy Planning, Mihir Gokhale

Masters Theses

"The selection of an appropriate strategy is critical for a university's success, and each university needs to capitalize on its own specialties and competencies for a competitive advantage. Strategy creation and planning in universities is generally a collective effort which relies on consensus. It is a complex process involving the setting of objectives and goals to achieve the strategic vision, and an analysis tool for evaluating and comparing different options and prioritizing objectives and goals is required. A high deductive capacity is necessary for aggregating the different trade-offs while prioritizing, which is challenging for a human mind. This thesis demonstrates …

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Bridge Damage Detection Using An Intelligent Engineering System, Dionysios N. Danilatos Jan 2007

Bridge Damage Detection Using An Intelligent Engineering System, Dionysios N. Danilatos

Masters Theses

"This thesis concerns the design of an algorithm that is capable to detect structural damage in civil infrastructure bridges. The algorithm, which will be dubbed Damage Diagnostics System throughout the thesis, is the software component of a broader Bridge Health Monitoring System. This broader system integrates software and hardware,such as sensors and data acquisition components...The rationale for the Structural Damage Diagnosis is based on the principle of the structural vibration testing. The Health Monitoring System captures the vibration signals, as the bridge responds to excitation from various sources. The purpose of the Diagnostic System is to extract information from the …

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An Indirect Loss Estimation Methodology To Account For Regional Earthquake Damage To Highway Bridges, Chakkaphan Tirasirichai Jan 2007

An Indirect Loss Estimation Methodology To Account For Regional Earthquake Damage To Highway Bridges, Chakkaphan Tirasirichai

Doctoral Dissertations

"This study proposes an integrated framework to estimate the indirect economic loss due to damaged bridges within the highway system from an earthquake event. The framework is designed to be general and convenient to apply to other study regions. In this dissertation, a simulated earthquake scenario centered in St. Louis Missouri with a magnitude 7.0 was used as a case study. The research results have clearly shown that the indirect losses are significant when compared to the direct loss. Policymakers can apply this study framework and the results as a guide and decision tool for developing an appropriate preventive action …

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Quality Loss Function - Common Methodology For Nominal-The-Best, Smaller-The-Better, And Larger-The-Better Cases, Naresh Kumar Sharma, Kenneth M. Ragsdell Jan 2007

Quality Loss Function - Common Methodology For Nominal-The-Best, Smaller-The-Better, And Larger-The-Better Cases, Naresh Kumar Sharma, Kenneth M. Ragsdell

Engineering Management and Systems Engineering Faculty Research & Creative Works

The quality loss function developed by Dr. Genichi Taguchi considers three cases including nominal-the-best, smaller-the-better, and larger-the-better. the methodology used to deal with the larger-the-better case is slightly different from that for the smaller-the-better and nominal-the-better cases. This paper attempts to bring about similarity among the three cases by introducing a term called the "target-mean ratio" and proposing a common formula for all three cases. the "target-mean ratio" can take different values to represent all three cases to bring about consistency and simplify the model. Also, it eliminates the assumption of target performance at infinite level and brings the model …

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Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes Jan 2007

Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents two neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control signals are calculated using local signals only, the transient and overall system stabilities can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system and the interconnection terms, thus the requirements for exact system parameters are released. Simulation studies with a three machine power system demonstrate the effectiveness of the proposed controller designs.

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A Comparison Of Techniques To Forecast Consumer Satisfaction For Vehicle Ride, Elizabeth A. Cudney, David Drain, Kenneth M. Ragsdell, Kioumars Paryani Jan 2007

A Comparison Of Techniques To Forecast Consumer Satisfaction For Vehicle Ride, Elizabeth A. Cudney, David Drain, Kenneth M. Ragsdell, Kioumars Paryani

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents a comparison of methods for the identification of a reduced set of useful variables using a multidimensional system. the Mahalanobis-Taguchi System and a standard statistical technique are used reduce the dimensionality of vehicle ride based on consumer satisfaction ratings. the Mahalanobis-Taguchi System and cluster analysis are applied to vehicle ride. the research considers 67 vehicle data sets for the 6 vehicle ride parameters. This paper applies the Mahalanobis-Taguchi System to forecast consumer satisfaction and provides a comparison of results with those obtained from a standard statistical approach to the problem. Copyright © 2007 SAE International.

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Identifying Useful Variables For Vehicle Braking Using The Adjoint Matrix Approach To The Mahalanobis-Taguchi System, Elizabeth A. Cudney, Kenneth M. Ragsdell, Kioumars Paryani Jan 2007

Identifying Useful Variables For Vehicle Braking Using The Adjoint Matrix Approach To The Mahalanobis-Taguchi System, Elizabeth A. Cudney, Kenneth M. Ragsdell, Kioumars Paryani

Engineering Management and Systems Engineering Faculty Research & Creative Works

The Mahalanobis Taguchi System (MTS) is a diagnosis and forecasting method for multivariate data. Mahalanobis distance (MD) is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. MTS is of interest because of its reported accuracy in forecasting small, correlated data sets. This is the type of data that is encountered with consumer vehicle ratings. MTS enables a reduction in dimensionality and the ability to develop a scale based on MD values. MTS identifies a set of useful variables from the complete data set …

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Technology And Competence Alignment To The Roadmap, Mihir M. Gokhale, Donald D. Myers Jan 2007

Technology And Competence Alignment To The Roadmap, Mihir M. Gokhale, Donald D. Myers

Engineering Management and Systems Engineering Faculty Research & Creative Works

Roadmaps serve as a useful graphical tool to integrate strategic objectives, technology-assessment, technology-road mapping and product road mapping against an axis of time. Much research has been done related to linking specific technologies with the product roadmap. It is equally important to understand how the sourcing of this technology can be done. This leads to the discussion of competence development to meet the technology gap. the paper describes the alignment of this technology and competence with the overall roadmap. © 2008 IEEE.

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Neural Network Control Of Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks Jan 2007

Neural Network Control Of Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as opposed …

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Architecting System Of Systems: Artificial Life Analysis Of Financial Market Behavior, Nil Hande Ergin Jan 2007

Architecting System Of Systems: Artificial Life Analysis Of Financial Market Behavior, Nil Hande Ergin

Doctoral Dissertations

"This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as …

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Understanding Behavior Of System Of Systems Through Computational Intelligence Techniques, Nil H. Kilicay, Cihan H. Dagli Jan 2007

Understanding Behavior Of System Of Systems Through Computational Intelligence Techniques, Nil H. Kilicay, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The world is facing an increasing level of systems integration leading towards systems of systems (SoS) that adapt to changing environmental conditions. The number of connections between components, the diversity of the components and the way the components are organized can lead to different emergent system behavior. Therefore, the need to focus on overall system behavior is becoming an unavoidable issue. The problem is to develop methodologies appropriate for better understanding behavior of system of systems before the design and implementation phase. This paper focuses on computational intelligence techniques used for analysis of complex adaptive systems with the aim of …

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Adaptive Neural Network Based Stabilizing Controller Design For Single Machine Infinite Bus Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani, Mariesa Crow Jan 2007

Adaptive Neural Network Based Stabilizing Controller Design For Single Machine Infinite Bus Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani, Mariesa Crow

Engineering Management and Systems Engineering Faculty Research & Creative Works

Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. In power system control literature, the performances of the proposed controllers were mostly demonstrated using simulation results without any rigorous stability analysis. This paper proposes a stabilizing neural network (NN) controller based on a sixth order single machine infinite bus power system model. The NN is used to approximate the complex nonlinear dynamics of power system. Unlike the other indirect adaptive NN control schemes, there is no offline training process and the NN can be directly …

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An Evaluation Of Mahalanobis-Taguchi System And Neural Network For Multivariate Pattern Recognition, Jungeui Hong, Rajesh Jugulum, Kioumars Paryani, K. M. Ragsdell, Genichi Taguchi, Elizabeth A. Cudney Jan 2007

An Evaluation Of Mahalanobis-Taguchi System And Neural Network For Multivariate Pattern Recognition, Jungeui Hong, Rajesh Jugulum, Kioumars Paryani, K. M. Ragsdell, Genichi Taguchi, Elizabeth A. Cudney

Engineering Management and Systems Engineering Faculty Research & Creative Works

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis- Taguchi System and a neural-network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

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Effective Use Of Process Capability Indices For Supplier Management, Elizabeth A. Cudney, David Drain Jan 2007

Effective Use Of Process Capability Indices For Supplier Management, Elizabeth A. Cudney, David Drain

Engineering Management and Systems Engineering Faculty Research & Creative Works

Process capability indices were originally invented to enable an organization to make economically sound decisions for process management. Process capability is a comparison of the voice of the process with the voice of the customer. Current practice is to use Cp and Cpk regardless of the validity of the underlying assumptions necessary for their use. Even if all necessary assumptions are satisfied, important problems can be missed if these indices are the sole process evaluation examined. Customer-supplier axioms are introduced to motivate more useful process evaluations and foster long-term harmonious relationships. This paper explores the alternative capability indices Cpm, Cpmk, …

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