通常工程の状態は当初管理状態にあり,それが何らかの要因により管理状態から逸脱し,管理外れ状態に移行するものと考えられる.現工程の状態を判定する場合,現工程から得られたサンプル・データと併せて,これ以前に得られていたサンプル・データを十分に活用して現工程の状態を判定することは有意義な方法であるといえる.ここに,連続するサンプリングにおけるサンプル・データを活用し,工程状態を解析する方法として,CUSUM管理図やEWMA管理図といった時系列管理図法が存在する.ただし,従来の時系列管理図の検出特性の評価においては,時系列管理図の適用にあたって,適用当初から工程が管理外れ状態であった場合の検出特性が取り上げられている.これに対して,既述のように初期状態において管理状態にあった工程が何らかの要因によりある時点から管理外れ状態に移行する状況が一般的であると考えられる.そこで本研究では,まず工程の品質特性の管理状態の分布からの乖離の程度をKullback-Leibler情報量に基づき定量化し,これを打点統計量としそ工程の状態判定を行う(x,s)同時管理図をもとに,CUSUM管理図およびEWMA管理図といったいくつかの時系列管理図を設計する.さらに,管理図の適用当初から管理外れ状態にある状況のもとでの検出特性を評価する従来の研究とは別に,管理状態のままでいくらかの時間を経過した後に管理外れ状態に移行する状況を想定し,このもとでの管理外れ状態に対する検出特性を評価することを考える.くわえて,管理外れ状態を検出するばかりでなく,工程の管理状態からの逸脱期を特定することは品質管理・工程管理において重要な課題である.このことに鑑み,工程を時系列的に解析する上述の管理図をもとに,情報量規準に関する概念を応用し,どの時点において管理状態からの逸脱が生じたのかを解析的に識別する方法を提案する.

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‘Measurement is not an end in itself …’ might seem to be a paradoxical way of introducing a book about measurement. But measurement is important to the majority since it gives objective evidence on which to base decisions. The need for quality-assured measurement has evolved and widened over the centuries. Quality-assured measurement remains a topic of burgeoning and increasingly multidisciplinary interest.

Ĉpk is used as an estimate of process capability and can reflect performance degradation due to both shifts in the process mean and variability. Exact upper and lower confidence limits for the actual parameter value are elusive. Objections to existing estimators focus on two areas: the difficulty of executing the needed computations and the excessive widths of those confidence bounds. The CPE estimator (so called since it relies on Ĉp ) which we derive in this paper is formed by simply multiplying Ĉpk by a value from the inverse chi-square distribution. A PASCAL computer program is supplied which generates those percentiles. Our CPE is compared via simulation to other estimators and is shown to be the preferred selection in most cases by providing the requisite probability coverage and narrow interval widths. Copyright © 2001 John Wiley & Sons, Ltd.

task; therefore, experimental methods based on statistical algorithms of data processing are being developed now. Specific features of application of these algorithms are considered.

single sampling plan when the fraction of nonconforming items is a fuzzy number and being modeled based on the fuzzy Poisson distribution. We have shown that the operating characteristic (oc) curves of the plan is like a band having a high and low bounds whose width depends on the ambiguity proportion parameter in the lot when that sample size and acceptance numbers is fixed. Finally we completed discuss opinion by a numerical example. And then we compared the oc bands of using of binomial with the oc bands of using of Poisson distribution.

p-Charts and np-Charts are commonly used in monitoring variables of the fraction type and these charts assume that the monitored variables are binomially distributed. In this paper we propose a new control chart called Beta Charts, for monitoring fraction data (p). The Beta Chart presents the control limits based on the Beta probability distribution. It was applied for monitoring the variables in three real studies, and it was compared to the control limits with three schemes. The comparative analysis showed that: (i) Beta approximation to the Binomial distribution was more appropriate with values confined in the [0,1]-interval; and (ii) the charts proposed were more sensitive to the average run length (ARL), in both in-control and out-of-control processes monitoring. The Beta Charts outperform the control charts analyzed for monitoring fraction data.

of the Dissertation .................................................................................................ii Acknowledgements ............................................................................................................. iv Table of

model for the process of evaluating and combining preferences is here proposed. After determining the preferences according to each criterion, these partial preferences are combined into global ones. Different combination rules are set, in a probabilistic framework. Attention is centered to the case of indicators measured in different levels of aggregation.

iii LIST OF TABLES vii LIST OF FIGURES ix CHAPTER I INTRODUCTION 1 1.1. Construction of Xbar Charts Via Estimators 3 1.2. Evaluation Criteria 4 CHAPTER II Tasks Methods and Assumptions 8 2.1. Summary of tasks 8 2.2. Criteria used for comparing methods 8 2.3. Control Chart Development and use procedures 9 CHAPTER III Research Questions 12 CHAPTER IV Simulation Method and Details 14 CHAPTER V – Generation and Evaluation of Simulation Results 17 5.1 Overview 17 5.2 Results from Question 1 Simulations 17 5.3 Results from Question 2 Simulations 25 5.4 Results from Question 3 Simulations 29 CHAPTER VI Conclusions and Recommendations 36 6.1 General Considerations Concerning Application Environments 36 6.2 Recommendations for Environment 1 38 6.3 Recommendations for Environment 2 38 6.4 Recommendations for Environment 3 39

ii Dedication v Acknowledgements vi List of Tables ix List of Figures xi Chapter One Assembly Time Estimation Methods: A Review 1 1.1 Process Based Assembly Time Estimation 2 1.2 Product Based Assembly Time Estimation 11 Chapter Two Defining the Research Motivation 21 2.1 Research Questions 27 2.2 Research Roadmap 30 2.3 Exploratory Study 35 Chapter Three Interference Detection Method – Graph Generation 46 3.1 Demonstration of IDM 48 3.2 Interference Detection Method Graph Generation Test Cases 50 3.3 Comparison of Graph Generation Methods 53 Chapter Four Application of IDM During The Conceptual Design Stage 67 4.1 Set of Models 68 4.2 Reducing Model Fidelity 70 4.3 Artificial Neural Network Generation 71 4.4 Experimental Sets 72 4.5 Conceptual Model Time Estimate Results 74 4.6 Conclusions and Future Work 81 Chapter Five Testing of Interference Detection Method 83 5.1 Internal Testing – CEDAR Products 83

ii Acknowledgements iv List of Figures vii List of Tables viii List of Symbols ix List of Abbreviations xi Chapter 1 Introduction 1 Overview 1 1.1 Literature Review 2 1.2 Organization of the Thesis 3 1.3 Chapter 2 Linear Projection Techniques 4 Introduction 4 2.1 Principal Component Analysis 4 2.2 Factor Analysis 6 2.3 Missing Data Analysis 7 2.4 Chapter 3 Novelty Detection under Environmental Variability 9 Mahalanobis Distance as Novelty Metric 9 3.1 Mitigation of Environmental Effects using Linear Projection Techniques 10 3.2 3.2.1 Mitigation of Environmental Effects using Principal Component Projection 10 3.2.2 Mitigation of Environmental Effects using Factor Analysis 12 3.2.3 Mitigation of Environmental Effects using Missing Data Analysis 17 Chapter 4 Change Detection 19 Introduction 19 4.1 Overview on SPRT 19 4.2 CUSUM Chart 20 4.3 4.3.1 CUSUM Chart for Univariate Samples 20 4.3.2 CUSUM Chart for Multivariate Samples 26 Chapter 5 Application 30 Introduction 30 5.1

i Acknowledgements ii Table of contents iii List of figures vii List of tables xi Terminology xii Chapter 1: Introduction 1 1.1 Manufacturing SMEs: current challenges and issues 3 1.1.1 Packaging machinery manufacturers 4 1.2 Research aims and objectives 6 1.2.1 Aim of the research 6 1.2.2 Objectives 6 1.3 Overview of the thesis 8 Chapter 2: Product development: current state and issues in SMEs 12 2.1 Product development 13 2.1.1 Benefits of following a systematic product development process model 17 2.1.2 Types of design activities 18 2.2 Product development in SMEs 22 2.2.1 Important findings from the literature 24 2.3 Relevant techniques to improve product development 26 2.3.1 Standardisation 26 2.3.2 Modular design 26 2.3.3 Product platform development 26 2.3.4 Development of a product architecture 27 2.4 Application in SMEs 28 2.4.1 Challenges for SMEs 31

doi:10.1016/j.jtcvs.2004.03.022 I f surgical performance—often measured by postoperative outcome of initial hospital stay—is monitored at all, the most common means is by risk-adjusted annual or semiannual audit. Observed occurrence of outcome measures (eg, in-hospital death and complications) as a proportion of cases performed is compared with expected performance using, for example, the Society of Thoracic Surgeons’ regression equations or EuroSCORE, which account for many aspects of case mix. Sometimes observed (O) and expected (E) proportions are subtracted, sometimes divided (O/E ratio); sometimes confidence limits of these comparisons are provided, and occasionally P values are given. Is this periodic, widespread, but rather coarse monitoring of surgical performance sufficient?

detection of shifts in a process is not a new problem. In manufacturing, engineers have employed Statistical Quality Control (SQC) to detect shifts in key response variables of production equipment for over seventy years. These same control techniques can be applied to air traffic systems. In this paper, the FAA Engineering and Integration Services Branch (ACT-250) presents SQC charting techniques that perform better than existing simple threshold checking techniques for determining when an aircraft flight is laterally deviating from its flight plan. Background the goals of the Federal Aviation Administration's (FAA) Free Flight Phase One (FFP1) Program, advances in ground and airborne automation are required. The FAA has sponsored the development of two ground based air traffic management decision support tools (DSTs) to aid en route and terminal air traffic controllers. The User Request Evaluation Tool (URET), developed by the MITRE Center for Advanced Aviation System Development, facilitates the controller's management of en route air traffic by identifying potential air traffic conflicts. The Center TRACON Automation System (CTAS), developed by the NASA Ames Research Center, supports the controller in the development of arrival sequencing plans and the assignment of aircraft to runways to optimize airport capacity. A fundamental component of both URET and CTAS is the trajectory modeler, upon which the functionality provided by these tools is based. For example, URET uses aircraft trajectories to predict conflicts; CTAS uses its predicted trajectories to calculate meter fix crossing times. Thus, the deviation between the predicted trajectory and the actual path of the aircraft has a direct effect on the overall accuracy of the tool (5). Flights that deviate from their known cleared route cause the DST trajectory prediction to become less accurate (1)(2)(5). NASA and MITRE researchers have built heuristic algorithms to reduce these errors by estimating the aircraft's intent. These trajectories still produce significant deviations from the known route of flight and have considerably reduced accuracy. Therefore, improvements in the detection of these flight deviations

bone substitutes manufacturing is highly important due to a vast number casualties are stepped to the society as a result of mainly traffic accidents, natural disasters and civil wars. Though casualties are grouped into several categories, a considerable number of patients is fallen into the bone associated injuries. It is also notable that especially the traffic related accidents and natural disasters may occur in populated regions. Due to financial reasons all the hospitals in the developing countries cannot maintain sophisticated scanning equipments along with their software solutions. Therefore having a lightweight software solution that facilitates bone profiling will be beneficial for patients and it also helps surgeons to prepare a care plan depending on the disorder. However, the artificial tools that are inserted to the human body can vary upon the injury. Hence, they should be highly customizable. Though computerized 3D modeling started around two decades ago, a few tools are available to assist surgeons in such situations. The available applications and techniques have limited functionalities thus, the manufactured bone grafts may not perfectly be suited to the lesion or injury. In this paper we propose a minimally invasive procedures to model bone grafts. In which, quality control methods for noise removals and 3D data compression mechanisms are coupled to the software solution that runs even on typical personal computer systems. The end result of the 3D modeled bone can be employed to extract the cavity, clip regions of interest and even to test the manufactured bone graft before the surgical procedure. Thereby, the process of manufacturing the prosthetic and the clinical procedures will be efficient and reliable.

a production process should be measured or controlled by another measurable variable. The immeasurable quality characteristic is a function of the directly measurable variable. For this type of quality characteristic, if the tolerance design is unbalanced, or if the relationship between the measurable variable and immeasurable quality characteristic is nonlinear, setting the process mean at the target value of the measurable variable will not minimise the expected quality loss. In this paper, the optimal process mean of the measurable variable is determined such that the expected quality loss is minimised for the above conditions. The results are compared with those of a directly measurable variable.

a b s t r a c t This study examines whether a fish stock can be managed using cumulative sum (CUSUM) control charts if limited historical information is available for the fish stock. We use the term 'limited' in the sense of having a minimal number of historical observations of relevant stock indicators and their respective control means (or reference points) that are aligned with the objectives of fisheries management. In the present study, a Decision Interval Cumulative Sum (DI-CUSUM) control chart was used to monitor two indices from a simulated fishery; the recruitment indicator and the large fish indicator (LFI). The fishery was subsequently managed using a harvest control rule (HCR) that triggered only when a significant deviation in the indicator trend was detected by the DI-CUSUM. The HCR was constructed using methods adopted from engineering process control theory where the adjustment in total allowable catch was determined by estimating the size of the shift in the indicator time series. We found that monitoring a combined indicator of both recruitment and LFI was more successful in controlling the fishery irrespective of the initial state of the fish stock. We discuss how DI-CUSUM could be incorporated into the management process for data poor fisheries.

[This abstract is based on the author's abstract.] Ensuring the financial success of a new product often requires decreasing the amount of inspection needed as soon as processes become more stable and the quality levels of products improve. This is ..

[This abstract is based on the author's abstract.] Another method for estimating the process standard deviation is presented. The method is based on the Downton estimator, which is a highly efficient estimator proposed by Downton. The control lim..