Design for Quality Using Response Surface Methods: An Alternative to Taguchi's Parameter Design Approach

ABSTRACT

This article presents the response surface methodology as an alternative approach to Taguchi's parameter design methods for optimizing designs for quality. The method is briefly explained, and its application is illustrated by an example of a preliminary design study of an advanced space transportation vehicle. The results indicate that the response surface methodology is a systematic and efficient approach that can help engineering managers design for quality, performance, and cost.     

The weighted priors approach for combining expert opinions in logistic regression experiments

ABSTRACT

When modeling the reliability of a system or component, it is not uncommon for more than one expert to provide very different prior estimates of the expected reliability as a function of an explanatory variable such as age or temperature. Our goal is to incorporate all information from the experts when choosing a design about which units to test. Bayesian design of experiments has been shown to be very successful for generalized linear models, including logistic regression models. We use this approach to develop methodology for the case where there are several potentially non-overlapping priors under consideration. While multiple priors have been used for analysis in the past, they have never been used in a design context. The Weighted Priors method performs well for a broad range of true underlying model parameter choices and is more robust when compared to other reasonable design choices. We illustrate the method through multiple scenarios and a motivating example. Additional figures for this article are available in the online supplementary information.     

Engineering Design Processes: A Comparison of Students and Expert Practitioners

In this paper we report on an in‐depth study of engineering design processes. Specifically, we extend our previous research on engineering student design processes to compare the design behavior of students and expert engineers. Nineteen experts from a variety of engineering disciplines and industries each designed a playground in a lab setting, and gave verbal reports of their thoughts during the design task. Measures of their design processes and solution quality were compared to pre‐existing data from 26 freshmen and 24 seniors. The experts spent significantly more time on the task overall and in each stage of engineering design, including significantly more time problem scoping. The experts also gathered significantly more information covering more categories. Results support the argument that problem scoping and information gathering are major differences between advanced engineers and students, and important competencies for engineering students to develop. Timeline representations of the expert designers' processes illustrate characteristic distinctions we found and may help students gain insights into their own design processes.     

The Process of Engineering and Its Management

ABSTRACT

The central theme of this article is that engineering should be viewed primarily as a process rather than as a body of knowledge and that only by understanding this process can it be effectively managed. As a consequence, engineering management cannot be synonymous with project management. The article then gives an overview of engineering as a process, emphasizing the important part played by the top-down systems engineering methodology.     

Integrating Reliability in Systems Engineering Management

Abstract

Understanding how engineering disciplines, activities, and artifacts are connected between them is key to success as an engineering manager. However, engineering managers are often unaware of these relationships in the area of reliability engineering. This results in omitting necessary reliability activities, incorrectly executing them, and/or executing them at the wrong time. This paper discusses how reliability activities effectively integrate in the system’s life cycle, enabling engineering managers to understand what has to be done, when and why, from a reliability point of view. We contend that this understanding facilitates communication between systems engineers and reliability experts. Eight real cases are presented to show the negative consequences of managing engineering projects without a systemic understanding of reliability. Based on such experience, a number of recommendations are given for engineering managers.     

FESOLE-fuzzy expert system for determining the optimal level of enforcement

Electronic payment for using road infrastructure is an area that is gaining importance in the realm of intelligent transportation systems (ITS). While preparing such an electronic fee collection system, the level of enforcement needed must be determined. This is not a trivial task, because it is affected by many technical and monetary factors. At the same time, it cannot be expected that all decision makers are deeply involved in this field and are able to make an optimal decision depending on the recommendations of external experts. In order to decrease the possibility of manipulating this decision process by lobbyists and to save the money used for external consultants, a decision support system is needed. This paper describes a fuzzy expert system for determining the optimal level of enforcement ? FESOLE. It bridges a missing part in the research concerning ITS. The author focuses mainly on the major technical features of this model. A fuzzy expert system is used to approach this problem. It can extract and exploit the knowledge of human experts and convert it to a robust computational model. Suitability of this methodology for a given task is also demonstrated.     

PLM Software Selection Model for Project Management Using Hierarchical Decision Modeling With Criteria From PMBOK® Knowledge Areas

Abstract:

This article presents a methodology for product lifecycle management (PLM) software selection utilizing the nine knowledge areas of the Project Management Body of Knowledge (PMBOK®) (A Guide to the Project Management Body of Knowledge (PMBOK® Guide, 2008). The methodology utilizes a five-process gate approach where the PLM offerings are researched, sorted, paired down, evaluated, and implemented. The primary decision model utilizes hierarchical decision modeling (HDM). Prioritizations are assigned to the PMBOK® knowledge areas utilizing a pair-wise comparison survey and then assessed across the different PLM system offerings. The proposed decision methodology is meant to serve only as a guide to assist with selection of PLM software from a project management perspective. To validate the model, several applications are presented that apply the selection methodology to different industries: semiconductor, information technology (IT), and automotive supply.     

Justification for the selection of manufacturing technologies: a fuzzy-decision-tree-based approach

In this paper, a developed model for the justification of alternative manufacturing technologies is presented. The approach, based on fuzzy decision trees, provides a methodology capable of identifying patterns within a technology case repository to support the evaluation of manufacturing systems. Experts are highly influential individuals in the decision process; they provide support and guidance when selecting investments. The experience-oriented task is founded on previous cases or an experts’ experience, and therefore difficult to express in a rational form. The concept is based on a number of characteristics of the case-based reasoning, rule induction and expert system theory. Structured around the fuzzy-decision-tree data-mining technique, the framework provides the ability of using regulated case information to act as structured experience for assisting in the decision process. Fuzzy induction extracts formal rules from a set of experience data, and the expert system philosophy computes the experience base of human expertise for problem-solving. A test case indicates the stability of the classification algorithm and verifies the applicability within the domain.     

Probabilistic inversion for chicken processing lines

We discuss an application of probabilistic inversion techniques to a model of campylobacter transmission in chicken processing lines. Such techniques are indicated when we wish to quantify a model which is new and perhaps unfamiliar to the expert community. In this case there are no measurements for estimating model parameters, and experts are typically unable to give a considered judgment. In such cases, experts are asked to quantify their uncertainty regarding variables which can be predicted by the model. The experts’ distributions (after combination) are then pulled back onto the parameter space of the model, a process termed “probabilistic inversion”. This study illustrates two such techniques, iterative proportional fitting (IPF) and PARmeter fitting for uncertain models (PARFUM). In addition, we illustrate how expert judgement on predicted observable quantities in combination with probabilistic inversion may be used for model validation and/or model criticism.     

On the performance of social network and likelihood-based expert weighting schemes

Using expert judgment data from the TU Delft's expert judgment database, we compare the performance of different weighting schemes, namely equal weighting, performance-based weighting from the classical model [Cooke RM. Experts in uncertainty. Oxford: Oxford University Press; 1991.], social network (SN) weighting and likelihood weighting. The picture that emerges with regard to SN weights is rather mixed. SN theory does not provide an alternative to performance-based combination of expert judgments, since the statistical accuracy of the SN decision maker is sometimes unacceptably low. On the other hand, it does outperform equal weighting in the majority of cases. The results here, though not overwhelmingly positive, do nonetheless motivate further research into social interaction methods for nominating and weighting experts. Indeed, a full expert judgment study with performance measurement requires an investment in time and effort, with a view to securing external validation. If high confidence in a comparable level of validation can be obtained by less intensive methods, this would be very welcome, and would facilitate the application of structured expert judgment in situations where the resources for a full study are not available. Likelihood weights are just as resource intensive as performance-based weights, and the evidence presented here suggests that they are inferior to performance-based weights with regard to those scoring variables which are optimized in performance weights (calibration and information). Perhaps surprisingly, they are also inferior with regard to likelihood. Their use is further discouraged by the fact that they constitute a strongly improper scoring rule.     

Information sharing in multiviewpoint injection moulding design and manufacturing

Global competitiveness challenges manufacturing industry to bring to market well designed and manufactured new products at competitive prices in as short a lead-time as possible. To achieve this, inputs are needed from experts in a multitude of disciplines as well as from customers and suppliers. While the use of design teams is achieving success, there is a need for modern software tools that support design and manufacture to be radically improved. Typically, design for manufacture software systems does not support the multiple views of information needed for information systems to be able to support multiple manufacturing perspectives. This paper proposes a linked, multiviewpoint, product model structure as a means of supporting the many views of product information necessary in team-based design and manufacturing systems. To test the ideas developed, an experimental product model has been implemented using an object-oriented database that focuses on views related to the design and manufacture of injection-moulded products. The experimental system explores the relationships between three views: mouldability, cavity design and cavity machining views.     

基于不确定语言信息的物流供应商选择

为解决传统供应商的选择与评价过程中专家评判的模糊性和不确定性问题,提出了一种基于不确定语言信息的物流服务供应商选择方法。该方法以不确定语言变量来表征专家的评判从而避免了决策信息的丢失。继而建立了物流服务质量评价属性的权重优化模型,并构造拉格朗日函数来求解该模型,得到评价属性的最优权重。接着利用不确定语言加权几何平均（ULWG）算子集结专家评判信息,则得到供应商的优先排序。给出了基于不确定语言信息的供应商选择的算法步骤和应用实例。     

Post-Certification Engineering Taxonomy and Task Value Optimization in the Aerospace Industry

Abstract:

This article proposes a novel taxonomy of post-certification engineering activities as a first step toward true lean product development (PD). Relying on key notions developed in a novel lean engineering performance model, the authors compare the leanness of post-certification versus pre-certification tasks for the design of aerospace parts. Discrete event simulation and integer linear programming models are developed to help ascertain the influence of factors such as multitasking, concurrency, task size, task value, and post-certification budget decision making on lean engineering PD performance. The models developed provide the foundation for enhanced PD performance and the establishment of optimal PD process parameters.     

Analyzing the behavior and reliability of voting systems comprising tri-state units using enumerated simulation

Voting is a common technique used in combining results from peer experts, for multiple purposes, and in a variety of domains. In distributed decision making systems, voting mechanisms are used to obtain a decision by incorporating the opinion of multiple units. Voting systems have many applications in fault tolerant systems, mutual exclusion in distributed systems, and replicated databases. We are specifically interested in voting systems as used in decision-making applications.In this paper, we describe a synthetic experimental procedure to study the behavior of a variety of voting system configurations using a simulator to: analyze the state of each expert, apply a voting mechanism, and analyze the voting results. We introduce an enumerated-simulation approach and compare it to existing mathematical approaches. The paper studies the following behaviors of a voting system: (1) the reliability of the voting system, R; (2) the probability of reaching a consensus, P_c; (3) certainty index, T; and (4) the confidence index, C. The configuration parameters controlling the analysis are: (1) the number of participating experts, N, (2) the possible output states of an expert, and (3) the probability distribution of each expert states. We illustrate the application of this approach to a voting system that consists of N units, each has three states: correct (success), wrong (failed), and abstain (did not produce an output). The final output of the decision-making (voting) system is correct if a consensus is reached on a correct unit output, abstain if all units abstain from voting, and wrong otherwise. We will show that using the proposed approach, we can easily conduct studies to unleash several behaviors of a decision-making system with tri-state experts.     

Bayesian reliability: Combining information

ABSTRACT

One of the most powerful features of Bayesian analyses is the ability to combine multiple sources of information in a principled way to perform inference. This feature can be particularly valuable in assessing the reliability of systems where testing is limited. At their most basic, Bayesian methods for reliability develop informative prior distributions using expert judgment or similar systems. Appropriate models allow the incorporation of many other sources of information, including historical data, information from similar systems, and computer models. We introduce the Bayesian approach to reliability using several examples and point to open problems and areas for future work.     

Application of Dempster-Shafer Theory in Aircraft Maintenance Time Assessment: A Case Study

Abstract:

The goal of this case study is to demonstrate the use of the Dempster-Shafer Theory of Evidence as a decision aid to predict aircraft maintenance times during Turkish Air Force wartime tactical operations. A questionnaire was administered to aircraft maintenance experts to investigate maintenance times of major aircraft failure modes (ignition, fuel, and electrical). An example examining jet engine aircraft failure is presented to demonstrate use of the developed approach. The results of this methodology provide a useful and specific application area for the Dempster-Shafer Theory to aid a decision maker in making wartime tactical decisions.     

Dynamic Systems-Engineering Process: The Application of Concurrent Engineering

ABSTRACT

In recent years, the traditional systems engineering and project management approaches used by the U.S. Government have been ineffective in incorporating complete life cycle impacts and rapidly evolving technologies and operations concepts into large system developments or upgrades. This is particularly true for the National Aeronautics and Space Administration (NASA) and the U.S. Department of Defense (DOD). Although this article concentrates on large government systems, these shortcomings also apply to the industrial product development process. Solving this problem will strengthen manufacturing competitiveness for any international competitor.

This article addresses the problem and describes a systems engineering methodology, dynamic systems-engineering, which enables users to accommodate changing needs; incorporate emerging technologies; identify, quantify, and manage system risks; manage evolving functional requirements; track the changing environment; and reduce system life cycle costs.     

Optimizing in a complex world: A statistician's role in decision making

ABSTRACT

As applied statisticians increasingly participate as active members of problem-solving and decision-making teams, our role continues to evolve. Historically, we may have been seen as those who can help with data collection strategies or answer a specific question from a set of data. Nowadays, we are, or strive to be, more deeply involved throughout the entire problem-solving process. An emerging role is to provide a set of leading choices from which subject matter experts and managers can choose to make informed decisions. A key to success is to provide vehicles for understanding the trade-offs between candidates and interpreting the merits of each choice in the context of the decision makers’ priorities. To achieve this objective, it is helpful to be able (a) to help subject matter experts identify quantitative criteria that match their priorities, (b) eliminate non-competitive choices through the use of a Pareto front, and (c) provide summary tools from which the trade-offs between alternatives can be quantitatively evaluated and discussed. A structured but flexible process for contributing to team decisions is described for situations when all choices can easily be enumerated as well as when a search algorithm to explore a vast number of potential candidates is required. A collection of diverse examples ranging from model selection, through multiple response optimization, and designing an experiment illustrate the approach.     

TU Delft expert judgment data base

We review the applications of structured expert judgment uncertainty quantification using the “classical model” developed at the Delft University of Technology over the last 17 years [Cooke RM. Experts in uncertainty. Oxford: Oxford University Press; 1991; Expert judgment study on atmospheric dispersion and deposition. Report Faculty of Technical Mathematics and Informatics No.01-81, Delft University of Technology; 1991]. These involve 45 expert panels, performed under contract with problem owners who reviewed and approved the results. With a few exceptions, all these applications involved the use of seed variables; that is, variables from the experts’ area of expertise for which the true values are available post hoc. Seed variables are used to (1) measure expert performance, (2) enable performance-based weighted combination of experts’ distributions, and (3) evaluate and hopefully validate the resulting combination or “decision maker”. This article reviews the classical model for structured expert judgment and the performance measures, reviews applications, comparing performance-based decision makers with “equal weight” decision makers, and collects some lessons learned.     

Expert judgement combination using moment methods

Moment methods have been employed in decision analysis, partly to avoid the computational burden that decision models involving continuous probability distributions can suffer from. In the Bayes linear (BL) methodology prior judgements about uncertain quantities are specified using expectation (rather than probability) as the fundamental notion. BL provides a strong foundation for moment methods, rooted in work of De Finetti and Goldstein. The main objective of this paper is to discuss in what way expert assessments of moments can be combined, in a non-Bayesian way, to construct a prior assessment. We show that the linear pool can be justified in an analogous but technically different way to linear pools for probability assessments, and that this linear pool has a very convenient property: a linear pool of experts’ assessments of moments is coherent if each of the experts has given coherent assessments. To determine the weights of the linear pool we give a method of performance based weighting analogous to Cooke's classical model and explore its properties. Finally, we compare its performance with the classical model on data gathered in applications of the classical model.