Implementation of the Product Reliability Program

Combining REACH, environmental and economic performance indicators for strategic sustainable product development

<div class="htmlview paragraph">A major focus of many implementation discussions on Systems Engineering revolves around developing a large database of information, to be reused later, benefiting the company's product development process. Business needs for agile and flexible responses place pressure on the maintenance of the database during Sustaining Engineering of the product. The business needs of the product management functions are discussed to frame the problem of maintaining the Systems Engineering database. This paper focuses on the maintenance of the Systems Engineering database during Sustaining Engineering activities. The process for ensuring the ‘Systems Engineering Documentation Set’ is being kept current is analyzed and described. Experiences regarding the dynamics of a Matrix Organization for Sustaining Engineering are discussed.</div>

The following case study evaluates the New Product Development (NPD) techniques utilized by Forest City Technologies, Incorporated (FCT). Through insight gathered via interviews conducted with the company's product development and materials purchasing management teams, and supported by literature, this study attempts to show how Forest City Technologies, Inc. integrates specific components into its product development process to: 1. Meet its NPD goals, and 2. Achieve better supplier and customer relationships. This study focuses on the components of: NPD models employed by FCT, early customer and supplier involvement, NPD-innovation integration techniques, demand change factors during the NPD process, and risk-mitigation strategies implemented by FCT during the NPD process. The study is segmented into three main sections: Introduction to NPD and FCT, the components of FCTs new product development process, and NPD implications on FCTs supplier and customer relationships.

A-dec Inc. is a dental equipment manufacturer headquartered in Newberg, Oregon. A-dec implemented an innovative product development process to support the launch of a new product line. The change in process motivated the A-dec Marketing Communications team to address how to align their process with the company's product development direction. The group decided to focus on content. They faced the challenge of how to control and manage content, which started their education in the area of content management and content management systems. This ongoing case study discusses their journey towards implementing a content management system. Located in Vancouver, BC, Intentional Design Inc. is a technical communication consultancy focused on improving the performance of communication products. The strength of the organization lies in the wildbrain approach (see Dale Dauten's The Gifted Boss) to performance improvement and its impact on the solutions provided to clients.

Worst-case uncertainty measurement in ADC-based instruments

How can we discern whether the covariance operator of a stochastic process is of reduced rank, and if so, what its precise rank is? And how can we do so at a given level of confidence? This question is central to a great deal of methods for functional data, which require low-dimensional representations whether by functional PCA or other methods. The difficulty is that the determination is to be made on the basis of i.i.d. replications of the process observed discretely and with measurement error contamination. This adds a ridge to the empirical covariance, obfuscating the underlying dimension. We build a matrix-completion inspired test statistic that circumvents this issue by measuring the best possible least square fit of the empirical covariance’s off-diagonal elements, optimised over covariances of given finite rank. For a fixed grid of sufficiently large size, we determine the statistic’s asymptotic null distribution as the number of replications grows. We then use it to construct a bootstrap implementation of a stepwise testing procedure controlling the familywise error rate corresponding to the collection of hypotheses formalising the question at hand. Under minimal regularity assumptions, we prove that the procedure is consistent and that its bootstrap implementation is valid. The procedure circumvents smoothing and associated smoothing parameters, is indifferent to measurement error heteroskedasticity, and does not assume a low-noise regime. An extensive simulation study reveals an excellent practical performance, stably across a wide range of settings and the procedure is further illustrated by means of two data analyses.

We study a problem of scheduling search-and-detection activities in complex technological and organizational systems. Prior probabilities of failures are known for each element, and the decision maker has to sequentially inspect the components so that to find a failed component within a given level of confidence. The inspections are imperfect; namely, a probability of overlooking the failed element and a probability of a “false alarm” exist. An index-based algorithm for finding the optimal search strategy is developed. An example for robotic search systems is discussed.KeywordsSearch-and-detectionDiscrete searchImperfect inspectionsFalsepositive detectionFalse-negative detectionGreedy algorithm

PREDICTIVE GEOLOGY IN NUCLEAR-WASTE MANAGEMENT

Extension of the Carter-Yang polynomial growth curve model to allow unique times of measurement for subjects

The Model Confidence Set

Purpose– While increased mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and imperfect planned or routine maintenance prohibit the maximum possible utilization of sophisticated mining equipment and require significant amount of extra capital investment. Traditional preventive/planned maintenance is usually scheduled at a fixed interval based on maintenance personnel's experience and it can result in decreasing reliability. This paper deals with reliability analysis and prediction for mining machinery. A software tool called GenRel is discussed with its theoretical background, applied algorithms and its current improvements. In GenRel, it is assumed that failures of mining equipment caused by an array of factors (e.g. age of equipment, operating environment) follow the biological evolution theory. GenRel then simulates the failure occurrences during a time period of interest based on Genetic Algorithms (GAs) combined with a number of statistical procedures. The paper also discusses a case study of two mine hoists. The purpose of this paper is to investigate whether or not GenRel can be applied for reliability analysis of mine hoists in real life.Design/methodology/approach– Statistical testing methods are applied to examine the similarity between the predicted data set with the real-life data set in the same time period. The data employed in this case study is compiled from two mine hoists from the Sudbury area in Ontario, Canada. Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.Findings– The case studies shown in this paper demonstrate successful applications of a GAs-based software, GenRel, to analyze and predict dynamic reliability characteristics of two hoist systems. Two separate case studies in Mine A and Mine B at a time interval of three months both present acceptable prediction results at a given level of confidence, 5 percent.Practical implications– Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.Originality/value– Compared to conventional mathematical models, GAs offer several key advantages. To the best of the authors’ knowledge, there has not been a wide application of GAs in hoist reliability assessment and prediction. In addition, the authors bring discrete distribution functions to the software tool (GenRel) for the first time and significantly improve computing efficiency. The results of the case studies demonstrate successful application of GenRel in assessing and predicting hoist reliability, and this may lead to better preventative maintenance management in the industry.

The Representative Soil Sampling Scheme of England and Wales has recorded information on the soil of agricultural land in England and Wales since 1969. It is a valuable source of information about the soil in the context of monitoring for sustainable agricultural development. Changes in soil nutrient status and pH were examined over the period 1971–2001. Several methods of statistical analysis were applied to data from the surveys during this period. The main focus here is on the data for 1971, 1981, 1991 and 2001. The results of examining change over time in general show that levels of potassium in the soil have increased, those of magnesium have remained fairly constant, those of phosphorus have declined and pH has changed little. Future sampling needs have been assessed in the context of monitoring, to determine the mean at a given level of confidence and tolerable error and to detect change in the mean over time at these same levels over periods of 5 and 10 years. The results of a non‐hierarchical multivariate classification suggest that England and Wales could be stratified to optimize future sampling and analysis. To monitor soil quality and health more generally than for agriculture, more of the country should be sampled and a wider range of properties recorded.

Optimal Search and Detection of Clustered Hidden Targets under Imperfect Inspections

A fast and efficient conformal regressor with regularized extreme learning machine

When estimating a population proportion p within margin of error m, a preliminary sample of size n is taken to produce a preliminary sample proportion y/n, which is then used to determine the required sample size (y/n)(1-y/n)(z/m)2, where z is the critical value for a given level of confidence. The population is assumed to be infinite, so these Bernoulli(p) observations are mutually independent. Upon taking a new sample based on the required sample size, the coverage probabilities on p are determined exactly for various values of m, n, p, and z, using a commonly-used formula for a confidence interval on p. The coverage probabilities tend to be somewhat smaller than their nominal values, and tend to be a lot smaller when np or n(1 - p) is small, which would result in anti-conservative confidence intervals. As a more minor conclusion, since the given margin of error m is not relative to the population proportion p, then the required sample size is larger for values of p nearest to 0.5. The mean and standard deviation of the required sample size are also computed exactly to provide prospective, regarding just how large or how small these required sample sizes need to be.