James T. Luxhøj

A Situational Maintenance Model

The role that effective maintenance management plays in contributing to overall organizational productivity has received increased attention. Presents the development of a situational maintenance model that may be used to analyse and design the elements of a maintenance system. The situational approach to maintenance builds on contingency theory and considers both internal and external corporate dynamics. Using ideas from total productive maintenance (TPM), discusses how this model may be used to link corporate goals with maintenance policies. Defines design variables for maintenance systems that include the perspectives of individual behaviour, decision support systems, management systems and organizational structure, and corporate culture.

A hybrid econometric--neural network modeling approach for sales forecasting

Abstract Business sales forecasting is an example of management decision making in an ill-structured, uncertain problem domain. Due to the dynamic complexities of both internal and external corporate environments, many firms resort to qualitative forecasting techniques. However, these qualitative techniques lack the structure and extrapolation capability of quantitative forecasting models, and forecasting inaccuracies typically lead to dramatic disturbances in production planning. This paper presents the development of a hybrid econometric-neural network model for forecasting total monthly sales. This model attempts to integrate the structural characteristics of econometric models with the non-linear pattern recognition features of neural networks to create a “hybrid” modeling approach. A three-stage model is created that attempts to sequentially “filter” forecasts where the output from one stage becomes part of the input to the next stage. The forecasts from each of the individual sub-models are then “averaged” to compute the hybrid forecast. Model development is discussed in the content of an actual sales forecasting problem from a Danish company that produces consumer goods. Actual model performance is reported for a six-month time period. Knowledge gained from the modeling approach is placed in the context of organizational learning about the nature of sales forecasting for this particular company.

Trends and perspectives in industrial maintenance management

Abstract With increased global competition for manufacturing, many companies are seeking ways to gain competitive advantages with respect to cost, service, quality, and on-time deliveries. The role that effective maintenance management plays in contributing to overall organizational productivity has received increased attention. This paper presents an overview of trends and perspectives in industrial maintenance. The results of benchmarking studies from Scandinavia and the United States are presented and compared. Implications of the trends and perspectives for the management of maintenance are highlighted. Case studies that examine maintenance methods, knowledge, organization, and information systems in three Danish manufacturing firms are used to motivate the discussion.

A general model for accelerated life testing with time-dependent covariates

This paper introduces a general or “distribution-free” model to analyze the lifetime of components under accelerated life testing. Unlike the accelerated failure time (AFT) models, the proposed model shares the advantage of being “distribution-free” with the proportional hazard (PH) model and overcomes the deficiency of the PH model not allowing survival curves corresponding to different values of a covariate to cross. In this research, we extend and modify the extended hazard regression (EHR) model using the partial likelihood function to analyze failure data with time-dependent covariates. The new model can be easily adopted to create an accelerated life testing model with different types of stress loading. For example, stress loading in accelerated life testing can be a step function, cyclic, or linear function with time. These types of stress loadings reduce the testing time and increase the number of failures of components under test. The proposed EHR model with time-dependent covariates which incorporates multiple stress loadings requires further verification. Therefore, we conduct an accelerated life test in the laboratory by subjecting components to time-dependent stresses, and we compare the reliability estimation based on the developed model with that obtained from experimental results. The combination of the theoretical development of the accelerated life testing model verified by laboratory experiments offers a unique perspective to reliability model building and verification.

Monitoring multivariate aviation safety data by data depth: control charts and threshold systems

Abstract Aviation safety analysis is increasingly needed in regulating air traffic and safety, in light of the rapid growth in air traffic density. With the recent advances in computer technology, large amounts of multivariate aviation safety data are now routinely collected in databases. Many existing analysis methods prescribed in those databases and corresponding safety indictors are based on classical statistical analysis, and their applicability are considerably restricted by the requirement of normality. An alternative nonparametric methodology based on data depthis pursued in this paper. For a given multivariate sample, a data depth can be used to measure their depth or outlyingness with respect to the underlying distribution. The measure of depth leads to a center-outward ordering of the sample points. Derived from this ordering, Liu (1995) introduced a simple, yet effective, control chart for monitoring multivariate observations. The control chart is combined here with properly chosen false alarm rates to develop meaningful threshold systems for multivariate aviation safety data for both regulating and monitoring purposes. The developed procedure is applied to the aviation inspection results collected by the Federal Aviation Administration (FAA) inspection system. The threshold system serves as a standard for evaluating the performance of aircraft operators, and provides clear guidelines for identifying unexpectedperformances and for assigning appropriate corrective actions.

Applying a knowledge management modeling tool for manufacturing vision (MV) development

This paper introduces an empirical application of an experimental model for knowledge management within an organization, namely a case‐based reasoning model for manufacturing vision development (CBRM). The model integrates the development process of manufacturing vision with the methodology of case‐based reasoning. This paper briefly describes the models theoretical fundamentals and its conceptual structure; conducts a detailed introduction of the critical elements within the model; exhibits a real world application of the model; and summarizes the review of the model through academia and practice. Finds that the CBRM is supportive to the decision‐making process of applying and augmenting organizational knowledge. It provides a new angle to tackle strategic management issues within the manufacturing system of a business operation. Explores a new proposition within strategic manufacturing management by enriching and extending the concept of MV while trying to lead the CBR methodology into a new domain by applying it in strategic management.

APPLICATION OF CHANCE-CONSTRAINED PROGRAMMING TO CAPITAL RATIONING PROBLEMS WITH ASYMMETRICALLY DISTRIBUTED CASH FLOWS AND AVAILABLE BUDGET

ABSTRACT Most applications of chance-constrained programming are based on either normally distributed random variables or random variables with symmetric distributions such as uniform, which can be approximated rather accurately by the normal distribution. In this paper we study pure capital rationing with selection of the best project mix when cash flows and available budget are random variables with asymmetric distributions. We show that solutions obtained by chance-constrained programming using normality approximation for asymmetrically distributed random variables fail to satisfy budget constraints when cash outflows are skewed to the left, indicating that realized cash outflows are more likely to be higher than expected.

Reliability curve fitting for aging helicopter components

This paper presents a comparison of alternative reliability curve fitting techniques for components of two model types of helicopters. Both mathematical function-based and neural network models were investigated. Preliminary results suggest that the neural network models compare very favorably with standard curve fitting techniques, and may provide better curve fitting for component reliability data from sparse data sets where the hazard rates are either constant or monotonically increasing.

Predictive safety analytics: inferring aviation accident shaping factors and causation

This paper illustrates the development of an object-oriented Bayesian network (OOBN) to integrate the safety risks contributing to an in-flight loss-of-control aviation accident. With the creation of a probabilistic model, inferences about changes to the states of the accident shaping or causal factors can be drawn quantitatively. These predictive safety inferences derive from qualitative reasoning to conclusions based on data, assumptions, and/or premises, and enable an analyst to identify the most prominent causal factors leading to a risk factor prioritization. Such an approach facilitates a mitigation portfolio study and assessment. The model also facilitates the computation of sensitivity values based on perturbations to the estimates in the conditional probability tables. Such computations lead to identifying the most sensitive causal factors with respect to an accident probability. This approach may lead to vulnerability discovery of emerging causal factors for which mitigations do not yet exist that then informs possible future R&D efforts. To illustrate the benefits of an OOBN in a large and complex aviation accident model, the in-flight loss-of-control accident framework model is presented.

A RISK-BASED DECISION SUPPORT TOOL FOR EVALUATING AVIATION TECHNOLOGY INTEGRATION IN THE NATIONAL AIRSPACE SYSTEM

Commercial aviation, one of the most critical national and international modes of transport, is a highly complex, dynamic domain. From a systems perspective, there are numerous interrelated infrastructural components and stakeholders that challenge analytical modeling. Perhaps more than any other domain, aviation is typically on the forefront of developing and applying new technologies. The Aviation System Risk Model (ASRM) is a risk-based decision support system prototype designed to evaluate the impacts of new safety technologies/ interventions. The process utilizes an analytic generalization framework to develop an integrated approach to model the complex interactions of causal factors. Bayesian probability theory is being used for model quantification and Bayesian decision theory provides an analytical method to evaluate the possible impacts of new interventions. The entire process is supported by expert judgments. Subsequently, the analytical methodology is encoded as a Probabilistic Decision Support System (PDSS). The resultant PDSS is a riskinformed decision support tool that aids the evaluation of the possible relative impact of single as well as multiple technologies on aviation safety system risk. Presenting a maintenance-related accident scenario provides an illustration of the possible use of the PDSS.