Desirability functions have been one of the most important multiresponse optimization technique since the early eighties. Main reasons for this popularity might be counted as the convenience of the implementation of the method and the availability of it in many experimental design software packages. Technique itself involves somehow subjective parameters such as the importance coefficients between response characteristics that are used to calculate overall desirability, weights used in determining the shape of each individual response and the size of the specification band of the response. However, the impact of these sensitive parameters on the solution set is mostly uninvestigated. This paper proposes a procedure to analyze the sensitivity of the important characteristic parameters of desirability functions and their impact on pareto-optimal solution set. The proposed procedure uses the experimental design tools on the solution space and estimates a prediction equation on the overall desirability to identify the sensitive parameters. For illustration, a classical desirability example is selected from the literature and results are given along with the discussion.
Reliability is an important aspect of product perception and manufacturers are compelled to take corrective actions on the items failing within the warranty period. Automotive manufacturers are being exposed to significant operating costs as a result of warranty claims affecting an individual unit or mandatory (sometimes voluntary) recalls affecting a batch. Underlying principles of warranty modelling are built by considering both subjective issues and objective constraints such as competition, quality, and performance under the goal of achieving desired levels of reliability and cost in a balanced manner. This paper reviews the warranty cost models with an emphasis on the failure analysis of used vehicles. Expected warranty costs are calculated by taking into account the age, usage, and maintenance data of the product in question. Failure intensities and characteristics are identified in order to propose a policy that highlights the trade-off between the cost and the warranty length. A case study on a popular brand's initiation of factory certified pre-owned program for the local automobile market of Turkey is presented in detail.
Purpose – Reliability evaluation of healthcare services has been a challenging task for both operations managers and system engineers working in the respective field. The purpose of this paper is to develop a data envelopment analysis-based reliability allocation model. Design/methodology/approach – A two-phase optimization scheme for the reliability evaluation and allocation of homogeneous system entities, namely, hospitals, operating in a healthcare network is proposed. First, reliability evaluation is performed nonparametrically through the frontier estimation technique data envelopment analysis by considering several failure modes and failure free discharged patients as the inputs and output of the service system. Subsequently, optimal reliability allocation that maximizes the overall network reliability subject to a budget constraint is carried out by utilizing weights of the inputs and output calculated on the Pareto optimal frontier, which is constructed from the most reliable hospitals operating in the network. Findings – The popular performance assessment methodology DEA is found to be an invaluable reliability assessment and allocation tool, where optimal weights of the associated envelopment model, under certain budget restrictions, are used to maximize overall network reliability. Originality/value – An empirical illustration of the proposed model is presented on a set of hospital network data from Turkey. Modeling implications can be carried out on similar service operations where identification of the critical performance indicator costs is possible.
Originally developed in the late 1970s to assess the efficiency of comparable operating units, Data Envelopment Analysis (DEA) has since been used in a variety of contexts. Although incomplete data sets are often encountered in practice, the best approach in such situations is unclear in general. This paper explores methods such as multiple imputation, bootstrapping and smart dummy variable replacement, borrowed from similar missing data problems in regression analysis. Each missing data method is tested on a library of DEA problems that are gathered from the DEA literature. These problems are selected in such a way as to represent a thorough cross-section of problem sizes (small, medium, large) and types (type of DEA model, number of decision-making units, number of inputs, number of outputs, etc.). The results are illustrated by comparing the solutions of complete data sets against the simulated versions of the same data sets with missing data. The sensitivity of each method on the efficiency scores and ranking of the decision-making units is presented.
Classical reliability analysis techniques of manufacturing and defense industries are not perfect fit for the assessment of reliability of services. This is partly due to the lack of proper and valid reliability testing procedures in service systems and complications faced in identifying critical service parameters. Since the most prominent performance indicators of a system can be associated with the maximum overall reliability it achieves, then factors that degrade the reliability can be identified with respect to its superior peers. This study utilizes the data envelopment analysis for the evaluation of reliability in service systems with focus on healthcare. Our approach comparably evaluates the performance of a service provider over a period of time by means of failure rates and identifies the factors affecting unreliable time phases. Application of the proposed method is illustrated on a private Turkish hospital along with an example of FMEA for inpatient treatment.
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