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Auditors and accountants have an accepted reputation of being conservative. However, Antle and Nalebuff (1991) conclude in their analytical model on auditor‐client negotiations that auditors are not conservative and that a conservative audit report is never issued. This paper extends the Antle and Nalebuff (1991) results. By replacing the Antle and Nalebuff (1991) assumption that an auditor has a symmetric loss function (financial statement overstatements have the same impact as financial statement understatements) with the assumption that an auditor has an asymmetric loss function (losses to an auditor for financial statement overstatement are greater than the losses of an equal understatement), I find that auditors can be conservative and that conservative audit reports are issued to the users.

The purpose of this paper is to develop a double-objective economic statistical design (ESD) of (X ‾) control chart under Weibull failure properties with the Linex asymmetric loss function. The authors have expressed the probability of type II error (β) as the statistical objective and the expected cost as the economic objective.

The design used in this study is based on a double-objective economic statistical design of (X ‾) control chart with Weibull shock model via applying Banerjee and Rahim's model for non-uniform and uniform schemes with Linex asymmetric loss function. The results in the least average cost and β in uniform and non-uniform schemes by Linex loss function, compared with the same schemes without loss function.

Numerical results indicate that it is not possible to reduce the second type of error and costs at the same time, which means that by reducing the second type of error, the cost increases, and by reducing the cost, the second type of error increases, both of which are very important. Obtained based on the needs of the industry and which one has more priority has the right to choose. These designs define a Pareto optimal front of solutions that increase the flexibility and adaptability of the X ‾ control chart in practice. When the authors use non-uniform schemes instead of uniform schemes, the average cost per unit time decreases by an average and when the authors apply loss function, the average cost per unit time increases by an average. Also, this quantity for double-objective schemes with loss function compared to without loss function schemes in cases uniform and non-uniform increases. The reason for this result is that the model underestimated the costs before using the loss function.

This research adds to the body of knowledge related to flexibility in process quality control. This article may be of interest to quality systems experts in factories where the choice between cost reduction and statistical factor reduction can affect the production process.

The cost functions for double-objective uniform and non-uniform sampling schemes with the Weibull shock model based on the Linex loss function are presented for the first time.

This paper seeks to provide a tool for decision makers to make more informed decisions regarding their outsourcing decisions and selection of the appropriate supplier.

The method uses the Taguchi loss function for the inclusion of intangibles in the evaluation and selection of suppliers. Intangibles are defined as factors that have an impact on the selection of an appropriate supplier but are not easily quantified to be included in the financial evaluation. These intangibles are classified as the benefits and risks of using a supplier to perform the outsourcing function. A decision maker has certain expectations regarding these intangibles and a loss occurs when a supplier's performance does not meet the decision maker's expectations. The Taguchi loss function has been selected as a means of measuring the loss. The decision maker defines the target value and the specification limits for each benefit and risk category. The weighted loss scores are calculated where the weights are the importance ratings assigned to benefit/risk categories by the decision maker. Based on this analysis each supplier will receive a weighted loss score for all the pertinent benefit categories and one weighted loss score for all the risk categories. To achieve a single measure, the aforementioned weighted loss scores are combined to determine a single aggregate loss score for each supplier, which is then used to rank them. The supplier who receives the highest ranking (minimum loss score) will be selected to perform the outsourcing function.

The procedure proposed here can help companies to identify the best supplier to perform an outsourcing function.

The paper presents a phased decision model that begins with economic evaluation and then uses Taguchi functions to measure the impact of intangibles.

The purpose of this research is to provide a new loss function‐based risk assessment method so the likelihood and consequence resulting from the failure of a manufacturing or environmental system can be evaluated simultaneously.

Instead of using risk matrices of the occurrence and consequence separately for evaluating manufacturing and environmental risks, an integrated approach by exploring the relationship between process capability indices: C_p, C_pk and C_pm, and three different loss functions: Taguchi's loss function; Inverted normal loss function (INLF); and Revised inverted normal loss function (RINLF) is proposed.

The new method of quantitative risk assessment linking the likelihood and expected loss of failure is illustrated by two numeric examples. The results suggest that the revised inverted normal loss function (RINLF) be used in assessing manufacturing and environmental risks.

It gives decision‐makers a concrete tool to assess the likelihood and consequence of their processes. Linking the process capability indices and loss functions is particularly promising, as this provides a useful risk assessment tool for practitioners who want to reduce hazardous waste and manufacturing losses from their facilities.

The manufacturing and environmental risks are determined by paring the process capability indices and loss function. From the loss function‐based estimation, one can quantify the consequence of a manufacturing loss and get the severity rating in an objective way.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

The process capability indices have been widely used to measure process capability and performance. In this paper, we proposed a new process capability index which is based on an actual dollar loss by defects. The new index is similar to the Taguchi’s loss function and fully incorporates the distribution of quality attribute in a process. The strength of the index is to apply itself to non‐normal or asymmetric distributions. Numerical examples were presented to show superiority of the new index against Cp, Cpk, and Cpm which are the most widely used process capability indices.

The purpose of this paper is to propose and empirically assess three comparative approaches to measuring service quality: modified gap model, TOPSIS and loss function. Aims to argue for the use of TOPSIS from decision sciences, and Loss function from operations research and engineering, as alternative approaches to the gap model.

The empirical evidence is provided by large sample consumer data on the service quality for leading Indian commercial banks. The service quality evaluations obtained from these three distinct methods are compared and tested for their mutual agreement.

Fndings show that the rankings obtained from different methods are statistically in agreement, suggesting that the alternative approaches can provide equally good measurement of service quality. But they should not be used in an interchangeable manner.

Research shows that a single measure of overall service quality based on gap model is over‐simplistic. It would be more useful to explore a richer profile of customer service quality provided by different measurement approaches. Each methodology has its own advantages and disadvantages, and should be used based on its suitability for a particular application.

This research offers profound practical implications. It offers managers with a framework of service quality improvement that measures service quality gaps, selects an optimal combination of attribute levels to deliver customer satisfaction, and focuses on reducing the future loss caused by poor quality.

Extant marketing literature is replete with gap model applications for measuring service quality. Drawing from interdisciplinary literature, alternatives are provided to the traditional gap model, which show equally good measurement with greater suitability of application under certain conditions.

The purpose of this paper is to develop a decision model to help decision makers with selection of the appropriate supplier.

Supplier selection is a multi‐criteria decision‐making process encompassing various tangible and intangible factors. Both risks and benefits of using a vendor in supply chain are identified for inclusion in the evaluation process. Since these factors can be objective and subjective, a hybrid approach that applies to both quantitative and qualitative factors is used in the development of the model. Taguchi loss functions are used to measure performance of each supplier candidate with respect to the risks and benefits. Analytical hierarchy process (AHP) is used to determine the relative importance of these factors to the decision maker. The weighted loss scores are then calculated for each supplier by using the relative importance as the weights. The composite weighted loss scores are used for ranking of the suppliers. The supplier with the smallest loss score is recommended for selection.

Inclusion of both risk and benefit categories in the evaluation process provides a comprehensive decision tool.

The proposed model provides guidelines for supply chain managers to make an informed decision regarding supplier selection.

Combining Taguchi loss function and AHP provides a novel approach for ranking of potential suppliers for outsourcing purposes.

Selection of logistics service provider (LSP) (also known as Third-party logistics (3PL) is a critical decision, because logistics affects top and bottom line as well. Companies consider logistics as a cost driver and at the time of LSP selection decision, many important decision criteria’s are left out. 3PL selection is multi-criteria decision-making process. The purpose of this paper is to develop an integrated approach, combining quality function deployment (QFD), and Taguchi loss function (TLF) to select optimal 3PL.

Multiple criteria are derived from the company requirements using house of quality. The 3PL service attributes are developed using QFD and the relative importance of the attributes are assessed. TLFs are used to measure performance of each 3PL on each decision variable. Composite weighted loss scores are used to rank 3PLs.

QFD is a better tool which connects attributes used in a decision problem to decision maker’s requirements. In total, 15 criteria were used and TLF provides performance on these criteria.

The proposed model provides a methodology to make informed decision related to 3PL selection. The proposed model may be converted into decision support system.

Proposed approach in this paper is a novel approach that connects the 3PL selection problem to practice in terms of identifying criteria’s and provides a single numerical value in terms of Taghui loss.

Proposing an improved model for evaluating criticality of non-value added (waste) in operation is necessary for realizing sustainable manufacturing practices. The purpose of this paper is concerning on improvement of the decision support model for evaluating risk criticality lean waste occurrence by considering the weight of modified FMEA indices and the influence of waste-worsening factors causing the escalation of waste risk magnitude.

Integration of entropy and Taguchi loss function into decision support model of modified FMEA is presented to rectify the limitation of previous risk reprioritization models in modified FMEA studies. The weight of the probability components and loss components are quantified using entropy. A case study from industry is used to test the applicability of the integration model in practical situation.

The proposed model enables to overcome the limitations of using subjective determination on the weight of modified FMEA indices. The inclusion of the waste-worsening factors and Taguchi loss functions enables the FMEA team to articulate the severity level of waste consequences appropriately over the use of ordinal scale in ranking the risk of lean waste in modified FMEA references.

When appraising the risk of lean waste criticality, ignorance on weighting of FMEA indices may be inappropriate for an accurate risk-based decision-making. This paper provides insights to scholars and practitioners and others concerned with the lean operation to understand the significance of considering the impact of FMEA indices and waste-worsening factors in evaluating criticality of lean waste risks.

The method adopted is for quantifying the criticality of lean waste and inclusion of weighting of FMEA indices in modified FMEA provides insight and exemplar on tackling the risk of lean waste and determining the most critical waste affecting performability of company operations.

Integration of the entropy and Taguchi loss function for appraising the criticality of lean waste in modified FMEA is the first in the lean management discipline. These findings will be highly useful for professionals wishing to implement the lean waste reduction strategy.