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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 aim of this article is to show how Taguchi methods can be applied to health care.

The quadratic loss function is at the heart of Taguchi methods. It is a powerful motivator for a quality strategy and can be used to adequately model the loss to society in health care. It also establishes a relationship between cost and variability. Therefore, it can be integrated with the performance and parameters of the design of medical applications. Signal‐to‐noise ratios give a sense of how close is the performance to the ideal. By maximizing the signal‐to‐noise ratio, quality‐engineering activities can be aimed at identifying near‐optimum levels of factors and making quality equal to zero.

This article shows that, when the patients' requirements are consistently met, lower losses can provide an impetus to improve patient satisfaction.

The article outlines areas in health care where Taguchi methods can easily be applied.

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 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.

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.

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.

Quality improvement decisions are the catalyst for substantial technological improvements being made in the manufacturing sector. The new technology, however, has developed faster than techniques for evaluating capital investments in such improvements. This is largely because the benefits of quality improvement technology are difficult to quantify. The Taguchi loss function is incorporated into a net present value capital budgeting technique to provide an estimate of these benefits. Describes the loss function in relation to key quality costs: appraisal and prevention costs, and internal and external failure costs. External failure cost savings are generated by reducing variability in the manufacturing process. These savings are then compared with the cost of the quality improving technology. Results indicate that these savings can be substantial, depending on the achieved reduction in the process variability, the cost of capital, and on the estimate of the cost of processing a customer’s return of the product.

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.

Taguchi defines quality in a negative manner as “the loss imparted to society from the time the product is shipped”. Explains the concept and the approach to quality improvement that arises. This approach involves statistical process control and can be daunting but the paper stresses and makes clear the underlying conceptual framework of a methodology for quality improvement and process robustness.

In this paper we incorporate Taguchi's loss function in the economic design of the control chart. This is done by redefining the in‐control and out‐of‐control costs using Taguchi's loss function. The new model brings Taguchi's off‐line concepts to the classical SPC approach and continues to have the advantages of the economic design model by taking into consideration the cost consequences of the design. Sensitivity analysis with respect to important model parameters is discussed.