Search results

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 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 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.

Most research on the process control charts concentrates on the economic design of its parameters. Reduction of variance as a control decision has not been researched. Presents a model for optimal decision on variance reduction and includes the rejection losses of the non‐conforming units increasing due to increased variance, Taguchi loss of the conforming units, and the cost of reducing variance. Optimal policies are derived analytically for uniform distribution and numerically for normal distribution. Applications of the model to the area of machine replacement and global manufacturing are suggested.

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.

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.

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.

The purpose of this paper is to provide a tool for decision makers to consider both tangible and intangible factors while making decisions regarding investment in advanced manufacturing technologies (AMT).

Traditional financial approaches are often used for evaluation of advanced technologies. However, the difficulty arises when the result of financial techniques cannot provide a conclusive recommendation for a technology adoption. The current research is an attempt to address this issue by including factors that allow distinction between technology alternatives with similar financial results. This task is accomplished in a two step process. First, a process is developed for identifying all potential benefits associated with adoption of an AMT. These are the benefits that were not measurable for inclusion in the financial analysis. Second, a mechanism is developed for quantifying these benefits to be used for ranking of the technology alternatives. This task is done by soliciting decision maker's input on importance of the benefits, required benefit goals, and his/her perception of how well each technology meets the benefit goals. This information is then used in Taguchi's loss functions to assign ranks to technology alternatives.

Investing in new technologies is the only way for manufacturers to survive in today's competitive market. Thus, there is a need by these manufacturers to have access to a decision model that will help them with their investment decisions.

The procedure proposed here helps companies to rank the technology alternatives and identify the best technology for adoption.

The identification of intangible benefits associated with adoption of a new technology and use of Taguchi's loss function to quantify these benefits.

An economic statistical design approach takes statistical properties into account while designing control charts economically. It improves both statistical design and economic design. In this paper, we present a statistically constrained economic model for the optimal design of S control chart for controlling process variability. In the model, the process quality can be affected by an assignable cause resulting in a shift of the variance of the distribution of output when it is operating according to its capability. The parameters are obtained by minimizing a total cost function proposed by Lorenzen and Vance, which is embellished with Taguchi loss function, subject to additional statistical constraints on average run length or average time‐to‐signal (ATS). Sensitivity analysis of the minimum cost will be performed to depict the effect of the choice of ATS bounds.

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.