Jami Kovach

Development of a multidisciplinary–multiresponse robust design optimization model

Robust design is an efficient process improvement methodology that combines experimentation with optimization to create systems that are tolerant to uncontrollable variation. Most traditional robust design models, however, consider only a single quality characteristic, yet customers judge products simultaneously on a variety of scales. Additionally, it is often the case that these quality characteristics are not of the same type. To addresses these issues, a new robust design optimization model is proposed to solve design problems involving multiple responses of several different types. In this new approach, noise factors are incorporated into the robust design model using a combined array design, and the results of the experiment are optimized using a new approach that is formulated as a nonlinear goal programming problem. The results obtained from the proposed methodology are compared with those of other robust design methods in order to examine the trade-offs between meeting the objectives associated with different optimization approaches.

A D-optimal design approach to constrained multiresponse robust design with prioritized mean and variance considerations

The traditional measures of ensuring quality, derived from viewing quality as simply conforming to specifications, does not adequately address todays global markets and value-driven customers. Consequently, process improvement projects are often performed to improve operational performance in order to increase customer satisfaction. Therefore, process improvement methods, such as robust design, are important to industrial quality improvement initiatives. Yet, little of the work in the area of robust design has specifically addressed problems involving physical processing constraints that create an irregularly shape experimental region and the simultaneous consideration of multiple quality characteristics. To address these issues, we propose a new approach to robust design that utilizes D-optimal experimental designs in the context of multiresponse optimization problems in order to overcome the limitations of standard experimental approaches often used in robust design studies. Specifically, we formulate our optimization models as a preemptive nonlinear goal programming problem that focuses on consideration of the mean and variance. We also investigate the extension of optimization models traditionally used in robust design investigations to address multiple responses and compare the outcomes of our proposed approaches using a numerical example.

A D-optimal design approach to robust design under constraints: a new Design for Six Sigma tool

Due to the success of Six Sigma, its methodology quickly spread backwards through the manufacturing chain into the design phase, which became known as Design for Six Sigma. One of the most well-known tools of Design for Six Sigma is robust design, which is a powerful and cost-effective quality improvement methodology. Although several approaches to robust design have been proposed in the literature, little attention has been given to its use when experiments are subject to constraints. It is often the case that todays complex manufacturing processes exhibit non-standard experimental characteristics. When such situations arise, experimental design techniques traditionally used in robust design may no longer be appropriate, and a logical alternative is the use of computer-generated designs, specifically D-optimal designs. In this paper, we propose an extension to the traditional robust design methodology within the Design for Six Sigma framework that incorporates D-optimal design techniques to facilitate the application of robust design to real-world situations. A numerical example is used to show our proposed approach, and the results determined from our proposed model are compared with that of the traditional robust design approach.

Development of a censored robust design model for time‐oriented quality characteristics

Robust design (RD) techniques, which are based on the concept of building quality into products or processes, are increasingly popular in industry primarily because of their practicality. Traditional RD principles have often been applied to situations in which the quality characteristics of interest are time-insensitive. However, when time-oriented quality characteristics are studied, censored data often occur. As a result, current RD models reported in the literature may not be effective in finding solutions based on such data. To address such practical needs, this paper develops a censored RD model. We also propose an estimation method that is closely related to the expectation–maximization algorithm and compare it with the method of maximum likelihood estimation via a numerical example. Model validation is conducted, and comparative studies are discussed for model verification. Copyright

Development of a variance prioritized multiresponse robust design framework for quality improvement

Purpose – Robust design is a well‐known quality improvement method that focuses on building quality into the design of products and services. Yet, most well established robust design models only consider a single performance measure and their prioritization schemes do not always address the inherent goal of robust design. This paper aims to propose a new robust design method for multiple quality characteristics where the goal is to first reduce the variability of the system under investigation and then attempt to locate the mean at the desired target value.Design/methodology/approach – The paper investigates the use of a response surface approach and a sequential optimization strategy to create a flexible and structured method for modeling multiresponse problems in the context of robust design. Nonlinear programming is used as an optimization tool.Findings – The proposed methodology is demonstrated through a numerical example. The results obtained from this example are compared to that of the traditional ...

Continuous improvement efforts in healthcare: a case study exploring the motivation, involvement and support necessary for success

In production environments, improvement efforts focus on reducing defects and/or improving efficiency. Yet, in service industries, customer satisfaction is often the primary concern; therefore, improvement efforts generally focus on reducing errors or mistakes. One specific type of service environment is a healthcare facility. Because the health and safety of human beings is the primary consideration in the healthcare industry, error reduction is paramount in this field. Healthcare facilities are, however, businesses, so in addition to concerns relative to the quality of patient care and patient safety, they are also interested in reducing operating expenses and improving the efficiency of their operations. In this work, we use a focus group composed of experts within the healthcare field to explore the current issues in healthcare and investigate how the represented healthcare facilities use continuous improvement initiatives such as Lean principles and the Six Sigma methodology to address these issues.

Development of product family-based robust design: a case study

As markets continue to globalise, product diversification and manufacturing flexibility have become requirements for competitive advantage. However, companies have difficultly developing new high-quality products that simultaneously minimise manufacturing complexity. Interrelated products, such as product families, are one solution to this problem. Robust design techniques based on the concept of building quality into a design are increasingly popular in industry. However, developing product families using robust design techniques has not been fully addressed in the literature and consequently is not being used in industry. Therefore, in this paper we develop a new design methodology called Product Family-Based Robust Design to address this area of research. A case study using this approach is presented, and the results are compared to that of the traditional robust design methodology.

Designing efficient Six Sigma experiments for service process improvement projects

The Six Sigma and Design for Six Sigma methodologies often use experiments to improve product or service quality. However in many situations, there are several challenges associated with planning and designing experiments. Specifically for service industries, the qualitative nature of the parameters involved in quality improvement often causes project teams to struggle with how to design effective experiments that are manageable in both size and cost. To address this issue, we propose the development of a new Six Sigma design/improvement methodology in which the quality function deployment process is used to narrow the scope of experimental factors. The purpose of this method is to provide a systematic method for choosing between potentially important factors, in order to facilitate the design, execution, and analysis of experiments in service sectors. To illustrate the proposed approach, we provide a numerical example concerning a quality improvement project for a banking operation.

The interconnectedness among auxiliary benefits and supporting practices within the Quality Function Deployment process

Due to the difficult nature of successfully launching new products, many firms have implemented Design for Six Sigma (DFSS) initiatives within their organisations. One well-known tool within DFSS is Quality Function Deployment (QFD), which is a structured methodology for translating customer needs into technical design requirements. The QFD methodology and its benefits are well-known, yet little has been discussed in the literature concerning how the qualitative benefits of QFD interact to create successful project outcomes and what specific tools support these achievements. The auxiliary benefits discussed here include the promotion of teamwork, provision of documentation, improved communication, deeper understanding of the design problem, increased effectiveness of the decision-making process and improved design creativity. In this paper, a model is developed to show the interconnections among these benefits and the implications of this model are discussed.

Experimental examination on the effectiveness of loss prevention technology: a case study

The negative impact of inventory shrinkage on bottom line performance in the retail industry reaches billions of dollars each year. Previous recommendations suggest that inventory shrinkage is a function of the merchandise tagging strategy. The current technology available for securing merchandise includes both electronic article surveillance (EAS) and radio frequency identification (RFID) systems. However, few studies have empirically examined the impact of tagging strategies on inventory management. The present study attempts to fill this gap in the literature by experimentally investigating the relationship between merchandise tagging and inventory control. Our work includes a field experiment to test the effect of EAS systems on inventory shrinkage in a retail setting. Our findings indicate that merchandise tagging systems exhibit strong capabilities in terms of reducing inventory shrinkage and enhancing retail profitability. These conclusions provide evidence in favour of the logistical and financial benefits of this technology with respect to shrinkage reduction.