Testing and analysing capability performance for products with multiple characteristics

Process capability analysis is a vital part of an overall quality improvement programme. Numerous techniques and tools have been proposed for process capability analysis. Among these, indices and charts of process capability are simple and effective tools and widely used in the manufacturing industry. Many scholars have revealed numerous valuable aspects of previously developed tools and methods. Due to the rising demands of product quality, the current tools and methods are insufficient for enabling managers to make informed decisions. To address this gap, this study proposes a hypothesis testing procedure which determines whether the process capabilities satisfy the target level. Furthermore, this study proposes an integrated quality test chart (IQTC), which can display the process potential and performance for an entire product with smaller-the-better, larger-the-better and nominal-the-best specifications. The proposed procedure and IQTC incorporate the quality-level concept of the Six Sigma model and can be used to quantitate the relationships among the quality level, capability indices and process yield. They can be applied to assist managers in measuring, monitoring, analysing and improving process performance in a timely manner which will help ensure that the quality levels of their products meet customer demands. Finally, an example is provided to illustrate how to use the proposed procedure and IQTC.     

The construction and application of Six Sigma quality indices

Process quality is the primary factor in facilitating product sales. In accordance with the concept of Six Sigma, numerous studies have employed process capability indices for the determination of process quality levels. Unfortunately, existing indices present only a range of quality levels rather than a specific quality level value. This paper aims to propose Six Sigma quality indices for the assessment of quality levels associated with unilateral as well as bilateral specifications. To ensure the reliability in process assessment, we employ the lower confidence limit of the indices to serve as a standard and develop a process quality analysis chart for the simultaneous evaluation of larger-the-better, smaller-the-better and nominal-the-best quality characteristics with the aim of identifying the causes of sub-standard quality. The resulting chart also provides a valuable reference by which to guide efforts aimed at improvement. Finally, we present three cases and numerical experiments to demonstrate the practical applicability of the proposed method.     

Statistical Reason for the 1.5σ Shift

Ever since Motorola, Inc. introduced its Six Sigma quality initiative, quality practitioners have questioned why followers of this philosophy add a 1.5σ shift to the average before estimating process capability. Six Sigma advocates claim such an adjustment is necessary but offer only empirical studies as justification for this decision. By examining the sensitivity of control charts to detect changes of various magnitudes, this article provides a statistically based reason for including such a shift in the process average. A new capability index, called dynamic C_pk, incorporating this shift is introduced.     

A rough set based data mining approach for house of quality analysis

As the first phase of quality function deployment (QFD) and the only interface between the customers and product development team, house of quality (HOQ) plays the most important role in developing quality products that are able to satisfy customer needs. No matter in what shape or form HOQ can be built, the key to this process is to find out the hidden relationship between customers’ requirements and product design specifications. This paper presents a general rough set based data mining approach for HOQ analysis. It utilises the historical information of customer needs and the design specifications of the product that was purchased, employs the basic rough set notions to reveal the interrelationships between customer needs and design specifications automatically. Due to the data reduction nature of the approach, a minimal set of customer needs that are crucial for the decision on the correlated design specifications is derived. The end result of the approach is in the form of a minimal rule set, which not only fulfils the goal of HOQ, but can be used as supporting data for marketing purposes. A case study on the product of electrically powered bicycles is included to illustrate the approach and its efficiency.     

Winning customer loyalty in an automotive company through Six Sigma: a case study

Six Sigma is a disciplined approach to improving product, process and service quality. Since its inception at Motorola in the mid 1980s Six Sigma has evolved significantly and continues to expand to improve process performance, enhance business profitability and increase customer satisfaction. This paper presents an extensive literature review based on the experiences of both academics and practitioners on Six Sigma, followed by the application of the Define, Measure, Analyse, Improve, Control (DMAIC) problem‐solving methodology to identify the parameters causing casting defects and to control these parameters. The results of the study are based on the application of tools and techniques in the DMAIC methodology, i.e. Pareto Analysis, Measurement System Analysis, Regression Analysis and Design of Experiment. The results of the study show that the application of the Six Sigma methodology reduced casting defects and increased the process capability of the process from 0.49 to 1.28. The application of DMAIC has resulted in a significant financial impact (over U.S. $110 000 per annum) on the bottom‐line of the company. Copyright © 2006 John Wiley & Sons, Ltd.     

A study on the improvements of new product development procedure performance–an application of design for Six Sigma in a semi-conductor equipment manufacturer

With the pressure of intensive global competition and the trend of shortening product life-cycle, the procedure for new product development (NPD) has become the focus of business concerns and investment of large corporations and the key strategy to upgrade competitiveness of corporations. An effective NPD procedure can meet the demands on quality, time-to-delivery and cost limitations of a corporation. Measuring and analysing the performance of the existing NPD procedure and pinpointing and resolving the problems of the existing NPD procedure is the key to improve the NPD performance to meet a corporation's demands. Six Sigma is a systemic procedure commonly used to improve product quality. This paper proposes a model to evaluate and improve the performance of NPD procedures by following the systemic procedure of Six Sigma and applying criteria defined by application of performance matrix, factor analysis, and theory of constraints. The resulting model is then verified with real life NPD procedures from a certain semi-conductor equipment manufacturer such that corporative competitiveness can be enhanced through the elevating cycle of performance evaluation, problem excavation, causes analysis, and countermeasure determination and execution proposed by this paper.     

Evaluating Six Sigma failure rate for inverse Gaussian cycle times

Six Sigma is a quality philosophy and methodology that aims to achieve operational excellence and delighted customers. The cost of poor quality depends on the sigma quality level and its corresponding failure rate. Six Sigma provides a well-defined target of 3.4 defects per million. This failure rate is commonly evaluated under the assumption that the process is normally distributed and its specifications are two-sided. However, these assumptions may lead to implementation of quality-improvement strategies that are based on inaccurate evaluations of quality costs and profits. This paper defines the relationship between failure rate and sigma quality level for inverse Gaussian processes. The inverse Gaussian distribution has considerable applications in describing cycle times, product life, employee service times, and so on. We show that for these processes attaining Six Sigma target failure rate requires higher quality efforts than for normal processes. A generic model is presented to characterise cycle times in manufacturing systems. In this model, the asymptotic production is described by a drifted Brownian motion, and the cycle time is evaluated by using the first passage time theory of a Wiener process to a boundary. The proposed method estimates the right efforts required to reach Six Sigma goals.     

Prioritizing quality characteristics in dynamic quality function deployment

Due to the combination of rapid influx of new technology, high pressure on time-to-market and increasing globalization, the number of products that have highly uncertain and dynamic specifications or customer requirements might significantly increase. In order to deal with these inherently volatile products or services, we need to adopt a more pro-active approach in order not to produce an unwanted product or service. Thus, based on the idea of the quality loss function and the zero-one goal programming, an intuitively simple mathematical model is developed to prioritize the quality characteristics (QCs) in the dynamic quality function deployment (QFD). It incorporates a pro-active approach towards providing products and services that meet the future voice of the customer (FVOC). The aim is to determine and prioritize only the ‘important’ QCs with a greater confidence in meeting the FVOC. It is particularly useful when the number of the potentially dominant QCs is very large so that, by using the prioritization, the size of the QFD can be effectively reduced. Some constraints, such as minimum customer satisfaction level and limitation on budget are also taken into consideration. A sensitivity analysis is suggested to give an insight to the QFD users in the change of parameters of the proposed model.     

服务产品设计质量保证的质量功能展开集成方法

为处理服务产品设计中散乱的模糊顾客需求信息,客观反映用户质量需求,解决传统质量功能展开方法中判断一致性差和整体满意度的展开精度低等问题,将模糊聚类分析、模糊层次分析法和质量屋方法集成,引入到服务产品设计的质量保证中。通过模糊聚类对顾客质量要求进行层次划分,在此基础上使用模糊层次分析法确定顾客质量需求权重,继而在质量屋关系矩阵中引入模糊集进行评判,从而将自然语言与模糊数相联系,更加准确地把握顾客需求。同时采用模糊数表示权重分析和关系度评判结果,提高了评判的精度和稳定性,为满足服务产品质量设计阶段质量保证工作提供一种量化分析方法。     

Quality evaluation of internal cylindrical grinding process with multiple quality characteristics for gear products

Gears are among the most crucial components in the transmission systems of machine tools. Gear manufacturing includes a number of processing procedures. The grinding process is an important procedure involving high precision and fairly small grinding surfaces. For this reason, this study aimed at developing a quality assessment model for the internal cylindrical grinding process of gears. The Six Sigma quality indices (SSQIs) were used to directly assess the quality of the internal cylindrical grinding process due to their ability to directly reflect quality level and process yield. Since the process may include nominal-the-best (NTB), larger-the-better (LTB) and smaller-the-better (STB) quality characteristics, so we used the variable transformation method to normalise the specifications of each quality characteristic for the convenient and effective management and analysis of process performance for multiple quality characteristics. We then constructed a multi-characteristic process quality analysis chart (MPQAC) to simultaneously assess the quality levels of various quality characteristics. Furthermore, the MPQAC can provide references for process improvement. This ensures the quality of internal cylindrical grinding and enhances the quality of gear and machine tool products. Finally, a real-world application and numerical experiments demonstrate the effectiveness and practical applicability of the proposed method.     

Quality Evaluation of a Manufactured Product with Multiple Characteristics

A majority of manufactured products have many quality characteristics that are important to the customer. To assess or evaluate the capability of a manufactured product with multiple characteristics, the quality characteristics need to be determined in advance and some characteristics can be correlated with each other. Also, the specification limits of the quality characteristics can be one‐sided or two‐sided. In order to deal with such multivariate data, there is a need to develop a new approach. In this study, the geometric distance variable is used to combine the correlated or uncorrelated quality characteristics. Then, the Luceño capability index is used to summarize the performance for each geometric distance variable. Finally, a composite capability index, MC_pc, composed of several univariate capability indices, is proposed to analyze the capability of a manufactured product with multiple characteristics. In addition, the probability of the product non‐conforming is also proposed. The application of the proposed methodology to a real‐life case study is presented. Copyright © 2005 John Wiley & Sons, Ltd.     

Using quality function deployment to capture the voice of the customer and translate it into the voice of the provider.

BACKGROUND: Health care has a number of historical barriers to capturing the voice of the customer and to incorporating customer wants into health care services, whether the customer is a patient, an insurer, or a community. Quality function deployment (QFD) is a set of tools and practices that can help overcome these barriers to form a process for the planning and design or redesign of products and services. The goal of the project was to increase referral volume and to improve a rehabilitation hospital's capacity to provide comprehensive medical and/or legal evaluations for people with complex and catastrophic injuries or illnesses. HIGH-LEVEL VIEW OF QFD AS A PROCESS: The steps in QFD are as follows: capture of the voice of the customer, quality deployment, functions deployment, failure mode deployment, new process deployment, and task deployment. The output of each step becomes the input to a matrix tool or table of the next step of the process. CASE STUDY EXAMPLE: In 3 1/2 months a nine-person project team at Continental Rehabilitation Hospital (San Diego) used QFD tools to capture the voice of the customer, use these data as the basis for a questionnaire on important qualities of service from the customer's perspective, obtain competitive data on how the organization was perceived to be meeting the demanded qualities, identify measurable dimensions and targets of these qualities, and incorporate the functions and tasks into the delivery of service which are necessary to meet the demanded qualities. DISCUSSION: The future of providing health care services will belong to organizations that can adapt to a rapidly changing environment and to demands for new products and services that are produced and delivered in new ways.     

Joint production control and product quality decision making in a failure prone multiple-product manufacturing system

This paper considers joint production control and product quality specifications decision making in unreliable multiple-product manufacturing system. This is with the knowledge that an optimum compromise should guide the decision making process. In fact, tight process specifications will generally lead to products with good quality and higher market values, but at the same time associated with a higher rate of non-conforming parts rejection leading to higher non quality costs and lower plant productivity. Moreover, in unreliable manufacturing context the decision maker should adopt an adequate production policy to hedge against future capacity shortages caused by machine failures in order to meet customer demand. This paper intends to extend previous findings to tackle this problem and study the overall decision making process aiming to guide the production and quality specification decisions in multiple-product context. The overall optimal decision policy is defined here as one that maximises the long term average per unit time profit of a combined measure of quality and quantity dependent sales revenue, minus inventory and backlog costs, in the presence of random plant failures and random repair durations.     

A data-driven approach for the optimisation of product specifications

In order to develop the profit-maximising, market share-maximising or cost-minimising bundle of product engineering specifications with proper performance levels, an optimisation model driven by operating data is proposed. The operating data are input as the sources to conduct the optimisation and a data-based customer satisfaction function can be formed. Then, a customer choice model developed from the customer satisfaction is constructed to estimate the customer choice probability. The expected market share (EMS) then can be derived from the choice probability. After all, a multi-objective model is constructed to maximise the EMS and minimise the total engineering cost. The candidate Pareto-optimal solutions can be obtained by solving the optimisation model. Then a membership function is defined to select the optimal solution from the Pareto-optimal solutions. A case study for optimising the smartphone’s specifications is conducted to demonstrate the effectiveness of the newly developed approach. Compared with the commonly used Conjoint Analysis (CA) method in determining the most desired levels for product specifications, the proposed data-driven method can avoid the situation where the user’s preferences are irrational, making the proposed method be more practical in measuring customer preferences than the utility-based model.     

Integrating Customer Perception into Process Capability Measures

Process capability indices provide a measure of the output of an in‐control process that conforms to a set of specification limits. These measures, which assume that process output is approximately normally distributed, are intended for measuring process capability for manufacturing systems. When the performance of a system results in a product that fails to fall within a given specification range, however, the product is typically scrapped or reworked, and the actual distribution that the customer perceives after inspection is truncated. In this paper, the concept of a truncated measure for three types of quality characteristics is introduced as the key to linking customer perception to process capability. Subsequently, a set of customer‐perceived process capability indices is presented as an extension of traditional manufacturer‐based counterparts. Finally, data transformation‐based process capability indices are also discussed. A comparative study and numerical example reveal considerable differences among the traditional and proposed process capability indices. It is believed that the proposed process capability index for various quality characteristics may more aptly lead to process improvement by facilitating a better understanding of the integrated effects found in engineering design problems. Copyright © 2015 John Wiley & Sons, Ltd.     

Probability Constrained Optimization as a Tool for Functional Design for Six Sigma

An important up-stream activity in the overall design of a system is the so-called functional design wherein the means and tolerances of the design variables are determined with respect to the competing demands of quality and cost. In this article probability constrained optimization is invoked to produce a functional design that focuses on the goal of design for Six Sigma (i.e., improved customer satisfaction, robustness, and predictable cost levels). Herein, a maximum system probability of nonconformance is obtained from a prescribed defect rate that in turn provides the primary design constraint. The production cost provides the objective function to be minimized in order to allocate the design parameters. All three quality metrics (e.g., target/larger/smaller-is-best) and robustness are inherent in the approach. The design of an electro-mechanical servo system serves as a case study wherein three responses are related to three control variables and two noise variables by mechanistic models. Designs for selected defect rates show the practicality and potential of the approach.     

An Objective-Oriented Rating System of Vendor Quality Performance

This article establishes a rating system for vendor's performance from the orientation of customer satisfaction by objective-orientation driving. Its purpose is to provide a methodology of “how to integrate the capability between vendor and manufacturer and to apply different strategies under the needs of quality improvement.”

The study is undertaken to specify the interaction and the mutual movement among the three groups in the supply chain “Vendor-Manufacturer-Customer” and to integrate the results of four factors: incoming inspection, performance of line reject, service quality of vendor, and product reliability. It tries to transform these factors into measurable, quantitative, just-in-time (JIT) parameters that are utilized in the planning and establishment of this rating system of vendor's performance. The concept of objective orientation is adopted in the “performance” calculations.

We obtain a very satisfactory completeness, flexibility, effectiveness, and sensitivity of parameters-to-model from the results of simulation and demonstration. The multi-parameters rating system of this study can be very beneficial to manufacturers in selecting the best vendor and in driving quality improvements.     

Circuit Design Optimization Based on Quality Cost Estimation

The variations in component parameter values embodied in an electronic circuit can result in it failing to conform to its specification. Such failures can cause the manufacturer to incur significant quality costs, arising from factors including warranty returns and legal liabilities. It can be difficult to predict both the probability that a circuit will fail to meet its specification and the consequent quality costs. It can also be difficult to determine which component parameter variations are most significant in causing faults. In this paper we introduce the concept of functional capability, which is a variation on the familiar process capability, as a measure of the performance variation found in a circuit relative to the specified range of acceptable performance. Based on this capability measure, along with a failure modes and effects analysis and a cost model, we are able to relate functional capability to quality costs. In addition, we introduce a capability breakdown which allows the effect of different parameter variations to be readily visualized so that the designer may focus effort where it will be most effective in reducing quality costs. The resulting technique is primarily intended to aid the design of printed circuit board level analogue and mixed signal circuits, particularly in safety critical applications. The paper illustrates the technique through analysis of a simple circuit and the analysis of a commercial mixed signal circuit. It also shows how the technique may be integrated within a capable design, manufacture and test analysis process. Copyright © 2005 John Wiley & Sons, Ltd.     

Two-phase selection framework that considers production costs of suppliers and quality requirements of buyers

Buyers are faced with selecting the optimal supplier, while suppliers are left to consider production costs. In this study, we developed a two-phase selection framework that allows buyers to evaluate the performance of suppliers while taking production costs into account for value maximisation. This scheme is a win-win solution capable of promoting long-term relationships between buyers and suppliers. Under the assumption of normality, the first phase involves constructing a new Six Sigma quality capability analysis chart (SSQCAC) which takes production costs into account. The objective is to evaluate all potential suppliers using the 100?×?(1–α)% upper confidence limit (UCL) of an integrated Six Sigma quality index (SSQI) Q_PIh when dealing with products with smaller-the-better (STB), larger-the-better (LTB), or nominal-the-best (NTB) quality characteristics. According to interval estimation theory, this method can have a significant impact on the consumption of resources; i.e. the production costs of the supplier can be decreased by reducing the production quality to below that required by the buyer. The proposed method also filters out unsuitable suppliers in order to simplify the decision problem and reduce computational demands and operational risks/costs without compromising the quality of the final product. In the second phase, a detailed analysis is conducted using Euclidean distance measure to select the optimal supplier from among the remaining candidates. We conducted a real-world case study to evaluate the efficacy of the proposed method. We also conducted comparisons with existing methods to demonstrate the advantages of the proposed method and its managerial implications. Suggestions for future study are also provided.     

Quality-yield measure for production processes with very low fraction defective

Process yield is the most common criterion used in manufacturing industry for measuring process performance. A more advanced measurement formula, called the quality yield index, Y _q , is proposed to calculate the quality yield for arbitrary processes by taking customer loss into consideration. Y _q penalizes yield for the variation of the product characteristics from its target, which presents a measure of the average product loss. Quality yield could be expressed as the traditional yield minus the truncated expected relative loss within the specifications to quantify how well a process can reproduce product items satisfactory to the customers. The paper proposes a reliable approach for measuring quality yield by converting the estimate into a lower confidence bound for processes with a very low fraction of defectives. The lower confidence bound not only provides information about actual process performance that is tightly related to both the fraction of defective units and customer quality loss, but also is useful in making decisions for capability testing.