Search results

A majority of products for manufacturing or consumers have multiple characteristics that must meet the requirements of the customer. For example, a steel beam any have dimensional tolerances on its length, width, or height and functional tolerances on its strength. The characteristics are influenced by different processes that create the product. For an individual characteristic, process capability measures exist that convey the degree to which the characteristic meets the specification requirements. Such measures may indicate the proportion of nonconforming product related to the particular characteristic, under some distributional assumptions of the characteristic. For products with multiple characteristics, the unit costs of rectification may be different, making the satisfaction of some characteristics meeting customer requirements more important than others. In this paper, an aggregate process capability performance measure is developed that considers the relative importance of the characteristic based on unit costs of nonconformance. Based on the aggregate measure, appropriate process capability measures for the individual measures are also derived. Bounds on the aggregate capability measures are also established.

The purpose of this paper is to seek effective measurement methods that reflect the real value of process capital.

From a system model perspective, the authors refined the existing knowledge of process measurement by distinguishing three kinds of indicator for the value of process capital: input, output, and the capability to manage process capital. The design of this study, therefore, incorporates a longitudinal analysis of the content of process capital and traces its evolution by attaching a monetary value to activities and assets.

The tested results reveal that the input measure is a less effective measure for process capital, while the output measure is a valid one for measuring operational and managerial performance of process capital. The capability to manage process capital can predict all dimensions of process capital in both the short‐ and long‐term periods.

A practical view of process capital enhances the current understanding of process capital by highlighting the sustainability of process value and the validity of measuring output and management capability of the process capital. Second, the study results also explain the productivity paradox because of the complexity of the hidden cost of process input and the distinctive capability of organizations in managing technology and complementary resources. Finally, the system view of process capital, from input through process to output of the process capital, with operationalized measures, provides a useful reference for examining intellectual capital.

The findings offer a more robust definition of process capital as a firm's established capability to exploit the knowledge of business processes and organize resources in designing and managing business activities for sustained value.

Assert that capability indices quantify process improvement in a simple way and, when used correctly, provide relevant benchmarks. Considers it important that managers fully understand the power and limitations of this quality tool. Asserts the process is on target when Cp and Cpk are equal. Contends that the observed differences are due to sampling error and that any capability index is simply an estimate of an unknown value. Concludes that the measurement of process capability and the assessment on internal and external suppliers performance using indices, is now widespread. Despite any problems, process capability indices are preferable to many other measures of process or supplier performance.

The purpose of this paper is to obtain a better understanding on robust performance of a hardening and tempering process producing component worm shaft used in the steam power plant. This research is capable to explaining the variation of process capability in terms of robustness.

This paper proposed a methodology (a combination of simulation, regression modelling and robust design technique) to study robustness of a hardening and tempering process producing component worm shaft used in the steam power plant and process capability acts as a surrogate measure of robustness. In each experimental run, the values of responses and the corresponding multivariate process capability indices across the outer array are determined. The variation of process performance (process capability values) due to random noise variation is studied using a general purpose process control chart (R-chart).

The results provide useful information in term of insensitiveness of the process against the noise (raw material and process noise) variation where the process capability acts as a surrogate measure of process robustness and explains the variation of process capability in term of robustness.

This paper adds to the body of knowledge on robustness of a manufacturing process. This paper may be of particular interest to practicing engineers as it suggests what factors should be more emphasis to achieve robust (consistent) performance from the process.

The originality of this paper lies within the context in which this study is to address key relationships between process robustness and process capability in a manufacturing industry.

Overall equipment effectiveness (OEE) and process capability (PC) are commonly used and well‐accepted measures of performance in industry. These measures, however, are traditionally applied separately and with different purposes. The purpose of this paper is to investigate the relationship between OEE and PC, how they interact and impact each other, and the possible effect that this relationship may have on decision making.

The paper reviews the OEE and PC background. Then, a discrete‐event simulation model of a bottling line is developed. Using the model, a set of experiments are run and the results interpreted using graphical trend and impact analyses.

The paper demonstrates the relationship between OEE and PC and suggests the existence of a “cut‐off point” beyond which improvements in PC have little impact on OEE.

PC uses the capability indices (CI) to help in determining the suitability of a process to meet the required quality standards. Although statistically a Cp/Cpk equal to 1.0 indicates a capable process, the generally accepted minimum value in manufacturing industry is 1.33. The results of this investigation challenge the traditional and prevailing knowledge of considering this value as the best PC target in terms of OEE.

This paper presents a study where the relationship between two highly used measures of manufacturing performance is established. This provides a useful perspective and guide to understand the interaction of different elements of performance and help managers to take better decisions about how to run and improve their processes more efficiently and effectively.

The current paradigm for assessing overall equipment effectiveness (OEE) is challenged as being anachronistic to the needs of businesses that now require a more holistic indicator of plant and process effectiveness. The purpose of this paper is to introduce a new framework that expands the original OEE measure to inform business performance at multiple levels focusing on adding benchmarkable indicators of asset management effectiveness and process capability. The ability to compare internal performance against external competition and vice verse is argued as being a critical attribute of any performance measurement system.

The research methodology taken incorporated an action research approach using a pilot study combining case study research with an action research process of planning, observing and reflecting summarized as taking an action case research design.

The OEE and related literature is replete with many different enhancements to the original OEE framework. Many of the revised OEE frameworks move away from a standard OEE format taking away the opportunity to benchmark against plant and process performance at multiple levels.

The enhanced OEE framework is developed and tested in situ at a single factory manufacturing large batches of similar products. Future research should look to further develop the OEE framework in both continuous process environments and asset intensive service industry environments.

The enhanced OEE framework introduces a measure of Six Sigma process capability using extant data from the OEE framework. Similarly, indicators of plant reliability, maintainability and asset management effectiveness are calculated taking extant data from the OEE framework. This enhanced OEE framework combines measures of process effectiveness, asset management effectiveness, gross process performance, net process performance and Six Sigma process capability into a single lean Six Sigma key performance indicator of process/plant performance.

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.

Statistical process control seeks to monitor the quality characteristics stipulated in the product design specifications to assure that these are achieved by the operation. Measuring devices are subject to variations but usually the possibility of obtaining fluctuating results is often neglected and the measurements provided by these devices are taken for granted as true values. Presents, briefly, process control and shows the interaction between process capability and measuring device error. Presents a model for determining process target limits which minimize cost of production taking into account measuring device variability. Provides a criterion for establishing optimal process capability as well.

The purpose of this paper is to implement statistical process control (SPC) in service quality using three-level SERVQUAL, quality function deployment (QFD) and internal measure.

The SERVQUAL questionnaire is developed according to internal services of train. Also, it is verified by reliability scale and factor analysis. QFD method is employed for translating SERVQUAL dimensions’ importance weights which are derived from Analytic Hierarchy Process into internal measures. Furthermore, the limits of the Zone of Tolerance are used to determine service quality specification limits based on normal distribution characteristics. Control charts and process capability indices are used to control service processes.

SPC is used for service quality through a structured framework. Also, an adapted SERVQUAL questionnaire is created for measuring quality of train’s internal services. In the case study, it is shown that reliability is the most important dimension in internal services of train for the passengers. Also, the service process is not capable to perform in acceptable level.

The proposed algorithm is practically applied to control the quality of a train’s services. Internal measure is improved for continuous data collection and process monitoring. Also, it provides an opportunity to apply SPC on intangible attributes of the services. In the other word, SPC is used to control the qualitative specifications of the service processes which have been measured by SERVQUAL.

Since SPC is usually used for manufacturing processes, this paper develops a model to use SPC in services in presence of qualitative criteria. To reach this goal, this model combines SERVQUAL, QFD, normal probability distribution, control charts, and process capability. In addition, it is a novel research on internal services of train with regard to service quality evaluation and process control.

In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions.

In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMC _p , RNMC _pm and RNMC _pu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMC _p and NMC _pm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case.

A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices.

Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system.

Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions.