Search results

Assessing the impact of a quality improvement intervention on an organization is particularly difficult in a high contact service operation where the intangible service encounter is the unit of output. Frequently, accounting or financial data must be used to evaluate the effectiveness of the intervention; however, these data may be problematic with respect to sample size and masking effects due to aggregation. Presents a systems model which describes metaphorically how an unstable process can continue to show no performance gains despite continued input of resources into improvement initiatives. A special type of Shewart control chart, known as the X‐chart, is developed as a methodology for assessing process performance after an improvement programme has been implemented. An X‐chart is used to analyse performance data collected in a real service setting where service quality standards were deployed in the front line phase of the operation. Although traditional analysis of variance concluded that there was no significant improvement in performance, the X‐chart indicates that real performance gains were occurring. The X‐chart provides management with an easy‐to‐use decision tool which can help assess the effectiveness of many different types of organizational change initiatives.

The purpose of this paper is to examine extreme value charts and analyse means based on half logistic distribution.

Variable control charts with subgroup observations based on the extreme values at each subgroup are constructed without specially going to any subgroup statistic. The control chart constants depend on the probability model of the extreme order statistic of each subgroup and the size of the subgroup. Accordingly the proposed chart is normal as extreme value chart. As a by‐product the technique of analysis of means for a skewed population is exemplated through half logistic distribution and extreme value control charts. The results are illustrated by examples on live data.

H.L.D is found to be better test for the data of the three examples, ANOM gave a larger (complete) homogeneity of data than those of Ott.

Supposing arithmetic means of k subgroups of size “n” each drawn from a half logistic model. If these subgroup means are used to develop control charts to assess whether the population from which these subgroups are drawn is operating with admissible quality variations. Depending on the basic population model, we may use the control chart constants developed by the authors or the popular Shewart constants given in any SQC text book. Generally the authors say that the process is in control if all the subgroup means fall within the control limits. Otherwise it is said that the process lacks control.

Half logistic distribution is a better model, exhibiting significant linear relation between sample and population quantiles.

Explains that the shifts of a process may be classified into a set of modes (or classifications), each of which is incurred by an assignable cause. Presents an algorithm to determine the process shift mode and estimate the run length when an out‐of‐control status is signalled by the x‐ or s chart in statistical process control. The information regarding the process shift mode and run length is very useful for diagnosing the assignable cause correctly and promptly. The algorithm includes two stages. First, the process shift modes are established using the sample data acquired during an explorative run. Afterwards, whenever an out‐of‐control case is detected, Bayes’ rule is employed to determine the active process shift mode and estimate the run length. In simulation tests, the proposed algorithm attains a fairly high probability (around 0.85) of correctly determining the active process shift mode and estimating the run length.

Control charts are widely used for both manufacturing and service industries. Traditional Shewart control chart is a very simple procedure, it works well for detecting large process shifts. A criticism of Shewhart charts is that they only use the information about the process contained in the last observation. One method of increasing the sensitivity of Shewhart charts to smaller process shifts is by introducing what are known as supplementary rules.

This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is deployed for recognizing new unseen activities without access to the ground truth. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the recognition model without explicit detection of changes in the model performance.

The approach determines the variation between reference activity data belonging to different classes and newly classified unseen data. If there is coherency between the data, it means the model is correctly classifying the instances; otherwise, a significant variation indicates wrong instances are being classified to different classes. Thus, the approach is formulated as a two-level architectural framework comprising of the off-line phase and the online phase. The off-line phase extracts of Shewart Chart change parameters from the training data set. The online phase performs classification of new samples and the detection of the changes in each class of activity present in the data set by using the change parameters computed earlier.

The approach is evaluated using a real activity-recognition data set. The results show that there are consistent detections that correlate with the error rate of the model.

The developed approach does not use ground truth to detect classifier performance degradation. Rather, it uses a data discrimination method and a base classifier to detect the changes by using the parameters computed from the reference data of each class to discriminate outliers in the new data being classified to the same class. The approach is the first, to the best of the authors’ knowledge, that addresses the problem of detecting within-user and cross-user variations that lead to concept drift in activity recognition. The approach is also the first to use statistical process control method for change detection in activity recognition, with a robust integrated framework that seamlessly detects variations in the underlying model performance.

In the economic design of •‐control charts the two extreme approaches used are that the design parameters are: (i) fixed once determined, and (ii) change from sample to sample. Administrative convenience favours the first approach in practice, although research work indicates, for instance, that constant sampling intervals are not necessarily optimal. This article provides an economic but partially dynamic model where the sampling intervals are of random lengths but other parameters are kept fixed once determined. Random sampling intervals are practical especially in labour‐intensive economies and job‐shop situations where assignable causes are introduced by external factors such as operator skill, fatigue, carelessness and other physical environments. In such work environments, operators have been found to take undue advantage of constant sampling intervals. The model developed here incorporates statistical, economic and administrative considerations in addition to other practical work situations. Some guidelines for implementing the model in the production environment are provided.

We propose a simple control‐charting scheme for simultaneously displaying univariate and multivariate process information. The proposed chart can be used as a diagnostic tool for understanding the nature of out‐of‐control conditions in multivariate statistical process control (SPC). The chart is easy to implement and interpret. Two examples are given for illustration purposes.

Bayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The paper aims to discuss this issue.

The BN provides a graphical and numerical tool to help a manager understand the effect of sample observations on the probability that the process is out-of-control and requires investigation. The parameters for the BN SPC model are statistically designed to minimize the out-of-control average run length (ARL) of the process at a specified in-control ARL and sample size.

The BN model outperforms adaptive np control charts in all experiments, except for some cases where only a large change in the proportion of sample defects is relevant. The BN is particularly useful when small sample sizes are available and when managers need to detect small changes in the proportion of defects produced by the process.

The BN model is statistically designed and parameters are chosen to minimize out-of-control ARL. Future advancements will address the economic design of BNs for SPC with attribute data.

The BNs allow qualitative knowledge to be combined with sample data, and the average percentage of defects can be modeled as a continuous random variable. The framework of the BN easily permits classification of the system operation into two or more states, so diagnostic analysis can be performed simultaneously with statistical inference.

Discusses Shewhart’s control charts and how they have been confined traditionally to the shopfloor in manufacturing industry. Contends that now practitioners are leading a growing interest in the charts’ wider application in areas such as sales, marketing, customer service, and inventory management. Shewhart discovered that variation in a process can result either from common causes (part of the process) or special causes (not part of the process). Shewhart’s charts enable us to learn about processes and improve them with the aid of his plan‐do‐study‐act continuous improvement cycle. Research conducted in Japan showed that companies which won the Deming Prize consistently outperformed the averages in financial measures for the industry.