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The purpose of this paper is to devise a robust statistical process control methodology that will enable service managers to better monitor the performance of correlated service measures.

A residuals control chart methodology based on least absolute value regression (LAV) is developed and its performance is compared to a traditional control chart methodology that is based on ordinary least squares (OLS) regression. Sensitivity analysis from the goal programming formulation of the LAV model is also performed. The methodology is applied in an actual service setting.

The LAV based residuals control chart outperformed the OLS based residuals control chart in identifying out of control observations. The LAV methodology was also less sensitive to outliers than the OLS approach.

The findings from this study suggest that the proposed LAV based approach is a more robust statistical process control method than the OLS approach. In addition, the goal program formulation of the LAV regression model permits sensitivity analysis whereas the OLS approach does not.

This paper shows that compared to the traditional OLS based control chart, the LAV based residuals chart may be better suited to actual service settings in which normality requirements are not met and the amount of data is limited.

This paper is the first study to use a least absolute value regression model to develop a residuals control chart for monitoring service data. The proposed LAV methodology can help service managers to do a better job monitoring related performance metrics as part of a quality improvement program such as six sigma.

This article deals with how to test for and evaluate interdependence among control practices in a management control system using structural equation modeling. Empirical research on the levers of control (LOC) framework is used as an example. In LOC research, a path model approach to interdependence has been developed. The appropriateness of this approach is evaluated, developed, and compared with the correlation of residuals approach (seemingly unrelated regression) implemented in the wider complementarity literature. Empirical examples of the different models are shown and compared by using a data set on LOC of 120 SBUs in Sweden. The empirical results show that modeling interdependence among control practices in a management control system as non-recursive (bi-directional) paths or as residual correlations evidently affects the conclusions drawn about interdependence in terms of both presence and magnitude. The two models imply different views on how to conceptualize interdependence and are not statistically and empirically comparable. If using non-recursive path models, several model specification issues appear. To be able to identify such models, this needs to be carefully considered in the theory and research design prior to data collection.

The study aims to design a control chart based on an exponentially weighted moving average (EWMA) chart of Pearson's residuals of a model of negative binomial regression in order to detect possible anomalies in mortality data.

In order to evaluate the performance of the proposed chart, the authors have considered official historical records of death of children of Ecuador. A negative binomial regression model was fitted to the data, and a chart of the Pearson residuals was designed. The parameters of the chart were obtained by simulation, as well as the performances of the charts related to changes in the mean of death.

When the chart was plotted, outliers were detected in the deaths of children in the years 1990–1995, 2001–2006, 2013–2015, which could show that there are underreporting or an excessive growth in mortality. In the analysis of performances, the value of λ = 0.05 presented the fastest detection of changes in the mean death.

The proposed charts present better performances in relation to EWMA charts for deviance residuals, with a remarkable advantage of the Pearson residuals, which are much easier to interpret and calculate. Finally, the authors would like to point out that although this paper only applies control charts to Ecuadorian infant mortality, the methodology can be used to calculate mortality in any geographical area or to detect outbreaks of infectious diseases.

Using the NBER Shared Capitalism Database comprised of over 40,000 employee surveys from 14 firms, we investigate worker attitudes toward employee ownership, profit sharing, and variable pay. Specifically, our study uses detailed survey questions on preferences over profit sharing, forms of employee ownership like company stock and stock option ownership, as well as preferences over variable pay in general, to explore how preferences for these different types of output-contingent pay vary with worker risk aversion, residual control, and views of co-workers and management. Our key results show that, on average, workers want at least a part of their compensation to be performance-related, with stronger preferences for output-contingent pay schemes among workers who have lower levels of risk aversion, greater residual control over the work process, and greater trust of co-workers and management.

Process monitoring is a way to manage the quality characteristics of products in manufacturing processes. Several process monitoring based on machine learning algorithms have been proposed in the literature and have gained the attention of many researchers. In this paper, the authors developed machine learning-based control charts for monitoring fraction non-conforming products in smart manufacturing. This study proposed a relevance vector machine using Bayesian sparse kernel optimized by differential evolution algorithm for efficient monitoring in manufacturing.

A new approach was carried out about data analysis, modelling and monitoring in the manufacturing industry. This study developed a relevance vector machine using Bayesian sparse kernel technique to improve the support vector machine used to both regression and classification problems. The authors compared the performance of proposed relevance vector machine with other machine learning algorithms, such as support vector machine, artificial neural network and beta regression model. The proposed approach was evaluated by different shift scenarios of average run length using Monte Carlo simulation.

The authors analyse a real case study in a manufacturing company, based on best machine learning algorithms. The results indicate that proposed relevance vector machine-based process monitoring are excellent quality tools for monitoring defective products in manufacturing process. A comparative analysis with four machine learning models is used to evaluate the performance of the proposed approach. The relevance vector machine has slightly better performance than support vector machine, artificial neural network and beta models.

This research is different from the others by providing approaches for monitoring defective products. Machine learning-based control charts are used to monitor product failures in smart manufacturing process. Besides, the key contribution of this study is to develop different models for fault detection and to identify any change point in the manufacturing process. Moreover, the authors’ research indicates that machine learning models are adequate tools for the modelling and monitoring of the fraction non-conforming product in the industrial process.

Industrial robots are extensively deployed to perform repetitive and simple tasks at high speed to reduce production time and improve productivity. In most cases, a compliant gripper is used for assembly tasks such as peg-in-hole assembly. A compliant mechanism in the gripper introduces flexibility that may cause oscillation in the grasped object. Such a flexible gripper–object system can be considered as an under-actuated object held by the gripper and the oscillations can be attributed to transient disturbance of the robot itself. The commercially available robots do not have a control mechanism to reduce such induced vibration. Thus, this paper aims to propose a contactless vision-based approach for vibration suppression which uses a predictive vibrational amplitude error-based second-stage controller.

The proposed predictive vibrational amplitude error-based second-stage controller is a real-time vibration control strategy that uses predicted error to estimate the second-stage controller output. Based on controller output, input trajectories were estimated for the internal controller of the robot. The control strategy efficiently handles the system delay to execute the control input trajectories when the oscillating object is at an extreme position.

The present controller works along with the internal controller of the robot without any interruption to suppress the residual vibration of the object. To demonstrate the robustness of the proposed controller, experimental implementation on Asea Brown Boveri make industrial robot (IRB) 1410 robot with a low frame rate camera has been carried out. In this experiment, two objects have been considered that have a low (<2.38 Hz) and high (>2.38 Hz) natural frequency. The proposed controller can suppress 95% of vibration amplitude in less than 3 s and reduce the stability time by 90% for a peg-in-hole assembly task.

The present vibration control strategy uses a camera with a low frame rate (25 fps) and the delays are handled intelligently to favour suppression of high-frequency vibration. The mathematical model and the second-stage controller implemented suppress vibration without modifying the robot dynamical model and the internal controller.

The theory and method of the gray model control with removed residuals is used first for the study of active vibration control of a rotor system. For the symmetric rotor bearing system having a single disk, a scheme of gray model control with removed residuals about the rotor vibration is designed in this paper. The results of simulated calculation showed that this control scheme not only has good effectiveness, but also can be realized easily.

The purpose of this paper is to develop a simple analytical model for predicting the through-thickness distribution of residual stresses in a cold spray (CS) deposit-substrate assembly.

Layer-by-layer build-up of residual stresses induced by both the peening dominant and thermal mismatch dominant CS processes, taking into account the force and moment equilibrium requirements. The proposed model has been validated with the neutron diffraction measurements, taken from the published literature for different combinations of deposit-substrate assemblies comprising Cu, Mg, Ti, Al and Al alloys.

Through a parametric study, the influence of geometrical variables (number of layers, substrate height and individual layer height) on the through-thickness residual stress distribution and magnitude are elucidated. Both the number of deposited layers and substrate height affect residual stress magnitude, whereas the individual layer height has little effect. A good agreement has been achieved between the experimentally measured stress distributions and predictions by the proposed model.

The proposed model provides a more thorough explanation of residual stress development mechanisms by the CS process along with mathematical representation. Comparing to existing analytical and finite element methods, it provides a quicker estimation of the residual stress distribution and magnitude. This paper provides comparisons and contrast of the two different residual stress mechanisms: the peening dominant and the thermal mismatch dominant. The proposed model allows parametric studies of geometric variables, and can potentially contribute to CS process optimisation aiming at residual stress control.

The purpose of this paper is to develop an approach that can detect abnormal deviations in the time series models for technology forecasting. The detected modifications provide a basis for understanding the determinants and impact of the corresponding change.

The proposed approach is based on monitoring residual values (the difference between the observation and the forecasted value) continuously using statistical control charts (SCCs). The residuals that are out of the expected limits are considered an alert indicating a remarkable change. To demonstrate the use of the proposed approach, a time series model was fitted to a number of TV-related patent counts. Subsequently, model residuals were used to determine the limits of the SCCs.

A number of patents granted in the year 2012 violated the upper control limit. A further analysis has shown that there is a linkage between the abnormal patent counts and the emergence of LCD TVs.

Change in technology may dramatically affect the accuracy of a forecasting model. The need for a parameter update indicates a significant change (emergence or death of a technology) in the technological environment. This may lead to the revision of managerial actions in R&D plans and investment decisions.

The proposed methodology brings a novel approach for abnormal data detection and provides a basis for understanding the determinants and impact of the corresponding change.

The purpose of this paper is to demonstrate the application of the control chart procedure to monitor the characteristics whose profile over time resembles a set of connected line segments.

Fit a regression spline model by taking the subgroup average of the characteristic as response variable and time as the explanatory variable. Then monitor the response variable using the regression spline control chart with the fitted model as center line and upper and lower control limits at three standard deviation units of the response variable above and below the center line.

The proposed chart is successfully deployed to monitor the daily response time profile of a client server of an application support process. The chart ensured the stability of the process as well as detected the assignable cause leading to the slowing down of the server performance.

The methodology can be used to monitor any characteristics whose performance profile over time resembles a set of connected line segments. Some of the examples are the consumption profile of utility providers like power distribution companies, usage profiles of telecom networks, loading profile of airline check-in process, e-commerce websites, etc.

To the best of the author’s knowledge, construction of control charts using regression spline is new. The usage of the control chart to monitor the performance characteristics which exhibits a nonlinear profile over time is also rare.