Process Variance Research Articles

A Maintenance Model Applying Loss Function Based on the Cpm+ in the Process Mean Shift Problem in Which the Production Volume Decreases

Machines and facilities are physically or chemically degenerated by continuous usage. The representative type of the degeneration is the wearing of tools, which results in the process mean shift. According to the increasing wear level, non-conforming products cost and quality loss cost are increasing simultaneously. Therefore, a preventive maintenance is necessary at some point . The problem of determining the maintenance period (or wear limit) which minimizes the total cost is called the ‘process mean shift problem’. The total cost includes three items: maintenance cost (or adjustment cost), non-conforming cost due to the non-conforming products, and quality loss cost due to the difference between the process target value and the product characteristic value among the conforming products. In this study, we set the production volume as a decreasing function rather than a constant. Also we treat the process variance as a function to the increasing wear rather than a constant. To the quality loss function, we adopted the Cpm+, which is the left and right asymmetric process capability index based on the process target value. These can more reflect the production site. In this study, we presented a more extensive maintenance model compared to previous studies, by integrating the items mentioned above. The objective equation of this model is the total cost per unit wear. The determining variables are the wear limit and the initial process setting position that minimize the objective equation.

Ruin Probability for a Gaussian Process with Variance Attaining its Maximum on Discrete Sets

Ruin probability for a Gaussian locally stationary process is considered in the case where the process variance attains its maximum in a finite number of points. The double sum method is applied to calculate exact asymptotics of the corresponding probability. Also, we consider a family of processes with variance that has a countable set of maximum points containing a limit point.

Fuzzy Xbar and S Control Charts Based on Confidence Intervals

There have been changes since the companies have realized the important role of quality improvement in their success. If they are able to produce high quality products and satisfy demands, then they can survive in competitive global markets. Quality improvement applications aim to decrease variability, which leads to less cost, production time, number of defects, scrap, rework and more customer satisfaction. Quality can be improved by reducing product variability. On the other hand, uncertainty or subjectivity is a part of many engineering and real life problems. However, these problems cannot be solved by traditional methods. This study focuses on constructing Xbar and S control charts in fuzzy environment. The approach is developed by considering the theoretical structure of the Shewhart control charts. The core of the approach depends on the combination of parametric interval estimation and fuzzy statistics. Control limits and samples are presented by fuzzy numbers which ensures to maintain fuzziness in control charts. An important property of the approach is that the fuzzy charts can be reduced to Shewhart control charts. A simulation study was conducted for the performance evaluation of fuzzy Xbar and S control charts. The proposed fuzzy control chart is sensitive to process mean shifts and variance changes, and outperforms the traditional control charts under the changes of variance. In addition, an example from the literature shows that the approach is an effective way of presenting fuzziness in the quality characteristics, which enables the approach to have high applicability to the real life problems.

A Bayesian Control Chart for Monitoring Process Variance

Automation in the service industry is emerging as a new wave of industrial revolution. Standardization and consistency of service quality is an important part of the automation process. The quality control methods widely used in the manufacturing industry can provide service quality measurement and service process monitoring. In particular, the control chart as an online monitoring technique can be used to quickly detect whether a service process is out of control. However, the control of the service process is more difficult than that of the manufacturing process because the variability of the service process comes from widespread and complex factors. First of all, the distribution of the service process is usually non-normal or unknown. Moreover, the skewness of the process distribution can be time-varying, even if the process is in control. In this study, a Bayesian procedure is applied to construct a Phase II exponential weighted moving average (EWMA) control chart for monitoring the variance of a distribution-free process. We explore the sampling properties of the new monitoring statistic, which is suitable for monitoring the time-varying process distribution. The average run lengths (ARLs) of the proposed Bayesian EWMA variance chart are calculated, and they show that the chart performs well. The simulation studies for a normal process, exponential process, and the mixed process of normal and exponential distribution prove that our chart can quickly detect any shift of a process variance. Finally, a numerical example of bank service time is used to illustrate the application of the proposed Bayesian EWMA variance chart and confirm the performance of the process control.

Structured neural network models to improve robust design solutions

The primary objective of robust design (RD) is to identify the ranges of input factors by reducing the process bias (i.e., the deviation between the mean and the desirable target value) and variance to be as small as possible to improve product quality in the manufacturing process offline stage. Well estimated models for the process mean and variance (or standard deviation) in RD have traditionally involved several error assumptions. However, the functional relationships between input and out output factors can be well estimated without these error assumptions by using a neural network (NN). In this paper, we therefore propose an NN-based estimation method as a RD modeling approach. The modeling method based on a feedback NN approach is first integrated in the RD response functions estimation. Two new feedback NN structures are then proposed. Next, the existing recurrent NNs (i.e., Jordan-type and Elman-type NNs) and the proposed feedback NN approaches are suggested as an alternative RD modeling method. Simulation studies are also performed to verify the potential of the proposed new NN-based estimation method. A case study is conducted to determine the effectiveness of the proposed method. Comparative studies between the proposed NN structure-based methods and existing methods (i.e., conventional NN structures, statistical LSM, IP, and RSM) were conducted. The results of these particular simulation and case studies results represents that the proposed approaches may provide slightly better solutions.

A trimmed EWMA control charts for non-normal processes

The traditional exponentially weighted moving average (EWMA) control chart is very popular in statistical process control (SPC) for detecting small shifts in process mean and variance. It performs well under the assumption of normality but when data violate this assumption, a modified approach is needed. In this paper, we proposed a variable control chart for monitoring the process mean by modifying the traditional EWMA control chart for non-normal processes based on the modified trimmed standard deviation (MTSD). The proposed EWMA control charts are based on three different heuristic methods, namely, the Trimmed Exponentially Weighted Moving Average (T-EWMA) control chart, the Trimmed Weighted Standard Deviation Exponentially Weighted Moving Average (TWSD-EWMA) control chart and the Trimmed Weighted Variance Exponentially Weighted Moving Average (TWV-EWMA) control chart. The performances of the control charts under study are evaluated via a Monte-Carlo simulation study by calculating expected out-of-control points and expected widths under non-normal distributions (i.e. Gamma and Exponential). Also, since the EWMA statistic $${Z}_{i}$$ is sensitive to detect mean shifts in the process, the out-of-control average run length ( $${ARL}_{1}$$ ) is considered in the simulation as a best tool to measure the sensitivity. The simulation studies are being carried out for the purpose and results showed that the proposed trimmed control charts performed well for non-normal processes in terms of their shorter expected widths, more number of expected out-of-control points and quick out-of-control signals as compared to non-trimmed versions of control charts. Moreover it is also observed that as weighing constant $$\lambda$$ increases, the sensitivity of the proposed trimmed control charts increases. For illustration purposes, a real-life data set from a public service industry is analyzed which confirming our conclusions from the simulation analysis. Consequently, for estimating of the process mean when the distribution of the data is non-normal, we recommend the trimmed versions of EWMA control charts for the practitioners to be used.

Accounting for sources of uncertainty when forecasting population responses to climate change.

In Focus: Jaatinen, K., Westerbom, M., Norkko, A., Mustonen, O., & Koons, D. N. (2021). Detrimental impacts of climate change may be exacerbated by density-dependent population regulation in blue mussels. Journal of Animal Ecology, 90, 562-573, https://doi.org/10.1111/1365-2656.13377. Conservation strategies for threatened species are increasingly dependent on forecasts of population responses to climate change. For such forecasts to be accurate, they must account for multiple sources of uncertainty, including those associated with projections of future climate scenarios and those associated with the models used to describe population dynamics. While many population forecasts incorporate parameter uncertainty in abiotic effects and process variance related to unexplained temporal variation, most forecasts overlook the importance of evaluating uncertainty in the structure of the population model itself. By accounting for structural uncertainties in a model of population growth for blue mussels, Jaatinen et al. (2021) demonstrated that density-dependent processes are likely to exacerbate adverse effects of climate change and reduce population viability of this keystone species. These findings highlight the importance of incorporating structural unknowns in population forecasts and the value of approaches that account for multiple sources of climate and model uncertainties. Forecasts that capture a range of possible population trajectories under climate change will help ensure efficient allocation of limited conservation resources.

Cognitive Predictors of Precautionary Behavior During the COVID-19 Pandemic.

The attempts to mitigate the unprecedented health, economic, and social disruptions caused by the COVID-19 pandemic are largely dependent on establishing compliance to behavioral guidelines and rules that reduce the risk of infection. Here, by conducting an online survey that tested participants’ knowledge about the disease and measured demographic, attitudinal, and cognitive variables, we identify predictors of self-reported social distancing and hygiene behavior. To investigate the cognitive processes underlying health-prevention behavior in the pandemic, we co-opted the dual-process model of thinking to measure participants’ propensities for automatic and intuitive thinking vs. controlled and reflective thinking. Self-reports of 17 precautionary behaviors, including regular hand washing, social distancing, and wearing a face mask, served as a dependent measure. The results of hierarchical regressions showed that age, risk-taking propensity, and concern about the pandemic predicted adoption of precautionary behavior. Variance in cognitive processes also predicted precautionary behavior: participants with higher scores for controlled thinking (measured with the Cognitive Reflection Test) reported less adherence to specific guidelines, as did respondents with a poor understanding of the infection and transmission mechanism of the COVID-19 virus. The predictive power of this model was comparable to an approach (Theory of Planned Behavior) based on attitudes to health behavior. Given these results, we propose the inclusion of measures of cognitive reflection and mental model variables in predictive models of compliance, and future studies of precautionary behavior to establish how cognitive variables are linked with people’s information processing and social norms.

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states.

Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The application of Nesbet's theorem makes it possible to recast a typical extrapolation task, training on correlation energies for small molecules and predicting correlation energies for large molecules, into an interpolation task based on the properties of orbital pairs. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for different datasets covering total and relative energies for thermally accessible organic and transition-metal containing molecules, non-covalent interactions, and transition-state energies. MOB-ML requires training data from only 1% of the QM7b-T dataset (i.e., only 70 organic molecules with seven and fewer heavy atoms) to predict the total energy of the remaining 99% of this dataset with sub-kcal/mol accuracy. This MOB-ML model is significantly more accurate than other methods when transferred to a dataset comprising of 13 heavy atom molecules, exhibiting no loss of accuracy on a size intensive (i.e., per-electron) basis. It is shown that MOB-ML also works well for extrapolating to transition-state structures, predicting the barrier region for malonaldehyde intramolecular proton-transfer to within 0.35 kcal/mol when only trained on reactant/product-like structures. Finally, the use of the Gaussian process variance enables an active learning strategy for extending the MOB-ML model to new regions of chemical space with minimal effort. We demonstrate this active learning strategy by extending a QM7b-T model to describe non-covalent interactions in the protein backbone-backbone interaction dataset to an accuracy of 0.28 kcal/mol.

Quantification of spatial tumor heterogeneity in immunohistochemistry staining images.

Quantitative immunofluorescence is often used for immunohistochemistry quantification of proteins that serve as cancer biomarkers. Advanced image analysis systems for pathology allow capturing expression levels in each individual cell or subcellular compartment. However, only the mean signal intensity within the cancer tissue region of interest is usually considered as biomarker completely ignoring the issue of tumor heterogeneity. We propose using immunohistochemistry image-derived information on the spatial distribution of cellular signal intensity (CSI) of protein expression within the cancer cell population to quantify both mean expression level and tumor heterogeneity of CSI levels. We view CSI levels as marks in a marked point process of cancer cells in the tissue and define spatial indices based on conditional mean and conditional variance of the marked point process. The proposed methodology provides objective metrics of cell-to-cell heterogeneity in protein expressions that allow discriminating between different patterns of heterogeneity. The prognostic utility of new spatial indices is investigated and compared to the standard mean signal intensity biomarkers using the protein expressions in tissue microarrays incorporating tumor tissues from 1000+ breast cancer patients. . Supplementary data are available at Bioinformatics online.

An improved multiple quality characteristic analysis chart for simultaneous monitoring of process mean and variance of steering knuckle pin for green manufacturing

To solve the problems of overconsumption and the increasing environmental pollution brought by rapid industrial development, governments around the world have been introducing a series of green manufacturing (GM) policies to reduce the negative effects of manufacturing. A high-quality product means less time wasted on interruptions and restarts in the production process as well as less waste and defective products. However, once the quality of the product reaches a certain level (e.g., five Sigma), it becomes harder to attain greater economic and environmental benefits. Clearly, both excessive and inadequate quality exert a profound impact on corporations, society, and the environment. This study presents an improved Qpm multiple quality characteristic analysis chart (Qpm MQCAC) to ensure that product quality meets a specified standard with minimum input. Quality optimization is used to improve quality characteristics which do not meet the standard by joint monitoring of possible shifts in the process mean µ and/or process standard deviation σ. A guide is also provided to ensure successful quality improvement. The proposed approach not only identifies the factors that influence production quality, but avoids excessive consumption of resources, thereby achieving GM goals. The proposed approach is demonstrated in a step-by-step manner using the example of a steering knuckle pin. Future works are also discussed.

Modified control chart for monitoring the variance

ABSTRACT.
 Goal: The main objective of this research paper is to propose a chart, named Modified Control Chart, where the process variance () is allowed to be larger than the in-control variance value () until a maximum value (), as long as the process remains capable, in the sense that it produces a specified (tolerated) small fraction of non-conforming items.
 Design / Methodology / Approach: The research methodology was quantitative approach with statistical analysis of simulated data, to assess the practical impact of variance increase in process control in terms of quality requirements.
 Results: The analysis of the simulated data showed that by using the proposed Modified Control Chart the number of unnecessary interventions in the process could be decreased, contributing to improve its efficiency.
 Limitations of the investigation: The analysis assumed mean and variance known, which is unlikely to occur in real applications. Research considering unknown parameters is also in progress.
 Practical implications: The S2 Modified Control Chart detects only genuinely increases in the process variance, which significantly increase the rate of non-conforming items being produced, preventing unnecessary process stop and assessment for assignable causes if only a small increase in the process variance occurs, contributing for higher process efficiency and reduce costs.
 Originality / Value: This paper introduces a new control chart to monitor the variance of quality characteristics being produced by capable processes, preventing process overcontrol.

Orchestrating Innovation Networks: A Dual Network-Field Perspective

The advent of the digital age has fueled the networked aspect of innovation at an unprecedented scale. In this paper, we examine how organizations orchestrate different types of innovation networks to achieve superior outcomes. We argue that innovation networks are embedded in institutional fields, which in turn imprint those networks with a constellation of beliefs and practices that network actors hold in common. As such, network orchestration is contextualized within the relational and institutional settings within which it takes place. We distinguish among three innovation network types along the exploration-exploitation continuum: research networks, application networks, and dominant design networks. We then analyze the three innovation networks in terms of both their network and field attributes and highlight the corresponding variance of orchestration processes. Importantly, we propose that each innovation network has a distinctive orchestration anchor, a field-level concept, that guides and prioritizes the network orchestration processes for different innovation networks.

Modeling Domestic Price Volatility for Cereal Crops in Ethiopia

The volatility in the domestic prices of maize and teff crops has been found to vary over time from month to month. Thus, Families of time series models namely, ARCH with their extensions to generalized ARCH, GARCH and EGARCH models with ARIMA mean equations were considered to the data. The best fitting model among each family of models was selected based on how well the model captures the variations in the data. The optimal lag specification for the models are accessed via AIC and SBIC. Comparisons of the symmetric and asymmetric selected models were carried out based on the significance of asymmetric term in the EGARCH model. Thus, statistically significance of asymmetric term and least forecast error from the model established that the EGARCH model with GED for residuals was superior to the GARCH model. Therefore, the ARIMA(2,0,3)-EGARCH(1,1) and ARIMA(0,0,3)-EGARCH(2,3) were chosen to be the best fitting models among the ARIMA(p, d, q)-GARCH(P, Q) family for monthly domestic price volatility of maize and teff crops, respectively. However, the volatility in the domestic price of wheat and barley was found to be not changing over time. Hence, the variance of the ARIMA process was used as the measure of volatility in the prices of these two crops which were 0.00112 and 0.0004, respectively. Moreover, it was found that from candidate exogenous variables, import prices for maize crop, fuel oil price, exchange rate (dollar-birr), inflation from non-food items, past shock and volatility on the domestic price had statistically significant effect on the current month domestic price volatility for maize and teff crops.

An adjusted-range based self-normalization test for correlation change

This paper proposes an adjusted-range based self-normalized test for change in correlation. Unlike the self-normalization approach proposed by Lobato (2001) and Shao (2010), which relies on the variance of the partial sum process as a self-normalizer, and used by Choi and Shin (2020) to formulate the self-normalized Q test statistic, here we propose the use of an adjusted-range of the partial sum process instead. Similar to the self-normalized Q test statistic in Choi and Shin (2020), our adjusted-range based correlation change test is also nuisance parameter and distribution free, which helps to circumvent the kernel-based long-run variance (LRV) estimation in Wied et al. (2012) and the computationally expensive bootstrapping in Wied (2017). Moreover, the proposed adjusted-range based correlation change test is a portmanteau test with a neater formulation than Q; it is found to have adequate sizes and powers in many cases and it has much better powers than Q under serial dependence with either homoscedasticity or heteroscedasticity. The empirical analysis also suggests the adequacy of the proposed test.

High order random coefficient INAR model and simulations

This paper develops a high-order INAR model based on random environmental processes, the conditional expectation, conditional variance, and correlation structure of this new process are discussed, Yule-Walker estimators of the parameters in the new model are obtained, and strong consistency of the Yule-Walker estimators is proved. Numerical simulation is studied to test the performance of the estimators in a finite sample.

On the computational estimation of high order GARCH model

To guarantee the non-negativity of the conditional variance of the GARCH process, it is sufficient to assume the non-negativity of its parameters. This condition was empirically violated besides rendering the GARCH model more restrictive. It was subsequently relaxed for some GARCH orders by necessary and sufficient constraints. In this paper, we generalized an approach for the QML estimation of the GARCH(p,q) parameters for all orders $p\geq 1$ and $q\geq1$ using a constrained Kalman filter. Such an approach allows a relaxed QML estimation of the GARCH without the need to identify and/or apply the relaxed constraints to the parameters. The performance of our method is demonstrated through Monte Carlo simulations and empirical applications to real data.

Comparative Analysis Four Different Ways of Calculating Yield Index SSSpk Based on Information of Control Chart, and Six Sigma, to Measuring the Process Performance in Industries: Case Study in Aden’s Oil Refinery, Yemen

Process capability index PCIs have long been used to assess process efficiency in the manufacturing. While PCIs assess the process stability and efficiency, real measurements of product quality depended on estimate the standard deviation for a short-term variance was calculated as the base approach in several previous studies for measuring PCIs. Studies in recent years seek to estimate shift of the process mean from target. In today’s smart manufacturing production climate, manufacturers keep track of production performance. Since the deviance of process mean comparative to the target is different. This allows a more robust norm to be implemented by estimate the relative shift to describe the variance of process in Six Sigma ( SS ). For that purpose, this paper suggests an evaluation method by comparative analysis to four different ways of calculating yield indexes $S_{pk}$ based on six sigma and control information, of $\bar {X}_{S}$ chart. Moreover, this paper extends the univariate process index yield $SSS_{pk}$ to a multi- generalized yield index is called $SSMS_{pk}$ . An established an updated approach to assess univariate and multi-characteristic, based on understanding variation which is critical to quality (CTQ) and focusing on the position and actions of natural process tolerance between defined tolerances limits. To demonstrate the trueness verification proficiency of this approach, this paper includes an industrial case study to assess the process efficiency in Aden’s oil refinery, Yemen. The findings of this study indicated that the indexes which are calculating based on six sigma outperformed existing indices and that this study has important implications for industrial practitioners, researchers and quality control experts interested in the evaluation of process performance.

Noise Reduction in the Accelerometer and Gyroscope Sensor with the Kalman Filter Algorithm

Noise is unwanted signals in a communication or information system. Kalman filter has a good ability to handle noise. This study uses the Kalman filter algorithm that works to reduce noise at the accelerometer and gyroscope sensor output. Data were taken using the accelerometer sensor and the gyroscope sensor in a stationary condition. The device is Arduino Uno for processing the data and MPU6050 for accelerometer and gyroscope sensor. In the Kalman filter algorithm, there is process variance matrix and measurement variance matrix parameters that affect noise attenuation or reduction at the accelerometer and gyroscope output. If the difference between the two parameters is too large, then the attenuation becomes very large and eliminates the original value of the sensor output. Thus, the value cannot be chosen carelessly. The best value is the measurement variance matrix must bigger than the process variance matrix.

The Importance of Knowledge-Based Risk Processes to Risk Analysis

Knowledge-based risk processes are suitable key elements within organizations since they can minimize the possibility and impact of an information technology (IT) project. The aim of this paper is to explain how the alignment between knowledge-based risk processes to validate risk analysis. A questionnaire was developed and distributed to a sample of 135 respondents who were actively engaged in IT. The proposed research model explained that 50 to 62% from of the variance in knowledge-based risk processes to risk analysis. The results showed that the two selected factors (identification and sharing) have a partially mediate and significant impact on risk analysis by knowledge-based risk repository. On the other hand, the examination is not mediated and not a significant impact on risk analysis. The findings of this study imply the understanding of knowledge-based risk processes to risk analysis This study will contribute to the field by examining the effects knowledge-based risk on risk analysis for IT projects.