Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory

In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.

Critical Factors in Process Quality of Engineering Construction Projects during Building Design Phase

Quality in construction is an important topic in the design phase due to the quick change in technological advancement. The current study focuses on the identification, measurement, and analysis of the critical elements which impact the process quality of building construction projects during the design stage in Pakistan. Elements were graded using the conservative Relative Importance Index (RII) and Grey Absolute Decision Analysis (GADA). The findings indicate that during the design phase, the critical element impacting process quality is the quality of codes and standards and was facilitated using a questionnaire survey. Similarly, the teamwork of parties in design firms and constructability analysis of the design are key factors during the design phase in Pakistan. Measures for the improvement of process quality in Pakistan were suggested. The building construction projects need to be modified to cover the important aspects of constructability analysis, continuous working on training programs, and many other related activities. These are performed through a proper commitment to continuous quality improvement in building construction projects. The results are important for improving process quality during the construction projects' design phase.

Policy Spotlight Effect in Critical Time-Sensitive Diseases

Background: Categorization of hospital emergency capability (CHEC) is a policy implemented worldwide to regionalize critical emergent care. The CHEC policy mainly uses time-based indicators as emergency care quality measurements.Objectives: We aimed to explore the CHEC policy spotlight effect on critical time-sensitive diseases with and without the influence of time-based surveillance indicators and guidelines. Research Design: We conducted a nationwide retrospective cohort study between 2005–2011. Regarding critical time-sensitive diseases, our study targeted acute ischemic stroke (AIS), ST-segment elevation myocardial infarction (STEMI), septic shock, and major trauma. We selected diagnosis and treatment guideline adherence as process quality measures and defined medical utilization, upward transfer rate, and short-term mortality rate as outcome indicators. Subjects: The Taiwan National Health Insurance 2005 Longitudinal Health Insurance Database contains one million random cases, including medical records and hospital information. Results: During this 7-year study AIS, STEMI, septic shock, and major trauma, respectively. AIS and STEMI cohorts had significantly higher rates of guideline adherence and better process quality than those of septic shock and major trauma cohorts. Furthermore, AIS and STEMI cohorts had a significant increase in diagnosis costs. Conclusion: The CHEC policy spotlight effect exists in critical time-sensitive diseases with time-based quality indicators. Importantly, disease entities without these indicators may experience decreases in diagnosis and treatment guideline adherence, indirectly jeopardizing their outcomes.

Outcome Quality of Inpatient and Day-Clinic Treatment in Child and Adolescent Psychiatry—A Naturalistic Study

Background: Child and adolescent psychiatry has only recently been established as a separate specialty and is practiced in different settings. The epidemiology of psychological problems in childhood is high and varied, thus qualitative work is essential. Assessment of outcome as part of quality management is central to assure the service of psychiatric care to be effective. Method: Over a three-year period consecutively admitted patients from inpatient and day-clinic treatment were prospectively evaluated. A total of 200 from 442 patients (m = 80, f = 120; age 15.1 ± 2.8 y) agreed to participate. Patients, caregivers, and therapists answered a range of questionnaires to provide a multi-personnel rating. Questionnaires used for outcome assessment were Child Behavior Checklist (CBCL) and Youth-Self-Report (YSR) (at admission, discharge, and 6 weeks after discharge) and the problem score of the Inventory of Quality of Life for children (ILK), treatment satisfaction, and process quality by the Questionnaire for Treatment Satisfaction (FBB, at discharge) and as real-life outcome control assessment of quality of life (ILK) was added (admission, discharge, and 6 wks after discharge). Results: There was a significant reduction in psychopathologicalsymptoms (CBCL, YSR) and in the problem score. Furthermore, there was a significant increase in quality of life. QoL score and YSR/CBCL scores returned to normal levels. Treatment satisfaction was high and so was satisfaction with process quality. Factors significantly influencing outcome were severity of disease and the relationship to the therapist. No differences were found for gender and setting. Conclusion: The quality management analysis revealed significant improvements of symptom load, a significant increase in QoL and a high treatment satisfaction. Furthermore, process quality was scored highly by parents and therapists.

Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing

We develop a data-driven decision model to improve process quality in manufacturing. A challenge for traditional methods in quality management is to handle high-dimensional and nonlinear manufacturing data. We address this challenge by adapting explainable artificial intelligence to the context of quality management. Specifically, we propose the use of nonlinear modeling with Shapley additive explanations to infer how a set of production parameters and the process quality of a manufacturing system are related. Thereby, we contribute a measure of process importance based on which manufacturers can prioritize processes for quality improvement. Grounded in quality management theory, our decision model selects improvement actions that target the sources of quality variation. The decision model is validated in a real-world application at a leading manufacturer of high-power semiconductors. Seeking to improve production yield, we apply our decision model to select improvement actions for a transistor chip product. We then conduct a field experiment to confirm the effectiveness of the improvement actions. Compared with the average yield in our sample, the experiment returns a reduction in yield loss of 21.7%. Furthermore, we report on results from a postexperimental rollout of the decision model, which also resulted in significant yield improvements. We demonstrate the operational value of explainable artificial intelligence by showing that critical drivers of process quality can go undiscovered by the use of traditional methods. This paper was accepted by Charles Corbett, operations management.

The Association Between Toddlers’ Temperament and Well-Being in Norwegian Early Childhood Education and Care, and the Moderating Effect of Center-Based Daycare Process Quality

Children who experience well-being are engaging more confidently and positively with their caregiver(s) and peers, which helps them to profit more from available learning opportunities and support current and later life outcomes. The goodness-of-fit theory suggests that children’s well-being might be a result of the interplay between their temperament and the environment. However, there is a lack of studies that examined the association between children’s temperament and well-being in early childhood education and care (ECEC), and whether this association is affected by ECEC process quality. Using a multilevel random coefficient approach, this study examines the association between toddlers’ (N = 1,561) temperament (shyness, emotionality, sociability, and activity) and well-being in Norwegian ECEC and investigates whether process quality moderates this association. Results reveal an association between temperament and well-being. Staff-child conflict moderates the association between shyness and well-being, and between activity and well-being. Moreover, high emotional behavioral support moderates the association between activity and well-being. Extra attention should be paid by the staff to these children’s needs.

Understanding online review behaviors of patients in online health communities: an expectation-disconfirmation perspective

PurposeAlthough online health communities (OHCs) and online patient reviews can help to eliminate health information asymmetry and improve patients' health management, how patients write online reviews within OHCs is poorly understood. Thus, it is very necessary to determine the factors influencing patients' online review behavior in OHCs, including the emotional response and reviewing effort.Design/methodology/approachBased on expectation-disconfirmation theory, this study proposes a theoretical model to analyze the effects of service quality perception (i.e. outcome quality and process quality perceptions) and disconfirmation (i.e. outcome quality and process quality disconfirmations) on patients' emotional response and reviewing effort. The authors test the research model by using empirical data collected from a popular Chinese OHC and applying ordinary least squares (OLS) regression and zero-truncated negative binomial (ZTNB) regression models.FindingsBoth service quality perception and disconfirmation have a positive effect on patients' positive emotional intensity in textual reviews, and disease severity enhances these relationships of process quality. Moreover, there is an asymmetric U-shaped relationship among service quality perception, disconfirmation and reviewing effort. Patients who perceive low service quality have higher reviewing effort, while service quality disconfirmation has the opposite relationship. Specifically, patients' effort in writing textual reviews is lowest when perceived outcome quality is 3.5 (on a five-point scale), perceived process quality is 4 or outcome quality and process quality disconfirmations are −1.Originality/valueThis study is the first to examine patients' online review behavior and its motivations and contributes to the literature on online reviews and service quality. In addition, the findings of this study have important management implications for service providers and OHC managers.