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In many applied and industrial settings, the use of Artificial Intelligence (AI) for quality technology is gaining growing attention. AI refers to the broad set of techniques which replicate human ...

Gait analysis is a standard practice used by clinicians and researchers to identify abnormalities, examine disease progression, or assess the success of interventions. Traditionally, assessments were performed with visual inspection by a trained professional. However, with the recent breakthroughs in sensing technologies, there is a growing body of literature that uses features extracted from sensing data as inputs to machine learning methods. These models require a large representative sample of gait cycles labeled according to each category of interest (e.g. standard, anomalous) for model training. This paper provides a personalized, nonparametric statistical framework that can be used for detecting and interpreting gait changes in individuals while requiring only a small number of baseline gait cycles. This framework can be applied using the acceleration trajectory or features from a single Intertial Measurement Unit (IMU). The individualized framework does not require the gait cycles to be labeled and does not require the assumption that the observed patterns are consistent across subjects. The personalized framework is applied to gait cycles extracted from a material handling task that simulates moving heavy loads in a warehouse. Twelve subjects were monitored and significant changes in personalized gait patterns consistent with perceived exertion were observed. Further interpretation of the changes illustrates that participants exhibit individualized patterns in gait as they approach the fatigued state.

Although confirmatory modeling has dominated much of applied research in medical, business, and behavioral sciences, modeling large data sets with the goal of accurate prediction has become more widely accepted. The current practice for fitting predictive models is guided by heuristic-based modeling frameworks that lead researchers to make a series of often isolated decisions regarding data preparation and cleaning that may result in substandard predictive performance. In this article, we use an experimental design to evaluate the impact of six factors related to data preparation and model selection (techniques for numerical imputation, categorical imputation, encoding, subsampling for unbalanced data, feature selection, and machine learning algorithm) and their interactions on the predictive accuracy of models applied to a large, publicly available heart transplantation database. Our factorial experiment includes 10,800 models evaluated on 5 independent test partitions of the data. Results confirm that some decisions made early in the modeling process interact with later decisions to affect predictive performance; therefore, the current practice of making these decisions independently can negatively affect predictive outcomes. A key result of this case study is to highlight the need for improved rigor in applied predictive research. By using the scientific method to inform predictive modeling, we can work toward a framework for applied predictive modeling and a standard for reproducibility in predictive research.

We are in the midst of a “Data Revolution” that is transforming our economy. This revolution is as large and profound as other major economic shifts from the introduction of the printing press to t...

Background Socially vulnerable communities are at increased risk for adverse health outcomes during a pandemic. Although this association has been established for H1N1, Middle East respiratory syndrome (MERS), and COVID-19 outbreaks, understanding the factors influencing the outbreak pattern for different communities remains limited. Objective Our 3 objectives are to determine how many distinct clusters of time series there are for COVID-19 deaths in 3108 contiguous counties in the United States, how the clusters are geographically distributed, and what factors influence the probability of cluster membership. Methods We proposed a 2-stage data analytic framework that can account for different levels of temporal aggregation for the pandemic outcomes and community-level predictors. Specifically, we used time-series clustering to identify clusters with similar outcome patterns for the 3108 contiguous US counties. Multinomial logistic regression was used to explain the relationship between community-level predictors and cluster assignment. We analyzed county-level confirmed COVID-19 deaths from Sunday, March 1, 2020, to Saturday, February 27, 2021. Results Four distinct patterns of deaths were observed across the contiguous US counties. The multinomial regression model correctly classified 1904 (61.25%) of the counties’ outbreak patterns/clusters. Conclusions Our results provide evidence that county-level patterns of COVID-19 deaths are different and can be explained in part by social and political predictors.

BACKGROUND Socially vulnerable communities are at increased risk for adverse health outcomes during a pandemic. Although this association has been established for H1N1, Middle East respiratory syndrome (MERS), and COVID-19 outbreaks, understanding the factors influencing the outbreak pattern for different communities remains limited. OBJECTIVE Our 3 objectives are to determine how many distinct clusters of time series there are for COVID-19 deaths in 3108 contiguous counties in the United States, how the clusters are geographically distributed, and what factors influence the probability of cluster membership. METHODS We proposed a 2-stage data analytic framework that can account for different levels of temporal aggregation for the pandemic outcomes and community-level predictors. Specifically, we used time-series clustering to identify clusters with similar outcome patterns for the 3108 contiguous US counties. Multinomial logistic regression was used to explain the relationship between community-level predictors and cluster assignment. We analyzed county-level confirmed COVID-19 deaths from Sunday, March 1, 2020, to Saturday, February 27, 2021. RESULTS Four distinct patterns of deaths were observed across the contiguous US counties. The multinomial regression model correctly classified 1904 (61.25%) of the counties’ outbreak patterns/clusters. CONCLUSIONS Our results provide evidence that county-level patterns of COVID-19 deaths are different and can be explained in part by social and political predictors.

With the continued technological advancements in mobile computing, sensors, and artificial intelligence methodologies, computer acquisition of human and physical data, often called cyber-physical convergence, is becoming more pervasive. Consequently, personal device data can be used as a proxy for human operators, creating a digital signature of their typical usage. Examples of such data sources include: wearable sensors, motion capture devices, and sensors embedded in work stations. Our motivation behind this paper is to encourage the quality community to investigate relevant research problems that pertain to human operators. To frame our discussion, we examine three application areas (with distinct data sources and characteristics) for human performance modeling: (a) identification of physical human fatigue using wearable sensors/accelerometers; (b) capturing changes in a driver’s safety performance based on fusing on-board sensor data with online API data; and (c) human authentication for cybersecurity applications. Through three case studies, we identify opportunities for applying industrial statistics methodologies and present directions for future work. To encourage future examination by the quality community, we host our data, Code, and analysis on an online repository.

Objective The COVID-19 pandemic in the U.S. has exhibited a distinct multiwave pattern beginning in March 2020. Paradoxically, most counties do not exhibit this same multiwave pattern. We aim to answer three research questions: (1) How many distinct clusters of counties exhibit similar COVID-19 patterns in the time-series of daily confirmed cases? (2) What is the geographic distribution of the counties within each cluster? and (3) Are county-level demographic, socioeconomic and political variables associated with the COVID-19 case patterns? Materials and methods We analyzed data from counties in the U.S. from March 1, 2020 to January 2, 2021. Time series clustering identified clusters in the daily confirmed cases of COVID-19. An explanatory model was used to identify demographic, socioeconomic and political variables associated with the outbreak patterns. Results Three patterns were identified from the cluster solution including counties in which cases are still increasing, those that peaked in the late fall, and those with low case counts to date. Several county-level demographic, socioeconomic, and political variables showed significant associations with the identified clusters. Discussion The pattern of the outbreak is related both to the geographic location within the U.S. and several variables including population density and government response. Conclusion The reported pattern of cases in the U.S. is observed through aggregation of the daily confirmed COVID-19 cases, suggesting that local trends may be more informative. The pattern of the outbreak varies by county, and is associated with important demographic, socioeconomic, political and geographic factors.

Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naive method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of three or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute error (MAE) 1 . 24 and median MAE 1 . 22 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker’s condition and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.

The k-chart, based on support vector data description, has received recent attention in the literature. We review four different methods for choosing the bandwidth parameter, s, when the k-chart is designed using the Gaussian kernel. We provide results of extensive Phase I and Phase II simulation studies varying the method of choosing the bandwidth parameter along with the size and distribution of sample data. In very limited cases, the k-chart performed as desired. In general, we are unable to recommend the k-chart for use in a Phase I or Phase II process monitoring study in its current form. Copyright © 2017 John Wiley & Sons, Ltd.

The increasing availability of high-volume, high-velocity data sets, often containing variables of dfferent data types, brings an increasing need for monitoring tools that are designed to handle these big data sets. While the research on multivariate st..

It is impossible to deny the significant impact from the emergence of big data and business analytics” on the fields of Information Technology, Quantitative Methods, and the Decision Sciences. Both industry and academia seek to hire talent in these areas with the hope of developing organizational competencies. This article describes a multi-method research agenda that was executed to ascertain insights regarding which knowledge, skills, and abilities, (KSAs) are valued by employers seeking to hire entry-level analytics professionals from schools of business. Current undergraduate business analytics programs are first examined to define the research scope. A triangulated mixed-method research approach is then used to determine the knowledge, skills, and abilities that are in demand for entry-level jobs in this area. Finally, the multi-method triangulation of data is combined with experiences in building academic programs in business analytics at two nationally-ranked state universities to offer insights for those seeking to develop academic programs in this area.

The recommended size of the Phase I data set used to estimate the in-control parameters has been discussed many times in the process monitoring literature. Collecting baseline data, however, can be difficult or slow in some applications. Such issues hav..

In quality control, a proper Phase I analysis is essential to the success of Phase II monitoring. A literature review reveals no distribution-free Phase I multivariate techniques in existence. This research develops a Phase I location control chart for multivariate elliptical processes. The resulting in-control reference sample can then be used to estimate the parameters for Phase II monitoring. Using Monte Carlo simulation, the proposed method is compared with the Hotelling's T2 Phase I chart. Although Hotelling's T2 chart is preferred when the data are multivariate normal, the proposed method is shown to perform significantly better under nonnormality. This article has supplementary material online.

Although considerable work has been done to develop new educational innovations, few have found widespread acceptance in the classroom. To improve the likelihood of adoption of educational innovations, researchers need to understand why some innovations are adopted and routinely used, while others are not. An initial aspect of the diffusion of innovations, as defined in the classical sociological literature, involves the communication of ideas and concepts related to innovations between individuals. This article presents an expert panel's answer to the following question: “What are the most important characteristics that relate to the dissemination of educational innovations?” As dissemination is a critical facet of the diffusion of an innovation, 45 researchers who received technology and engineering grants from the National Science Foundation (NSF) participated in a Delphi study designed to address this research question. In three rounds, the experts identified and ranked 11 characteristics of educational innovations, 6 characteristics of students, 13 characteristics of faculty members, and 5 characteristics of administrators that can relate to the successful dissemination of educational innovations. The results of this study led to the formation of a Characteristics of Dissemination Success (CODS) framework. This framework offers useful guidance for educational innovators seeking a better understanding of the influences on the dissemination of educational innovations.

Statistical process control (SPC) is a collection of problem-solving tools used to achieve process stability and improve process capability through variation reduction. Because of its sound statistical basis and intuitive use of visual displays, SPC has been extensively used in manufacturing and health care and service industries. Deploying SPC involves both a technical aspect and a proper environment for continuous improvement activities based on management support and worker empowerment. Many of the commonly used SPC tools, including histograms, fishbone diagrams, scatter plots, and defect concentration diagrams, were proposed prior to the advent of microcomputers as efficient methods to record and visualize data for single (or few) variable(s) processes. As the volume, variety, and velocity of data continues to evolve, there are opportunities to supplement and improve these methods for understanding and visualizing process variation. In this paper, we propose enhancements to some of the basic quality tools that can be easily applied with a desktop computer. We demonstrate how these updated tools can be used to better characterize, understand, and/or diagnose variation in a case study involving a US manufacturer of structural tubular metal products. Finally, we create the quality visualization toolkit to allow practitioners to implement some of these visualization tools without the need for training, extensive statistical background, and/or specialized statistical software. Copyright © 2014 John Wiley & Sons, Ltd.

Today׳s supply chain professionals are inundated with data, motivating new ways of thinking about how data are produced, organized, and analyzed. This has provided an impetus for organizations to adopt and perfect data analytic functions (e.g. data science, predictive analytics, and big data) in order to enhance supply chain processes and, ultimately, performance. However, management decisions informed by the use of these data analytic methods are only as good as the data on which they are based. In this paper, we introduce the data quality problem in the context of supply chain management (SCM) and propose methods for monitoring and controlling data quality. In addition to advocating for the importance of addressing data quality in supply chain research and practice, we also highlight interdisciplinary research topics based on complementary theory.

An overview and perspective is provided on the Phase I collection and analysis of data for use in process improvement and control charting.

Purpose – Enterprise architecture (EA) aligns information systems with business processes to enable firms to reach their strategic objectives and, when effectively employed by organizations, can lead to enhanced levels of performance. However, while many firms may adopt EA, it is often not used extensively. The purpose of this paper is to examine how performance expectancy (PE) and training affect the degree to which organizations use EA. Design/methodology/approach – The paper employed a survey method to gather data from IT professionals, senior managers, and consultants who work within organizations that have adopted EA. Covariance-based structural equation modeling was used to analyze the research model and test the hypotheses. Findings – The paper found PE to be a significant predictor of EA use. In addition, training is also shown to enhance use of EA while also playing a mediating role within the relationship between PE and use of EA. Research limitations/implications – The study is limited by the focus only on training as an intervention. Other mediators and/or moderators such as top management support and organization culture may also play an important role and should be examined in future studies. Nonetheless, the study demonstrates the critical role that training can play in facilitating widespread use of EA within organizations. Practical implications – Widespread use is a critical success factor for organizations that want to gain the maximum possible benefit from EA. To achieve extensive use, the study suggests that organizations that adopt EA should consider implementing a formal and robust education and training program. Originality/value – This study extends the research on information technology training by examining the role of training as an intervention within the technology acceptance paradigm. The paper also contributes to the literature regarding post-adoption innovation diffusion by demonstrating the efficacy of organizational training in promoting widespread usage.

As the volume and variety of available data continue to proliferate, organizations increasingly turn to analytics in order to enhance business decision-making and ultimately, performance. However, the decisions made as a result of the analytics process are only as good as the data on which they are based. In this article, we examine the data quality problem and propose the use of control charting methods as viable tools for data quality monitoring and improvement. We motivate our discussion using an integrated case study example of a real aircraft maintenance database. We include discussions of the measures of multiple data quality dimensions in this online process. We highlight the lack of appropriate statistical methods for the analysis of this type of problem and suggest opportunities for research in control chart methods within the data quality environment. This article has supplementary material online.

This study evaluates the robustness of multisample confirmatory factor analysis approaches to measurement equivalence/invariance (ME/I) evaluations under a specific model misspecification: when independence among the groups is assumed, but groups are dependent. Monte Carlo simulation is used to investigate the robustness of several ME/I evaluation procedures including the likelihood ratio test ( ), ΔCFI, ΔSRMR, and ΔRMSEA. The effect of this model misspecification on the factor loadings and their standard errors is also considered. Assuming the groups are independent when they are not is shown to have no practical effect on the results of the , ΔCFI, ΔSRMR, or ΔRMSEA procedures because the effect of model misspecification is canceled out through differencing. Similarly, the estimated factor loadings and standard errors are not significantly affected by incorrectly assuming among-group dependence. These results are used to develop recommendations for researchers who conduct multigroup analyses using struct...

Firms are beginning to find that the adoption of certain reverse logistics practices may offer a lucrative approach to greening their supply chain. Consisting of remanufacturing, reusing, and recycling, this idea of green reverse logistics is currently being diffused throughout the supply chain. A recently published logistics diffusion model with its basis in resource-advantage theory suggests that a logistics innovation is more likely to be adopted if it enhances competitive advantage for the firm considering adoption. Our study uses diffusion of innovation and resource-advantage theories as a foundation to investigate consumer reactions to firms that implement green reverse logistics practices. We investigate whether a firm's adoption of green reverse logistics leads to higher levels of consumer loyalty and a willingness to pay more for the firm's products. Our findings suggest that consumers' satisfaction with firms that adopt green reverse logistics leads to increased levels of loyalty to the firm, wh...

PurposeThe purpose of this paper is to employ organizational information processing theory to assess how a firm's information processing requirements and capabilities combine to affect the intention to adopt cloud computing as an enabler of electronic supply chain management systems. Specifically, the paper examines the extent to which task uncertainty, environmental uncertainty, and inter‐organizational uncertainty affect intention to adopt cloud computing technology and how information processing capability may moderate these relationships.Design/methodology/approachThe paper uses a multiple method approach, thus examining the hypothesized model with both quantitative and qualitative methods. To begin, the paper incorporates a Delphi study as a way in which to choose a practically relevant characterization of the moderating variable, information processing capability. The authors then use a survey method and hierarchical linear regression to quantitatively test their hypotheses. Finally, the authors employ interviews to gather additional qualitative data, which they examine via use of content analysis in order to provide additional insight into the tenability of the proposed model.FindingsThe quantitative analysis suggests that significant two‐way interactions exist between each independent variable and the moderating variable; each of these interactions is significantly related to intention to adopt cloud computing. The qualitative results support the assertion that information processing requirements and information processing capability affect intention to adopt cloud computing. These findings support the relationships addressed in the hypothesized model and suggest that the decision to adopt cloud computing is based upon complex circumstances.Research limitations/implicationsThis research is limited by the use of single key informants for both the quantitative and qualitative portions of the study. Nonetheless, this study enhances understanding of electronic supply chain management systems, and specifically cloud computing, through the application of organizational information processing theory. The authors’ mixed‐methods approach allowed them to draw more substantive conclusions; the findings provide a theoretical and empirical foundation for future research in this area, and also suggest the use of additional theoretical perspectives.Practical implicationsThis study provides insight that can help supply chain managers to better understand how requirements, when coupled with capabilities, may influence the decision to adopt cloud computing as an enabler of supply chain management systems.Originality/valueAs an emerging technology, cloud computing is changing the form and function of information technology infrastructures. This study enhances the understanding of how this technology may diffuse within the supply chain.