Extracting Business Intelligence from Online Product Reviews: An Experiment of Automatic Rule-Induction

Online product reviews are a major source of business intelligence (BI) that helps managers and market researchers make important decisions on product development and promotion. However, the large volume of online product review data creates significant information overload problems, making it difficult to analyze users’ concerns. In this paper, we employ a design science paradigm to develop a new framework for designing BI systems that correlate the textual content and the numerical ratings of online product reviews. Based on the framework, we developed a prototype for extracting the relationship between the user ratings and their textual comments posted on Amazon.com’s Web site. Two data mining algorithms were implemented to extract automatically decision rules that guide the understanding of the relationship. We report on experimental results of using the prototype to extract rules from online reviews of three products and discuss the managerial implications

4-D Printing of Pressure Sensors and Energy Harvesting Devices for Engineering Education

This paper elaborates on the development of laboratory project modules in the Industrial manufacturing and systems engineering department at The University of Texas El Paso based on Four-Dimensional (4D) printing technology. These modules are aimed at introducing the students to interdisciplinary manufacturing and emerging dimensions in manufacturing technology. 4D printing is a new dimension in additive manufacturing wherein, the 3D printed structures react to the change of parameters within the environment such as temperature, and humidity, resulting in shape change or in functionality such as electricity output, and self-healing. Recently 4D printing of simple devices for pressure sensors application were identified and show high feasibility for commercialization due to low cost, freedom of design, and agile manufacturing process. This enables a high interdisciplinary platform for research and project modules suitable to be used in the academic environment for hands-on students training. Laboratory Modules based on 4D printing of pressure sensors is developed for student training that includes: 1) Design of piezoelectric nanocomposites; 2) 3-D model design of pressure sensor devices; 3) Using 3-D printers for 4-D printing, and involved post-processing techniques by which students can experience emerging manufacturing technologies, and; 4) Testing for piezoelectric properties

PLAYING THE LOTTERY GAMES?

The lottery is a huge business. In 2011, $57.6 billion worth of lottery tickets were sold in 43 states and the District of Columbia. There are three major parties (governments, lottery players, and retailers) involved in the lottery industry, plus many more stakeholders. This paper examines the lottery from the viewpoints of these three primary parties. From the lottery players\u27 viewpoint, we show how to statistically determine the expected value of a lottery ticket and discuss when to conclude it is profitable to buy lottery tickets. We explore the question of whether lottery players are rational. State governments have, for years, relied on lottery money to fund education and other expenses. We examine the economic benefits as well as the societal costs of operating the lottery business. Finally, we examine the economics of selling lottery tickets from the retailers\u27 viewpoint

Degradation Modeling and RUL Prediction Using Wiener Process Subject to Multiple Change Points and Unit Heterogeneity

Degradation modeling is critical for health condition monitoring and remaining useful life prediction (RUL). The prognostic accuracy highly depends on the capability of modeling the evolution of degradation signals. In many practical applications, however, the degradation signals show multiple phases, where the conventional degradation models are often inadequate. To better characterize the degradation signals of multiple-phase characteristics, we propose a multiple change-point Wiener process as a degradation model. To take into account the between-unit heterogeneity, a fully Bayesian approach is developed where all model parameters are assumed random. At the offline stage, an empirical two-stage process is proposed for model estimation, and a cross-validation approach is adopted for model selection. At the online stage, an exact recursive model updating algorithm is developed for online individual model estimation, and an effective Monte Carlo simulation approach is proposed for RUL prediction. The effectiveness of the proposed method is demonstrated through thorough simulation studies and real case study

Exploring the Quality of Course Deployment in Engineering Education: A Quantitative Assessment using Quality Function Deployment

Due to the rapid changes of the industrial landscape, engineering education is becoming more dynamic in meeting the needs of the 21st century. Many industries may likely prefer special skills over traditional degrees, which necessitates the to keep updating our course curricula in response to the required skillsets. At the same time, it is very important to understand students’ perceptions of this rapidly changing educational portfolio. This paper attempts to explore how our rapidly changing course curricula can develop students’ skillsets while maintaining their expectations and adaptability. To do so, we conduct a well-organized anonymous student survey on the different aspects of a particular course and evaluate using the Quality Function Deployment (QFD) tool, subsequently. The course titled “Design for Manufacturability” (MFG 5311) is used as the case study in this study, where 17 students enrolled in this course were considered as the study population. The course was offered as one of the core courses of the Industrial, Manufacturing, and Systems Engineering (IMSE) department at the University of Texas at El Paso (UTEP) in the Spring 2021 Semester. From this study, we extract several key findings regarding curricular enhancement, students’ expectations, and technical skillsets development from students’ perspectives

Multiple-Change-Point Modeling and Exact Bayesian Inference of Degradation Signal for Prognostic Improvement

Prognostics play an increasingly important role in modern engineering systems for smart maintenance decision-making. In parametric regression-based approaches, the parametric models are often too rigid to model degradation signals in many applications. In this paper, we propose a Bayesian multiple-change-point (CP) modeling framework to better capture the degradation path and improve the prognostics. At the offline modeling stage, a novel stochastic process is proposed to model the joint prior of CPs and positions. All hyperparameters are estimated through an empirical two-stage process. At the online monitoring and remaining useful life (RUL) prediction stage, a recursive updating algorithm is developed to exactly calculate the posterior distribution and RUL prediction sequentially. To control the computational cost, a fixed-support-size strategy in the online model updating and a partial Monte Carlo strategy in the RUL prediction are proposed. The effectiveness and advantages of the proposed method are demonstrated through thorough simulation and real case studies

Opportunities in Green Supply Chain Management

The supply chain consists of those activities associated with manufacturing from raw material acquisition to final product delivery. Because of the recently changed environmental requirements that affect manufacturing operations and transportation systems, growing attention is given to the development of environment management strategies for supply chains. A green supply chain aims at confining the wastes within the industrial system so as to conserve energy and prevent the dissipation of harmful materials into the environment. In this paper, we compare and contrast the traditional and green supply chains. Moreover, we discuss several important opportunities in green supply chain management in depth, including those in manufacturing, bio-waste, construction, and packaging

Building Web Directories in Different Languages for Decision Support: A Semi-Automatic Approach

Web directories organize voluminous information into hierarchical structures, helping users to quickly locate relevant information and to support decision-making. The development of existing Web directories either relies on expert participation that may not be available or uses automatic approaches that lack precision. As more users access the Web in their native languages, better approaches to organizing and developing non-English Web directories are needed. In this paper, we have proposed a semi-automatic approach to building domain-specific Web directories in different languages by combining human precision and machine efficiency. Using the approach, we have built Web directories in the Spanish business (SBiz) and Arabic medical (AMed) domains. Experimental results show that the SBiz and AMed directories achieved significantly better recall, F value, and satisfaction rating than benchmark directories. These encouraging results show that the approach can be used to build high-quality Web directories to support decision-making

Exploring Systems Performance Using Modeling and Simulation – Project-based Study and Teaching

Modeling and Simulation (M&S) provides a risk-free environment allowing the users to experiment in a computer-generated virtual platform and analyze the what-if scenarios for effective decision support systems. Due to its pervasive usefulness, the concept of M&S is widely used across many sectors, including manufacturing, warehouse operations, supply chain, logistics, transportation, mining, and many more. The field of M&S requires computer-intensive and software-based training, which is very different from teaching in a regular classroom setting. Hence, we develop a three-stage (mimic-guide-scaffold) project-based teaching strategy to enhance students learning experience in M&S education. Here, students first follow the instructor to understand basics of simulation and become familiar with AnyLogic software. Second, the students work on a group project under the passive supervision of the instructor to enhance their problem-solving capability. In the third step, students work independently on a similar but extensive project to scaffold their knowledge. The project was designed to answer three high-level key research questions for a hospital system including systems throughput, resource utilization, and patients’ length of stay reduction. We performed a thorough evaluation using an anonymous survey, where thirty-one students participated to provide their feedback. This paper provides a detailed description of the projects including problem statements, learning objectives, evaluation rubrics, data collection criteria, and evaluation outcomes with detailed discussion