1,130 research outputs found
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Enhancing Excel Skills in Teaching Undergraduates in Busin
This paper tries to enhance Excel skills in teaching an undergraduate operations management course since improving basic Excel skills is one of the key areas necessary to close the feedback loop. As a required core business course, OM101 Operations Management is a natural starting point to enhance basic Excel skills along with quantitative analysis and modeling skills. In light of overwhelming topics to be covered in the course, we believe it is best to enhance basic Excel skills by means of homework assignments with specific Excel instructions, instead of overburdening the students with extra Excel lectures or general-purpose Excel instructions
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Business Intelligence and Big Data Analytics: An Overview
This research investigates the current status of big data business analytics and critical skills necessary to create business value. Business analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to provide actionable insights. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. Big data is used to characterize data sets that are large, diverse and rapidly-changing, as seen by ever- increasing numbers of organizations. Big data require database management systems with capabilities beyond those seen in standard SQL-based systems. According to Manyika et al. (2011), the projected demand for deep business analytical positions could exceed the supply produced with the current trend by 140,000 to 190,000 positions, in addition to the projected need of 1.5 million managers and analysts in dealing with big data business analytics in the United States. Specifically, the emphasis of this research is on how organizations are using big data business analytics and how business school in the United States and across the globe are designing their programs to fill in the talent gap, which leads to a more in-depth analysis on the graduate degree programs in the Greater New York Metropolitan area and potential applications in various industries
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The Effect of Supply Chain Strategy on Quality and U. S. Competitiveness
This research investigates the effect of global supply chain strategy on product quality and U. S. competitiveness by analyzing the case of Boeing 787 Dreamliner recent lithium-ion battery overheating incident. Boeing 787, a new and complex product, has outsourced 70% of its parts and components with a redesigned global supply chain strategy. The grounding of all 50 Boeing 787s already in service by the U. S. FAA on January 16, 2013, has trigged a renewed debate on product quality as a result of extensive outsourcing and its impact on the overall U. S. competitiveness. While this incident is a result of in-flight battery fire with Japan Airlines, along with a similar case occurred earlier in January 2013 in Boston with the same airline company, many believe Boeing’s new aggressive supply chain strategy may have contributed to its quality and safety problems. Managerial implications are discussed to generalize the impact of various global supply chain strategies on product quality and overall U. S. competitiveness
The ERP Implementation in China
We investigate the current status of enterprise resource planning (ERP) implementation in China by means of executive survey and statistical analysis. Pilot studies were conducted to help facilitate the design of the survey questionnaire; follow-up phone interviews were made to ensure the usable return of the research. We sent out survey questionnaires in July 2001 to senior executives of 150 companies and received 86 returns, with 82 usable ones. Statistic analyses were conducted using SAS to determine the trend of ERP implementation in China, its key success factors, and managerial implications of ERP in China
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Forecasting Gasoline Price with Time Series Models
This research forecasts the gasoline price in U.S. and analyzes its managerial implications by means of both univariate and multivariate time series forecasting models. Gasoline price forecast is among the most difficulty time series variables during its importance to the economy and extremely volatile nature. The average regular gasoline price in U.S. reached 3.41 at the beginning of 2022, a 48% increase. While gasoline prices had been rising over the first half of 2022 due to supply chain disruptions as a result of global Covid 19 lockdowns and the Russia invasion of Ukraine since February 24, 2022, they then surprisingly came down in the second half of 2022 to $3.85 in November 2022, which has caught many consumers and business organizations off- guard. Both univariate time series forecasting models, such as exponential smoothing and autoregressive integrated moving average, and multivariate time series forecasting models, such as time series regression models, are used in this research with the data for the period January 2002 through November 2022. We find that the time series regression model with trend, season, GDP, CPI, and crude oil price turns out the be the best forecasting model both on the training data and the testing data, even with the testing data containing significant turning points. Managerial implications and future research directions are also discussed
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Crude Oil Prices Forecasting: Time Series vs. SVR Models
This research explores the weekly crude oil price data from U.S. Energy Information Administration over the time period 2009 - 2017 to test the forecasting accuracy by comparing time series models such as simple exponential smoothing (SES), moving average (MA), and autoregressive integrated moving average (ARIMA) against machine learning support vector regression (SVR) models. The main purpose of this research is to determine which model provides the best forecasting results for crude oil prices in light of the importance of crude oil price forecasting and its implications to the economy. While SVR is often considered the best forecasting model in the main stream literature, this research investigates its computational insights in terms of parameter selections and overfitting potential, in addition to exploring forecasting accuracy and model comparison. The results of this research can be generalized to forecast other business and economic time series data such as stock market prices, product sales, and government statistics
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Intelligent Web User Interfaces
This paper investigates the key components of an intelligent web user interface to facilitate online investment as a novel approach to compensating for the impersonality of e-commerce. By analyzing challenges to online brokerage services and evaluating key criteria for a viable intelligence system, we develop a decision tree based intelligent web user interfaces model. The resulting intelligent model is intended to help online shoppers avoid common mistakes by means of implicit reasoning, flexible knowledge granularity, and effective reasoning-by-exception, which is significantly different from the traditional approaches that largely rely on assistance from remote control knowledge engines. One of the key contributions of the intelligent web user interfaces model introduced in this paper is that it provides a heuristic guidance behind the scene for both online shoppers and online stores without extra structural constraints or financial burdens
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Enterprise Documentation: A Formal-Model Approach
Most digital documents within an enterprise reside outside traditional databases. Search engines have been used to retrieve these digital documents but the results are often unsatisfactory. Digital libraries yield search results with greater precision than general search engines and are capable of compartmentalizing a wide variety of digital documents. However, digital libraries cannot subtly control application-specific document retrievals because they lack a formal model for recognizing the intrinsic relationships among digital documents. This research proposes a formal model, the Entity-Oriented Enterprise Documentation Model (EOEDM), to facilitate document retrievals for enterprise applications. This EOEDM is a meta-level modeling schema, exploiting the entity-relationship model in database design as well as the object model in application design. Since enterprise documents usually result from business activities, the proposed model targets the enterprise documentation needs and relies on the patterns of business activities to associate and locate digital documents with higher precision and better recall
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Impact of Team Attitude and Behavior on IS Project Success
Although the success rate of information systems (IS) projects has increased over the past two decades, the result is still far from satisfactory. To improve IS project success rate, this study proposes a research model to examine IS project performance from a team attitude and behavior perspective. A total of 91 IS projects were collected by an online survey from CIOs/CTOs/IT professionals. The collected data were analyzed by using AMOS 7.0. We found, via hypotheses testing, that (1) A project team’s goal commitment is positively related to its teamwork quality, (2) A project team’s goal commitment is positively related to IS project success, and (3) A project team’s teamwork quality is positively related to IS project success. Managerial implications to IS project practitioners were discusse
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Crude Oil Price Prediction with Decision Tree Based Regression Approach
Crude oil is an essential commodity for industry and the prediction of its price is crucial for many business entities and government organizations. While there have been quite a few conventional statistical models to forecast oil prices, we find that there is not much research using decision tree models to predict crude oil prices. In this research, we develop decision tree models to forecast crude oil prices. In addition to historical crude oil price time series data, we also use some predictor variables that would potentially affect crude oil prices, including crude oil demand and supply, and monthly GDP and CPI during the period 1992 through 2017 with a total of 312 observations. In this research, we use decision tree models to predict crude oil price. We find that the decision tree models developed in this research are expected to have higher forecasting accuracy than that of such benchmark models as multiple linear regression and time series autoregressive integrated moving average (ARIMA)
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