Advances in Business and Management Forecasting

DR. KENNETH D. LAWRENCE is a Professor of Management Science and Business Analytics in the Tuchman School of Management at the New Jersey Institute of Technology. Professor Lawrence’s research is in the areas of applied management science, data mining, forecasting, and multi-criteria decision-making. His current research works include multi-criteria mathematical programming models for productivity analysis, discriminant analysis, portfolio modeling, quantitative finance, and forecasting/data mining. He is a full member of the Graduate Doctoral Faculty of Management at Rutgers, The State University of New Jersey in the Department of Management Science and Information Systems and a Research Fellow in the Center for Supply Chain Management in the Rutgers Business School. His research work has been cited over 1,750 times in various research publications.DR. RONALD K. KLIMBERG is a Professor in the Department of Decision Systems Sciences of the Haub School of Business at Saint Joseph’s University. Dr. Klimberg has published 3 books, including his Fundamentals of Predictive Analytics Using JMP, edited 9 books, over 50 articles and made over 70 presentations at national and international conferences. His current major interest include multiple criteria decision making (MCDM), multiple objective linear programming (MOLP), data envelopment analysis (DEA), facility location, data visualization, data mining, risk analysis, workforce scheduling, and modeling in generation. He is currently a member of INFORMS, DSI, and MCDM. Ron was the 2007 recipients of the Tenglemann Award for his excellence in scholarship, teaching, and research.

• 1, The Effect of Released Information on Searching for Missing Children: The Case of the Baby Back Home Network; Yang, F.Nuermarti Y. Huang, Z. 2, Enhanced Multicriteria Scoring Model; Ko, K. 3, Forecasting Treatment Outcomes for the Futures Drug and Alcohol Rehabilitation Content; Miori, V., Campbell Garwood, K., Cardamone, C. 4, An oversampling technique for classifying imbalanced datasets; Nguyen, S.,Quinn, J., Olinsky, A. 5, Funding Analytics: A Predictive Analysis in a Major State Research University; Lawrence, K., Kudbya, S., Lawrence, S. M.6, A Novel Approach to Forecasting Regression and Cluster Analysis; Klimberg, R., Ratick, S., Smith, H. 7, Forecasting Development of a Practical and Effective Forecasting Performance Measure; Klimberg, R., Ratick, S. 8, Assessing the Design of Control Charts for Intermittent Data; Lindsey, M., Pavur, R. 9, On the Causal Models of Fuzzy Time Series; Duru, O. 10, Modeling and Forecasting with Fuzzy Time Series and Artificial Neural Networks, Duru, O.; Butler, M. 11, Forecasting in Service Supply Chain Systems: A State-of-the-Art Review Using Latent Semantic Analysis; Sudhanshu, J.

Business scholars discuss forecasting applications; predictive analytics, regression analysis, and clustering in forecasting; and time series, intermittent data, and supply chain applications. Among the topics are the effect of releasing information on searching for missing children: the case of the Baby Back Home network, forecasting treatment outcomes for the Futures drug and alcohol rehabilitation center, a novel approach to forecasting regression and cluster analysis, developing a practical and effective forecasting performance measure, and forecasting in-service supply chain systems: a state-of-the-art review using latent semantic analysis.