Decision Sciences Seminar Series
Speaker: Clark Pixton, PhD
School of Business, Brigham Young University
Presentation: “Temporal Hierarchies for Time Series Forecasting: Benefits and the Way Forward”
Presentation: “Signaling Quality with Return Insurance: Theory and Empirical Evidence”
Presentation: “A Dark Future for AI: The Looming Spectre of SkyNet?”
Presentation: “Cooperative Distributed Inferencing (CDI): An Intelligent Control Agent Based Technology”
Connecting the Parts with the Whole: Toward an Information Ecology Theory of Digital Innovation Ecosystems
Advances in new product forecasting
Platform Strategies for Organizations: Transforming the Business Model
PhD Candidate Jin fang will discuss her research based on a HITS-based model for facility location decision.
PhD Candidate Yuyue Chen will present research that proposes a holistic view to explore the intrinsic connections between intelligence aggregation and signal disaggregation using optimization models.
Dr. Triche will present his paper, Aligning BI & Analytics Undergraduate Programs with Industry Demand Using the Labor Market Alignment Framework.
Benjamin Osei-Poku and Aasha Gupta present their research: Analysis of Patient Volume and Diagnosis Distribution During the Global Health Pandemic & Analysis of Uncertainties.
Dr. Wu’s research interests are Big Data Analytics, Artificial Intelligence, Enterprise Social Media, Innovation, Entrepreneurship, Productivity
Dr. Mankad research interests are Operations, Technology and Information Management.
Dr. Jung will be presenting a paper titled “The secret to finding love: A field experiment of choice structure in online dating platform”
Dr. Li will be presenting, “Last-mile Commute: Impact of Bike-sharing on Restaurants”
Dr. Arinze will be presenting, “A Research Agenda for the use of IoT technologies in the Supply Chain”
Yaqin Sun will present her job market paper titled, “Can learning-by-doing hurt profit? The case of outsourcing and contract manufacturer encroachment”
Yuyun Zhong will present her job market paper titled, “Information Provision under Showrooming and Webrooming”
Decision Sciences and MIS guest speaker, Amina Shahzad, will discuss her industry experience and tools used in the Supply Chain industry.
Dr. Schneider will be presenting “The Effects of Data Protection on Forecasting Models.”
Tim Jordan will be discussing how supply chain strategies are shifting to support digitization and the technologies that are changing the competitive landscape
Dr. Fang is a professor at the University of Delaware and will be presenting A Deep Learning Approach to Industry Classification
Yasamin Salmani will present Investment Decision Making in Improving Multiple Sales Channels
Dr. Li is a Professor at UMASS Lowell’s Manning School of Business and will be presenting Predictive Analytics with Strategically Missing Data
Gbemileke Ogunranti will present Models Addressing Sustainability and Risk Issues in Global Supply Chains
Qi Lin, PhD candidate from Beihang University, will present Cold Chain Transportation Decision in the Vaccine Supply Chain
This talk focuses on current efforts of my research group in the direction of establishing data-driven intelligent systems.
‘Nobody pays retail anymore: Optimal pricing in distribution channels with bargain-hunting consumers’
This talk illustrates capabilities in Mathematica 11 and other Wolfram technologies that are directly applicable for use in teaching and research on campus.
Senior Scientist of Outcomes Research, Predictive and Economic Modeling
Merck & Co, Inc.
Yunan Liu, NC State University
Abstract: Queueing theory is a field driven by applications. But unfortunately, there still remains a large gap between tractable theoretical studies and practical applications, such as call centers and health care systems, which have many realistic features (e.g., time-varying arrivals, customer abandonment, non-exponential distributions, and complicated network structures). In response to the challenge, we study a general G_t/GI/s_t+GI queueing model, which has a non-stationary non-Poisson arrival process (the G_t), non-exponential service times (the first GI), and allows customer abandonment according to a non-exponential patience distribution (the +GI). To bridge the gap between mathematical tractability and model applicability, we develop fundamental principles and optimal control policies for such a general queueing model.
Analytic formulas are developed to set the time-dependent number of servers in order to stabilize important service-level indicators, including: mean customer delay, probability of abandonment, and tail probability of delay (TPoD). Taking the TPoD for example: for any delay target w > 0 and probability target 0 < alpha < 1, we determine appropriate time-dependent staffing levels (the s_t) so that the time-varying probability that the waiting time exceeds a maximum acceptable value w is stabilized at alpha at all times. In addition, effective approximating formulas are provided for other important performance functions such as the probabilities of delay and abandonment, and the means of delay and queue length. Many-server heavy-traffic limit theorems in the efficiency-driven regime are developed to show that (i) the proposed staffing function achieves the goal asymptotically as the scale increases, and (ii) the proposed approximating formulas for other performance measures are asymptotically accurate as the scale increases. Extensive simulations show that both the staffing functions and the performance approximations are effective, even for smaller systems having an average of 3 servers.
U.S. hospitals join GPOs for procurement cost savings. It is generally believed that GPOs lower costs through demand aggregation. Until recently hospitals purchasing supplies from a GPO vendor had to pay a GPO-negotiated price. The novel practice of custom contracting allows GPO member hospital to negotiate with GPO vendors to improve on the GPO negotiated prices. Hospital procurement departments have welcomed this practice, lauding the opportunity to further lower hospital costs. In this paper, we use economic modeling to investigate the practice of custom contracting. We develop a game-theoretic model treating as endogenous the pricing and negotiations decisions of the GPO vendor and of member hospitals. We show why – counter to the hospital industry’s expectations – expected purchase prices will not decrease with the introduction of custom contracting. The practice benefits GPO vendors at the expense of the member hospitals.