2015 Informs Annual Meeting

SD01

INFORMS Philadelphia – 2015

Sunday, 4:30pm - 6:00pm

3 - Assessing the Relationship of Supply Chain Risk Management to Quality Management Tyler Florio, East Carolina University, College of Business, Greenville, NC, 27858, United States of America, floriot11@students.ecu.edu, Scott Dellana This research explores the relationship between an organization’s supply chain risk management maturity (SCRMM) and quality management (QM) practices. QM practices and SCRMM were measured using a survey questionnaire of organizations in the USA. ISO 9001 status was also determined. Preliminary results suggest that ISO 9001 is not related to SCRMM, while QM is related to SCRMM. Organizations with more mature quality management programs appear Alireza Azimian, Wilfrid Laurier University, 55 Hickory St, E, Waterloo, ON, N2J 3J5, Canada, azim9110@mylaurier.ca, Hamid Noori, Marc Kilgour Rivals often benefit from each other’s failures because of demand shifts; however some incidents may adversely affect an entire industry causing all firms to suffer. We explore whether investment in the safety measure of the rivals who just comply with minimum standards can be employed as a risk mitigation strategy. 5 - Information Security Investments: Better Together? Lisa Yeo, Assistant Professor, Loyola University Maryland, 4501 N. Charles St., Sellinger Hall 306, Baltimore, MD, 21210, United States of America, mlyeo@loyola.edu, Erik Rolland, Bora Kolfal, Raymond Patterson, Hooman Hidaji Security breaches are often the result of the weakest link in a chain of related businesses; a firm may have strong security practices and yet still experience a loss due to a weaker business partner. We propose allocating some firm resources to a supplier in order to improve that supplier’s security posture with possible positive spillover to a competitor. In a symmetric duopoly setting, where demand reacts to breach notification, we find the conditions in which a shared supplier is desirable. Software Demonstration Cluster: Software Demonstrations Invited Session 1 - Provalis Research – How to Analyze Big Text Data with Provalis Text Analytics Tools Normand Peladeau, CEO, Provalis Research Provalis Research will showcase its integrated collection of text analytics software. QDA Miner is an easy to use qualitative and mixed methods software that meets the needs of researchers performing qualitative data analysis and would like to code more quickly and more consistently larger amounts of documents. It offers high level computer assistance for qualitative coding with innovative text search tools that help users speed up the coding process as well as advanced statistical and visualization tools. Users with even bigger text data, can also take advantage of WordStat. This add-on module to QDA Miner can be used to analyze huge amounts of unstructured information, quickly extract themes, find trends over time, and automatically identify patterns and references to specific concepts using categorization dictionaries. 2 - SAS Education Practice – Introduction to SAS Data Mining to also have more mature risk management programs. 4 - Mitigating Contagion Risk in Supply Chains SC79 79-Room 302, CC

SD01 01-Room 301, Marriott Military O.R. and Applications II Sponsor: Military Applications Sponsored Session

Chair: Michael Hirsch, ISEA TEK, 620 N. Wymore Rd., Ste. 260, Maitland, FL, 32751, United States of America, mhirsch@iseatek.com 1 - Efficient Information Distribution in the Presence of Unknown Cognitive Capacity in HRI Siddhartha Mehta, Research Assistant Scientist, University of Florida, 1350 N. Poquito Rd., Shalimar, FL, 32579, United States of America, siddhart@ufl.edu, Monali Malvankar, Eduardo Pasiliao In military applications, unmanned aerial vehicles (UAVs) remotely operated by a ground crew may require significant human interaction. UAVs share information with human operators to perform geographically-dispersed priority-based tasks within a specified time. Asymmetric information including cognitive capacity and efficacy exists within the groups of operators. We develop a model incorporating asymmetric information to optimally share information by maximizing efficiency of the entire system. 2 - Robust and Adaptive Optimization Applied to Operator Task Scheduling Luca Bertuccelli, UTRC, 411 Silver Lane, East Hartford, 06107, United States of America, bertuclf@utrc.utc.com, Taimoor Khawaja, Cali Fidopiastis This work is concerned with human-in-the-loop decision making when the human is interacting with robust scheduling systems. We provide a comprehensive literature review of the area of robust optimization as applied to human-in-the-loop scheduling. We then demonstrate the impact of uncertainty in scheduling problems solved in both a “one-shot” optimization and a receding horizon framework with a simulated user-in-the-loop. 3 - A Bayesian Framework for Integrating Human and Machine Perceptions Emily Doucette, Air Force Research Laboratory, 101 W. Eglin Blvd., Eglin AFB, United States of America, emily.doucette@us.af.mil Current methods for human-autonomy interaction are largely implemented through rigid schemes that transfer control between humans and autonomy. The authors propose a generalizable framework utilizing Bayesian estimation and decision theory that incorporates the concepts confidence and consequence in all agents in a human-autonomous system to yield a decision structure for heterogeneous teams, where a human supervisor is aided by a risk-based representation of the target state estimate. 4 - A Framework Supporting the Separation of Cognitive Performance from Execution Environment

Katie Mcconky, Assistant Professor, Rochester Institute of Technology, 81 Lomb Memorial Dr, Rochester, NY, 14623, United States of America, ktmeie@rit.edu, Moises Sudit, Hector Ortiz-pena

Tom Bohannon, Analytical Consultant, SAS, tom.bahannon@sas.com, F. Michael Spped, PhD, Analytical Consultant, SAS, mike.speed@sas.com

This project aims to enhance a decision maker’s mission planning capabilities by providing decision aids that predict anticipated human performance based on civil, environmental and physical conditions. A framework is presented that isolates human performance factors from environmental and situational mission characteristics that facilitates the ability to estimate the capability of units to accomplish tasks to expected standards under specified conditions.

This introduction covers the basic skills required to assemble analysis flow diagrams using the rich tool set of SAS Text Mining. Also, participates will be shown how unstructured data can be converted into numeric data that can utilized for pattern discovery and predictive modeling. Simple example will be used to illustrate the basic concepts of text mining.

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