Thomas J. Goldsby, Ph.D.
Harry T. Mangurian, Jr. Foundation Professor in Business, Professor of Logistics
Chair, Department of Marketing & Logistics
Fisher College of Business, The Ohio State University Columbus, Ohio (USA)
Biography: Dr. Thomas J. Goldsby is the Harry T. Mangurian, Jr. Foundation Professor in Business and Professor of Logistics at The Ohio State University. Dr. Goldsby holds a B.S. in Business Administration from the University of Evansville, M.B.A. from the University of Kentucky, and Ph.D. in Marketing and Logistics from Michigan State University.Dr. Goldsby is Co-Editor-in-Chief of the Journal of Business Logistics and former Editor of Transportation Journal. He serves as Associate Director of the Center for Operational Excellence (COE), a Research Fellow of the National Center for the Middle Market, and a research associate of the Global Supply Chain Forum, all housed at Ohio State’s Fisher College of Business.His research interests include logistics strategy, supply chain integration, and the theory and practice of lean and gile supply chain strategies. He has published more than 50 articles in academic and professional journals and serves as a frequent speaker at academic conferences, executive education seminars, and professional meetings. He is co-author of five books: Logistics Management: Enhancing Competitiveness and Customer Value (MyEducator, 2015),The Definitive Guide to Transportation (Financial Times, 2013), Global Macrotrends and Their Impact on Supply Chain Management (Financial Times, 2013), The Design and Management of Sustainable Supply Chains (Cambridge University Press, 2016), and Lean Six Sigma Logistics: Strategic Development to Operational Success (J. Ross Publishing, 2005).
Dr. Goldsby is a recipient of the Best Paper Award at the Transportation Journal(2012-2013), Bernard J. LaLonde Award at the Journal of Business Logistics (2007), and has twice received the Accenture Award for best paper published in theInternational Journal of Logistics Management (1998 and 2002).Dr. Goldsby has received recognition for excellence in teaching at Iowa State University, The Ohio State University, and the University of Kentucky. He delivered a course on Business Operations for The Great Courses’ Critical Business Skills series in 2015. He is recognized as one of the most productive researchers all-time in the field of Logistics Management.
Dr. Goldsby has delivered keynote addresses and conducted workshops throughout the world and served as a Visiting Professor at the Copenhagen Business School (2015), WHU-Otto Beisheim School of Management (2013), and Politecnico di Milano (2008). He is a member of the selection committees for several industry awards. Dr. Goldsby has supervised more than 100 Lean/Six Sigma supply chain projects with industry partners, chaired seven Ph.D. dissertations, and served as an investigator on five federally funded research projects, exceeding $2 million in grant proceeds. In his spare time, Dr. Goldsby competes as one of the top masters (over-40) runners in America for distances between the mile and the marathon.
Title : Supply Chain 2.0: Expecting More from our Supply Chains and the Businesses that Compose Them
Abstract : A supply chain consists of the network of companies that works together to provide a product or service for the end-customer. Each company in the network has a responsibility to yield economic returns to its shareholders. In order to achieve this outcome, most businesses focus on the profitable provision of forward deployment of materials and products to support the needs of their next-stage customers. This notion of exchange dates back to the beginnings of modern commerce. Times are changing, though, as companies and society expect more from their supply chains today. This session explores a new realm of supply chain management that calls for not just end-to-end management of physical flows, but instead a circular economy that manages material and products through multiple lifecycles. Also, the singular objective of economic returns is giving way to the management of the triple bottom lines, finding balance among the economic, environmental, and social outcomes of business decisions and actions. Further complicating these challenges is the need to manage the business and its supply chain relations with the requisite transparency and security. We will review modern methods and best practices leading to this new revolution in supply chain management – Supply Chain 2.0.
Professor: University of Indonesia
Professor of Computational Intelligence and Intelligent Systems
Biography: Dr. Benyamin Kusumoputro is Professor in Computational Intelligence and Intelligent Systems since 2004. He holds a Bachelor degree in Physics from Bandung Institute of Technology, Indonesia, and Magister of Engineering from Universitas Indonesia in 1981 and 1984, respectively. Dr. Kusumoputro received his Dr. Eng. degree from Dept. of Electrical and Electronic Engineering, Tokyo Institute of Technology in 1993. He has served as Principal Investigator in various multi years reseach grants from 1995, and spent a year as a Visiting Professor at KAIST, Korea during 2006-2007 under KFAS Foundation. He also serves numerous times as a Visiting Scholars in Tokyo Institute of Technology, and Nagoya University in Japan.
Dr. Kusumoputro has received various awards as a recognition for excellence in research from Universitas Indonesia in 2002, Ministry of Research and Technology of Indonesia in 2006, and the University Award from University of Indonesia in 2016. Dr. Kusumoputro has supervised more than 12 Ph.D dissertations with 6 candidates in progress. His research interests include the development of 3D face recognition using Hemispherical Structure of Hidden Layer Neural Networks, odor recognition system, and recently, the development of autonomous control system of unmanned vehicle systems. He published more than 70 articles in academic and professional journals and serves as a frequent Invited Speaker at various academic conferences and professional meetings.
Title: Autonomous control system for a trajectory flight dynamics of an unmanned aerial vehicle systems using artificial neural networks
Abstract :Unmanned Aerial Vehicle (UAV) system is an unmanned aircraft that can be categorized into a fixed-wing UAV and a rotor wing UAV. UAV system is firstly developed to perform various dangerous military purposes of aerial missions due to its simplicity in the flying process, lower operating cost and the low safety risk to the human operator. Recently, the development of the UAV system is also designed for its utilization in many civil applications such as for land and forestry mapping, agriculture purposes, and for search and rescue (SAR) tasks. UAV system is also capable of carrying out a high-risk task, such as in the areas exposed to a nuclear radiation or a disaster area that is difficult to be reached. UAV system is a complex dynamic flying system with the characteristics of multiple input multiple output (MIMO), under actuated, nonlinear, highly coupled, time-varying and inherently unstable and varies widely across its full flight condition. The control system of a UAV could be developed using a linear control method or a nonlinear control methods, where any developed control methods required an adequate model of flight dynamics for the design, verification and analysis of the system. The UAV flight dynamics model are widely developed mathematically, derived from its physical based model to the non-parametric modeling technique, and also from the complete and complex nonlinear model to a more simplified linear model. However, as the UAV system is a very dynamic system with the ability to move at a six degree of freedom that might be prone to various disturbance within a very fast change of flight conditions, there are many problems still remain a challenge for researchers to develop a reliable robust controller system for an autonomous UAV movements. In this presentation, a neural networks control system has been developed and implemented as an autonomous trajectory flight control of a UAV system. Instead of using mathematically derived adaptive controller system, an artificial neural networks algorithm is used as the adaptive controller system for the uncertain flight dynamics and conditions of the UAV system, reducing the need for tuning offline of the usually utilized PID controller system. The artificial neural networks based autonomous controller system is developed to be able to identify and control the UAV flight dynamics, by modeling the non-linear systems and the DIC control techniqure using multi-layer neural network approximation.