Board 106: Enhancing Critical Life-Cycle Decision Making in Complex Engineering Projects in the Context of Engineering Economy Courses
Author(s) -
K. Jo Min,
John Jackman,
Farshad Niayeshpour
Publication year - 2020
Publication title -
2018 asee annual conference and exposition proceedings
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--29868
Subject(s) - computer science , engineering economics , valuation (finance) , curriculum , intuition , context (archaeology) , management science , operations research , engineering , economics , finance , paleontology , philosophy , epistemology , biology , economic growth
Complex engineering projects (CEPs) such as electric transmission networks and transportation infrastructure are becoming increasingly important to the public in general and even more so to engineers. These projects are large-scale in terms of money and time and contain significant uncertainties over their lifecycle, with fluctuations in input and output costs. Due to these uncertainties, there are conditional opportunities (e.g., on prices) to make critical decisions such as investment in construction of power generation facilities or decommissioning of such facilities. Such decisions constitute strategic flexibilities or "real options" because the decision maker can alter the course of an investment over time when an uncertain aspect of the project such as the price becomes known. The current practice in engineering curricula, however, does not address the declarative and procedural knowledge necessary for critical economic decision making. We propose to (1) develop a module in an introductory course emphasizing the concept of the aforementioned strategic flexibilities and (2) develop an advanced course that is mathematically rigorous, yet with in-depth case studies for the CEPs. The module addresses the valuation of the strategic flexibilities over the life of CEPs to provide managerial insights and economic intuition. The advanced course emphasizes project experience including data-based parameter estimation and computation for optimal decisions. Both the module and course teaching materials will be complemented by a set of visualization aids for the key concepts and applications.
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