[CCoE Notice] Thesis Defense: A New Approach to Measure Intangible in Economic Analysis of Advanced Technology

[Grayson, Audrey A]

A New Approach to Measure Intangibles in Economic Analysis of Advanced Technology Projects

A Dissertation Defense Presented to

The Faculty of the Department of Industrial Engineering
University of Houston
By
Khaled Eldressi
April 18, 2016
9:30 am – 11:30am
Scott T. Poage IE Conference Room, E214

Current global demand for products with more advanced features and capabilities, less weight, and increased aesthetic has driven manufacturers to make significant investments in machinery and tools.   Company decisions to invest in advanced technology are often strategically aimed toward short-term return and frequently do not conform to traditional cost accounting practices; which, in many cases, may lead to rejection of investment due to inappropriate measurement techniques.

In revitalizing the manufacturing sector of the United States, thus creating higher paying jobs, manufacturing companies, particularly those engaged in designing and producing more advanced products, have been encouraged by a multitude of incentives to invest capital in plant and equipment enhancements to meet and exceed market expectations.  This capital investment made by these companies is expected to enhance capacity toward making new products, not simply expanding current production capacities. The investment in advanced manufacturing and technology systems are often extensive and their successful implementation requires the full support and commitment of top management.  Traditional justification methods are often directly tied to company cash flow and, with the high investment required, and short return period expected by shareholders, investments in advanced technology projects are often rejected due to the long-term return commitment.

In this research, we have  developed a methodology to measure the intangible attributes, yet include both tangible and intangible attributes, in the economic decision-making process. Multiple attributes that may influence the decision process are included and measured in the method.  We present a comprehensive numerical example demonstrating the capability of the  methodology. Additionally, we present conclusions and recommendations for future research in this important subject area to the manufacturing sector of the US economy.
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