Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/54174
Title: Transactional risk reduction via prototyping in design outsourcing
Authors: Ang, Aston Prananto
Keywords: DRNTU::Engineering::Mechanical engineering
Issue Date: 2013
Abstract: Design outsourcing is increasingly popular in various industries, such as electronics, automobiles and aircrafts etc. However, given design’s innovative nature, it bears more transactional risks than the traditional outsourcing (manufacturing, servicing). To elicit customers’ needs and convey designers’ capabilities, such that transactional risks can be reduced, prototypes are generally employed in design outsourcing. Nonetheless, in certain capital intensive industries, prototypes usually take a lot of resources (money, labor, time) to build. Quantitative analysis on the trade-off between a prototype’s value and its cost is conducted in this research in hope to answer questions like is it cost effective to build the prototype? Who should pay for it, customer or designer? And how much the final product should be priced? An innovative feature of this research is employment of Bayesian updating to assess the amount of risk reduction through prototyping. Combination of real option method and Bayesian updating have resulted in Bayesian- Based Multiple-Step Real Option Method. In this model, the customer is assumed to invest in a series of prototypes during a long time horizon. The customer can make multiple investment decisions along the development phase of the prototypes. The result shows that the Bayesian-based option valuation model gives a lower option value than that of the traditional model (where volatility is assumed to be constant). This result suggests that the traditional model overestimates the design value, which may lead to excessive investment. In order to complete this model and easily use it as a decision tool, a Matlab program is created. To which, management could input the necessary figure and a calculated decision will be made by the system. In addition, to see how far some parameters may affect to the option value itself, a sensitivity analysis is presented by varying selected variable while keeping other variables fix.
URI: http://hdl.handle.net/10356/54174
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

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