Modeling and quantifying uncertainty in the product design phase for effects of user preference changes

Authored by Hamid Afshari, Qingjin Peng

Date Published: 2015

DOI: 10.1108/imds-04-2015-0163

Sponsors: National Science and Engineering Research Council of Canada (NSERC)

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

Purpose - The purpose of this paper is to quantify external and internal uncertainties in product design process. The research addresses the measure of product future changes. Design/methodology/approach - Two methods are proposed to model and quantify uncertainty in the product life cycle. Changes of user preferences are considered as the external uncertainty. Changes stemming from dependencies between components are addressed as the internal uncertainty. Both methods use developed mechanisms to capture and treat changes of user preferences. An agent-based model is developed to simulate sociotechnical events in the product life cycle for the external uncertainty. An innovative application of Big Data Analytics (BDA) is proposed to evaluate the external and internal uncertainties in product design. The methods can identify the most affected product components under uncertainty. Findings - The results show that the proposed method could identify product changes during its life cycle, particularly using the proposed BDA method. Practical implications - It is essential for manufacturers in the competitive market to know their product changes under uncertainty. Proposed methods have potential to optimize design parameters in complex environments. Originality/value - This research bridges the gap of literature in the accurate estimation of uncertainty. The research integrates the change prediction and change transferring, applies data management methods innovatively, and utilizes the proposed methods practically.

Tags

Management Big data information System Conceptual design Business intelligence Change propagation Structure matrix Analytics Life