Creating Knowledge Environment during Lean Product Development Process of Jet Engine

Zehra C Araci,Ahmed Al-Shaab,Muhammad Usman,M C,Jan H

doi:10.14569/ijacsa.2020.0110509

Zehra C Araci, Ahmed Al-Shaab + Show 3 more

Open Access

https://doi.org/10.14569/ijacsa.2020.0110509

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Abstract

Organizations invest intense resources in their product development processes. This paper aims to create a knowledge environment using trade-off curves during the early stages of the set-based concurrent engineering (SBCE) process of an aircraft jet engine for a reduced noise level at takeoff. Data is collected from a range of products in the same family as the jet engine. Knowledge-based trade-off curves are used as a methodology to create and visualize knowledge from the collected data. Findings showed that this method provides designers with enough confidence to identify a set of design solutions during the SBCE applications.

Highlights

Due to rapid technological changes, organizations are under pressure to be agile enough in order to respond to the fast-changing demand [8]
This paper aims to create an initial knowledge environment for designers by using trade-off curves in order to identify a set of possible design solutions from the previous projects of an aircraft jet engine [19]
While it was understood from the Trade-off curves (ToCs) that Design X could not be used as a whole system concept, the fan design might be an inspiring idea for a new design solution

Summary

Introduction

Due to rapid technological changes, organizations are under pressure to be agile enough in order to respond to the fast-changing demand [8]. This agility can be gained by improving its product development activities. Designers face several challenges, especially during the early stages of developing a new product [1]. These challenges could be addressed by the lean product development (LeanPD) approach [22]. During the LeanPD process, it is essential to have the right knowledge environment in order to achieve a robust optimal design [27]. Trade-off curves (ToCs) provide this environment by creating and visualizing the knowledge that is based on the physical insights of the product as well as experienced data (e.g., outcomes of R&D, data from successful or failed projects) [13]

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