Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61856
Title: A Semiautomatic Experience-Based Tool for Solving Product Innovation Problem
Contributor(s): Waris, Mohammad Maqbool (author); Sanin, Cesar  (author)orcid ; Szczerbicki, Edward (author); Shafiq, Syed Imran (author)
Publication Date: 2017
Early Online Version: 2017-03-02
DOI: 10.1080/01969722.2016.1276776
Handle Link: https://hdl.handle.net/1959.11/61856
Abstract: 

In this paper, we present the idea of Smart Innovation Engineering (SIE) System and its implementation methodology. The SIE system is semiautomatic system that helps in carrying the process of product innovation. It collects the experiential knowledge from the formal decisional events. This experiential knowledge is collected from the group of similar products having some common functions and features. The SIE system behaves like a group of experts in its domain as it collects, captures, and stores the experiential knowledge from similar products as well as reuses this experiential knowledge that ultimately enhances the innovation process of manufactured goods. Moreover, with SIE in hand, entrepreneurs and manufacturing organizations will be able to take proper, enhanced decisions and most importantly at appropriate time. The system gains expertise each time a decision is taken and stored in the form of set of experience that can be used in future for similar queries. Implementation of the SIE system using Set of Experience Knowledge Structure and Decisional DNA for case study suggests that the SIE system is capable of capturing and reusing the innovation-related experiences of the manufactured products. The case study confirmed that the SIE system can be beneficial for entrepreneurs and manufacturing organizations for efficient decision making in the product innovation process.

Publication Type: Journal Article
Source of Publication: Cybernetics and Systems, 48(3), p. 231-248
Publisher: Taylor & Francis Inc
Place of Publication: United States of America
ISSN: 1087-6553
0196-9722
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

Files in This Item:
1 files
File SizeFormat 
Show full item record

SCOPUSTM   
Citations

9
checked on Nov 23, 2024
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.