Innovation Project Selection Considering Stochastic Weighted Product Model

Guilherme A. Barucke Marcondes, Claudia Barucke Marcondes

2024

Abstract

Nowadays, organizations and companies are increasingly seeking to innovate in products and processes. In general, innovation materializes in the form of project execution. However, such projects are complex to implement, the evaluation of which requires understanding and knowledge of many factors. This makes its selection also difficult, given that it is necessary to experiment, which may or may not be successful, involving volatility and uncertainty, which increases when the open approach is implemented (to compensate for a lack of information, resources and skills). The selection of innovation projects can be supported by appropriate tools, in order to assist the decision maker in their choices. Multi-criteria decision methods (MCDM) can provide this support, especially if they take uncertainty into account. This work proposes the application of the Weighted Product Model (WPM), an MCDM, in the selection of innovation projects. To address uncertainty, assessments performed by more than one expert are translated into three-point estimates and Monte Carlo simulation applied for a stochastic approach. The proposed method is applied to the selection in a group of 13 innovation projects, as an example.

Paper Citation

in Harvard Style

A. Barucke Marcondes G. and Barucke Marcondes C. (2024). Innovation Project Selection Considering Stochastic Weighted Product Model. In Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES; ISBN 978-989-758-681-1, SciTePress, pages 207-212. DOI: 10.5220/0012270000003639

in Bibtex Style

@conference{icores24,
author={Guilherme A. Barucke Marcondes and Claudia Barucke Marcondes},
title={Innovation Project Selection Considering Stochastic Weighted Product Model},
booktitle={Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES},
year={2024},
pages={207-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012270000003639},
isbn={978-989-758-681-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES
TI - Innovation Project Selection Considering Stochastic Weighted Product Model
SN - 978-989-758-681-1
AU - A. Barucke Marcondes G.
AU - Barucke Marcondes C.
PY - 2024
SP - 207
EP - 212
DO - 10.5220/0012270000003639
PB - SciTePress