Citation: ¸Sengüne¸s, B.; Öztürk, N.
An Artificial Neural Network Model
for Project Effort Estimation. Systems
2023, 11, 91. https://doi.org/
10.3390/systems11020091
Academic Editors: Shuai Li,
Dechao Chen, Mohammed
Aquil Mirza, Vasilios N. Katsikis,
Dunhui Xiao and Predrag
S. Stanimirovic
Received: 23 December 2022
Revised: 2 February 2023
Accepted: 7 February 2023
Published: 9 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
An Artificial Neural Network Model for Project Effort Estimation
Burcu ¸Sengüne¸s * and Nursel Öztürk
Industrial Engineering Department, Faculty of Engineering, Bursa Uluda˘g University, 16059 Bursa, Turkey
* Correspondence: burcusenguenes@gmail.com
Abstract:
Estimating the project effort remains a challenge for project managers and effort estimators.
In the early phases of a project, having a high level of uncertainty and lack of experience cause
poor estimation of the required work. Especially for projects that produce a highly customized
unique product for each customer, it is challenging to make estimations. Project effort estimation
has been studied mainly for software projects in the literature. Currently, there has been no study
on estimating effort in customized machine development projects to the best of our knowledge.
This study aims to fill this gap in the literature regarding project effort estimation for customized
machine development projects. Additionally, this study focused on a single phase of a project, the
automation phase, in which the machine is automated according to customer-specific requirements.
Therefore, the effort estimation of this phase is crucial. In some cases, this is the first time that the
company has experienced the requirements specific to the customer. For this purpose, this study
proposed a model to estimate how much work is required to automate a machine. Insufficient effort
estimation is one of the main reasons behind project failures, and nowadays, researchers prefer more
objective approaches such as machine learning over expert-based ones. This study also proposed an
artificial neural network (ANN) model for this purpose. Data from past projects were used to train the
proposed ANN model. The proposed model was tested on 11 real-life projects and showed promising
results with acceptable prediction accuracy. Additionally, a desktop application was developed to
make this system easier to use for project managers.
Keywords: artificial neural network; project effort estimation; customized machine development
1. Introduction
One of the most crucial issues in project management and a continuing challenge for
project managers is accurate project effort estimation. Effort estimates are one of the most
critical inputs for project planning activities such as developing a schedule and estimating
the required budget. Therefore, the accuracy of the estimates has a direct impact on the
project’s success [
1
]. The inaccurate estimation of project effort can result in unachievable
schedules and budgets [
2
]. One study on software development projects reported that 13 to
15 percentage of software projects failed because of inadequate planning [
3
]. Another study
reported that only 17% of the projects were completed on schedule and within budget, and
that effort overruns result in unsatisfied customers, poor quality of product, and frustrated
employees [4].
Especially in the early phases of a project, making realistic estimates is difficult due
to the high level of uncertainty [
5
]. There is an inherent tendency to be optimistic in
effort estimation in environments with high levels of uncertainty, and estimates made by
experts are often biased [
4
]. Additionally, due to human nature, decision-makers are often
optimistic [
6
]. Furthermore, it is difficult to demonstrate realistic effort when competing
with other companies for a project [
4
]. One study reported that optimism in effort estimation
is one of the primary causes of project failures [
7
]. Underestimation of project effort results
in the approval of projects that exceed the budgeted amount [
1
]. In addition, assigning
fewer resources than necessary for the project may result in staff burnout due to the high
Systems 2023, 11, 91. https://doi.org/10.3390/systems11020091 https://www.mdpi.com/journal/systems
