3 research outputs found
The EC context for private forestry incentive evaluation.
No full textXVIII IUFRO World Congress, Ljubljana 1986
ERP-Related Issues and Challenges in Turkey: An Overview from ERP Experts
No full text
Abstract
The Enterprise Resource Planning System (ERP) is an integrated information system for competitive enterprises in the era of
globalization, especially for managing their activities effectively. These systems are enormously complex systems that require
tremendous investment on especially consulting, training, hardware, and software within corporate time and resources.
Moreover, their implementation processes often entail significant challenges, difficulties, and risks. In this paper, it is aimed to
introduce the most important issues and challenges of implementing an ERP system, in both large enterprises and SMEs in
Turkey. Exploratory research was conducted by using a small-scale survey among 31 ERP experts of 31 Turkish companies
from different industries. The findings show that user resistance is the most compelling factor influencing ERP implementation
success in Turkish companies. Additionally, lack of well- planned project duration and implementation steps, as well as
inadaptability with ERP product are the other notable factors affecting native ERP implementation success.
Editor: H. Kemal İlter, Ankara Yıldırım Beyazıt University, Turkey
Received: August 19, 2018, Accepted: October 18, 2018, Published: November 10, 2018
Copyright: © 2018 IMISC Ekren et al. This is an open-access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited.
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The Importance of Feature Selection Methods for the Error Prediction Process of a Digital Twin
No full text
Abstract
The idea of building a digital twin is related to simultaneously creating a model that becomes a transportation vehicle for
data within the information life cycle. In order to create such model, there should be well-defined feature space. Because of
the "curse of dimensionality", while the complexity of the model exponentially increases, the accuracy rate of the model
decreases. In this study, the importance of the methods chosen for dimensionality reduction while creating a model setup,
which can predict the error on a digital twin, is presented with an exemplary implementation. Four different dimension
reduction methods, PCA, Conventional PCA, WPCA, and Mars, were applied to dataset with 89016 observation values and
590 different attributes, in order to predict error via Non-linear SVM with Polynomial kernel. According to results WPCA
and MARS methods, predicted the error more successfully than others. As a result, the feature extraction solutions, that the
methods provide, affected the performance of the designed models.
Editor: H. Kemal İlter, Ankara Yıldırım Beyazıt University, Turkey
Received: August 19, 2018, Accepted: October 18, 2018, Published: November 10, 2018
Copyright: © 2018 IMISC Özdemir et al. This is an open-access article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.
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