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Dr. Gürdal Ertek's Publications

Dr. Gürdal Ertek's Publications

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Analytics Automotive Industry Data Envelopment Analysis (DEA) Data Science Decision Support Systems (DSS) Industry Insurance Statistical Hypothesis Testing 

Mind the perception gap: An integrative performance management framework for service supply chains

November 17, 2018May 23, 2019 Dr. Gurdal Ertek

Download the full published paper from Emerald. Download the abstract & developed framework as pdf. Visit the website or the

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Analytics Data Science Industry Operations Research Optimization Retail Science Supply Chain Management (SCM) Uncategorized 

Location-based pricing and channel selection in a supply chain: a case study from the food retail industry

November 13, 2018May 23, 2019 Dr. Gurdal Ertek

Download the final draft of this paper. Many retailers nowadays operate in an Internet-enabled dual-channel supply chain setting, referred to

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text-mining-of-wind-turbine-accidents
Analytics Data Science Design Analytics Energy Industry Project Management Risk Management Sustainability Text Mining Wind Energy 

Text Mining Analysis of Wind Turbine Accidents: An Ontology-Based Framework

March 19, 2018May 23, 2019 Dr. Gurdal Ertek Text Mining Analysis

As the global energy demand is increasing, the share of renewable energy and specifically wind energy in the supply is

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Data_Mining_of_Project_Management_Data
Association Mining Construction Data Science Decision Support Systems (DSS) Healthcare Research Manufacturing Project Management 

Data Mining of Project Management Data: An Analysis of Applied Research Studies

March 19, 2018May 23, 2019 Dr. Gurdal Ertek

Data collected and generated through and posterior to projects, such as data residing in project management software and post-project review

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Analytics Association Mining Data Science Healthcare Research Industry Machine Learning Optimization Text Mining 

A Data Mining Framework for the Analysis of Patient Arrivals into Healthcare Centers

March 18, 2018May 23, 2019 Dr. Gurdal Ertek Data Mining

We present a data mining framework that can be applied for analyzing patient arrivals into healthcare centers. The sequentially applied

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