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Development of Functional Cost Estimation Models by Machine Learning

Parvizi Nejad, Alborz | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 53812 (51)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Haji, Alireza; Fatahi Valilai, Omid; Siadat, Ali
  7. Abstract:
  8. Organizations in the oil and gas industry often deal with a variety of logistic challenges. These organizations need to utilize innovative technologies like machine learning and other IT-based technologies to reduce costs and help achieve a lower-emissions environment. These industries involve a global supply chain comprised of domestic and international transportation, inventory control, materials handling, import/export facilitation, and information technology. Currently, in these industries, a classic model for executing supply-chain management techniques is considered. However, companies can optimize their supply chains to generate more productivity for better financial returns. Applying some of the new techniques can diminish costs and reduce the uncertainty in the supply chain. The demand for digital oil-field attention will grow once there is a reduction in oil costs. To improve supply chain management, it is necessary to use an optimization model.This thesis conducts a comprehensive literature review on cost estimation models which use historical data in the supply chain. Considering the practical aspects of the thesis, the focus will be on real-world operation data of the GPC (Global Project Center) department in SubSea 7 company. Studying the oil and gas supply chain literature, the thesis proposes a framework for enabling the collection and processing of historical operational data to enable a cost estimation tool. The capabilities of this framework will be examined for being quickly usable by buyers. Different case studies will be designed and implemented using Air studio, R shiny, and R studio
  9. Keywords:
  10. Machine Learning ; Cost Estimation ; Supply Chain ; Cost Management ; Big Data Analytics

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