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Investigation of Corruption in the Privatization of State Resources
IEM-C-35
Shai Ben Haim; shay6476@gmail.com Shlomi Vigdor; uhh3k1@gmail.com Vadim Yafe Dov; efredoff@gmail.com
Advisor: Dr. Doron Klonover
SCE - Shamoon College of Engineering, Ashdod
Privatization of state assets, like oil, gas, minerals and water, can drive economic growth but may lead to corruption if not properly privatized. Lack of oversight often results in exploitation, poor management, and corruption, such as awarding contracts without transparent tenders or selling assets at low prices to political elites. In countries like Nigeria, Russia and Venezuela, this leads to the concentration of wealth in a few hands, while the public gains little. Corruption damages public trust, reduces competition, and limits investment in public services. Nevertheless, examples like Norway show that, with proper oversight and transparency, natural resources may be managed fairly. The goal of this project is to explore corruption in privatization and propose solutions for better resource privatization.
Keywords: conflict of interest, corruption, natural resources, privatization, state assets, transparency
Decision Support Tool for Project and Task Prioritization Based on Pre-Assignment Characteristics
IEM-C-36
Gitit Hania; gitit.hania@gmail.com Yonati Yukelson; yonati10@gmail.com Eden Talker; eden101198@gmail.com
Advisor: Dr. Alisa Voslinsky
SCE - Shamoon College of Engineering, Ashdod
This project outlines the pre-development phase of a decision support system (DSS) with the goal of optimizing task and project prioritization at “ESI.” Currently, prioritization is done manually by the product manager, which can cause delays and subjective decision-making. The proposed tool automates this process by analyzing tasks based on predefined criteria, such as urgency, strategic importance, and service level agreement (SLA)—generating an automatic priority ranking.
By leveraging ‘multi-criteria decision analysis’ (MCDA), ‘business intelligence’ (BI) tools, and ‘rule- based automation’ (RBA), our system enhances decision-making speed and accuracy, also providing clear data visualization and full transparency in prioritization. This new tool is expected to: improve operational efficiency; minimize biases; optimize resource allocation; and enhance customer service— ultimately strengthening organizational decision-making.
Keywords: AI-powered DSS, decision-making effectiveness, decision support, efficiency, employee placement, project management, task prioritization