Naghmeh Khosrowabadi

PhD Candidate

Naghmeh Khosrowabadi

PhD Candidate

Naghmeh Khosrowabadi joined Kühne Logistics University as a PhD Candidate in the field of Supply Chain Management under the supervision of Prof. Dr. Kai Hoberg in May 2019. Her PhD research concentrates on supply chain analytics using data science approaches.

Prior to the KLU she received her Master degree in Industrial Engineering at Iran University of Science and Technology in Iran in Jun. 2018. The title of her Master thesis was “Resilience in occupational safety of paint halls of an industrial unit”, using three approaches, data mining, OR and control charts on a real case study. Naghmeh received her Bachelor degree in Iran University of Science and Technology in Sep. 2016.

She won the top student awards in Bachelor, and entered to Master degree straightly without entrance examination with brilliant talent ranking in 2016. In addition, she also finished her Master degree with rank 1 among all of her co-faculty’ students.

The candidate is a holder of Awards and Facilities of the National Elite Foundation in Iran during (2017- 2018). Beside her study, she gained some experiences as a member of SSID (Scientific Society of Industrial Engineering Department) in IUST for one year. Moreover, she was research assistant for more than 9 months during Master study. Naghmeh is also interested in big data and AI topics.




Academic Positions

2017-2018 Research Assistant at Iran University of Science and Technology


Since 2019 PhD Candidate in Supply Chain Management, Kühne Logistics University, Hamburg, Germany
2018 M.Sc. Industrial Engineering, Iran University of Science and Technology (IUST), Teheran, Iran
2016 B.Sc. Industrial Engineering, Iran University of Science and Technology (IUST), Teheran, Iran



Abstract: With regard to the industry’s development, occupational safety is a key factor in protecting the worker’s health, achieving organizational goals and increasing productivity. Therefore, research is needed to investigate the factors affecting occupational safety. This research, based on the information gathered from the paint halts of one of the industrial units of Tehran, uses data mining technique to identify the important factors.Initially with Literature review to 2018, an insight into existing approaches and new ideas earned. Then, with a significant 5600 units of data, the results of the charts, association rules and K-means algorithm were used to extract the latent knowledge with the least error without human intervention from the six-step methodology of Crisp for data mining.The results of charts, association rules, and K-means algorithm for clustering are in a line and have been successful in determining effective factors such as important age groups and education, identifying important events, identifying the halls and finally, the root causes of major events that were the research questions.The results reveal the importance of very young and young age with often diploma education and low experience, in major accidents involving bruising, injury, and torsion, often due to self-unsafe act and unsafe conditions as slipping or collision with things. In addition, the important body members, hands and feet in the color retouching and surface color cabins are more at risk. These results help improve safety strategies. Finally, suggestions for future research were presented.

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Abstract: Various advanced systems deploy artificial intelligence (AI) and machine learning (ML) to improve demand forecasting. Supply chain planners need to become familiar with these systems and trust them, considering real-world complexities and challenges the systems are exposed to. However, planners have the opportunity to intervene based on their experience or information that the systems may not capture. In this context, we study planners’ adjustments to AI-generated demand forecasts. We collect a large amount of data from a leading AI provider and a large European retailer. Our dataset contains 30 million forecasts at the SKU-store-day level for 2019, plus variables related to products, weather, and holidays. In our two-phase analysis, we aim to understand the adjustments made by planners and the quality of these adjustments. Within each phase, we first identify the drivers of adjustments and their quality using random forest, a well-known ML algorithm. Next, we investigate the collective effects of the different drivers on the occurrence and the quality of the adjustments using a decision tree approach. We find that product characteristics such as price, freshness, and discounts are important factors when making adjustments. Large positive adjustments occur more frequently but are often inaccurate, while large negative adjustments are generally more accurate but fewer in number. Thus, planners do not contribute to accuracy on average. Our findings provide insights for the better use of human knowledge in judgmental forecasting.

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