Iurii Konovalenko

PhD Candidate

Iurii Konovalenko

PhD Candidate

Iurii Konovalenko joined KLU in 2016 as a PhD candidate to conduct a research at the intersection of supply chain management and information systems. He is doing his PhD under the supervision of Prof. Dr. André Ludwig (Kühne Logistics University) and focuses on supply chain event management and its realization through existing technologies and information/decision support systems.

Prior to embarking on a PhD program at KLU Iurii received BSc degree with honors in International Economics and MSc degree with honors in International Economics and Finances from Kyiv National Economic University named after Vadym Hetman. After graduation Iurii focused on the problems of transport risk management, hazardous materials transportation, and supply chain security management in his research at the Kyiv National University of Trade and Economics and at the Hamburg University of Technology. Research activities were supplemented by professional experience in the field of logistics in a private Ukrainian company.

Iurii’s current work centers on how the data collected from multiple sources (e.g. IoT) along supply chain in real time should be aggregated and processed to timely identify the deviations from desired business process views and how the consequences of such deviations can be minimized through the actions triggered by a set of pre-defined rules.

Contact

Tel: +49 40 328707-304
Fax: +49 40 328707-109
iurii.konovalenko@the-klu.org

Networks

Professional Experience

2009 - 2016

Manager of International Relations and Logistics, Landcenter, Kyiv, Ukraine

2012 - 2013

Guest Researcher, Hamburg University of Technology, Hamburg, Germany

2009 - 2013

Research Associate, Kyiv National University of Trade and Economics, Kyiv, Ukraine

2009Internship, National Securities and Stock Market Commission, Kyiv, Ukraine
2008Manager of International Relations, Maleta Cyclic Distillation, Tallinn, Estonia
2006 - 2008Interpreter/Translator, UniCredit Bank, Kyiv, Ukraine

Education

2016

PhD Studies in Logistics, Kühne Logistics University, Hamburg, Germany

2009

MSc in International Economics and Finances at Kyiv National Economic University named after Vadym Hetman, Ukraine

2008BA in International Economics at Kyiv National Economic University named after Vadym Hetman, Ukraine

Publications

DOI: 10.1016/j.compind.2018.12.009 

Abstract: Increasing supply chain complexity poses new challenges to managers. On the other hand, evolving information and communication technology offers ample opportunity for more reliable supply chain management practices. Event processing has established itself in many applications in logistics. Although the topic has enjoyed increasing popularity, there is no study taking stock of prior developments and guiding future research. Therefore, a systematic literature review on the topic of event processing in supply chain management from 2005 until the present is undertaken. Extant literature is synthesized and analyzed from technological and supply chain management perspectives to inform scholars and practitioners of existing field developments. Additionally, to guide future scholarly endeavors, a research agenda is derived from promising topics raised in papers and unfulfilled practical requirements. We find that current solutions primarily focus on a limited number of supply chain core processes and a restricted number of supply chain actors. The majority of publications focused on time-temperature sensitive products. Additionally, the domination of road transportation can be observed, while other modes of transport are often ignored in solution implementations. Decision support in terms of object traceability within the supply chain is found in most articles. RFID, typically accompanied by the Electronic Product Code Information Services standard, is the dominant enabling technology. Future research should focus on the topics of standardization, granularity, data sources, and cooperation. Moreover, holistic event processing supported by big data and machine learning techniques could create interfaces with other legacy business intelligence applications. Another promising area includes the exploration of new technologies, i.e. IoT, to enable new smart solutions.

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