Implementation of RPA Bots in Cold Supply Chain Logistics

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Resumo

Due to the significant use of low-temperature logistics in the transportation of perishable goods, the demand for the cold chain has increased. To ensure delivery efficiency and reduce damage, logistics companies should monitor the status of deliveries over short time intervals. Tracking the delivery status is a time-consuming, resource-intensive, inefficient and repetitive process. Therefore, robotic Process Automation (RPA) applications have attracted the attention of practitioners in the cold chain logistics industry. By studying the workflow of cold chain logistics, this study helps to identify possible areas that require automation. As part of the case study, the performance of two automatic RPA robots used in a forwarding company to check the condition of cargo and temperature conditions was tested and evaluated. The results showed that the introduction of RPA into the workflow significantly reduces data processing time.

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Sobre autores

Alexander Medvedev

Russian Biotechnological University (ROSBIOTECH)

Autor responsável pela correspondência
Email: medvedevav@mgupp.ru
ORCID ID: 0000-0003-1918-1967
Código SPIN: 6369-3593

Cand. Sci. (Econ.), associate professor, Department of Computer Science and Computer Engineering of Food Production

Rússia, Moscow

Artem Medvedev

Russian Biotechnological University (ROSBIOTECH)

Email: medvedevav@mgupp.ru
ORCID ID: 0009-0001-5215-7427
Código SPIN: 8498-0024

postgraduate student, Department of Computer Science and Computer Engineering of Food Production

Rússia, Moscow

Nikita Kireychenkov

Russian Biotechnological University (ROSBIOTECH)

Email: medvedevav@mgupp.ru
ORCID ID: 0009-0001-8048-3705

Department of Computer Science and Computer Engineering of Food Production

Rússia, Moscow

Bibliografia

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  3. Ali I., Nagalingam S., Gurd B. A sustainability model for perishable food logistics in the cold chain. International Logistics Management. 2018. No. 29 (3). Pp. 922–941.
  4. Ribeiro J., Lima R., Eckhardt T. et al. Robotic process automation and artificial intelligence in Industry 4.0: Literature review. Procedia Comput. Sci. 2021. No. 181. Pp. 51–58.
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  7. Li C., Feng W.X., Han S. et al. Digital adaptive management, digital transformation and quality of service in logistics enterprises. J. Glob. Inf. Manag. 2022. No. 30 (1). Pp. 1–26.
  8. Ivanchich L., Susha Vugets D., Bosil Vuksic V. Robotic automation of processes: Matic review of the literature. In: Business process management: Blockchain and the forum of Central and Eastern Europe. BPM 2019. Lecture notes on business information processing. Vol. 361. Cham: Springer, 2019.
  9. Medvedev A.V., Romashevskaya S.V. Continuation of evolution: ERP system Integration. Scientific Review. 2016. No. 9. Pp. 270–277. (In Rus.). EDN: WBMJYB.
  10. Medvedev A.V., Medvedev A.A. Factors of production in assessing the results of economic activity. In: Advances in science and technology: Collection of articles of the LII International Scientific and Practical Conference. Moscow, April 30, 2023. Moscow: Actualnost.RF, 2023. Pp. 281–287. EDN: RWYOZW.
  11. Gorodetskaya O.Yu., Gobareva Ya.L., Medvedev A.V. Innovative technologies for distance learning in conditions of quarantine restrictions. Problems of Economics and Legal Practice. 2021. Vol. 17. No. 3. Pp. 118–125. (In Rus.). EDN: EASCCR.

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2. Fig. 1. Program command Lod to Excel

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3. Fig. 2. Program command: Loop

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4. Fig. 3. Program command Keystrokes

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5. Fig. 4. Outbound process for managing temperature-sensitive products

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6. Fig. 5. Improved process

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