Rationalization of network retail management with a shift trading function based on the mathematical description of processes in the mass service area
Economic Annals-ХХI: Volume 190, Issue 5-6(2), Pages: 136-148
Citation information:
Kelman, V., Ponevach, A., & Korolovych, O. (2021). Rationalization of network retail management with a shift trading function based on the mathematical description of processes in the mass service area. Economic Annals-XXI, 190(5-6(2)), 136-148. doi: https://doi.org/10.21003/ea.V190-13
Vitalii Kelman
PhD Student (Economics), Аccounting,
Taxation and Marketing Department,
Mukachevo State University
26 Uzhhorodska Str., Mukachevo,
Zakarpattia Region, 89600, Ukraine
kelvitalij600@gmail.com
ORCID ID: https://orcid.org/0000-0002-4048-2484
Attila Ponevach
PhD Student (Economics), Аccounting,
Taxation and Marketing Department,
Mukachevo State University
26 Uzhhorodska Str., Mukachevo,
Zakarpattia Region, 89600, Ukraine
atillaponevac3@gmail.com
ORCID ID: https://orcid.org/0000-0001-5766-8886
Oksana Korolovych
PhD (Economics),
Associate Professor of the Аccounting,
Taxation and Marketing Department,
Mukachevo State University
26 Uzhhorodska Str., Mukachevo,
Zakarpattia Region, 89600, Ukraine
oxyk_k@yahoo.com
ORCID ID: https://orcid.org/0000-0001-5878-0925
Rationalization of network retail management with a shift trading function based on the mathematical description of processes in the mass service area
Abstract. The authors highlight the high priority of rationalization in the management of the totality of transactions carried out in the sphere of network retail, in close contact with the service consumer.
For network retail objects with a shift trading function, it is important to implement a direct and permanent impact, both on the system structure and on the basic processes in the area of mass trade services. This impact focuses on studying the flow of requests, service inputs and outputs of the system, as well as the length of waiting times, and the length of queues. The success of development in such retail networks depends on the flexibility of the operations performed by the contractor in close contact with the service consumer.
It is envisaged to consider peculiarities in the rationalization of network retail management with trade turnover (flexibility) functions. The latter define the structure of the service delivery system for service consumers with processes running, in which client demands for services, as defined in the income chain, even though the intensity of the customer service flow is not constant.
The purpose of the research is to present the informative field for rationalization in network retail management with the function of shifting trade, based on the mathematical description and repeated «playing» of all processes within the area of mass service.
To represent the informative area for rationalization in network retail management with a shift trading function based on a mathematical description and repeated play of all processes within the public service area, Methods of probability theory and mathematical statistics have been used, as well as cloud computing in AnyLogic Claud environment, AnyLogic service.
The results of the study was the presentation of new possibilities for rationalizing network retail management by groups of network objects based on the concept of a mass service area, and in view of the fact that there is an n-channel system of mass service with an unlimited queue, where the request flow has the intensity λ, and the service flow is the intensity μ.
The study was implemented with the example of one of the hubs Walmart-Salvador, uniting 90 supermarkets of the company. All Walmart hubs combine only the same supermarket type, supporting the trade changeover function within a single graph (half-yearly). Similar Walmart hubs are developed in Mexico, Great Britain, Brazil, China, Canada, South Africa, Chile, Japan, Costa Rica, Guatemala, Argentina, Honduras, Nicaragua, El Salvador, and Ukraine. At the same time, all network nodes contain objects that apply multi-channel service systems, most common in the network retail with an unlimited queue and an option to add a new service node. It is the Walmart-Salvador hub that has a fairly high percentage of customers’ refusals due to the busy service devices (this estimate ranging from 19% to 25%). As a result, Walmart’s lost annual profit reaches up to USD 25.5 million approximately.
The rationalization in the management of the network retail for the Walmart-Salvador hub objects is implemented with a breakdown into 8 groups, united according to common input parameters, the latter providing a solution for the optimal number of service devices, and their required reserve and runoff are calculated, as well as efficient productivity resulting from the consistency of the input and output flows in the service channel and the stability in the mass service system. At the same time, programming for solving the problem of the management rationalization is realized using rate fixing for the basic processes in the area of mass service. In this way, mass service system sustainability is ensured, with the average timing for the application staying in the mass service system being crucial. In particular, implementing such a standard could allow avoiding losses caused by waiting for servicing and unproductive ones. Among other relevant factors are: associated timing, probability or other values (necessary for transformation operations in the characteristics of the mass service area, performed for generating target values of this indicator).
Perspectives of implementing the mathematical description of the processes in a mass-service area are in the fact that it will provide for significantly simplification in the processes of rationalizing the retail management in shift-trading facilities, regardless of the frequency of quantity and quality product range changes.
Keywords: Network Retail; Shift Trading; Rationalization; Management; Mass Service Area; Mass Service Model; Service User; System; Walmart Hub; Walmart-Salvador
JEL Classіfіcatіon: C46; C61; С80
Acknowledgements and Funding: The authors received no direct funding for this research.
Contribution: The authors contributed equally to this work.
Data Availability Statement: The dataset is available from the authors upon request.
DOI: https://doi.org/10.21003/ea.V190-13
References
- Aiman, Z. J., Norfadzlia, M. Y., & Tole, S. (2016). Arduino based paperless queue management system. Telkomnika, 14(3), 839-845.
https://doi.org/10.12928/TELKOMNIKA.v14i3.3114 - Chetan, S., & Indira, J. (2020). Virtual Queueing System using Cloud Computing. International Research Journal of Engineering and Technology, 7(5), 695-698.
https://www.irjet.net/archives/V7/i5/IRJET-V7I5137.pdf - Chih-Chuan, L., Chin-Chieh, W., Chi-Dan, Ch., & Kuan-Fu, Ch. (2019). Could we employ the queueing theory to improve efficiency during future mass causality incidents?, Scandinavion Journal of Trauma, Resuscitation and Emergency Medicine, 27, 41-42.
https://doi.org/10.1186/s13049-019-0620-8 - Ernst & Young Global Limited. (2021). Database.
https://www.ey.com/uk_ua (in Ukr.) - Gao, X., & Deng, S. J. (2016). Hydrodynamic limit of order book dynamics. Version 2.
https://arxiv.org/abs/1411.7502v2 - Green, L. V., Soares, J., Giglio, J. F., & Green, R. A. (2006). Using queueing theory to increase the effectiveness of emergency department provider staffing. Academic Emergency Medicine, 13(1), 61-68.
https://doi.org/10.1197/j.aem.2005.07.034 - Haghani, M., Ejtemai, O., Sarvi, M., Sobhani, A., Burd, M., & Aghabayk, K. (2014). Random utility models of pedestrian crowd exit selection based on SP-off-RP experiments. Transportation Research Procedia, 2, 524-532.
https://doi.org/10.1016/j.trpro.2014.09.070 - Hu, X., Barnes, S., & Golden, B. (2018). Applying queueing theory to the study of emergency department operations: a survey and a discussion of comparable simulation studies. International Transactions in Operational Research, 25(1), 7-49.
https://doi.org/10.1111/itor.12400 - Huang, W., Lehalle, Ch.-A., & Rosenbaum, M. (2014). Simulating and analyzing order book data: The queue-reactive model.
https://arxiv.org/pdf/1312.0563.pdf - Igwe, A., Onwumere, J. U. J., & Egbo, O. P. (2014). Efficient Queue Management in Supermarkets: A Case Study of Makurdi Town, Nigeria. European Journal of Business and Management, 39(6),
https://core.ac.uk/download/pdf/234626158.pdf - Karthik, A. (2016). Effective Queue Management System in a Supermarket. researchgate.
https://www.researchgate.net/publication/301326347_Effective_Queue_Management_System_in_a_Supermarket - Kelly, F., & Yudovina, E. (2015). A Markov model of a limit order book: thresholds, recurrence and trading strategies. (Version 4). Quantitative Finance.
https://arxiv.org/abs/1504.00579 - Kritchanchai, D., & Hoeur, S. (2018). Simulation modeling for facility allocation of outpatient department. International Journal of Healthcare Management, 11(3), 193-201.
https://doi.org/10.1080/20479700.2017.1359920 - Li, D., Miwa, T., Morikawa, T., & Liu, P. (2016). Incorporating observed and unobserved heterogeneity in route choice analysis with sampled choice sets. Transportation Research Part C: Emerging Technologies, 67, 31-46.
https://doi.org/10.1016/j.trc.2016.02.002 - Nityangini, J., & Pravin, B. (2017). Analysis and Application of Queuing Theory in Supermarket. International Journal of Innovative Research in Science, Engineering and Technology, 6(9).
http://www.ijirset.com/upload/2017/september/21_analysis%20of%20queueing%20theory%20in%20supermarkets%20_1_.pdf - Obulor, R., & Eke, B. O. (2016). Outpatient queueing model development for hospital appointment system. International Journal of Scientific Engineering and Applied Science (IJSEAS), 2(4), 15-22.
https://ijseas.com/volume2/v2i4/ijseas20160404.pdf - Peter, O. P., & Sivasamy, R. (2019). Queueing theory techniques and its real applications to health care systems – Outpatient visits. International Journal of Healthcare Management, 14(1), 114-122.
https://doi.org/10.1080/20479700.2019.1616890 - Tanaka, M., Yanagisawa, D., & Nishinari, K. (2019). Exclusive queueing model including the choice of service windows.
https://arxiv.org/pdf/1905.03583.pdf - Toke, I. M. (2014). The order book as a queueing system: average depth and influence of the size of limit orders. Quantitative Finance, 15(5).
https://doi.org/10.1080/14697688.2014.963654 - Tšernov, K. (2019). How to Implement a Queue Management System for Retail. qminder. Qminder.
https://www.qminder.com/implement-retail-queue-management-system
Received 24.03.2021
Received in revised form 19.04.2021
Accepted 29.04.2021
Available online 10.07.2021