Voronezh, Russian Federation
employee
Voronezh, Russian Federation
employee
Voronezh, Voronezh, Russian Federation
Clustering methods are widely used to divide goods into groups depending on sales volumes in order to build an optimal purchasing planning and inventory management strategy. Cluster analysis methods do not provide an unambiguous partition of the original set of objects, therefore, in the work, existing clustering methods were analyzed to study sales of auto parts at truck service stations. To solve the problem, the following methods were chosen: k-means, hierarchical agglomerative clustering and DBSCAN. Before using the k-means method, the elbow method found the optimal number of clusters. The DBSCAN method is based on object density and automatically determines the number of clusters. The initial data for cluster analysis was information on sales of spare parts at truck service stations for 3 years; clustering was applied to data by year. The DBSCAN algorithm showed unsatisfactory results, since most of the goods (86%) were identified in one cluster, while others contained units of goods. The k-means method gave the best partitioning result, each group has a different volume. The distribution of goods in clusters changes over three years, so managers should study the change in the affiliation of goods to one group or another. The obtained clustering results will help determine the real needs of spare parts at truck service stations and build an optimal procurement strategy.
Cluster analysis, Data Mining, k-means, DBSCAN, hierarchical agglomerative clustering, dendrogram, elbow method.
1. Dyshin, O.A. The calculation of the spare parts in the auto-service enterprise on the base of real / O.A. Dyshin, N.A. Karimov // Demand. Engineering Science. - 2017. - Vol. 2, № 3. - 2017. - Pp. 78-84. - DOI:https://doi.org/10.11648/j.es.20170203.14.
2. Evdokimova, S.A. Analiz tovarnogo assortimenta zapasnyh chastey dilerskogo predpriyatiya avtomobil'nogo servisa s pomosch'yu algoritma FP-Growth / S.A. Evdokimova, K.V. Frolov, A.I. Novikov // Modelirovanie sistem i processov. - 2022. - T. 15, № 4. - S. 24-33. - DOI:https://doi.org/10.12737/2219-0767-2022-15-4-24-33.
3. Ivahnenko, A.A. Modelirovanie strategiy upravleniya zapasami avtoservisnogo predpriyatiya / A.A. Ivahnenko, O.A. Ivaschuk // Sovremennye naukoemkie tehnologii. - 2022. - № 12-2. - S. 217-222. - DOI:https://doi.org/10.17513/snt.39462.
4. Shikov, N.N. Model' upravleniya zapasami centra servisnogo obsluzhivaniya / N.N. Shikov, N.Z. Boyko, R.N. Shikov // Ekonomicheskiy vestnik Donbasskogo gosudarstvennogo tehnicheskogo instituta. - 2022. - № 13. - S. 57-65.
5. Using digital twins to create an inventory management system / V. Kukartsev [et al.] // E3S Web of Conferences. - 2023. - Vol. 431(1). - C. 05016. - DOI:https://doi.org/10.1051/e3sconf/202343105016.
6. Tehnologii intellektual'nogo analiza dannyh v reshenii ekonomicheskih zadach / M.Yu. Ivanov [i dr.] // Baikal Research Journal. - 2022. - T. 13, № 2. - S. 27. - DOI:https://doi.org/10.17150/2411-6262.2022.13(2).27
7. Simchenko, N.A. System analysis of digital economy virtualization processes / N.A. Simchenko, N.V. Apatova, O.L. Korolev // Perspectives of Science and Education. - 2021. - № 2 (50). - S. 23-39. - DOI:https://doi.org/10.32744/pse.2021.2.2.
8. Evdokimova, S.A. Segmentation of store customers to increase sales using ABC-XYZ-analysis and clustering methods / S.A. Evdokimova // Journal of Physics: Conference Series. - 2021. - T. 2032. - C. 012117. -DOI:https://doi.org/10.1088/1742-6596/2032/1/012117.
9. Klinov, D.A. Razrabotka metodiki segmentacii pol'zovateley s pomosch'yu algoritmov klasterizacii i rasshirennoy analitiki / D.A. Klinov, K.A. Grigoryan // Elektronnye biblioteki. - 2022. - T. 25, № 2. - S. 137-147. - DOI:https://doi.org/10.26907/1562-5419-2022-25-2-137-147.
10. Defindal, I.P. Applying machine learning on ABC-XYZ inventory model using multivariate and hierarchical clustering / I.P. Defindal, N. Saputra // Proceedings of the 6th International Conference on Vocational Education Applied Science and Technology (ICVEAST 2023). - 2023. - Pp. 322-334. - DOI:https://doi.org/10.2991/978-2-38476-132-6_30.
11. Narkhede, G. Optimizing inventory carrying cost using rank order clustering approach for small and medium enterprises (SMES) / G. Narkhede, N.R. Rajhans // Journal of University of Shanghai for Science and Technology. - 2021. - Vol. 23, Is. 1. - Pp. 161-170. - DOI:https://doi.org/10.51201/Jusst12550.
12. Prianus, O. Inventory grouping to support IT business management with the k-means algorithm / O. Prianus // Journal of Computer Science and Information Technology. - 2022. - Vol. 8, Is. 3. - Pp. 66-73. - DOI:https://doi.org/10.35134/jcsitech.v8i3.39.
13. Ridwan, A.L. Clustering sales patterns of best selling and less selling products at El Jhon Bengkulu stores using the k-medoid method / A. L. Ridwan, S. Siswanto, R.T. Alinse // Jurnal Komputer, Informasi Dan Teknologi (JKOMITEK). - 2022. - Vol. 2(2). - Pp. 637-642. -DOI:https://doi.org/10.53697/jkomitek.v2i2.1048.
14. Deng, Y. A study on e-commerce customer segmentation management based on improved K-means algorithm / Y. Deng, Q. Gao // Information Systems and e-Business Management. - 2020. - № 18(4). - Pp. 497-510. - DOI:https://doi.org/10.1007/s10257-018-0381-3.
15. Chindyana, M. Segmentation of tourist interest on tourism object categories by comparing PSO K-means and DBSCAN method / M. Chindyana, L.A. Wulandhari // Revue d’Intelligence Artificielle. - 2021. - №35(1). - Pp. 23-37. - DOI:https://doi.org/10.18280/ria.350103.
16. Evdokimova, S.A. Algoritm analiza klientskoy bazy torgovoy organizacii / S.A. Evdokimova, T.P. Novikova, A.I. Novikov // Modelirovanie sistem i processov. - 2022. - T. 15, № 1. - S. 24-35. - DOI:https://doi.org/10.12737/2219-0767-2022-15-1-24-35.
17. Evdokimova, S.A. Primenenie algoritmov klasterizacii dlya analiza klientskoy bazy magazina / S.A. Evdokimova, A.V. Zhuravlev, T.P. Novikova // Modelirovanie sistem i processov. - 2021. - T. 14, № 2. - S. 4-12. - DOI:https://doi.org/10.12737/2219-0767-2021-14-2-4-12.
18. Durojaye, D.I. Analysis and visualization of market segmentation in banking sector using kmeans machine learning algorithm / D.I. Durojaye // FUDMA Journal of Sciences. - 2022. - Vol. 6, № 1. - Pp. 387-393. - DOI:https://doi.org/10.33003/fjs-2022-0601-910.
19. Gabova, E.I. Metodika reytingovaniya kompaniy IT-sektora po urovnyu riskov kreditosposobnosti / E.I. Gabova, N.A. Kazakova // Finansy: teoriya i praktika. - 2022. - T. 26, № 4. - S. 124-138. - DOI:https://doi.org/10.26794/2587-5671-2022-26-4-124-138.
20. Novikova, T.P. Issledovanie nabora tehnologicheskih operaciy podgotovki semennogo materiala hvoynyh porod dlya lesovosstanovleniya / T.P. Novikova // Lesotehnicheskiy zhurnal. - 2021. - T. 11, № 4 (44). - S. 150-160. - DOIhttps://doi.org/10.34220/issn.2222-7962/2021.4/13.
21. How can the engineering parameters of the NIR grader affect the efficiency of seed grading? / T.P. Novikova [et al.] // Agriculture. - 2022. - T. 12, № 12. - S. 2125. - DOI:https://doi.org/10.3390/agriculture12122125.
22. Novikova, T.P. The choice of a set of operations for forest landscape restoration technology / T.P. Novikova // Inventions. - 2022. - T. 7(1). - S. 1. - DOI:https://doi.org/10.3390/inventions7010001.
23. Orehov, A.V. Markovskiy moment ostanovki aglomerativnogo processa klasterizacii v Evklidovom prostranstve / A.V. Orehov // Vestnik Sankt-Peterburgskogo universiteta. Prikladnaya matematika. Informatika. Processy upravleniya. - 2019. - T. 15, № 1. - S. 76-92. - DOI:https://doi.org/10.21638/11702/spbu10.2019.106.
24. Clinical phenotypes of chronic cough categorized by cluster analysis / J. Kang // PloS ONE. - 2023. - Vol. 18(3). - e0283352. - DOI:https://doi.org/10.1371/journal.pone.0283352.
25. Davydov, O.A. Analiz suschestvuyuschih algoritmov klasterizacii (Chast' 1) / O.A. Davydov // Vestnik Tihookeanskogo gosudarstvennogo universiteta. - 2020. - № 1 (56). - S. 27-36.
26. Pranav Shetty, Suraj Singh. Hierarchical Clustering: A Survey. International Journal of Applied Research. - 2021. - № 7(4). - Pp. 178-181. - DOI:https://doi.org/10.22271/allresearch.2021.v7.i4c.8484.
27. Golovinskiy, P.A. Vyazkiy gravitacionnyy algoritm klasterizacii netochnyh dannyh / P.A. Golovinskiy // Vestnik Voronezhskogo gosudarstvennogo universiteta. Seriya: Sistemnyy analiz i informacionnye tehnologii. - 2022. - № 1. - S. 79-89. - DOI:https://doi.org/10.17308/sait.2022.1/9203.
28. Abdullah, A.N. A comparison between some hierarchical clustering techniques / A.N. Abdullah, S. Ahmed // International Journal of Agricultural and Statistical Sciences. - 2021. - Vol. 17(1). - Pp. 1221-1227.
29. Otradnov, K.K. Eksperimental'noe issledovanie effektivnosti metodik vektorizacii tekstovyh dokumentov i algoritmov ih klasterizacii / K.K. Otradnov, V.K. Raev // Vestnik Ryazanskogo gosudarstvennogo radiotehnicheskogo universiteta. - 2018. - № 64. - S. 73-84. - DOI:https://doi.org/10.21667/1995-4565-2018-64-2-73-84.
30. Zhuravleva, V.V. Uproschennyy pokazatel' silueta dlya opredeleniya kachestva klasternyh struktur / V.V. Zhuravleva, A.S. Manicheva // Izvestiya Altayskogo gosudarstvennogo universiteta. - 2022. - № 4 (126). - S. 110-114. - DOI:https://doi.org/10.14258/izvasu(2022)4-17.
31. Improvement of DBSCAN algorithm based on k-dist graph for adaptive determining parameters / L. Yin [et al.] // Electronics. - 2023. - Vol. 12. - S. 3213. - DOI:https://doi.org/10.3390/electronics12153213.
32. Zhang, X. WOA-DBSCAN: Application of whale optimization algorithm in DBSCAN parameter adaption / X. Zhang, S. Zhou // IEEE Access. - 2023. - Vol. 11. - Pp. 91861-91878. - DOI:https://doi.org/10.1109/ACCESS.2023.3307412.