A Data Mining Practical Approach to Inventory Management and Logistics Optimization

Bambang Pujiarto, Mukhtar Hanafi, Arief Setyawan, Asti Nur Imani, Eky Rizky Prasetya


The latent demand to optimize costs and customer service has been fostered in the current economic situations, characterized by high competitiveness and disruption in supply chains, placing inventories as a vital sector with significant potential to implement improvements in firms. Inventory management that is done correctly has a favorable impact on logistics performance indexes. Warehousing operations account for around 15% of logistics expenditures in terms of dollars. This article employs a method based on the Partitioning Around Medoids algorithm that incorporates, in a novel way, the application of a strategy for locating the optimal picking point based on cluster classification, taking into account the qualitative and quantitative factors that have the greatest impact or priority on inventory management in the company. The results obtained with this model improve the routes of distributed materials based on the identification of their characteristics such as the frequency of collection and handling of materials, allowing for the reorganization and expansion of storage capacity of the various SKUs, moving from a classification by families to a cluster classification. This article shows a suggestion for a warehouse distribution design using data mining techniques, which uses indicators and key qualities for operational success for a case study in a corporation, as well as an approach to improve inventory management decision-making.

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Network Management; NMS; Fault Management; SNMP; System Information

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A. Anđelković and M. Radosavljević, “Improving order-picking process through implementation of warehouse management system,” Strateg. Manag., vol. 23, no. 2, pp. 3–10, 2018, doi: 10.5937/straman1801003a.

E. Bottani, A. Volpi, and R. Montanari, “Design and optimization of order picking systems: An integrated procedure and two case studies,” Comput. Ind. Eng., vol. 137, no. September, p. 106035, 2019, doi: 10.1016/j.cie.2019.106035.

G. P. Cachon and M. Fisher, “Supply chain inventory management and the value of shared information,” Manage. Sci., vol. 46, no. 8, pp. 1032–1048, 2000, doi: 10.1287/mnsc.46.8.1032.12029.

B. Chandra, “Inventory Management,” PHI Learn., vol. 148, pp. 148–162.

M. S. Chen, J. Han, and P. S. Yu, “Data mining: An overview from a database perspective,” IEEE Trans. Knowl. Data Eng., vol. 8, no. 6, pp. 866–883, 1996, doi: 10.1109/69.553155.

X. Chen, M. Sim, D. Simchi-Levi, and P. Sun, “Risk aversion in inventory management,” Oper. Res., vol. 55, no. 5, pp. 828–842, 2007, doi: 10.1287/opre.1070.0429.

T. M. Choi, S. W. Wallace, and Y. Wang, “Big Data Analytics in Operations Management,” Prod. Oper. Manag., vol. 27, no. 10, pp. 1868–1883, 2018, doi: 10.1111/poms.12838.

D. Download, P. D. F. Pack, V. M. Invent, D. Formulas, and M. R. P. A. Segerst, “economics Trends in inventory management,” Trends Invent. Manag., vol. 35, no. 1–3, pp. 107–114, 1994, [Online]. Available: https://doi.org/10.1016/0925-5273(94)90070-1.%0A(https://www.sciencedirect.com/science/article/pii/0925527394900701).

E. H. Grosse and C. H. Glock, “The effect of worker learning on manual order picking processes,” Int. J. Prod. Econ., vol. 170, pp. 882–890, 2015, doi: 10.1016/j.ijpe.2014.12.018.

M. Grzegorz, “VALUE-BASED INVENTORY MANAGEMENT,” vol. 1, no. 1.

J. Gu, M. Goetschalckx, and L. F. McGinnis, “Research on warehouse operation: A comprehensive review,” Eur. J. Oper. Res., vol. 177, no. 1, pp. 1–21, 2007, doi: 10.1016/j.ejor.2006.02.025.

J. Han and M. Kamber, “Data Mining: Concepts and Techniques The Explosive Growth of Data: from terabytes to petabytes,” Data Min. Concepts Tech., 2007, [Online]. Available: www.cs.uiuc.edu/~hanj.

G. Hua, T. C. E. Cheng, and S. Wang, “Managing carbon footprints in inventory management,” Int. J. Prod. Econ., vol. 132, no. 2, pp. 178–185, 2011, doi: 10.1016/j.ijpe.2011.03.024.

D. P. Koumanakos, “The effect of inventory management on firm performance,” Int. J. Product. Perform. Manag., vol. 57, no. 5, pp. 355–369, 2008, doi: 10.1108/17410400810881827.


G. Michalski, “Value-Based Inventory Management,” pp. 82–90, 2008.

A. R. F. Pinto and M. S. Nagano, “An approach for the solution to order batching and sequencing in picking systems,” Prod. Eng., vol. 13, no. 3–4, pp. 325–341, 2019, doi: 10.1007/s11740-019-00904-4.

L. Rahmelina, F. Firdian, I. T. Maulana, H. Aisyah, and J. Na’am, “The effectiveness of the flipped classroom model using e-learning media in introduction to information technology course,” Int. J. Emerg. Technol. Learn., vol. 14, no. 21, pp. 148–162, 2019, doi: 10.3991/ijet.v14i21.10426.

G. K. Rand, “Inventory Management and Production Planning and Scheduling (Third Edition),” J. Oper. Res. Soc., vol. 52, no. 7, pp. 845–845, 2001, doi: 10.1057/palgrave.jors.2601154.

P. Readers, A. Search, P. Website, C. About, and S. Privacy, “Oops ! It looks like you ’ re in the wrong,” p. 404.

I. Scientifique and P. Inra, “Concepts et Techniques,” 2003.

I. B. Society, “Disease Clustering : A Generalization of Knox ’ s Approach to the Detection of Space-Time Interactions Author ( s ): M . C . Pike and P . G . Smith Reviewed work ( s ),” Jstor, vol. 24, no. 3, pp. 541–556, 2012.

S. H. W. Stanger, N. Yates, R. Wilding, and S. Cotton, “Blood Inventory Management: Hospital Best Practice,” Transfus. Med. Rev., vol. 26, no. 2, pp. 153–163, 2012, doi: 10.1016/j.tmrv.2011.09.001.

L. B. Toktay, L. M. Wein, and S. A. Zenios, “Inventory Management of Remanufacturable Products,” Manage. Sci., vol. 46, no. 11, pp. 1412–1426, 2000, doi: 10.1287/mnsc.46.11.1412.12082.

M. I. Uddin Adnan, R. Raz, T. Ahmed, and A. H. M. S. Islam, “Application and Analysis of Retail Inventory Using Data Mining Techniques,” Glob. J. Comput. Sci. Technol., vol. 20, no. 2, pp. 27–33, 2020, doi: 10.34257/gjcstgvol20is2pg27.

I. Umami, “Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset,” J. Appl. Data Sci., vol. 2, no. 2, pp. 14–25, 2021, doi: 10.47738/jads.v2i2.28.

T. wahyuningsih, “Text Mining an Automatic Short Answer Grading (ASAG), Comparison of Three Methods of Cosine Similarity, Jaccard Similarity and Dice’s Coefficient,” J. Appl. Data Sci., vol. 2, no. 2, pp. 45–54, 2021, doi: 10.47738/jads.v2i2.31.

Q. Zhang, S., Zhang, C., Yang, “Dara Prepartion for Data Mining,” Appl. Artif. Intel., vol. 17(5–6), pp. 375–381, 2003, doi: 10.1080/08839510390219264.


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