METHODS FOR ANALYZING THE EFFECTIVENESS OF INFORMATION SYSTEMS FOR INVENTORY MANAGEMENT
DOI:
https://doi.org/10.15588/1607-3274-2024-3-19Keywords:
inventory management efficiency, information system, management system, evaluation methods, factors, Big DataAbstract
Context. Information systems for inventory management are used to forecast, manage, coordinate, and monitor the resources needed to move goods smoothly, in a timely, cost-effective, and reliable manner. The more efficiently the system works, the better results a company can achieve. A common problem with existing performance measurement methods is the difficulty of interpreting the relationship between performance indicators and the factors that influence them.
Objective. The purpose of the study is to describe a method for evaluating the effectiveness of information systems, which allows to establish a link between performance indicators and factors that influenced these indicators.
Method. A set of indicators characterizing the effective operation of inventory management information systems is proposed. The rules for quantifying the factors that influence the performance indicators are proposed. The factors arise during events that affect the change in order, delivery, balance, target inventory level, parameters of the forecasting algorithm, etc. The proposed method performs an iterative distribution of the quantitative value of factors among performance indicators and thus establishes the relationship between performance indicators and factors.
Results. The implementation of the proposed method in the software was carried out and calculations were made on actual data.
Conclusions. The calculations carried out on the basis of the method have demonstrated the dependence of performance indicators on factors. The use of the method allows identifying the reasons for the decrease in efficiency and making the company’s management more efficient. Prospect for further research may be to detail the factors, optimize software implementations, and use the method in inventory management information systems in various areas of activity.
References
Elsayed K. Exploring the relationship between efficiency of inventory management and firm performance: an empirical research, International Journal of Services and Operations Management, 2015, Vol. 21, Issue 1, pp. 73–86. DOI: 10.1504/IJSOM.2015.068704
Chebet E., Kitheka S., Chogo C. et al. Effects of inventory management system on firm performance-an empirical study, The International Journal of Innovate Research & Development, 2019, Vol. 8, № 8, pp. 141–150.
Stević Z., Merima B. ABC/XYZ Inventory Management Model in a Construction Material Warehouse, Alphanumeric Journal, 2021, Vol. 9, № 2, pp. 325–334. DOI: 10.17093/alphanumeric.1052034
Khan M. A., Saqib S., Alyas T. et al. Effective demand forecasting model using business intelligence empowered with machine learning, IEEE access, 2020, Vol. 8, pp. 116013– 116023. DOI: 10.1109/ACCESS.2020.3003790
Abolghasemi M., Beh E., Tarr G. et al. Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion, Computers & Industrial Engineering, 2020, Vol. 142. pp. 106380. DOI: 10.1016/j.cie.2020.106380
[Ulrich M., Jahnke H., Langrock R. el al. Ulrich M. Classification-based model selection in retail demand forecasting, International Journal of Forecasting, 2022, Vol. 38, № 1, pp. 209–223. DOI: 10.1016/j.ijforecast.2021.05.010
Praveena S., Prasanna D. S. A Hybrid Demand Forecasting for Intermittent Demand Patterns using Machine Learning Techniques, 2022 1st International Conference on Computational Science and Technology (ICCST). Chennai, India, 09–10 November 2022 : proccedings, IEEE, 2022, pp. 557– 561. DOI: 10.1109/ICCST55948.2022.10040407
Perez H. D., Hubbs C. D., Li C. et al. Algorithmic Approaches to Inventory Management Optimization, Processes, 2021, Vol. 9, № 102, pp. 241–258. DOI: 10.3390/pr9010102
Singh N., Adhikari D. AI in inventory management: Applications, Challenges, and opportunities, International Journal for Research in Applied Science and Engineering Technology, 2023, Vol. 11, № 11, pp. 2049–2053. DOI: 10.22214/ijraset.2023.57010
Yanovsky D. V., Graf M. S. Analysis of existing demand forecasting methods and ways to assess their quality, Information Technology: Computer Science, Software Engineering and Cyber Security, 2023, Vol. 3, pp. 70–77. DOI: 10.32782/IT/2023-3-9
Idrees M. A., M. Abbas, S.Q. Ali et al. The Factors Influencing Effective Inventory Management: A Supply Chain Perspective, Journal of Policy Research, 2023, №9, pp. 380–394. DOI: 10.5281/zenodo.7997287
Chan S. W., Tasmin R., Nor Aziati A. H. et al. Factors influencing the effectiveness of inventory management in manufacturing SMEs, International Research and Innovation Summit (IRIS2017). Melaka, Malaysia, 6–7 May 2017 : proccedings, IOP Conf. Series: Materials Science and Engineering 226, 2017, P. 012024. DOI: 10.1088/1757899X/226/1/012024
Islam S. S., Pulungan A. H., Rochim A. Inventory management efficiency analysis: A case study of an SME company, Journal of Physics: Conference Series, 2019, Vol. 1402, № 2, P. 022040. DOI: 10.1088/1742-6596/1402/2/022040
Canco I. Opportunities for improving the inventory management based on the example of Albanian manufacturing companies, Socialiniai tyrimai, 2022, Vol. 45, № 1, pp. 91– 103. DOI: 10.15388/Soctyr.45.1.6
Yanovsky D. V. Optimization of supply chain: solving the «BullWhip Effect» for supplier orders, Technical engineering, 2023, №2(29), pp. 146–151. DOI: 10.26642/ten-2023-2(92)-146-151
Jun-jun G., Yongping H. A joint decision model of variant selection and inventory control based on demand forecasting, 2008 IEEE International Conference on Automation and Logistics. Qingdao, China, 1–3 september 2008 : proccedings, IEEE, 2008, pp. 362–367. DOI: 10.1109/ICAL.2008.4636176
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Д. В. Яновський, М. С. Граф
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
