Preview

Bulletin of "Turan" University

Advanced search

A model of target inventory level based on management accounting data of an industrial enterprise

https://doi.org/10.46914/1562-2959-2026-1-3-312-327

Abstract

Amid increasing production complexity and growing uncertainty, effective inventory management has become critical for ensuring the financial sustainability of industrial enterprises. Conventional approaches, largely based on normative calculations and retrospective control, are increasingly inadequate, highlighting the need to integrate management accounting techniques with lean manufacturing tools (Lean). This study develops and empirically substantiates an inventory forecasting and management framework grounded in management accounting data and supported by lean principles and predictive analytics. The empirical analysis is based on data from a machine-building assembly enterprise in the Republic of Kazakhstan for the period 2021–2025. The information base comprises trial balance statements for account 1300 “Inventories” and its sub-accounts, along with cost of goods sold indicators reported in the financial statements. The research includes a comprehensive assessment of inventory dynamics, structure, and flow formation. Particular attention is given to turnover ratios, inventory intensity, imbalances between material inflows and outflows, and time lags between operational scale adjustments and inventory levels. The findings indicate that inventory management practices remain predominantly reactive and are characterized by shifts in inventory control regimes – from the accumulation of work in progress to excessive stockpiling of components and materials. Based on the empirical results, a target inventory level model is proposed. The model defines an optimal inventory range and enables quantitative evaluation of the management gap between actual and target inventory levels. The practical contribution lies in the model’s applicability for bottleneck diagnostics and evidence-based managerial decision-making within the inventory management system of a machine-building assembly enterprise.

About the Authors

G. B. Ramazanova
S. Seifullin Kazakh Agro Technical Research University
Kazakhstan

doctoral student.

Astana



T. A. Kussaiynov
S. Seifullin Kazakh Agro Technical Research University
Kazakhstan

d.e.s., рrofessor.

Astana



M. A. Vakhrushina
Financial University under the Government of the Russian Federation
Russian Federation

d.e.s., professor.

Moscow



References

1. Kompleksnyj plan po razvitiju mashinostroitelnoj otrasli Respubliki Kazakhstan na 2024-2028 gody: utv. postanovleniem Pravitelstva RK ot 30.10.2023 № 991. URL: https://adilet.zan.kz/rus/docs/P2300000991 (data obrashhenija: 26.03.2026) (In Russian)

2. Appelbaum D., Kogan A., Vasarhelyi M., Yan Z. (2017) Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems. Vol. 25. P. 29–44. DOI: 10.1016/j.accinf.2017.03.003 (In English)

3. Agostino D., Arnaboldi M., Lema M. (2022) Digitalization, accounting and accountability: A literature review and reflections on future research. Financial Accounting & Management. Vol. 38. Iss. 2. P. 143–321. DOI: 10.1111/faam.12301 (In English)

4. Möller K., Schäffer U., Verbeeten F. (2020) Digitalization in management accounting and control: an editorial. Journal of Management Control. Vol. 31. P. 1–8. DOI: 10.1007/s00187-020-00300-5 (In English)

5. Korabayev B., Amanova G., Akimova B., Saduakassova K., Nurgaliyeva A. (2024) The model of environmental accounting and auditing as a factor in increasing the efficiency of management decisions at industrial enterprises in the Republic of Kazakhstan. Regional Science Policy & Practice. Vol. 16. Iss. 3. Art. 12727. DOI: 10.1111/rsp3.12727 (In English)

6. Rikhardsson P., Yigitbasioglu O. (2018) Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems. Vol. 29. P. 37–58. DOI: 10.1016/j.accinf.2018.03.001 (In English)

7. Rosin F., Forget P., Lamouri S., Pellerin R. (2020) Impacts of Industry 4.0 technologies on Lean principles. International Journal of Production Research. Vol. 58. No. 6. P. 1644–1669. DOI: 10.1080/00207543.2019.1672902 (In English)

8. Rossini M., Costa F., Tortorella G.L., Portioli-Staudacher A. (2019) Industry 4.0 and Lean Production: an empirical study. Procedia Manufacturing. Vol. 42. P. 59–66. (In English)

9. Zheng T., Ardolino M., Bacchetti A., Perona M. (2021) The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review. International Journal of Production Research. Vol. 59. No. 6. P. 1922–1954. DOI: 10.1080/00207543.2020.1824085 (In English)

10. Seifullina A., Er A., Nadeem S.P., et al. (2018) A Lean Implementation Framework for the Mining Industry. Procedia Manufacturing. Vol. 51. P. 1149–1154. (In English)

11. Goltsos T.E., Syntetos A.A., Glock C.H., Ioannou G. (2022) Inventory-forecasting: Mind the gap. European Journal of Operational Research. Vol. 299. No. 2. P. 397–419. DOI: 10.1016/j.ejor.2021.07.040 (In English)

12. van der Haar J.F., Wellens A.P., Boute R.N., Basten R.J.I. (2024) Supervised learning for integrated forecasting and inventory control. European Journal of Operational Research. Vol. 319. No. 2. P. 573–586. (In English)

13. Huber J., Müller S., Fleischmann M., Stuckenschmidt H. (2019) A data-driven newsvendor problem: From data to decision. European Journal of Operational Research. Vol. 278. No. 3. P. 904–915. DOI: 10.1016/j.ejor.2019.04.043 (In English)

14. Petropoulos F., et al. (2022) Forecasting: theory and practice. International Journal of Forecasting. Vol. 38. No. 3. P. 705–871. DOI: 10.1016/j.ijforecast.2021.11.001 (In English)

15. Serrano B., Minner S., Schiffer M., Vidal T. (2024) Bilevel optimization for feature selection in the data-driven newsvendor problem. European Journal of Operational Research. Vol. 315. No. 2. P. 703–714. (In English)

16. Makridakis S., Spiliotis E., Assimakopoulos V. (2018) The M4 Competition: Results, findings, conclusion and way forward. International Journal of Forecasting. Vol. 34. No. 4. P. 802–808. DOI: 10.1016/j.ijforecast.2018.06.001 (In English)

17. Ulrich M., Jahnke H., Langrock R., Pesch R., Senge R. (2021) Distributional regression for demand forecasting in e-grocery. European Journal of Operational Research. Vol. 294. No. 3. P. 831–842. DOI: 10.1016/j.ejor.2019.11.029 (In English)

18. Ngoc Anh Nguyen, Thi Xuan Hoa Nguyen, Ngoc Thang Tran, Thi Ha Nguyen, Phuong Anh Nguyen, Hai Anh Vu. (2026) Optimizing supply chain operations using advanced Time-Series Mixer models for demand forecasting and inventory under uncertain demand. Expert Systems with Applications. Vol. 296. Part B. Art. 128955. DOI: 10.1016/j.eswa.2025.128955 (In English)

19. Lingkon L.R., Hossain M.S., Chakrabortty R.K. (2026) An analytics-driven hybrid method for multiitem demand forecasting in supply chains. Supply Chain Analytics. Vol. 13. Art. 100194. DOI: 10.1016/j.sca.2026.100194 (In English)

20. Liu Y., Kalaitzi D., Wang M., Papanagnou C. (2025) A machine learning approach to inventory stockout prediction. Journal of Digital Economy. Vol. 4. P. 144–155. DOI: 10.1016/j.jdec.2025.06.002 (In English)

21. Mahin M.P., Shahriar M., Das R., Roy A., Reza A.W. (2025) Enhancing sustainable supply chain forecasting using machine learning for sales prediction. Procedia Computer Science. Vol. 252. P. 470–479. DOI: 10.1016/j.procs.2025.01.006 (In English)

22. Zabraoui O., Hmamou Y., Alami S. (2025) A comparative study of multi-algorithm optimization for inventory analytics in supply chains. Supply Chain Analytics. Vol. 12. P. 1–22. DOI: 10.1016/j.sca.2025.100154 (In English)

23. Guo Y., Liu F., Song J.-S., Wang S. (2025) Supply chain resilience: A review from the inventory management perspective. Fundamental Research. Vol. 5. Iss. 2. P. 450–463. DOI: 10.1016/j.fmre.2024.08.002 (In English)

24. Dikhanbayeva D., Shaikholla S., Suleiman Z., Turkyilmaz A. (2020) Assessment of Industry 4.0 Maturity Models by Design Principles. Sustainability. Vol. 12. No. 23. Art. 9927. DOI: 10.3390/su12239927 (In English)

25. TOO «Mashinostroitel’nyj zavod MTZ-Kazahstan». Oficial’nyj sajt. URL: https://tractor-belarus.kz/ (data obrashhenija: 29.05.2026) (In Russian)


Review

For citations:


Ramazanova G.B., Kussaiynov T.A., Vakhrushina M.A. A model of target inventory level based on management accounting data of an industrial enterprise. Bulletin of "Turan" University. 2026;(3):312-327. (In Russ.) https://doi.org/10.46914/1562-2959-2026-1-3-312-327

Views: 20

JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1562-2959 (Print)
ISSN 2959-1236 (Online)