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S. Karahan Orak, N. Aydin, E. Karatas / IJOCTA, Vol.15, No.1, pp.14-24 (2025)

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Periods of significant demand fluctuations may occur in
¨
˙
[5] Kasap, N., Bi¸cer, I., & Ozkaya, B. (2009). Inven-
real life, requiring the established model to reassess pro-
tory management system for critical spare parts
duction dynamics by adjusting them to achieve an op-
timal solution. To support the decision-making process used in repair of construction machinery using
within the organization, the model’s solution may be up- stochastic inventory model method. Istanbul Uni-
dated periodically. A key consideration here is the accu- versity Faculty of Business Administration Jour-
rate management of parameters such as inventory, demand, nal, 139(2), 310-334.
stock, and capacity, which should be analyzed according
to prevailing conditions and reintegrated into the model [6] Ceylan, Z., Bulkan, S., & Tozan, H. (2017). Sin-
as needed. Keeping the model updated is essential to ob- gle and multi-period inventory control models.
tain accurate and valuable outputs. As the SAA algorithm Journal of Engineering Sciences and Design, 5(2),
offers a scenario-based modeling approach, the proposed 441-455.
model finds promising solutions even under more complex
[7] Petrovic, R., Senborn, A. & Vujosevic, M. (1986).
demand patterns only if scenarios accurately represent the
condition, which highly depends on the applicator. Dur- Hierarchical spare parts inventory systems. Stud-
ing the multi-period demand cases, the scenario’s power ies in Production and Engineering Economics 5,
to represent the real condition decreases, negatively affect- Tokyo, Elsevier.
ing the model’s power to find a promising solution. Again,
[8] Ta¸s, A. (2007). Models for determining inventory
the model‘s power is highly dependent on the represen-
lot sizes under deterministic and stochastic de-
tative power of the scenarios. The number of scenarios
mand assumptions. Hacettepe University Journal
may increase to represent the real case better. However, in
such cases, heuristic algorithms may be required to find a of Economics and Administrative Sciences, 25(1),
promising solution in a short time. 215-237.
[9] C¸elebi, D., & Bayraktar, D. (2011). Creation and
Acknowledgments
validation of a stochastic inventory management
None. model in a distribution network. ITU Journal,
8(4).
Funding
[10] C¸eki¸c, B. (2015). A stochastic programming
None. approach for multi-stage inventory control man-
agement under non-stationary demand in supply
Conflict of interest chains. Hacettepe University Journal of Econom-
ics and Administrative Sciences, 33(1), 44-77.
The authors declare no conflict of interest.
[11] Sobel, M. J., & Zhang, R. Q. (2001).
Author contributions Inventory policies for systems with sto-
chastic and deterministic demand. Oper-
Conceptualization: All authors
ations Research, 49 (1), 157-162. h t t p s :
Formal analysis: Seyda Karahan Orak, Nezir Aydin
//doi.org/10.1287/opre.49.1.157.11197
Methodology: All authors
Writing–original draft: All authors [12] Ziemba, W. T. (2003). The stochastic program-
Writing–review & editing: All authors
ming approach to asset, liability, and wealth
management. Research Foundation of AIMR,
Availability of data
Scorpion Publications.
The data supporting this study’s findings are available [13] Bienstock, D., & Shapiro, J. F. (1988).
from the corresponding author upon reasonable request.
Optimizing resource acquisition deci-
sions by stochastic programming. Man-
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