You are here

Stokastik envanter model kullanılarak is makinelerinin onarımında kullanılanılan kritik yedek parçalar için envanter yönetim sistemi oluşturulması

Building inventory management system by using stochastic inventory model for critical spare parts used for maintenance of construction machines

Journal Name:

Publication Year:

Abstract (2. Language): 
In this study, we develop a stochastic inventory model for maintenance depot of the construction machines. With this model, the inventory of critical spare parts used for the maintenance of construction machines is managed. Proposed model is a nonlinear stochastic integer optimization problem. The aim of the model is to find out reorder points, and the maximum and minimum stocking levels of critical spare parts that minimize the inventory costs. Moreover, the availability of critical spare parts is increased. The average service level and replenishment frequency constraints are also considered in the model. To solve the model, we propose a heuristic that contains a modified ABC Analysis and can be implemented with the spreadsheet applications easily. Finally, an application and cost analysis with the proposed model are performed. The comparison of total costs for the current situation and optimum inventory level that the model proposes is performed during the cost analysis. Using inventory levels and reorder amount found by the model satisfies more than 5% cost reduction.
Abstract (Original Language): 
Bu çalısmada, is makinelerinin bakım onarım deposu için stokastik envanter modeli olusturulmustur. Bu model ile is makinelerinin onarımında kullanılan kritik yedek parçalara ait envanter yönetimi yapılmaktadır. Olusturulan model, lineer olmayan stokastik bir tam sayılı optimizasyon modelidir. Modelin hedefi, envanter maliyetlerini asgariye indirmek için yedek parçalara ait stok parametrelerini ve yeniden siparis noktasını belirlemektir. Aynı zamanda kritik yedek parçaların envanterde bulunabilirligi de arttırılmaktadır. Modelde, siparis sıklıgı ve servis düzeyi kısıtları da göz önünde bulundurulmustur. Çözüm metodu olarak gelistirilmis ABC analizini içeren ve elektronik tablolarda kolayca uygulanabilen sezgisel (heuristic) bir yöntem önerilmistir. Son olarak, önerilen model ile bir uygulama ve maliyet analizi yapılmıs ve mevcut durumdaki ile modelde önerilen optimum envanter düzeyindeki maliyetlerin karsılastırılması yapılmıstır. Model kullanımı maliyetlerde % 5’in üzerinde bir azalma saglamaktadır.
310-334

REFERENCES

References: 

[1] Ç. Saraçlar, Lojistik Faaliyetler çinde Envanter Yönetiminin Rolü ve Önemi. Yüksek
Lisans Tezi, Dokuz Eylül Üniversitesi, 2003.
[2] G. Satıcı, Enflasyonist Ortamda Endüstri sletmelerinde Envanter Model Analizleri
(Üretim ve Stok Sistemleri) ve Bir Uygulama. Doktora Tezi, Dokuz Eylül
Üniversitesi, 1995.
[3] F. Chen, et al., Quantifying the bullwhip effect in a simple supply chain: the impact
of forecasting, lead times, and information. Management Science, 46(3), 436-443
(2000).
[4] C. Chandra, S. Kumar, Taxonomy of inventory policies for supply-chain
effectiveness. International Journal of Retail and Distribution Management. 29 (4),
164-175 (2001).
[5] Y.T. Herer, et al., Transshipments: an emerging inventory recourse to achieve
supply chain leagility. International Journal of Production Economics. 80, 201-212
(2002).
[6] M. Khouja, Optimizing inventory decisions in a multi-stage multi-customer supply
chain. Transportation Research Part E. 39, 193-208 (2003).
[7] A. H. L. Lau, H. Lau, Effects of a demand-curve s shape on the optimal solutions of
a multi-echelon inventory/pricing model. European Journal of Operational Research.
147, 530-548 (2003).
[8] B. Sezen, Tedarik zincirinde stok yönetimi problemleri için elektronik tablolar
yardımı ile simülasyon uygulaması. Yönetim ve Ekonomi. 11, 1, 57-68 (2004).
[9] S. Banerjee, et al., Controlled partial shipments in two-echelon supply chain
networks: a simulation study. International Journal of Production Economics. 71,
91-100 (2001).
N. Kasap, . Biçer, B. Yüksel Özkaya / stanbul Üniversitesi sletme Fakültesi Dergisi 39, 2, (2010) 310-334 ©
2010
325
[10] F.T.S. Chan, et al., A simulation approach in supply chain management. Integrated
Manufacturing Systems. 13, 117-122. (2002).
[11] R. Ganeshan, et al., The impact of inventory and flow planning parameters on
supply chain performance: an exploratory study. International Journal of Production
Economics. 71, 111-118 (2001).
[12] J.S.K. Lau, G.Q. Huang, Web-based simulation portal for investigating impacts of
sharing production information on supply chain dynamics from the perspective of
inventory allocation. Integrated Manufacturing Systems. 13, 5, 345-358 (2002).
[13] F. Persson, J. Olhager, Performance simulation of supply chain designs.
International Journal of Production Economics. 77, 231-245 (2002).
[14] L. Lebel, J.S. Carruth, Simulation of woodyard inventory variations using a
stochastic model. Forest Products Journal. 47, 3, 52-57 (1997).
[15] W.J. Kennedy, et al., An overview of recent literature on spare parts inventories.
International Journal of Production Economic. 76, 201-215, (2002).
[16] S.C. Yang, Z.W. Du, Criticality evaluation for spare parts initial provisioning. IEEE.
507-513 (2004).
[17] R. Dekker, et al., A spare part stocking policy based on equipment criticality.
International Journal of Production Economics. 56, 69-77 (1998).
[18] T.S. Dhakar, et al., Base stock level determination for high cost low demand critical
repairable spares. Computer and Operations Research. 21(4), 411-420 (1994).
[19] T.S. Vaughan, Failure replacement and preventive maintenance spare parts
ordering policy. European Journal of Operational Research. 161, 183-190 (2005).
[20] R.Q. Zhang, et al., Spreadsheet implementable inventory control for a distribution
center. Journal of Heuristics. 7, 185–203 (2001).
[21] S. Nahmias, Production and Operation Analysis. McGraw Hill, New York, NY, 2005,
p.256.

Thank you for copying data from http://www.arastirmax.com