• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.19-27
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• 2011

This study investigates impact of smoothed replenishment ordering policy on the performance measures such as lead time, order fulfillment ratio, and inventory cost. We consider a two-echelon supply chain: a single retailer orders using smoothed order up to replenishment policy and a manufacturer produces the retailer's orders on a make to order basis. Simulation result confirms that lead time from the manufacturer can be reduced by smoothed ordering policy as expected. However, smoothing orders may deteriorate the customer order fulfillment ratio and inventory cost in a retailer. We also observe that variance of manufacturing time contributes more than mean of manufacturing time to both order fulfillment ratio and inventory cost. Therefore, variability of upstream manufacturing time should be minimized.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.1
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• pp.1-12
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• 2008

The Kanban system in Just-In-Time (JIT) production is very effective in reducing the inventories when consumption rate of the final product is relatively stable. When large fluctuations exist in the consumption rate, a new production ordering policy in which the production order quantity is determined by smoothing the demands exponentially is more suitable. This new ordering policy has not been investigated sufficiently. In this research, a multi-stage production and inventory system with stock points for materials and finished items located at each stage is considered. Approximations of average inventories at each stage in the system are derived theoretically. Numerical simulations are carried out to assess the accuracy of approximations and to evaluate the effectiveness of the new ordering policy as compared with the Kanban system. As a result, it is shown that the new ordering policy can achieve significantly lower inventory costs than the original Kanban system. The new ordering policy thus emerges as a key concept for an effective supply chain management.

• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.41-51
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• 2013

Tendency for small changes in end-consumer demand to be amplified as one moves further up the supply chain is known as bullwhip effect (BE). BE is usually defined as variance(order)/variance(demand). Since such distorted information throughout the supply chain can lead to inefficiencies, many studies to reduce variance(order) have been performed. However, in this study, we show that minimization of BE may increase inefficiencies of the supply chain. We introduce a new objective function to increase system efficiency using smoothed ordering policies. Simulation optimization is utilized to find optimal smoothed ordering policies.

• Journal of the Korea Society for Simulation
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• v.12 no.3
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• pp.31-40
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• 2003

This paper studies an optimal policy for a certain class of (s, S) inventory control systems, where the demands are characterized by the renewal arrival process. To minimize the average cost over a simulation period, we apply a stochastic optimization algorithm which uses the gradients of parameters, s and S. We obtain the gradients of objective function with respect to ordering amount S and reorder point s via a combined perturbation method. This method uses the infinitesimal perturbation analysis and the smoothed perturbation analysis alternatively according to occurrences of ordering event changes. The optimal estimates of s and S from our simulation results are quite accurate. We consider that this may be due to the estimated gradients of little noise from the regenerative system simulation, and their effect on search procedure when we apply the stochastic optimization algorithm. The directions for future study stemming from this research pertain to extension to the more general inventory system with regard to demand distribution, backlogging policy, lead time, and inter-arrival times of demands. Another direction involves the efficiency of stochastic optimization algorithm related to searching procedure for an improving point of (s, S).

• Journal of the Korea Society for Simulation
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• v.12 no.2
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• pp.1-11
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• 2003

In this paper, we focus an optimal policy focus optimal class of (s, S) inventory control systems. To this end, we use the perturbation analysis and apply a stochastic optimization algorithm to minimize the average cost over a period. We obtain the gradients of objective function with respect to ordering amount S and reorder point s via a combined perturbation method. This method uses the infinitesimal perturbation analysis and the smoothed perturbation analysis alternatively according to occurrences of ordering event changes. Our simulation results indicate that the optimal estimates of s and S obtained from a stochastic optimization algorithm are quite accurate. We consider that this may be due to the estimated gradients of little noise from the regenerative system simulation, and their effect on search procedure when we apply the stochastic optimization algorithm. The directions for future study stemming from this research pertain to extension to the more general inventory system with regard to demand distribution, backlogging policy, lead time, and review period. Another directions involves the efficiency of stochastic optimization algorithm related to searching procedure for an improving point of (s, S).