Abstract:
The aim of this thesis is to analyze the estimation performance for different characteristics of data in different supply chain systems. In this study, we analyze two different systems. The first section of this thesis covers the parameter estimation problem of a single retailer who observes price-dependent uncertain demand in additive form. To estimate the parameters of the demand, we use linear regression. Additionally, we jointly optimize the price and order quantity. We present a numerical analysis where parameter estimations are calculated for different experiment sets. We analyze the estimations for the number of observations, coefficient of variation, price, and profit margin. We show that as the number of observations increases and the coefficient of variation decreases, the accuracy of our estimations increases. The real optimal price of the data sets does not indicate a pattern of improvement and the profit margin of the product is observed to have no significant effect on the estimation performance. In the second section, we introduce a two-echelon system observing identical and non-identical exponentially distributed demand rates. The warehouse makes the allocation decision of its limited inventory based on the past demand data of retailers. The capacity constraint of the warehouse creates a conditional expected profit function. To find the expected profit, we propose a simulation method where the inventory allocation is repeated multiple times. To evaluate the performance of our method, we introduce experiment sets. Results show that for the systems with identical and non-identical demand rates, the percent loss in expected profit is smaller when the system utilization and the profit margin are larger. For the latter system, we analyze the effect of the ratio between the demand rates of two retailers. The results show no significant effect of this ratio on the percent loss in expected profit.
