Ayrıntılı Bilimsel Program ve Bildiri Özetleri - YAEM2010

YAEM 2010
YÖNEYLEM ARAÞTIRMASI VE ENDÜSTRÝ MÜHENDÝSLÝGI 30. ULUSAL KONGRESÝ
The supply chain analyzed includes two retailers that sell two
substitutable products and two suppliers. Each retailer faces a
stochastic demand for the product she sells and replenishes her
inventory from her supplier. The supplier provides a random fraction
of the quantity requested. A given percentage of customers with
unmet demand will substitute the product sold by the other retailer.
We assume that the two retailers who make ordering decisions are
rational players. Since each retailer's decision affects the single period
expected profit of the other retailer, game theory is used to find the
order quantities when the retailers use a Nash strategy.
Keywords: Inventory, substitution, game theory, random demand
and supply
A Bayesian Model for the Accurate Simulation of Multi-Product
Inventory Systems
Canan Gunes, Bahar Biller
Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA,
USA
The most common practice in performing inventory simulations is to
estimate demand models using historical data sets of finite length and
drive the simulations with the random variates generated from the
estimated demand models. However, this practice ignores the
uncertainty around the estimated demand model and its parameters
(i.e., parameter uncertainty) and accounts only for stochastic
uncertainty (i.e., the uncertainty that arises from the dependence of
the output on the simulation's random input streams) in the output
analysis. Consequently, it provides not only inaccurate estimates for
mean fill rates, but also low coverage for confidence intervals.
Motivated by the need to build multivariate demand models for multi-
product inventory simulations, we introduce a Bayesian model that
accounts for both stochastic uncertainty and parameter uncertainty in
the output analysis of multi-product inventory simulations with
correlated demands. We demonstrate that the resulting model
improves both the accuracy of the mean fill rates and the coverage of
the confidence intervals.
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