Reliable Prediction Intervals and Bayesian Estimation for Demand Rates of Slow-Moving Inventory Page: 2
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Lindsey, Matthew Douglas. Reliable Prediction Intervals and Bayesian Estimation
for Demand Rates of Slow-Movinq Inventory. Doctor of Philosophy (Management
Science), August 2007, 180 pp., 58 figures, 17 tables, references, 122 titles.
Inventory having intermittent demand has infrequent sales that appear at
random, with many periods that do not show any demand at all. Managing inventory
with intermittent demand has received less attention in the literature than that of fast-
moving products. This is due in part, perhaps, to the lack of observable historical sales
figures for inventory with intermittent demand or because slow-moving inventory does
not provide the bulk of sales, despite often being the bulk of inventory on hand.
Inventory management tools are proposed that provide estimation procedures for
the future demand rates of inventory with intermittent demand. Prediction intervals,
adapted from statistical procedures developed for software reliability, for the future
demand rate of a group of products that have no sales or no more than one sale over a
specified time frame are proposed. A Monte Carlo simulation study is conducted to
assess the reliability of these prediction intervals across various sizes of product groups
and demand rates as well as for mixtures of demand rates and identify reliable
parameter ranges. Sales data from a Fortune 500 company were used to assess the
performance of the proposed prediction intervals.
Inventory managers periodically update their predictions of future demand rates
for products. Two models - a Bayes model, using a prior probability distribution for the
demand rate and a Poisson model, using a Poisson distribution for demand - were
used to obtain optimal inventory levels over several periods assuming a known cost for
surplus and shortage. This procedure has been proposed in the literature. However, its
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Lindsey, Matthew Douglas. Reliable Prediction Intervals and Bayesian Estimation for Demand Rates of Slow-Moving Inventory, dissertation, August 2007; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc3946/m1/2/: accessed October 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .