J. Mostard (Julien)
http://repub.eur.nl/ppl/10619/
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RePub, Erasmus University RepositoryForecasting demand for single-period products: A case study in the apparel industry
http://repub.eur.nl/pub/21769/
Mon, 16 May 2011 00:00:01 GMT<div>J. Mostard</div><div>R. Teunter</div><div>M.B.M. de Koster</div>
The problem considered is that of forecasting demand for single-period products before the period starts. We study this problem for the case of a mail order apparel company that needs to order its products pre-season. The lack of historical demand data implies that other sources of data are needed. Advance order data can be obtained by allowing a selected group of customers to pre-order at a discount from a preview catalogue. Judgments can be obtained from purchase managers or other company experts. In this paper, we compare several existing and new forecasting methods for both sources of data. The methods are generic and can be used in any single-period problem in the apparel or fashion industries. Among the pre-order based methods, a novel 'top-flop' approach provides promising results. For a small group of products from the case company, expert judgment methods perform better than the methods based on advance demand information. The comparative results are obviously restricted to the specific case study, and additional testing is required to determine whether they are valid in general.The newsboy problem with resalable returns: A single period model and case study
http://repub.eur.nl/pub/64884/
Thu, 16 Feb 2006 00:00:01 GMT<div>J. Mostard</div><div>R.H. Teunter</div>
We analyze a newsboy problem with resalable returns. A single order is placed before the selling season starts. Purchased products may be returned by the customer for a full refund within a certain time interval. Returned products are resalable, provided they arrive back before the end of the season and are undamaged. Products remaining at the end of the season are salvaged. All demands not met directly are lost. We derive a simple closed-form equation that determines the optimal order quantity given the demand distribution, the probability that a sold product is returned, and all relevant revenues and costs. We illustrate its use with real data from a large catalogue/internet mail order retailer.The distribution-free newsboy problem with resalable returns
http://repub.eur.nl/pub/975/
Fri, 17 Oct 2003 00:00:01 GMT<div>J. Mostard</div><div>R.H. Teunter</div><div>M.B.M. de Koster</div>
We study the case of a catalogue/internet mail order retailer selling seasonal products
and receiving large numbers of commercial returns. Returned products arriving before
the end of the selling season can be resold if there is sufficient demand. A single order
is placed before the season starts. Excess inventory at the end of the season is salvaged
and all demands not met directly are lost. Since little historical information is available,
it is impossible to determine the shape of the distribution of demand. Therefore, we
analyze the distribution-free newsboy problem with returns, in which only the mean and
variance of demand are assumed to be known. We derive a simple closed-form expression
for the distribution-free order quantity, which we compare to the optimal order quantities when
gross demand is assumed to be normal, lognormal or uniform. We find that the distribution-free
order rule performs well in most realistic cases.The Newsboy Problem with Resalable Returns
http://repub.eur.nl/pub/268/
Thu, 07 Feb 2002 00:00:01 GMT<div>J. Mostard</div><div>R.H. Teunter</div>
We analyze a newsboy problem with resalable returns. A single order is
placed before the selling season starts. Purchased products may be
returned by the customer for a full refund within a certain time
interval. Returned products are resalable, provided they arrive back
before the end of the season and are undamaged. Products remaining at
the end of the season are salvaged. All demands not met directly are
lost. We derive a simple closed-form equation that determines the
optimal order quantity given the demand distribution, the probability
that a sold product is returned, and all relevant revenues and costs.
We illustrate its use with real data from a large catalogue/internet
mail order retailer.