Perishable products may be classified into two typestime dependent and
time independent perishable products. Products like green vegetable, fruits,
milk, flowers, meat, New Year greeting cards, Christmas trees, etc., are considered to
be time dependent perishable products as they have short fixed useful life. But
products like fashion merchandise, winter clothing, personal computers, cell phones, etc., are
time independent perishable products as they may be useful to customers or users
for a significant duration but have very less economic value. The supply chain of
perishable products is much more complicated compared to non-perishable products due to
short life cycle, low salvage value, long supply chain with fragmentation of supply
chain ownership, uncertainty in supply and demand and dynamic pricing.
Chopra and Peter (2008) mentioned two revenue management tactics used
for perishable assets or products. The first tactic is varying price dynamically over time
to maximize expected revenue and the second tactic is overbooking sales of the asset
or products to account for cancellations. One may find literatures regarding
inventory issues related to perishable products.
Rahim et al. (2000) developed a model for
jointly determining the optimal pricing policy and the order quantity for a class of
single period inventory systems of perishable products where deterioration starts at a
random point in time during the cycle and explained it through a numerical illustration.
Hsu (2003) presented an Economic Lot Size (ELS) model for perishable products
where the costs of holding inventory stocks (having backorders) in each period depends
on the age of inventories (backorders). They proposed a polynomial-time
dynamic programming algorithm to solve two-structured problems, one with
non-decreasing demands and the other with non-decreasing marginal backorder cost with respect
to the age of the backorder. Lin and Chen (2003) presented the dynamic allocation
problem with uncertain supply for the perishable
commodity-supply chain (PC-SC) to maximize the total net profit of the strategic alliance of the PC-SC and to
determine the optimal orders placed to suppliers and the resultant amount of
perishable commodities allocated to retailers. They also showed that extended-Genetic
Algorithm works best for the PC-SC with critical constraint.
Sezen (2004) suggested another method for perishable goods that utilizes
the probability values obtained from the past experiences and calculates an expected
profit value for each alternative discount policy. The decision maker then selects the
discount policy with the highest expected profit. Ramanathan (2006) developed an
empirical procedure to identify the number of units to be stocked, discount period and
the quantum of discount at the retail outlet for perishable products. He explained
this concept and the steps involved in the procedures using a numerical example.
Woensel et al. (2007) studied the customer behavior with regard to out-of-stocks
of perishable products (focused on bakery bread) based on 3,800 customer
interviews performed in three stores of a large Dutch grocery retail chain. Based on
intensive data-analysis, it was observed that bread consumers are often willing to
substitute, with some differences across the supermarkets in the sample.