Order policies for a perishable product in retail

Research output: Contribution to conferenceAbstractAcademic

Abstract

A challenge of inventory control of perishable products in retail is that in general the age distribution of the items in stock is not known. Only the total numbers of items delivered and sold are recorded, resulting in an estimate of the total items in stock. The exact number may be different from the inventory status according to the checkout system due to damaged items and more waste than expected. We investigate order policies for a product with a maximum shelf life of 3 days at delivery. Demand is non-stationary during the week, but stationary over the weeks. Lead time is one day.
For planning purposes in the supermarket, we search for order policies with fixed reorder days during the week, so we order at least 3 times a week, and at most every day. It is likely to have items of different ages in stock. Customers can pick the items in front of the shelf (FIFO), as preferred and stimulated by the store, or search for the freshest items (LIFO). The store has a target α-service level to meet demand.
A Stochastic Programming (SP) model is presented of the situation in the retailer practice. Several policies to determine the order quantity are studied and compared to a policy from literature. The base is a YS order policy where the reorder days Y are fixed and order-up-to levels S are used, with parameter values generated by an MILP approximation of the SP model. Numerical experiments compare the effectiveness of the policies with respect to costs and reached service levels.

Conference

Conference29th European Conference On Operational Research
CountrySpain
CityValencia
Period8/07/1811/07/18
Internet address

Fingerprint

Perishable products
Retail
Stochastic programming
Service levels
Approximation
Mixed integer linear programming
Order quantity
Shelf life
Lead time
LIFO
Planning
Inventory control
Numerical experiment
Costs
Supermarkets
Retailers

Keywords

  • Retail
  • Order policy
  • perishable product
  • non-stationary demand
  • service level constraint

Cite this

Pauls-Worm, K. G. J., & Hendrix, E. M. T. (2018). Order policies for a perishable product in retail. Abstract from 29th European Conference On Operational Research, Valencia, Spain.
Pauls-Worm, K.G.J. ; Hendrix, E.M.T. / Order policies for a perishable product in retail. Abstract from 29th European Conference On Operational Research, Valencia, Spain.
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Pauls-Worm, KGJ & Hendrix, EMT 2018, 'Order policies for a perishable product in retail' 29th European Conference On Operational Research, Valencia, Spain, 8/07/18 - 11/07/18, .

Order policies for a perishable product in retail. / Pauls-Worm, K.G.J.; Hendrix, E.M.T.

2018. Abstract from 29th European Conference On Operational Research, Valencia, Spain.

Research output: Contribution to conferenceAbstractAcademic

TY - CONF

T1 - Order policies for a perishable product in retail

AU - Pauls-Worm, K.G.J.

AU - Hendrix, E.M.T.

PY - 2018/7

Y1 - 2018/7

N2 - A challenge of inventory control of perishable products in retail is that in general the age distribution of the items in stock is not known. Only the total numbers of items delivered and sold are recorded, resulting in an estimate of the total items in stock. The exact number may be different from the inventory status according to the checkout system due to damaged items and more waste than expected. We investigate order policies for a product with a maximum shelf life of 3 days at delivery. Demand is non-stationary during the week, but stationary over the weeks. Lead time is one day.For planning purposes in the supermarket, we search for order policies with fixed reorder days during the week, so we order at least 3 times a week, and at most every day. It is likely to have items of different ages in stock. Customers can pick the items in front of the shelf (FIFO), as preferred and stimulated by the store, or search for the freshest items (LIFO). The store has a target α-service level to meet demand.A Stochastic Programming (SP) model is presented of the situation in the retailer practice. Several policies to determine the order quantity are studied and compared to a policy from literature. The base is a YS order policy where the reorder days Y are fixed and order-up-to levels S are used, with parameter values generated by an MILP approximation of the SP model. Numerical experiments compare the effectiveness of the policies with respect to costs and reached service levels.

AB - A challenge of inventory control of perishable products in retail is that in general the age distribution of the items in stock is not known. Only the total numbers of items delivered and sold are recorded, resulting in an estimate of the total items in stock. The exact number may be different from the inventory status according to the checkout system due to damaged items and more waste than expected. We investigate order policies for a product with a maximum shelf life of 3 days at delivery. Demand is non-stationary during the week, but stationary over the weeks. Lead time is one day.For planning purposes in the supermarket, we search for order policies with fixed reorder days during the week, so we order at least 3 times a week, and at most every day. It is likely to have items of different ages in stock. Customers can pick the items in front of the shelf (FIFO), as preferred and stimulated by the store, or search for the freshest items (LIFO). The store has a target α-service level to meet demand.A Stochastic Programming (SP) model is presented of the situation in the retailer practice. Several policies to determine the order quantity are studied and compared to a policy from literature. The base is a YS order policy where the reorder days Y are fixed and order-up-to levels S are used, with parameter values generated by an MILP approximation of the SP model. Numerical experiments compare the effectiveness of the policies with respect to costs and reached service levels.

KW - Retail

KW - Order policy

KW - perishable product

KW - non-stationary demand

KW - service level constraint

M3 - Abstract

ER -

Pauls-Worm KGJ, Hendrix EMT. Order policies for a perishable product in retail. 2018. Abstract from 29th European Conference On Operational Research, Valencia, Spain.