Periodic review inventory control with fluctuating purchasing costs

2004 ◽  
Vol 32 (4) ◽  
pp. 374-379 ◽  
Author(s):  
Srinagesh Gavirneni
Author(s):  
Leslie-Noelia Ceballos-Palomares ◽  
Andrés-Benjamín Nava-Jiménez ◽  
Santiago-Omar Caballero-Morales ◽  
Patricia Cano-Olivos

Food waste is an important economic and resource problem in all countries around the world.  Particularly, the restaurant sector highly contributes to food waste and limited efforts or studies have been performed to overcome this problem. In this context, the present study addresses an alternative to improve the supply planning for perishable products in the restaurant sector through the application of specific forecasting methods and a stochastic inventory control model. For this purpose, a real enterprise within this economic sector was considered. Our findings support that monthly forecasts can be more appropriate for accurate demand estimation and supply planning of perishable products, which is important to reduce unnecessary products. Also, the periodic review inventory control model can lead to a more appropriate supply scheme to reduce the waste of surplus food. These findings and the proposed techniques can be used for other economic entities to reduce product waste due to poor supply planning.


2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Baimei Yang ◽  
Chunyan Gao ◽  
Na Liu ◽  
Liang Xu

We consider a dynamic inventory control and pricing optimization problem in a periodic-review inventory system with price adjustment cost. Each order occurs with a fixed ordering cost; the ordering quantity is capacitated. We consider a sequential decision problem, where the firm first chooses the ordering quantity and then the sale price to maximize the expected total discounted profit over the sale horizon. We show that the optimal inventory control is partially characterized by as, s′, ppolicy in four regions, and the optimal pricing policy is dependent on the inventory level after the replenishment decision. We present some numerical examples to explore the effects of various parameters on the optimal pricing and replenishment policy.


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