scholarly journals Inventory Models Involving Lead Time Crashing Cost as an Exponential Function

2016 ◽  
Vol 7 (2) ◽  
pp. 29-39 ◽  
Author(s):  
Vijayashree M ◽  
Uthayakumar R
Keyword(s):  
Exponential Function ◽  
Lead Time ◽  
Inventory Models ◽  
Crashing Cost

Inventory models with back-order discounts and variable lead time

2001 ◽  
Vol 32 (7) ◽  
pp. 925-929 ◽  
Author(s):  
Jason Chao-Hsien Pan ◽  
Yu-Cheng Hsiao
Keyword(s):  
Lead Time ◽  
Inventory Models ◽  
Back Order

A Refinement of the Classical Order Point Model

2012 ◽  
Vol 5 (3) ◽  
pp. 43-57 ◽  
Author(s):  
Farhad Moeeni ◽  
Stephen Replogle ◽  
Zariff Chaudhury ◽  
Ahmad Syamil
Keyword(s):  
Control Systems ◽  
Inventory Control ◽  
Lead Time ◽  
Random Variables ◽  
Potential Effect ◽  
Inventory Models ◽  
Inventory Policies ◽  
Sampling Variability ◽  
Sampling Variation ◽  
Production And Inventory Control

Factors such as demand volume and replenishment lead time that influence production and inventory control systems are random variables. Existing inventory models incorporate the parameters (e.g., mean and standard deviation) of these statistical quantities to formulate inventory policies. In practice, only sample estimates of these parameters are available. The estimates are subject to sampling variation and hence are random variables. Whereas the effect of sampling variability on estimates of parameters are in general well known in statistics literature, literature on inventory control policies has largely ignored the potential effect of sampling variation on the validity of the inventory models. This paper investigates the theoretical effect of sampling variability and develops theoretically sound inventory models that can be effectively used in different inventory policies.

scholarly journals Exploring the Potential Use of the Birnbaum-Saunders Distribution in Inventory Management

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Peter Wanke ◽  
Víctor Leiva
Keyword(s):  
Inventory Management ◽  
Lead Time ◽  
Extreme Values ◽  
Lot Size ◽  
Inventory Models ◽  
Distributional Assumption ◽  
Statistical Software ◽  
Demand Distribution ◽  
Gamma Distributions ◽  
Potential Use

Choosing the suitable demand distribution during lead-time is an important issue in inventory models. Much research has explored the advantage of following a distributional assumption different from the normality. The Birnbaum-Saunders (BS) distribution is a probabilistic model that has its genesis in engineering but is also being widely applied to other fields including business, industry, and management. We conduct numeric experiments using the R statistical software to assess the adequacy of the BS distribution against the normal and gamma distributions in light of the traditional lot size-reorder point inventory model, known as (Q,r). The BS distribution is well-known to be robust to extreme values; indeed, results indicate that it is a more adequate assumption under higher values of the lead-time demand coefficient of variation, thus outperforming the gamma and the normal assumptions.

Sign in / Sign up

Export Citation Format

Share Document