<i>JIT</i>delivery with stochastic lead time

Hayya, Jack C.; Ramasesh, Ranga V.; Tyworth, John G.; Kim, John G.; Sun, Daewon

doi:10.1057/jors.2012.27

Cited by 3 publications

(2 citation statements)

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“…Hayya, Harrison, and He ( 2011 ) use deterministic demand and exponentially distributed lead times to show that reducing the mean of lead time reduces the variance caused by order crossover in the ( 1, ) R S = policy. Hayya et al ( 2013 ) use simulations to extract the distribution of ELT directly from the parent lead time and show that because ELT is a time series; that is, a joint distribution of mixtures of random variables, deriving a statistical distribution of ELT would be impossible.…”

Section: Noninterchangeabilitymentioning

confidence: 99%

“…We conducted the experiments in Wolfram Mathematica 10.4, where for each of the 46 lead time distributions or experimental treatments, we generated a lead time vector of length 100,000. We used the Hayya et al ( 2008Hayya et al ( , 2009Hayya et al ( , 2011Hayya et al ( , 2013 algorithm for generating ELT vectors from the random draws of lead time for three review periods, R = 1, 7, 30, representing typical supply chain practices of daily, weekly, and monthly inventory reviews, respectively. Assuming gamma-distributed demand with mean ( D μ ) of 1,000 units and 10 levels of standard deviation ( D σ ) determined by coeffi cient of variation of demand (CVD) ranging from 0.1 to 1, we generate 10 vectors of LTD E for each treatment of ELT.…”

Section: Effective Lead Timementioning

confidence: 99%

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Order Crossover Research: A 60‐Year Retrospective to Highlight Future Research Opportunities

Saldanha

Swan²

2017

Transp. j.

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We review the literature spanning 60 years of efforts to model the phenomenon of order crossover. The importance of this research has increased due to today's longer ocean supply chains, with their greater inherent uncertainty. The incidence of order crossover is higher in these supply chains, and ignoring it will result in overestimation of safety stock. The literature is grouped into four areas, which are described separately, and is mapped to reveal gaps. Gaps exist for combinations of lead time distributions, demand distributions, and inventory policies where extant methods do not address order crossover. The effective lead time approach has proven flexible enough to be used in a wide range of periodic review situations. However, our results show that its accuracy is undermined by bimodal skewed lead-time distributions, especially when lower bounds are nonzero. Future research needs to concentrate on exploring nonparametric methods or similar methods robust to different lead time distribution assumptions to provide a method of setting safety stock in “real world” situations where orders cross.

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Section: Noninterchangeabilitymentioning

confidence: 99%