Optimal ordering policies when anticipating a disruption in supply or demand

Weiss, Howard J.; Rosenthal, Edward C.

doi:10.1016/0377-2217(92)90194-e

Cited by 61 publications

(34 citation statements)

References 8 publications

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“…The goals of the expected resilience based on the amount of product delivered and the one based on the average delivery distance are both 0.96 under the maximum allowable recovery time as seven days, i.e., T a = 7 days. The node disruption time follows the exponential distribution, which is usually used in the previous study of the supply chain, see [32,33]. The node capacity degradation follows the discrete distribution, which is usually used in the stochastic flow network analysis (see [34,35]).…”

Section: Manufacturer Distribution Centers Nanjing Beijing Shenzhenmentioning

confidence: 99%

A New Resilience Measure for Supply Chain Networks

Dong

Jin

et al. 2017

Sustainability

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Currently, supply chain networks can span the whole world, and any disruption of these networks may cause economic losses, decreases in sales and unsustainable supplies. Resilience, the ability of the system to withstand disruption and return to a normal state quickly, has become a new challenge during the supply chain network design. This paper defines a new resilience measure as the ratio of the integral of the normalized system performance within its maximum allowable recovery time after the disruption to the integral of the performance in the normal state. Using the maximum allowable recovery time of the system as the time interval under consideration, this measure allows the resilience of different systems to be compared on the same relative scale, and be used under both scenarios that the system can or cannot restore in the given time. Two specific resilience measures, the resilience based on the amount of product delivered and the resilience based on the average delivery distance, are provided for supply chain networks. To estimate the resilience of a given supply chain network, a resilience simulation method is proposed based on the Monte Carlo method. A four-layered hierarchial mobile phone supply chain network is used to illustrate the resilience quantification process and show how network structure affects the resilience of supply chain networks.

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Section: Manufacturer Distribution Centers Nanjing Beijing Shenzhenmentioning

confidence: 99%

A New Resilience Measure for Supply Chain Networks

Dong

Jin

et al. 2017

Sustainability

View full text Add to dashboard Cite

show abstract

“…There has been recent work that focuses on deriving optimal multiperiod ordering policies where it is assumed that the current state of the supply process is known (either 'up' or 'down'). This includes Weiss and Rosenthal [22] who integrate disruption uncertainty in EOQ inventory systems by developing optimal inventory policies in anticipation of a random length interruption in the supply or demand process, but where the interruption starting time is known in advance. Parlar [14] and Parlar and Perry [15] invoke renewal theory to model how the multiperiod (q, r) replenishment policies can be extended to a setting that includes supply interruptions of random lengths of time.…”

Section: Introductionmentioning

confidence: 99%

The importance of decoupling recurrent and disruption risks in a supply chain

Chopra

Reinhardt

Mohan

2007

Naval Research Logistics

266

113

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This paper focuses on the importance of decoupling recurrent supply risk and disruption risk when planning appropriate mitigation strategies. We show that bundling the two uncertainties leads a manager to underutilize a reliable source while over utilizing a cheaper but less reliable supplier. As in Dada et al. (working paper, University of Illinois, Champaign, IL, 2003), we show that increasing quantity from a cheaper but less reliable source is an effective risk mitigation strategy if most of the supply risk growth comes from an increase in recurrent uncertainty. In contrast, we show that a firm should order more from a reliable source and less from a cheaper but less reliable source if most of the supply risk growth comes from an increase in disruption probability.

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“…They had obtained analytic solutions for the optimal order quantities from two sources and the minimum average cost (AC) in terms of all the parameter values for the EOQ model. Weiss and Rosenthal (1992) developed an optimal inventory policy for EOQ inventory systems which might have a disruption in either supply or demand. The start of the disruption was known a priori and it lasted a random length of time.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of an inventory model for two suppliers with random capacity considering supply disruption

Ahmed

Sultana

Azeem

2017

IJLSM

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Supply disruption occurs for diverse reasons including transportation problem, equipment failure, raw material shortages, natural calamities etc. As a result supplier may become unavailable at random times for random time length. In addition, suppliers may also have random capacities leading to uncertain yield in orders. Again under random supplier capacities, the retailer should order from a number of suppliers in order to diversify the risk associated with shortages. The objective of this paper is to develop an inventory model for two suppliers with random capacities considering supply disruption. A modified (Q, r) model is developed to tackle the problem of future supply uncertainty in response to the demand generated by Poisson process. Two suitable optimisation algorithms are applied to search for the optimal values of the decision variables which are state dependent order quantities and reorder point to minimise the cost per unit time. A hypothetical example and its solution are then provided to have a better understanding about the demonstration of the proposed model. Finally, sensitivity analysis is also carried out to have better insights about the model developed.

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Optimal ordering policies when anticipating a disruption in supply or demand

Cited by 61 publications

References 8 publications

A New Resilience Measure for Supply Chain Networks

A New Resilience Measure for Supply Chain Networks

The importance of decoupling recurrent and disruption risks in a supply chain

Development of an inventory model for two suppliers with random capacity considering supply disruption

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