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Deteriorating Inventory Policy in a Two-Warehouse System under Demand Disruption
Published in Dinesh Kumar, Kanika Prasad, Making Complex Decisions toward Revamping Supply Chains amid COVID-19 Outbreak, 2022
Ranveer Singh Rana, Leopoldo Eduardo Cárdenas-Barrón, Harshit Katurka, Dinesh Kumar
In 2020–20121, the world has witnessed waves of COVID-19, the most significant supply chain disruptive event. With around 21 million corona cases and more than 200,000 deaths, the pandemic indeed has taken a lot from us. With patients and deaths nearly quadrupled, the second wave has proven to be more dreadful and dangerous than its predecessor. Messina et al. (2020) discussed methodologies used by decision makers to deal with disruptions. According to Grida et al. (2020), the government has taken various containment measures to stop the spread of viruses that restricted movement, which affected almost all the supply chain stages. After that, Mahajan and Tomar (2021) discussed the disruption caused in the food supply chain due to lockdown imposed to stop the spread of the virus. Afterward, Shahed et al. (2021) proposed a model to lessen the effect of disruption in a supply chain network which consists of three stages: supplier, manufacturer, and retailer. Any disruptive event that starts from a point and propagates through the stages is called a ripple effect. Many researchers have mentioned the influence of the ripple effect on supply chain performance and resilience, such as Dolgui and Ivanov (2021), Li and Zobel (2020), Li et al. (2021), and Birkie and Trucco (2020). Measures need to be taken by the food supply chain post-COVID for revival are mentioned by Mor et al. (2020).
International maritime trade and international logistics
Published in Dong-Ping Song, Container Logistics and Maritime Transport, 2021
The complexity of physical distribution channel is further enhanced by the facts that a large number of parties (e.g. importer, exporter, different transport operators, various agents, and legal experts to check the contract and conditions) are involved, and each party has their own objectives. International logistics is often over long distance using multiple transport modes. Long distance and transferring between transport modes increase the degree of uncertainties, e.g. weather condition at sea, flooding and industrial actions at ports, and congestion in inland road traffic. The disruption can cause ripple effect along the international supply chain. For example, a delay of vessel arrival at a port implies a delay of cargo delivery to the importer that will affect the importer’s inventory availability and its downstream supply chain members’ operations. Container shipping is a weekly service. This may create a surge effect with peaks and valleys. Downstream manufacturers have to build flexibility into the daily production schedule to accommodate such surge effect. The physical distribution depends on the decisions of route choice, service choice, and carrier choice (e.g. direct shipment vs transhipment, port-to-port service vs door-to-door service, which ocean carriers among the same alliance or from different alliances). Equipment availability, quality of goods and services, lead time, reliability, and inventory management are also directly related to the physical distribution channel.
Software Maintenance
Published in B.S. Dhillon, Engineering Maintenance, 2002
With respect to technical maintenance problems, a change made at one place in the software system may have a ripple effect elsewhere. This means that understanding the consequences of changes is essential. For a change is to be consistent, maintenance persons must investigate the possibility of occurrence of all types of ripple effects. Ripple effect propagation may be defined as a phenomenon by which modifications made to a software element during the software life cycle (i.e., specification, design, code, or test phase) affects other elements or components.2
Mitigating ripple effect in supply networks: the effect of trust and topology on resilience
Published in International Journal of Production Research, 2022
Ilaria Giannoccaro, Anas Iftikhar
Resilience is the proper strategy to deal with the ripple effect (Ivanov, Sokolov and Dolgui, 2014). The latter refers to disruption propagation towards supply chain stages. Ripple effect occurs when high impact and low-frequent events cascade downstream in the network and impact the firm’s service level, revenues and profits (Simchi-Levi et al. 2015; Ojha et al. 2018; Kinra et al. 2020; Dolgui, Ivanov, and Rozhkov 2020; Ivanov and Dolgui 2020). The ripple effect impacts the structure of the supply network (Dolgui, Ivanov, and Sokolov 2018; Hosseini, Ivanov, and Dolgui 2020; Li et al. In Press) and can cause financial crisis (Kim, Ryu, and Nam 2010). Resilient supply networks mitigate the negative effect of disruption propagation on network performance (Dolgui, Ivanov, and Sokolov 2018; Ivanov 2018a, 2018b; Ivanov et al., 2019; Dolgui, Ivanov, and Rozhkov 2020). Thus, to mitigate the ripple effect, resilient supply networks should be designed.
Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach
Published in International Journal of Production Research, 2020
Seyedmohsen Hosseini, Dmitry Ivanov, Alexandre Dolgui
There are several limitations and future research directions associated with this study. We developed metrics to dynamically measure the ripple effect of supplier disruption on a manufacturer in terms of TEU and service level. Ripple effect can be also measured in terms of other SC performance indicators, such as lead time and market share loss (Ivanov 2017; Ivanov, Sokolov, and Dolgui 2014). Future research efforts could focus on developing metrics that include those mentioned above in addition to TEU and service level. Furthermore, the vulnerability of SCs with different network topologies is worthwhile to be examined using the proposed ripple effect metrics. In this study, we consider a two-stage SC. However, future studies should explore the disruption propagation impact of suppliers on distribution centres and retailers. The DBN is a powerful tool for simulating the disruption propagation of upstream entities on downstream ones. Our integrated DTMC-DBN approach enables ripple effect analysis in terms of structural dynamics. Neglected, however, remain the operational production-ordering decisions, which in some cases could have a significant impact on the ripple effect (Dolgui, Ivanov, and Rozhkov 2019; Ivanov 2019b). Finally, the probability elicitation of CPT could be exhaustive when the number of nodes in the DBN model increases exponentially. Hence, alternative methods, such as the Noisy – OR method, could be used to reduce the computational complexity of a DBN with a large number of nodes.
Assessing the benefits of labelling postponement in an export-focused winery
Published in International Journal of Production Research, 2018
Mauricio Varas, Sergio Maturana, Susan Cholette, Alejandro Mac Cawley, Franco Basso
All the above papers assume an environment without uncertainty. Nevertheless, in the real world, many forms of uncertainty may affect production. For a review on production planning models under uncertainty, we refer the reader to Mula and Raul Poler (2006). According to Ho (1989), uncertainties can be categorised into two groups: environmental uncertainty and system uncertainty. Environmental uncertainty includes uncertainties beyond the production process, such as demand uncertainty and supply uncertainty. System uncertainty relates to uncertainty within the production process, such as operation yields and production lead times. As both types of uncertainty degrade the performance of the wine supply chain, researchers have worked to find ways to diminish their effects. System uncertainty at the grape production stage is addressed by Bohle, Maturana, and Vera (2010), who propose a MILP model that handles harvesting productivity uncertainty using the robust optimisation approach of Bertsimas and Sim (2004). Environmental uncertainty at the packaging stage is addressed by Varas (2016), who develops a MILP model that handles demand uncertainty through a rolling horizon framework. A robust optimisation approach for handling demand uncertainty in multi-stage production systems is developed by Cheng and Tang (2018). A different type of uncertainty, which has been called ripple effect, has been recently analysed by Dolgui, Ivanov, and Sokolov (2017). The ripple effect refers to the impact of a disruption as it propagates through the supply chain.