Reliability evaluation and adjustment of supply chain network design with demand fluctuations

Hsu, Chaug‐Ing; Li, Hui-Chieh

doi:10.1016/j.ijpe.2011.03.020

Cited by 48 publications

(19 citation statements)

References 24 publications

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“…Supply chain network design problems can be classified into two categories in terms of available information on demand: those with known demand and those with demand fluctuations or uncertainties. In particular, network design with uncertain demand is well suited to capture changes on demand patterns, product mix, production processes, sourcing strategies, or operating costs (Hsu and Li, 2011;Lin and Wang, 2011;Benyoucef et al, 2013;Bouzembrak et al, 2013;Soleimani et al, 2014).…”

Section: Supply Chain Network Designmentioning

confidence: 99%

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Wang

Gunasekaran

Ngai

et al. 2016

International Journal of Production Economics

1,115

750

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The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) -that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.

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Section: Supply Chain Network Designmentioning

confidence: 99%

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Wang

Gunasekaran

Ngai

et al. 2016

International Journal of Production Economics

1,115

750

View full text Add to dashboard Cite

show abstract

“…One of the proposed method to define or quantify this supply chain risk is to use wholesale quantity contracts to recover the disrupted situation, based on the change in demand (Qi, Bard, and Yu 2004). In addition, some research reflects the disruption such as changes in the size of the market, the impact of asymmetric information, demand fluctuations or the recovery speed from disruption (Xia et al 2004;Chen and Xiao 2009;Hsu and Li 2011;Lei, Li, and Liu 2012). As well as external sources of risk, the disruptions caused inside the company were also measured by considering the fluctuation of stock prices (Tranvouez and Ferrarini 2006;Bogataj and Bogataj 2007).…”

Section: Literature Review 21 Supply Chain Risk and Its Impactmentioning

confidence: 99%

Evaluation mechanism for structural robustness of supply chain considering disruption propagation

Han

Shin

2015

International Journal of Production Research

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This paper aims to develop a novel evaluation mechanism for assessing the structural robustness of a supply chain considering disruption propagation. Disruption propagation means that the impact of risks propagates to the whole supply chain along the connected structure. Based on the propagation model, a structural robustness evaluation mechanism is devised by integrating two quantitative metrics, average path length and in degree-out degree. To validate the proposed mechanism, the result of the quantitative assessment of the structural robustness on random networks is compared with the probability of network disruption due to the random risk. From the results of the statistical verifications and sensitivity analysis, it can be said that the proposed mechanism is better at explaining the robustness of a supply chain. In other words, all components of a network, such as nodes and arcs, and their relationships should be considered altogether, in order to more accurately measure the robustness. It may be possible to apply the proposed mechanism to the very first step of designing the supply chain. Especially, in the case of it being hard to redesign a supply chain structure after practically launching and operating the designed network, the proposed mechanism may be utilised to verify whether the planned supply chain is robust to risks or not.

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“…They investigate the effect of disruption of suppliers and demand uncertainty in setting an optimal inventory system for the SC. Hsu and Li (2011) consider the SCND problem of a manufacturer with several plants in different regions. Their model determines the location and capacity of plants and the production plan through the SC, so as to minimize the average per unit product cost.…”

Section: This Papermentioning

confidence: 99%

Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs

Mohammaddust

Rezapour

Farahani

et al. 2017

International Journal of Production Economics

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This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC's network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works 2 based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet.

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Reliability evaluation and adjustment of supply chain network design with demand fluctuations

Cited by 48 publications

References 24 publications

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Evaluation mechanism for structural robustness of supply chain considering disruption propagation

Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs

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