Reliable Supply Chain Network Design

Abstract

Risk management in supply chains has been receiving increased attention in the past few years. In this article, we present formulations for the strategic supply chain network design problem with dual objectives, which usually conflict with each other: minimizing cost and maximizing reliability. Quantifying the total reliability of a network design is not as straightforward as total cost calculation. We use reliability indices and develop analytical formulations that model the impact of upstream supply chain on individual entities' reliability to quantify the total reliability of a network. The resulting multiobjective nonlinear model is solved using a novel hybrid algorithm that utilizes a genetic algorithm for network design and linear programming for network flow optimization. We demonstrate the application of our approach through illustrative examples in establishing tradeoffs between cost and reliability in network design and present managerial implications.

A Network‐Based Impact Measure for Propagated Losses in a Supply Chain Network Consisting of Resilient Components

“…Käki et al [23] also utilized the BN framework in modeling the propagation of risks across a supply chain network but combined it with probabilistic risk assessment (PRA) to evaluate supply network risks. A different approach was proposed by Yildiz et al [24], where the reliability of a supply chain network (using a metric that takes into account both a network component's intrinsic reliability and the reliability of its upstream) is used as a target for optimization, along with calculated cost, in a multiobjective nonlinear programming model. This model is then solved using a novel fusion of a genetic algorithm for the network design and linear programming for the optimization of the network flow.…”

The Role of Knowledge Management in Innovative Supply Chain Design

“…Indeed, there is no shortage of decision models or frameworks for guiding supply chain design and decision making, particularly under uncertainty [16] [17] [18] [19] [20] [21]. A study by Yildiz et al [22] attempts to reconcile and integrate the dual (and often conflicting) objectives of minimising costs and maximising reliability in the context of supply chain design formulation. Others advocate the use of supply chain network design to determine the structure of the supply chain and make decisions regarding facility location and size, inventory management, distribution and transport [23].…”

Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

“…Networking complexity in the SC frequently leads to disruption propagations through multiple stages: this is called the ripple effect (Liberatore, Scaparra, and Daskin 2012;Ivanov, Sokolov, and Dolgui 2014;Ivanov, Sokolov, and Pavlov 2014;Han and Shin 2016;Ivanov 2017a;Akkermans and van Wassenhove 2018;Dunke et al 2018;Scheibe and Blackhurst 2018;Dolgui, Ivanov, and Sokolov 2018). Uncertainty prediction and SC restoration or reconfiguration are two common research topics in SC disruption risk management that aim at mitigating the adverse effects of disruptions on SC financial and operational performance (Blackhurst et al 2005;Tang 2006aTang , 2006bCraighead et al 2007;Handfield and McCormack 2008;Blackhurst, Dunn, and Craighead 2011;Gurnani, Mehrotra, and Ray 2012;Sodhi, Son, and Tang 2012;Sodhi, Son, and Tang 2012;Habermann, Blackhurst, and Metcalf 2015;Yildiz et al 2016;Heckmann 2016;Khojasteh 2018;Ivanov 2018a). The management efforts and the resulting resource allocations have usually been directed towards disruption prediction, protective redundancy, and reactive capabilities.…”