Proactive and reactive models for disaster resilient supply chain

Elluru, Sahitya; Gupta, Hardik; Kaur, Harpreet; Singh, SP

doi:10.1007/s10479-017-2681-2

Cited by 124 publications

(78 citation statements)

References 35 publications

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“…The width of the ripple effect and how it impacts economic performance is reliant on redundancies such as inventory or capacity buffers, also called robustness reserves, and on the speed and extent of recovery measures (Elluru et al 2017). As a result, it is necessary that, in the proactive mode, risk and SC resilience are assessed and incorporated at the design and planning stages.…”

Section: Ripple Effect Control Frameworkmentioning

confidence: 99%

The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

Ivanov

Dolgui

Sokolov

2018

International Journal of Production Research

1,250

641

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The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two isolated areas, i.e. the impact of digitalisation on SC management (SCM) and the impact of SCM on the ripple effect control. To the best of our knowledge, this is the first study that connects business, information, engineering and analytics perspectives on digitalisation and SC risks. This paper does not pretend to be encyclopedic, but rather analyses recent literature and case-studies seeking to bring the discussion further with the help of a conceptual framework for researching the relationships between digitalisation and SC disruptions risks. In addition, it emerges with an SC risk analytics framework. It analyses perspectives and future transformations that can be expected in transition towards cyber-physical SCs. With these two frameworks, this study contributes to the literature by answering the questions of (1) what relations exist between big data analytics, Industry 4.0, additive manufacturing, advanced trace & tracking systems and SC disruption risks; (2) how digitalisation can contribute to enhancing ripple effect control; and (3) what digital technology-based extensions can trigger the developments towards SC risk analytics.

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Section: Ripple Effect Control Frameworkmentioning

confidence: 99%

The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

Ivanov

Dolgui

Sokolov

2018

International Journal of Production Research

1,250

641

View full text Add to dashboard Cite

show abstract

“…In this modern-day business environment, no organizations and individuals can survive against disruption and retain their competitive advantage as a sovereign entity (Bhamra et al, 2011). Defined as individuals' ability to adapt to, and recover from disturbing events (Cheshire et al, 2015), resilience has garnered currency in the literature as a result of several catastrophes and disasters most especially related to supply chain (Elluru et al, 2017;Li et al, 2017;Macdonald et al, 2018;Mandal & Sarathy, 2018). In an attempt to lessen the consequences of disruptions and crises, many empirical studies explore different predictors as well as antecedents of resilience in the context of supply chain such as supply chain relationships (Mandal & Sarathy, 2018) supply chain risk management strategies (Zineb et al, 2017), supply chain capabilities (Brusset & Teller, 2017) and technological capabilities (Rajesh, 2017).…”

Section: Review Of Literaturementioning

confidence: 99%

“…Recently, there have been a lot of undesirable events and persistent hitches that have ruthlessly upset the ability of the firms' managers in the products productions and distribution, including, terrorism, political crises, natural disasters and diseases (Aqlan & Lam, 2015;Chen et al, 2013;Ivanov et al, 2017;Sreedevi & Saranga, 2017). Such happenings have created mindfulness among both policy makers, practitioners and academics of the need to curtail the potentially devastating effects and consequences of interruptions by creating more resilient supply chains (Elluru et al, 2017). For example, World Economic Forum (2013) survey discovered that more than 80% of firm's managers are seriously concerned about their supply chains resilience.…”

Section: Introductionmentioning

confidence: 99%

“…For example, World Economic Forum (2013) survey discovered that more than 80% of firm's managers are seriously concerned about their supply chains resilience. Additionally, the notion of facing up to interruptions by constructing supply chain resilience (SCRES) has lately garnered substantial academic interest (Das, 2014;Datta, 2017;Elluru et al, 2017). Building SCRES presumes that firms and their managers can swiftly recover from a disrupting incidences -either progressing to an even better state of desired outcome or, at least, returning to normalcy (Li et al, 2017;Macdonald et al, 2018;Mandal & Sarathy, 2018;Tukamuhabwa et al, 2015) Indeed, a firm that possesses a defense and response mechanisms to a disruption performs superior than its competitors and the managers are more committed.…”

Section: Introductionmentioning

confidence: 99%

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Does the tripartite social capital predict resilience of supply chain managers through commitment?

Salisu¹,

Hashim²,

Ismail³

et al. 2019

10.5267/j.uscm

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Studies on supply chain resilience have been well documented, but most of these studies were conducted at organizational level and hence the role of facilitating managers in the supply chain is conspicuously neglected. The purpose of this paper is to explore the effect of tripartite social capital (bonding, bridging and linking) on managers' resilience building and to examine the underlying mechanism through which these relationships exist. Data were collected through selfadministered questionnaire from 452 supply chain managers in Nigeria, a country that has been rocked by series of environmental turbulences. The measurement and structural models were assessed by Partial Lease Square Structural Equation Modelling (PLS-SEM) using SMART-PLS 3 software. The findings suggest that linking social capital influenced manager's resilience, but bonding and bridging did not. Bonding, bridging and linking influence manager's commitment. Additionally, manager's commitment mediated the relationship between tripartite social capital and manager's resilience, Theoretical, practical and methodological implications were also discussed.

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“…In case of supply chain configurations, Shukla and Kiridena (2016) have introduced a novel approach for managing supply chain configurations in presence of environmental issues. In disaster response models of supply chains, various applications have been investigated earlier involving location planning (Kaur and Singh (2016), Elluru et al (2017) ). Similarly, a dynamic and sustainable procurement model for planning was also introduced recently in Kaur and Singh (2017).…”

Section: Introductionmentioning

confidence: 99%

A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network

et al. 2018

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Modern-day logistics companies require increasingly shorter lead-times in order to cater for the increasing popularity of on-demand services. There is consequently an urgent need for fast scheduling algorithms to provide high quality, real-time implementable solutions. In this work we model a spare part delivery problem for an on-demand logistics company, as a variant of vehicle routing problem. For large delivery networks, the optimisation solution technique of column generation has been employed successfully in a variety of vehicle routing settings and is often used in combination with exact methods for solving problems with a large number of variables. Challenges may arise when the pricing subproblem is difficult to solve in a realistic period due to complex constraints or a large number of variables. The problem may become intractable when the network structure varies daily or is known with less certainty over longer period. In such instances, a high quality heuristic solution may be more preferable than an exact solution with excessive run time. We propose an improved version of column generation approach integrating an efficient genetic algorithm to obtain fast and high-quality solutions for a sustainable spare parts delivery problem. More specifically, we propose to retain the traditional column generation iterative framework, with master problem solved exactly, but with pricing subproblem solved using a metaheuristic. Computational results on a real dataset indicate that this approach yields improved solutions compared to the current best-case business-as-usual costs. It also substantially decreases the computational time; allowing for high-quality, tractable solutions to be obtained in few minutes. We propose to strike a balance between the practical and efficient solution aspects of metaheuristic algorithms, and the exact decomposition and iterative aspect of the column generation solution technique.

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Proactive and reactive models for disaster resilient supply chain

Cited by 124 publications

References 35 publications

The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

Does the tripartite social capital predict resilience of supply chain managers through commitment?

A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network

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