Time traps in supply chains: Is optimal still good enough?

Dunke, Fabian; Heckmann, Iris; Nickel, Stefan; Saldanha-da-Gama, Francisco

doi:10.1016/j.ejor.2016.07.016

Cited by 49 publications

(27 citation statements)

References 34 publications

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“…Risk analytics in the digital SC: extensions of quantitative analysis applications to ripple effect Dunke et al (2018) underline that digitalisation and Industry 4.0 may significantly influence the optimisation techniques in the SC domain as well as disruption propagation impacts on SC performance. With the help of optimisation and simulation approaches, current research generates new knowledge about the influence of disruption propagation on SC output performance considering disruption location, duration and propagation and recovery policies.…”

Section: 2mentioning

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

1,194

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: 2mentioning

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

1,194

641

View full text Add to dashboard Cite

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“…Despite the fact that the multi-stage stochastic programming framework is well established [61,11], its application to distribution problems is still in its infancy. Recently, some authors [52,44,23] raise the need for such a decisional framework to tackle complex supply chain problems. The aim of this paper is thus to first define 2E-DDP under uncertain and time-varying demand, and time-varying DP opening costs, formulated as a multi-stage stochastic program with recourse.…”

Section: Introductionmentioning

confidence: 99%

Designing a two-echelon distribution network under demand uncertainty

Mohamed

Klibi

Vanderbeck

2020

European Journal of Operational Research

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This paper proposes a comprehensive methodology for the stochastic multi-period two-echelon distribution network design problem (2E-DDP) where product flows to ship-to-points are directed from an upper layer of primary warehouses to distribution platforms (DPs) before being transported to the ship-to-points. A temporal hierarchy characterizes the design level dealing with DP location and capacity decisions, as well as the operational level involving transportation decisions as origin-destination flows. These design decisions must be calibrated to minimize the expected distribution cost associated with the two-echelon transportation schema on this network under stochastic demand. We consider a multi-period planning horizon where demand varies dynamically from one planning period to the next. Thus, the design of the two-echelon distribution network under uncertain customer demand gives rise to a complex multi-stage decisional problem. Given the strategic structure of the problem, we introduce alternative modeling approaches based on two-stage stochastic programming with recourse. We solve the resulting models using a Benders decomposition approach. The size of the scenario set is tuned using the sample average approximation (SAA) approach. Then, a scenariobased evaluation procedure is introduced to post-evaluate the design solutions obtained. We conduct extensive computational experiments based on several types of instances to validate the proposed models and assess the efficiency of the solution approaches. The evaluation of the quality of the stochastic solution underlines the impact of uncertainty in the two-echelon distribution network design problem (2E-DDP).

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“…Another revolutionary design method is the online optimization. Online optimization can support to solve real-time challenges [12] in complex supply chain processes or in-plant material handling systems.…”

Section: Content Analysismentioning

confidence: 99%

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review

Bányai¹

2020

Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

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Time traps in supply chains: Is optimal still good enough?

Cited by 49 publications

References 34 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

Designing a two-echelon distribution network under demand uncertainty

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review

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