Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies

Ivanov, Dmitry; Павлов, А. В.; Dolgui, Alexandre; Pavlov, Dmitry; Sokolov, Boris

doi:10.1016/j.tre.2015.12.007

Cited by 142 publications

(91 citation statements)

References 76 publications

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“…The operational risk refers to those recurrent risks such as supply and demand uncertainties that are inherent in supply chains. A disruption risk usually refers to external disruptions caused by natural and man-made disasters [23]. The papers reviewed in the preceding subsection generally address operational risks.…”

Section: Disruption Risks In Supply Chainmentioning

confidence: 99%

Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions

2017

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Abstract:While biomass has been recognized as an important renewable energy source which has a range of positive impacts on the economy, environment, and society, the existence of feedstock seasonality and risk of service disruptions at collection facilities potentially compromises the efficiency and reliability of the energy supply system. In this paper, we consider reliable supply chain design for biomass collection against feedstock seasonality and time-varying disruption risks. We optimize facility location, inventory, biomass quantity, and shipment decisions in a multi-period planning horizon setting. A real-world case in Hubei, China is studied to offer managerial insights. Our computational results show that: (1) the disruption risk significantly affects both the optimal facility locations and the supply chain cost; (2) no matter how the failure probability changes, setting backup facilities can significantly decrease the total cost; and (3) the feedstock seasonality does not affect locations of the collection facilities, but it affects the allocations of collection facilities and brings higher inventory cost for the biomass supply chain.

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Section: Disruption Risks In Supply Chainmentioning

confidence: 99%

Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions

2017

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“…The author established that, along with cost, percentage of supplier uptime, disruption length, capacity, and flexibility play an important role in determining a buyer's disruption management strategy. The recent developments in the field of supply chain disruption management from a multidisciplinary perspective were summarized by Ivanov et al [17][18][19] who studied the Ripple effect in supply chains. They emphasized that the Ripple effect can consolidate research in supply chain disruption management, similar to the bullwhip effect regarding demand and lead time fluctuations.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic versus Deterministic Approach to Coordinated Supply Chain Scheduling

Sawik

2017

Mathematical Problems in Engineering

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The purpose of this paper is to consider coordinated selection of supply portfolio and scheduling of production and distribution in supply chains under regional and local disruption risks. Unlike many papers that assume the all-or-nothing supply disruption pattern, in this paper, only the regional disruptions belong to the all-or-nothing disruption category, while for the local disruptions all disruption levels can be considered. Two biobjective decision-making models, stochastic, based on the wait-and-see approach, and deterministic, based on the expected value approach, are proposed and compared to optimize the trade-off between expected cost and expected service. The main findings indicate that the stochastic programming wait-and-see approach with its ability to handle uncertainty by probabilistic scenarios of disruption events and the much simpler expected value problem, in which the random parameters are replaced by their expected values, lead to similar expected performance of a supply chain under multilevel disruptions. However, the stochastic approach, which accounts for all potential disruption scenarios, leads to a more diversified supply portfolio that will hedge against a variety of scenarios.

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“…Outsourced products are directly delivered to customers. The capacity of external facility is assumed to be infinite (Cortinhal et al, 2015;Thanh et al, 2011Thanh et al, , 2010Thanh et al, ,2008Hinojosa et al, 2008).Capacity expansion at undisrupted facilities is not permitted during disruption onset period td 1 as it is not practically feasible to implement structural changes in short term immediately after the disruption (Melo etal., 2006 andIvanov et al, 2016).…”

Section: Problem Description and Model Formulationmentioning

confidence: 99%

“…However, in practise the loss due to low probable catastrophic disruptions are significant as compared to repetitive operational risks. Ivanov et al, (2016) have presented a multi-stage SC re-planning model under facility and transportation disruptions considering gradual capacity recovery and disruption duration. The optimal proactive SC structure and recovery policies (such as opening new back-up suppliers, depots and transportation channels/modes and use of inventory and capacity buffers) against disruptions are determined using System dynamics and linear programming.…”

Section: Introductionmentioning

confidence: 99%

“…Klibi & Martel, (2013) incorporated operational response and structural adaptation decisions (such as additional expediting, backorder and overtime recourse decisions) in each SC planning period and developed a multi-criteria design evaluation approach to select the most effective and robust SCND among candidate solutions. Summarizing from literature, clearly SCNR models considering facility disruption received limited attention (Ivanov et al, 2016, Kristianto et al, 2014and Klibi & Martel, 2013. Motivated by this clear research gap, we develop a multi-objective multi-period SCNR model under facility disruptions.…”

Section: Introductionmentioning

confidence: 99%

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Multi Objective Supply Chain Network Reconfiguration Under Disruptions

Rajagopal¹,

Venkatesan²,

Agrawal³

2017

World Conference on Supply Chain Management

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Disruption in today's complex and global supply chain network (SCN) results in a negative impact on business performance. Companies try to manage disruptions by shifting the production/sourcing to undisrupted facilities, expanding the capacity at selected facilities, re-routing transportation and outsourcing the unmet demands. Models and methods to find a cost effective SCN reconfiguration to deal with disruption need attention. In this work, a SCN reconfiguration in a dynamic planning horizon under facility disruption is modelled mathematically with the objectives of minimizing the expected total cost and delivery time. The augmented ε-constraint method is proposed as a solution approach to obtain a set of Pareto optimal solutions. Numerical illustration with disruption scenarios is presented and our results show that facility with minimum distance to most customers serve majority of total customer demand. During disruption onset period, unmet demands are shifted to least utilised facility. During recovery periods, the facility with minimum distance to most customers is expanded to satisfy unmet demands. More capacity expansion occurred in minimum cost solution than in minimum time solution as more number of facilities are opened in latter than in former. The capacity utilization of facilities in minimum cost solutions are higher compared to that of minimum time solution.

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Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies

Cited by 142 publications

References 76 publications

Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions

Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions

Stochastic versus Deterministic Approach to Coordinated Supply Chain Scheduling

Multi Objective Supply Chain Network Reconfiguration Under Disruptions

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