Integrating financial risk measures into the design and planning of closed-loop supply chains

Cardoso, Sónia R.; Barbosa‐Póvoa, Ana Paula; Relvas, Susana

doi:10.1016/j.compchemeng.2015.10.012

Cited by 60 publications

(42 citation statements)

References 42 publications

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“…Cardoso et al [25] designed a mixed-integer linear programming model to maximize the expected NPV of SC and minimize financial risk. They considered four types of financial risk: the difference between the expected NPV and the NPV value of a given scenario, penalization according to lower-than-expected value, downside deviation from a given target for an expected NPV, and the risk of being lower than the conditional value-at-risk.…”

Section: Introductionmentioning

confidence: 99%

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A Linkage Model of Supply Chain Operation and Financial Performance for Economic Sustainability of Firm

2017

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Abstract:Although several studies have explored the relationship between the operation and performance of a supply chain (SC), a general SC model cannot deliver the expected financial results at a company-wide level. In this paper, we argue that this cannot guarantee the maximization of a firm's overall value because short-term financial performance metrics do not reflect the risk to businesses and the invested capital. Owing to the varying natures of risk and the capital invested, firms with multiple divisions should assess each division separately, and the results can be compared for decisions concerning the allocation of the firm's capital and resources to maximize the overall value of its businesses. We propose a linkage model to consider operational activities and financial performance simultaneously in a firm's supply chain model. To exhibit the superiority of the proposed model that connects SC operation and financial indicators, we first compare the differences between models for maximizing profit and enterprise-wise economic value added (EVA) as objective functions. To examine uncertainty in the operational and financial parameters of the SC, the results of sensitivity analyses are then reported. Experimental results showed that our model, using the EVA approach, is more effective and superior in terms of maximizing the firm's overall value from the long-term perspective while satisfying the target values for financial ratios set by the firm's executives and shareholders for all periods, unlike the results of the general model.

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Section: Introductionmentioning

confidence: 99%

“…Equations (21) to (25) explain the minimum and maximum inventory levels that can be on hand for each facility in each period:…”

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confidence: 99%

A Linkage Model of Supply Chain Operation and Financial Performance for Economic Sustainability of Firm

2017

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“…[41][42][43][44][45][46] Incorporating uncertainty metrics (e.g., downside risk 47 or conditional value at risk 48,49 ) in addition to the traditional economic criteria in a multi-objective optimization framework has been undertaken to address risk management in supply chain optimization. 21,[50][51][52] A few researchers have combined different methods for optimization under uncertainty to leverage the respective advantages and complement the corresponding drawbacks. A weighted sum of stochastic programming and robust optimization objectives has been considered as the objective to reflect the decision maker's preference between feasibility and optimality.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal supply chain design and operations under multi‐scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Yue

You

2016

AIChE Journal

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Although strategic and operational uncertainties differ in their significance of impact, a "one-size-fits-all" approach has been typically used to tackle all types of uncertainty in the optimal design and operations of supply chains. In this work, we propose a stochastic robust optimization model that handles multi-scale uncertainties in a holistic framework, aiming to optimize the expected economic performance while ensuring the robustness of operations. Stochastic programming and robust optimization approaches are integrated in a nested manner to reflect the decision maker's different levels of conservativeness toward strategic and operational uncertainties. The resulting multi-level mixed-integer linear programming model is solved by a decomposition-based column-and-constraint generation algorithm. To illustrate the application, a county-level case study on optimal design and operations of a spatially-explicit biofuel supply chain in Illinois is presented, which demonstrates the advantages and flexibility of the proposed modeling framework and efficiency of the solution algorithm. V C 2016 American Institute of Chemical Engineers AIChE J, 00: 000-000, 2016 Keywords: multi-scale uncertainties, stochastic robust optimization model, column-and-constraint generation algorithm, supply chain optimization IntroductionWhen hedging against uncertainties in the optimal design and operations of supply chains, only one uniform approach has been typically used to tackle all types of uncertainty. [1][2][3] This "one-size-fits-all" approach might be stochastic programming, robust optimization, fuzzy programming, or any other methods for optimization under uncertainty. 4 However, uncertainties at strategic and operational scales may differ in their significance of impact, 5 as shown in Figure 1. Strategic uncertainties have impacts over a significant duration of the project's lifetime. Once realized, strategic uncertainties would remain unchanged for a considerable period of time. Examples of strategic uncertainties include climate and weather, technology evolution, incentives and policies, and network stability. In contrast, operational uncertainties change more frequently and often lead to immediate adjustment in operational decisions. Examples of operational uncertainties include variations in raw material quality and composition, supply and demand, cost and price, as well as in lead times of production, transportation, and material handling activities. Moreover, the realizations of operational uncertainties may be associated with that of strategic uncertainties. For example, the yields of agricultural products are expected to be dependent on the climate and weather. In addition, a decision maker may hold different levels of conservativeness toward strategic and operational uncertainties. For instance, one might be less conservative toward strategic uncertainties and willing to explore the potentials of all possibilities, but be more conservative toward operational uncertainties considering factors such as demand fulfill r...

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“…Zeng and Yen (2017) applied topology theory and Markov chain technology to measure the resilience of supply chain financial risk from the perspective of partnership [12]. Cardoso et al (2016) proposed a mixed integer linear programming model to measure the financial risk in the design and planning of closed-loop supply chains [13]. Using empirical methodology-analytic hierarchy process AHP, Caniato et al (2016) analyzed the applications of 14 programs of global supply chain finance, and proposed 9 financial risk evaluation indexes, including economic risk, operational risk, external risk, etc.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Game Dynamics for Financial Risk Decision‐Making in Global Supply Chain

Jin

Duan

2018

Complexity

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This paper focuses on the game evolution process and its influencing factors of financial risk cooperation behavior between suppliers and manufacturers in global supply chain system. Using two-population evolutionary game theory, the performance of supply chain members under financial risk environment is modeled. Further, the proposed financial risk game model is applied to simulation cases of global supply chain. Based on the theory analysis and simulation results, it is shown that the cooperation strategy is the optimal evolutionarily stable strategy (ESS) for all supply chain members, when facing the high financial risk. The financial risksharing coefficient can be regarded as an adjuster that affects risk ESS of both suppliers and manufacturers under the low financial risk setting. By reducing the financial risk-sharing ratio of one supply chain player, his intention of adopting cooperation strategy would be enhanced. Finally, it is observed that financial risk sharing approach may lead to the alignment among supply chain members. Therefore, setting up an effective financial risk-sharing mechanism is beneficial to realize sustainable development of global supply chain.

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Integrating financial risk measures into the design and planning of closed-loop supply chains

Cited by 60 publications

References 42 publications

A Linkage Model of Supply Chain Operation and Financial Performance for Economic Sustainability of Firm

A Linkage Model of Supply Chain Operation and Financial Performance for Economic Sustainability of Firm

Optimal supply chain design and operations under multi‐scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Evolutionary Game Dynamics for Financial Risk Decision‐Making in Global Supply Chain

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