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Delayering the global production network into congruent subnetworks
Delayering the global production network into congruent subnetworks
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Delayering the global production network into congruent subnetworks

Ferdows, Kasra ; Vereecke, Ann ; De Meyer, Arnoud

Journal of operations management, 2016-01, Vol.41 (1), p.63-74 [Periódico revisado por pares]

Chicago: Elsevier B.V

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2
Inside the world of stepsiblings: Linking global production networks to sustainable supply chain management
Inside the world of stepsiblings: Linking global production networks to sustainable supply chain management
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Inside the world of stepsiblings: Linking global production networks to sustainable supply chain management

Yawar, Sadaat Ali ; Seuring, Stefan

Business strategy and the environment, 2024-02, Vol.33 (2), p.788-805 [Periódico revisado por pares]

Hoboken, NJ: Wiley

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3
A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks
A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks
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A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks

Niknejad, Ali ; Petrovic, Dobrila

International journal of production economics, 2016-09, Vol.179, p.44-58 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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4
Changing a multidomestic production network to a global function network: North America Heinz ketchup from 1960 to 2015
Changing a multidomestic production network to a global function network: North America Heinz ketchup from 1960 to 2015
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Changing a multidomestic production network to a global function network: North America Heinz ketchup from 1960 to 2015

Miltenburg, John

International journal of production economics, 2015-10, Vol.168, p.267-278 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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5
Industrial districts’ evolution and technological regimes: Italy and Taiwan
Industrial districts’ evolution and technological regimes: Italy and Taiwan
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Industrial districts’ evolution and technological regimes: Italy and Taiwan

Guerrieri, Paolo ; Pietrobelli, Carlo

Technovation, 2004-11, Vol.24 (11), p.899-914 [Periódico revisado por pares]

Amsterdam: Elsevier Ltd

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6
Social Upgrading in Global Production Networks (GPNs): Institution Role Perspective
Social Upgrading in Global Production Networks (GPNs): Institution Role Perspective
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Social Upgrading in Global Production Networks (GPNs): Institution Role Perspective

Xiaoxia, Zhao ; Xiaoni, Sun ; Fang, Tony

Journal of applied sciences (Asian Network for Scientific Information), 2013, Vol.13 (24), p.5759-5759

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7
R&D, Value Chain Location and Firm Performance in the Global Electronics Industry
R&D, Value Chain Location and Firm Performance in the Global Electronics Industry
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R&D, Value Chain Location and Firm Performance in the Global Electronics Industry

Shin, Namchul ; Kraemer, Kenneth L. ; Dedrick, Jason

Industry and innovation, 2009-06, Vol.16 (3), p.315-330 [Periódico revisado por pares]

Sydney: Taylor & Francis Group

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8
Tie diversity, ambidexterity and upgrading of the latecomer firm in global production networks: Evidence from China's plastic equipment industry
Tie diversity, ambidexterity and upgrading of the latecomer firm in global production networks: Evidence from China's plastic equipment industry
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Tie diversity, ambidexterity and upgrading of the latecomer firm in global production networks: Evidence from China's plastic equipment industry

Peng, Xin‐min ; Wu, Dong Yu, Jiang

Chinese management studies, 2013-01, Vol.7 (2), p.310-327 [Periódico revisado por pares]

Bradford: Emerald Group Publishing Limited

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9
A Bayesian network model for resilience-based supplier selection
A Bayesian network model for resilience-based supplier selection
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A Bayesian network model for resilience-based supplier selection

Hosseini, Seyedmohsen ; Barker, Kash

International journal of production economics, 2016-10, Vol.180, p.68-87 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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10
Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention
Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention
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Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention

Sehovac, Ljubisa ; Grolinger, Katarina

IEEE access, 2020, Vol.8, p.36411-36426 [Periódico revisado por pares]

Piscataway: IEEE

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