An Integrated Artificial Neural Network and System Dynamics Approach in Support of the Viable System Model to Enhance Industrial Intelligence: The Case of a Large Broiler Industry
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DOI: 10.1002/sres.2199
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References listed on IDEAS
- Ali Azadeh & Kosar Darivandi & Ehsan Fathi, 2012. "Diagnosing, Simulating and Improving Business Process Using Cybernetic Laws and the Viable System Model: The Case of a Purchasing Process," Systems Research and Behavioral Science, Wiley Blackwell, vol. 29(1), pages 66-86, January.
- Gerard J. Lewis, 1997. "A cybernetic view of environmental management: the implications for business organizations," Business Strategy and the Environment, Wiley Blackwell, vol. 6(5), pages 264-275, November.
- Kevin J. Foster, 1997. "Cybernetic Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 17(2), pages 215-225, April.
- Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
- J. D. R. de Raadt, 1990. "Information transmission in viable systems," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(2), pages 111-120, March.
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Cited by:
- Zeinab Rezaee & Adel Azar & Abbas Moghbel Ba Erz & Mahmoud Dehghan Nayeri, 2019. "Application of Viable System Model in Diagnosis of Organizational Structure," Systemic Practice and Action Research, Springer, vol. 32(3), pages 273-295, June.
- Mohamad Ghozali Hassan* & Che AzlanTaib & Muslim Akanmu & Afif Ahmarofi, 2018. "A Theoretical Review on the Preventive Measures to Landslide Disaster Occurrences in Penang State, Malaysia," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 753-759:6.
- Francesca Iandolo & Pietro Vito & Francesca Loia & Irene Fulco & Mario Calabrese, 2021. "Drilling down the viable system theories in business, management and accounting: A bibliometric review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 38(6), pages 738-755, November.
- Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
- Thangeda, Rahul & Kumar, Niraj & Majhi, Ritanjali, 2024. "A neural network-based predictive decision model for customer retention in the telecommunication sector," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
- Ali Akbar Arghand & Mahmood Alborzi & Ali Rajabzadeh Ghatari, 2021. "Banking System Modeling by Viable System Modeling (VSM)," Systemic Practice and Action Research, Springer, vol. 34(3), pages 269-290, June.
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