IDEAS home Printed from https://ideas.repec.org/a/bla/srbeha/v31y2014i2p236-257.html
   My bibliography  Save this article

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

Author

Listed:
  • Ali Azadeh
  • Kosar Darivandi Shoushtari
  • Mortezza Saberi
  • Ebrahim Teimoury

Abstract

Organizational cybernetics is one of the powerful systems approaches that benefits from the viable system model (VSM). The model is very general and is usually in need of complementary methods. In this article, one of artificial intelligence methods, artificial neural networks (ANNs), and system dynamics simulation have been used in support of the VSM. Iran broiler industry is conceived as a complex economic system and has been modelled using VSM. Operational elements, coordination, control, development, policy functions and environment of the industry are identified. ANN has been utilized in service of the controller (system 3) and the intelligence function (system 4) of the industry. ANN helps system 3 to anticipate market deviation from defined targets and reduce action delays for feeding the system back. A model in which ANN and system dynamics simulation are combined helps systems 4 and 5 manage external relationships by facilitation of defining imports tariff for maize and soybean, which are detected as critical environmental elements in identifying the industry environment. Maize and soybean cost contribute to more than 60% of chicken meat cost in Iran. Chicken meat is a high‐consumed product all over the world and one of the main sources of protein. Suitable price of chicken meat is an important factor for the industry managers in Iran. As illustrated in the paper, artificial intelligence can improve VSM subsystems functioning and enhance the industry intelligence and regulation against internal and external oscillations. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • Ali Azadeh & Kosar Darivandi Shoushtari & Mortezza Saberi & Ebrahim Teimoury, 2014. "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," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(2), pages 236-257, March.
  • Handle: RePEc:bla:srbeha:v:31:y:2014:i:2:p:236-257
    DOI: 10.1002/sres.2199
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/sres.2199
    Download Restriction: no

    File URL: https://libkey.io/10.1002/sres.2199?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Kevin J. Foster, 1997. "Cybernetic Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 17(2), pages 215-225, April.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Nora Mouhib & Slimane Bah & Abdelaziz Berrado, 2020. "Viability Theory and PSI Theory Interrelation Inspired by Bunge Systemic Classification: the Viable System Ontology Theory," Systemic Practice and Action Research, Springer, vol. 33(6), pages 675-701, December.
    3. Yuxin Zhang & Yifei Yang & Xiaosi Li & Zijing Yuan & Yuki Todo & Haichuan Yang, 2023. "A Dendritic Neuron Model Optimized by Meta-Heuristics with a Power-Law-Distributed Population Interaction Network for Financial Time-Series Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, March.
    4. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    5. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    6. Curry, Bruce, 2007. "Neural networks and seasonality: Some technical considerations," European Journal of Operational Research, Elsevier, vol. 179(1), pages 267-274, May.
    7. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    8. Hart, Diane & Paucar-Caceres, Alberto, 2017. "A utilisation focussed and viable systems approach for evaluating technology supported learning," European Journal of Operational Research, Elsevier, vol. 259(2), pages 626-641.
    9. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    10. Zhang, Rong & Ashuri, Baabak & Shyr, Yu & Deng, Yong, 2018. "Forecasting Construction Cost Index based on visibility graph: A network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 239-252.
    11. Lalou Panagiota & Ponis Stavros T. & Efthymiou Orestis K., 2020. "Demand Forecasting of Retail Sales Using Data Analytics and Statistical Programming," Management & Marketing, Sciendo, vol. 15(2), pages 186-202, June.
    12. Elena Tavella & L. Alberto Franco, 2015. "Dynamics of Group Knowledge Production in Facilitated Modelling Workshops: An Exploratory Study," Group Decision and Negotiation, Springer, vol. 24(3), pages 451-475, May.
    13. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    14. Icaro Romolo Sousa Agostino & Wesley Vieira da Silva & Claudimar Pereira da Veiga & Adriano Mendonça Souza, 2020. "Forecasting models in the manufacturing processes and operations management: Systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1043-1056, November.
    15. Soolmaz L. Azarmi & Akeem Adeyemi Oladipo & Roozbeh Vaziri & Habib Alipour, 2018. "Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus," Sustainability, MDPI, vol. 10(9), pages 1-18, August.
    16. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2011. "Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation," Energy, Elsevier, vol. 36(1), pages 348-359.
    17. Ben Moews & J. Michael Herrmann & Gbenga Ibikunle, 2018. "Lagged correlation-based deep learning for directional trend change prediction in financial time series," Papers 1811.11287, arXiv.org, revised Nov 2018.
    18. Jônatas Belotti & Hugo Siqueira & Lilian Araujo & Sérgio L. Stevan & Paulo S.G. de Mattos Neto & Manoel H. N. Marinho & João Fausto L. de Oliveira & Fábio Usberti & Marcos de Almeida Leone Filho & Att, 2020. "Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants," Energies, MDPI, vol. 13(18), pages 1-22, September.
    19. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    20. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:srbeha:v:31:y:2014:i:2:p:236-257. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1111/1092-7026 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.