Document Type : Original Research Paper


1 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Imam Hussein University, Tehran, Iran


Business logic is one of the most important logics based on the decision matrix. However, using this logic alone and environmental uncertainty leads to problems such as low accuracy and integrity in strategic planning. In this work, we use an intelligent model based on the neural-fuzzy approach aiming at a desired decision-making and reducing the uncertainty in the strategic planning in mineral holdings. Here, the strategies are presented based on three logics, namely business, added value, and capital market. After extracting the primary indices, the final indices of the three logics are selected by consulting with the mineral holding experts. Modelling of the indices is accomplished by the Matlab software, and the model computation is done by the root mean square error for the test data and train data. The case study (Shahab-sang holding) findings show that by a combination of these three logics, the proposed strategies include more integration and accuracy, which lead to a lower uncertainty and more speed in the strategy formulation. Also the test result indicates the validity of all the extracted strategies.


[1]. Du, J., Bai, T. and Chen, S. (2019). Integrating corporate social and corporate political strategies: Performance implications and institutional contingencies in China. Journal of Business Research. 98: pp.299-316.
[2]. Cosenz, F. and Noto, G., 2018. A dynamic business modelling approach to design and experiment new business venture strategies. Long Range Planning. 51 (1): pp.127-140.
[3]. Johnson G, R. Whittington. (2015). Exploring Corporate Strategies (7th Ed.) New York: Financial Times Prentice Hal.
[4]. Pidun, U., Rubner, H., Krühler, M., Untiedt, R., Boston Consulting Group and Nippa, M., 2011. Corporate portfolio management: Theory and practice. Journal of Applied Corporate Finance. 23 (1): pp.63-76.
[5]. Cummings, S. and Daellenbach, U. (2009). A guide to the future of strategy? The history of long range planning. Long Range Planning. 42 (2): pp.234-263.
[6]. Grant, R.M. (2010). Contemporary strategy analysis. 6th. Malden, MA: Blackwell Pub, 13(482), p.133.
[7]. Porter, M.E. (1987). From Competitive Advantage to Corporate Strategy. Harvard Business Review, May/June.
[8]. Andrews, K.R. (1971). Concept of corporate strategy.
[9]. Johnson, G., Scholes, K. and Whittington, R. (2005). Exploring Corporate Strategy Harlow Prentice Hall.
[10]. Campbell, A. and Goold, M. (1995). Corporate strategy: The quest for parenting advantage. Harvard business review. 73 (2): pp.120-132.
[11]. Li, S. and Li, J.Z. (2009). Hybridising human judgment, AHP, simulation and a fuzzy expert system for strategy formulation under uncertainty. Expert Systems with Applications. 36 (3): pp.5557-5564.
[12]. Chen, H., Ho, J.C. and Kocaoglu, D.F. (2009). IEEE Transactions on Engineering Management. IEEE Transactions on Engineering Management, Feb. 2010.
[13]. Bowman, E.H. and Helfat, C.E. (2001). Does corporate strategy matter?. Strategic Management Journal, 22(1), pp.1-23.
[14]. Nippa, M., Pidun, U. and Rubner, H. (2011). Corporate portfolio management: Appraising four decades of academic research. Academy of Management Perspectives. 25 (4): pp.50-66.
[15]. Moghbell, Ebrahimi. (2014). Design hybrid system for strategic planning.
[16]. Lloret, A. (2016). Modeling corporate sustainability strategy. Journal of Business Research. 69 (2): pp.418-425.
[17]. Campbell, A., Goold, M., Alexander, M. and Whitehead, J. (2014). Strategy for the corporate level: Where to invest, what to cut back and how to grow organisations with multiple divisions. John Wiley & Sons.
[18]. Negnevitsky, Michael, Hybrid intelligent systems: Neural expert systems and neuro-fuzzy systems, Pearson Education (2008).
[19]. Nikravesh, M., Zadeh, L.A. and Aminzadeh, F. eds. (2003). Soft computing and intelligent data analysis in oil exploration. Elsevier.
[20]. Nourani, V., Kisi, Ö. and Komasi, M. (2011). Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology. 402 (1-2): pp.41-59.
[21]. Gai, J. and Hu, Y. (2018). Research on fault diagnosis based on singular value decomposition and fuzzy neural network. Shock and Vibration.
[22]. Shaabani, M.E., Banirostam, T. and Hedayati, A. (2016). Implementation of neuro fuzzy system for diagnosis of multiple sclerosis. International Journal of Computer Science and Network. 5 (1): pp.157-164.
[23]. Suparta, W. and Alhasa, K.M. (2016). Modeling of tropospheric delays using ANFIS.