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End-To-End Strategic Planning Model Proposal with Artificial Intelligence

Year 2021, Volume 10, Issue 4, 3526 - 3545, 30.12.2021
https://doi.org/10.15869/itobiad.891561

Abstract

Organizations want to manage the future by being positioned correctly in the future with strategic planning, which is a part of strategic management. Strategic planning, which is a proactive approach, is a long-term type of planning and a case of forecasting based on data. It also contains many data that are complex, uncertain and linguistic, without statistical history. Real-life challenges, like strategic planning, are dynamic, unpredictable, and multi-criteria. It is known that the use of artificial intelligence techniques in such problems gives optimum results and minimizes human errors. At the same time, the use of multi-criteria-decision-making methods together with artificial intelligence techniques provides accurate and effective results to the decision maker. Artificial intelligence (AI) applications, which is the phenomenon of today, continue to enter all areas of our lives and its usage area is rapidly spreading. AI techniques are also used in the stages of strategic planning. There are applications of artificial intelligence (fuzzy logic) with SWOT analysis, which is a very common method to determine the current situation in strategic planning. Moreover, the number of models offering solutions with artificial intelligence techniques at all stages of planning from the very beginning to the end is low. In this study, a model proposal is presented for the preparation of end-to-end strategic planning with AI techniques. In the proposed model, strategic planning was examined in four stages as analyses of current situation, strategic concepts, assessment and evaluation. (Strategic planning was examined in four stages in the proposed model: current situation analyses, strategic concepts, assessment, and evaluation.) AI techniques are suggested for each stage. The proposed AI techniques have been chosen from among the most used techniques in the literature. The methods used are of 3 types. The first type is fuzzy logic (FL) which is one of the AI techniques, expert systems (ES), artificial neural networks (ANN), and genetic algorithms (GA). The second type is the combination of delphi technique used in data collection and multi-criteria decision-making methods, which is the decision-making methods, and FL. Fuzzy delphi and fuzzy multi-criteria-decision-making methods. The third type is the use of other AI techniques with FL. Fuzzy ES, fuzzy ANN. The aim of this study is to provide a perspective to organizations and experts that make strategic planning.

References

  • Adar, E., Karatop, B., İnce, M., & Bilgili, M. S. (2016). Comparison of methods for sustainable energy management with sewage sludge in Turkey based on SWOT-FAHP analysis. Renewable and Sustainable Energy Reviews, 62, 429-440.
  • Akbarian-Saravi, N., Mobini, M., & Rabbani, M. (2020). Development of a comprehensive decision support tool for strategic and tactical planning of a sustainable bioethanol supply chain: Real case study, discussions and policy implications . Journal of Cleaner Production, 244, 118871
  • Allahverdi, N. (2002). Expert Systems An Artificial Intelligence Application (Uzman Sistemler Bir Yapay Zekâ Uygulaması), Atlas Publisher& Distribution, Ankara.
  • Amin, S. H., Razmi, J., & Zhang, G. (2011). Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming. Expert Systems with Applications, 38(1), 334-342.
  • Azimi, R., Yazdani-Chamzini, A., Fouladgar, M. M., Zavadskas, E. K., & Basiri, M. H. (2011). Ranking the strategies of mining sector through ANP and TOPSIS in a SWOT framework. Journal of business economics and management, 12(4), 670-689.
  • Balcı, O., & Smith, EP (1986). Uzman sistem performansının doğrulanması. Bilgisayar Bilimleri Bölümü, Virginia Politeknik Enstitüsü ve Eyalet Üniversitesi.
  • Chen, H., Hsu, P., Orwig, R., Hoopes, L., and Nunamaker J. F., J. (1994). Automatic concept classification of text from electronic meetings. Communications of the ACM 37, 10, 56–73.
  • Cowgill, M. C., Harvey, R. J., & Watson, L. T. (1999). A genetic algorithm approach to cluster analysis. Computers & Mathematics with Applications, 37(7), 99-108.
  • Dağdeviren, M. 2007. “Personnel Selectıon with Fuzzy Analytical Hierarchy Process and an Applicatıon (Bulanık analitik hiyerarşi prosesi ile personel seçimi ve bir uygulama)”, Journal of the Faculty of Engineering and Architecture of Gazi University, 22(4), 791-799.
  • Dyson, R. G. (2004). Strategic development and SWOT analysis at the University of Warwick. European journal of operational research, 152(3), 631-640
  • EC HLEG AI (European Commission High-Level Expert Group on Artificial Intelligence). (2018). A definition of AI: Main capabilities and scientific disciplines, https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341 Accepted: 19.03.2019
  • Frohlich, H., Chapelle, O., & Scholkopf, B. (2003, November). Feature selection for support vector machines by means of genetic algorithm. In Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence (pp. 142-148). IEEE.
  • Fullér, R, 1995, The Lecture Notes, Neural Fuzzy Systems, Abo Akademi University, http://uni-obuda.hu/users/fuller.robert/ln1.pdf
  • Ghazinoory, S., Esmail Zadeh, A., & Memariani, A. (2007). Fuzzy SWOT analysis. Journal of Intelligent & Fuzzy Systems, 18(1), 99-108.
  • Göl, M. (1999). Top Management Information System and Expert Systems in Strategic Decision Making Environment (Stratejik Karar Alma Ortamında Üst Yönetim Bilgi Sistemi ve Uzman Sistemler). Dumlupınar University Journal of Social Sciences, (3), 357-364.
  • Güler, E. & Karatop, B. (2019). Using Artificial Intelligence in Massive Open Online Courses: A Conceptual View to Wise MOOCs. Handbook of Research on Learning in the Age of Transhumanism (pp.116-133), Kentucky: IGI Global Disseminator Knowledge Industry Publications, Inc.
  • Hall, N. G. (1988). Diagnosing problems with the user interface for a strategic planning fuzzy DSS. IEEE transactions on systems, man, and cybernetics, 18(4), 638-646.
  • Hebb, D.O. (1949). The Organization of Behavior -a Neuropsychologıcal Theory, John What & Sons. Inc.,
  • Hosseini-Nasab, H., Hosseini-Nasab, A., & Milani, A. S. (2011). Coping with imprecision in strategic planning: A case study using fuzzy SWOT analysis. IBusiness, 3(01), 23.
  • İçen, D., & Günay, S. (2014). Expert Systems and Statistics (Uzman Sistemler ve İstatistik). The Journal of Statisticians: Statistics and Actuaries, 7 (2), 37-45.
  • Kajanus, M., Kurttila, M., & Pesonen, M. (1996, June-July). Applying SWOT and AHP analysis when changing to ecolabelled forestry, Integrating Environmental Values into Forest Planning-Baltic and Nordic Perspectives. Paper presented at the Nordic-Baltic Research Course, Räpinä, Estonia.
  • Kangas, J., Pesonen, M., Kurttila, M., & Kajanus, M. (2001, August). A'WOT: Integrating the AHP with SWOT Analysis. Paper presented at the 6th International Symposium on the Analytic Hierarchy Process (ISAHP), Berne, Switzerland.
  • Karatop, B. (2015). Focus Strategy Decision Model in Domestic Automotive Investment (Yerli Otomotiv Yatırımında Odak Strateji Karar Modeli). Eastern Library (Doğu Kütüphanesi), İstanbul.
  • Karatop, B., Kubat, C., & Uygun, Ö. (2018). Determining the strategies on Turkish automotive sector using fuzzy AHP based on the SWOT analysis. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(5), 1314-1325.
  • Karatop, B., Taşkan, B. (2019). Corporate Performance Management Models, Methods and Historical Development (Kurumsal Performans Yönetimi Modeller, Yöntemler ve Tarihsel gelişimi), Night Library (Gece Kitaplığı), Ankara. ISBN 978-625-7958-39-4.
  • Kheirkhah, A. S., Esmailzadeh, A., & Ghazinoory, S. (2009). Developing strategies to reduce the risk of hazardous materials transportation in Iran using the method of fuzzy SWOT analysis. Transport, 24(4), 325-332.
  • Kubat, C. (2012). MATLAB: Artificial Intelligence and Engineering Applications (MATLAB: Yapay Zekâ ve Mühendislik Uygulamaları). Beşiz Publications, ISBN, 978-605.
  • Kroener, B. R. (1939). The “Frozen Blitzkrieg:” German Strategic Planning Against the Soviet Union and the Causes of Its Failure. From Peace to War: Germany, Soviet Russia, and the World, 1941, 135-150.
  • Leardi, R. (2000). Application of genetic algorithm–PLS for feature selection in spectral data sets. Journal of Chemometrics, 14(5‐6), 643-655.
  • Li, S., Davies, B., Edwards, J., Kinman, R., & Duan, Y. (2002). Integrating group Delphi, fuzzy logic and expert systems for marketing strategy development: the hybridisation and its effectiveness. Marketing Intelligence & Planning, 20(5):273–284
  • Maulik, U., & Bandyopadhyay, S. (2000). Genetic algorithm-based clustering technique. Pattern recognition, 33(9), 1455-1465.
  • Mockler, R. J. (1987). Computer information systems and strategic corporate planning. Business Horizons, 30(3), 32-37.
  • Orwig, R., Chen, H., Vogel, D., & Nunamaker, J. F. (1997). A multi-agent view of strategic planning using group support systems and artificial intelligence. Group Decision and Negotiation, 6(1), 37-59.
  • Öztemel, E. (2009). Introduction to Industrial Engineering (Endüstri Mühendisliğine Giriş). Papatya Publishing Education.
  • Öztemel, E. (2010). Intelligent Manufacturing Systems. In: Benyoucef L., Grabot B. (eds) Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management. Springer Series in Advanced Manufacturing. Springer, London.
  • Öztemel, E. (2012). Artificial Neural Networks (Yapay Sinir Ağları). Papatya Publishing Education.
  • Papapostolou, A., Karakosta, C., Apostolidis, G., & Doukas, H. (2020). An AHP-SWOT-Fuzzy TOPSIS Approach for Achieving a Cross-Border RES Cooperation. Sustainability, 12(7), 2886, 2-28.
  • Piercy, N., & Giles, W. (1989). Making SWOT analysis work. Marketing Intelligence & Planning, 7(5/6), 5-7.
  • Pinson, S. D., Louçã, J. A., & Moraitis, P. (1997). A distributed decision support system for strategic planning. Decision Support Systems, 20(1), 35-51.
  • Pochampally, K. K., & Gupta, S. M. (2008). A multiphase fuzzy logic approach to strategic planning of a reverse supply chain network. IEEE Transactions on Electronics Packaging Manufacturing, 31(1), 72-82.
  • Prusty, S. K., Mohapatra, P. K., & Mukherjee, C. K. (2010). GOS tree (Goal–Objective–Strategy tree) approach to strategic planning using a fuzzy-Delphi process: An application to the Indian Shrimp Industry. Technological Forecasting and Social Change, 77(3), 442-456.
  • Raikov, A. (2020). Megapolis Tourism Development Strategic Planning with Cognitive Modelling Support. In Fourth International Congress on Information and Communication Technology (pp. 147-155). Springer, Singapore.
  • Reed, H. P. (1946). The development of the terrain model in the war. Geographical Review, 36(4), 632-652.
  • Shahanipour, S., Amindoust, A., Sahraian, K., & Beiranvand, S. (2020). Identification and prioritization of human resource strategies with employees’ creativity approach in administrative organizations using SWOT–ANP. OPSEARCH, 57(1), 119-143.
  • Smith, R., & Farmer, E. (1946). Housing, population and decentralisation. Strategic planning in action: the impact of the Clyde Valley Regional Plan, 1982, 41-72.
  • Spaatz, C. (1946). Strategic Air Power: Fulfillment of a Concept. Foreign Affairs, 24(3), 385-396.
  • Steiner, G. A. (2010). Strategic planning. Simon and Schuster. books.google.com
  • Şen, Z. (2009). Fuzzy Logic Principles and Modeling (Engineering and Social Sciences) (Bulanık Mantık İlkeleri ve Modelleme (Mühendislik ve Sosyal Bilimler)), Water Foundation Publications (Istanbul Su Vakfı Yayınları), İstanbul.
  • Ünal, A., Kılınç, İ. (2020). A Revıew on Relatıonshıp Between Artıfıcıal Intellıgence And Busıness Management (Yapay Zekâ İşletme Yönetimi İlişkisi Üzerine Bir Değerlendirme), Journal of Management Information Systems (Yönetim Bilişim Sistemleri Dergisi), 6(1), 51-78.
  • Valentin, E. K. (2001). SWOT analysis from a resource-based view. Journal of marketing theory and practice, 9(2), 54-69.
  • Von Krogh, G. (2018). Artifıcial intelligence in organizations: new opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409,
  • Wang, H. F., & Chang, W. Y. (2001). Fuzzy Scenario Analysis in Strategic Planning. Internatıonal Journal Of General System, 30(2), 193-207.
  • Wang, Y., Xu, L., & Solangi, Y. A. (2020). Strategic renewable energy resources selection for Pakistan: Based on SWOT-Fuzzy AHP approach. Sustainable Cities and Society, 52, 101861.
  • Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4(2), 65-85.
  • Yavuz, S., Deveci, M. (2011). The Effect of Statıstıcal Normalızatıon Technıques on The Performance of Artıfıcıal Neural Network (İstatiksel Normalizasyon Tekniklerinin Yapay Sinir Ağın Performansına Etkisi). Journal of Erciyes University Faculty of Economics and Administrative Sciences, 0(40), 167-187.
  • Yiğit, S, Yiğit, A. (2011). The External Envıronment Analysıs in Strategıc Management: A Comparısıon Between Small and Medıum Sıze Busınesses and Large Busınesses (Stratejik Yönetimde Dış Çevre Analizi: Kobi’ler ve Büyük İşletmeler Arasında Bir Karşılaştırma). Journal of Erciyes University Faculty of Economics and Administrative Sciences, 0(38), 119-136.
  • Zadeh, L. (1965). Fuzzy Sets, Information and Control, 8, 338-353.
  • Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4), 83-93.

Yapay Zekâ ile Uçtan Uca Stratejik Planlama Modeli Önerisi

Year 2021, Volume 10, Issue 4, 3526 - 3545, 30.12.2021
https://doi.org/10.15869/itobiad.891561

Abstract

Stratejik yönetimin bir parçası olan stratejik planlama ile işletmeler gelecekte doğru konumlanarak geleceği yönetmek isterler. Proaktif bir yaklaşım olan stratejik planlama, uzun vadeli bir planlama türü ve veriye dayalı bir tahmin etme durumudur. Ayrıca, istatistiksel geçmişi olmayan karmaşık, belirsiz ve dilbilimsel birçok veri içerir. Stratejik planlamada olduğu gibi gerçek hayat problemleri de karmaşık, belirsiz ve çok kriterlidir. Bu tür problemlerde yapay zekâ tekniklerinin kullanılması optimum sonuçlar verdiği ve insan hatalarını en aza indirdiği bilinmektedir. Aynı zamanda çok kriterli karar verme yöntemlerinin yapay zekâ teknikleriyle birlikte kullanılması karar vericiye doğru ve etkili sonuçlar sağlamaktadır. Günümüzün fenomeni olan yapay zekâ uygulamaları hayatımızın her alanına girmeye devam etmekte, kullanım alanı hızla yaygınlaşmaktadır. Stratejik planlama aşamalarında yapay zekâ teknikleri de kullanılmaktadır. Stratejik planlamada mevcut durumu belirlemek için çok sık kullanılan metot olan SWOT analizi ile yapay zeka (bulanık mantık) uygulamaları bulunmaktadır. Dahası, planlamanın başından sonuna kadar tüm aşamalarında yapay zekâ teknikleri ile çözüm sunan model sayısı azdır. Bu çalışmada yapay zekâ teknikleriyle uçtan uca stratejik planlamanın hazırlanması için bir model önerisi sunulmuştur. Önerilen modelde stratejik planlama mevcut durum analizi, stratejik kavramlar, strateji oluşturma, ölçme ve değerlendirme olmak üzere 4 aşamada incelenmiştir. Her aşama için yapay zekâ teknikleri önerilmiştir. Önerilen teknikler literatürde en çok kullanılan tekniklerden seçilmiştir. Bu yöntemler üç tiptir. Birincisi yapay zekâ tekniklerinden olan bulanık mantık, uzman sistemler, yapay sinir ağları, genetik algoritmadır. İkinci tip veri toplamada kullanılan delfi tekniği ile karar verme yöntemlerinden olan çok kriterli karar verme yöntemlerinin bulanık mantık ile kullanılmasıdır. Bulanık delfi ve bulanık çok kriterli karar verme yöntemleri. Üçüncü tip ise bulanık mantıkla diğer yapay zekâ tekniklerinin kullanılmasıdır. Bulanık uzman sistemler, bulanık yapay sinir ağları. Bu çalışmanın amacı stratejik planlama yapan organizasyonlara ve uzmanlara bir bakış açısı sunmaktır.

References

  • Adar, E., Karatop, B., İnce, M., & Bilgili, M. S. (2016). Comparison of methods for sustainable energy management with sewage sludge in Turkey based on SWOT-FAHP analysis. Renewable and Sustainable Energy Reviews, 62, 429-440.
  • Akbarian-Saravi, N., Mobini, M., & Rabbani, M. (2020). Development of a comprehensive decision support tool for strategic and tactical planning of a sustainable bioethanol supply chain: Real case study, discussions and policy implications . Journal of Cleaner Production, 244, 118871
  • Allahverdi, N. (2002). Expert Systems An Artificial Intelligence Application (Uzman Sistemler Bir Yapay Zekâ Uygulaması), Atlas Publisher& Distribution, Ankara.
  • Amin, S. H., Razmi, J., & Zhang, G. (2011). Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming. Expert Systems with Applications, 38(1), 334-342.
  • Azimi, R., Yazdani-Chamzini, A., Fouladgar, M. M., Zavadskas, E. K., & Basiri, M. H. (2011). Ranking the strategies of mining sector through ANP and TOPSIS in a SWOT framework. Journal of business economics and management, 12(4), 670-689.
  • Balcı, O., & Smith, EP (1986). Uzman sistem performansının doğrulanması. Bilgisayar Bilimleri Bölümü, Virginia Politeknik Enstitüsü ve Eyalet Üniversitesi.
  • Chen, H., Hsu, P., Orwig, R., Hoopes, L., and Nunamaker J. F., J. (1994). Automatic concept classification of text from electronic meetings. Communications of the ACM 37, 10, 56–73.
  • Cowgill, M. C., Harvey, R. J., & Watson, L. T. (1999). A genetic algorithm approach to cluster analysis. Computers & Mathematics with Applications, 37(7), 99-108.
  • Dağdeviren, M. 2007. “Personnel Selectıon with Fuzzy Analytical Hierarchy Process and an Applicatıon (Bulanık analitik hiyerarşi prosesi ile personel seçimi ve bir uygulama)”, Journal of the Faculty of Engineering and Architecture of Gazi University, 22(4), 791-799.
  • Dyson, R. G. (2004). Strategic development and SWOT analysis at the University of Warwick. European journal of operational research, 152(3), 631-640
  • EC HLEG AI (European Commission High-Level Expert Group on Artificial Intelligence). (2018). A definition of AI: Main capabilities and scientific disciplines, https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341 Accepted: 19.03.2019
  • Frohlich, H., Chapelle, O., & Scholkopf, B. (2003, November). Feature selection for support vector machines by means of genetic algorithm. In Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence (pp. 142-148). IEEE.
  • Fullér, R, 1995, The Lecture Notes, Neural Fuzzy Systems, Abo Akademi University, http://uni-obuda.hu/users/fuller.robert/ln1.pdf
  • Ghazinoory, S., Esmail Zadeh, A., & Memariani, A. (2007). Fuzzy SWOT analysis. Journal of Intelligent & Fuzzy Systems, 18(1), 99-108.
  • Göl, M. (1999). Top Management Information System and Expert Systems in Strategic Decision Making Environment (Stratejik Karar Alma Ortamında Üst Yönetim Bilgi Sistemi ve Uzman Sistemler). Dumlupınar University Journal of Social Sciences, (3), 357-364.
  • Güler, E. & Karatop, B. (2019). Using Artificial Intelligence in Massive Open Online Courses: A Conceptual View to Wise MOOCs. Handbook of Research on Learning in the Age of Transhumanism (pp.116-133), Kentucky: IGI Global Disseminator Knowledge Industry Publications, Inc.
  • Hall, N. G. (1988). Diagnosing problems with the user interface for a strategic planning fuzzy DSS. IEEE transactions on systems, man, and cybernetics, 18(4), 638-646.
  • Hebb, D.O. (1949). The Organization of Behavior -a Neuropsychologıcal Theory, John What & Sons. Inc.,
  • Hosseini-Nasab, H., Hosseini-Nasab, A., & Milani, A. S. (2011). Coping with imprecision in strategic planning: A case study using fuzzy SWOT analysis. IBusiness, 3(01), 23.
  • İçen, D., & Günay, S. (2014). Expert Systems and Statistics (Uzman Sistemler ve İstatistik). The Journal of Statisticians: Statistics and Actuaries, 7 (2), 37-45.
  • Kajanus, M., Kurttila, M., & Pesonen, M. (1996, June-July). Applying SWOT and AHP analysis when changing to ecolabelled forestry, Integrating Environmental Values into Forest Planning-Baltic and Nordic Perspectives. Paper presented at the Nordic-Baltic Research Course, Räpinä, Estonia.
  • Kangas, J., Pesonen, M., Kurttila, M., & Kajanus, M. (2001, August). A'WOT: Integrating the AHP with SWOT Analysis. Paper presented at the 6th International Symposium on the Analytic Hierarchy Process (ISAHP), Berne, Switzerland.
  • Karatop, B. (2015). Focus Strategy Decision Model in Domestic Automotive Investment (Yerli Otomotiv Yatırımında Odak Strateji Karar Modeli). Eastern Library (Doğu Kütüphanesi), İstanbul.
  • Karatop, B., Kubat, C., & Uygun, Ö. (2018). Determining the strategies on Turkish automotive sector using fuzzy AHP based on the SWOT analysis. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(5), 1314-1325.
  • Karatop, B., Taşkan, B. (2019). Corporate Performance Management Models, Methods and Historical Development (Kurumsal Performans Yönetimi Modeller, Yöntemler ve Tarihsel gelişimi), Night Library (Gece Kitaplığı), Ankara. ISBN 978-625-7958-39-4.
  • Kheirkhah, A. S., Esmailzadeh, A., & Ghazinoory, S. (2009). Developing strategies to reduce the risk of hazardous materials transportation in Iran using the method of fuzzy SWOT analysis. Transport, 24(4), 325-332.
  • Kubat, C. (2012). MATLAB: Artificial Intelligence and Engineering Applications (MATLAB: Yapay Zekâ ve Mühendislik Uygulamaları). Beşiz Publications, ISBN, 978-605.
  • Kroener, B. R. (1939). The “Frozen Blitzkrieg:” German Strategic Planning Against the Soviet Union and the Causes of Its Failure. From Peace to War: Germany, Soviet Russia, and the World, 1941, 135-150.
  • Leardi, R. (2000). Application of genetic algorithm–PLS for feature selection in spectral data sets. Journal of Chemometrics, 14(5‐6), 643-655.
  • Li, S., Davies, B., Edwards, J., Kinman, R., & Duan, Y. (2002). Integrating group Delphi, fuzzy logic and expert systems for marketing strategy development: the hybridisation and its effectiveness. Marketing Intelligence & Planning, 20(5):273–284
  • Maulik, U., & Bandyopadhyay, S. (2000). Genetic algorithm-based clustering technique. Pattern recognition, 33(9), 1455-1465.
  • Mockler, R. J. (1987). Computer information systems and strategic corporate planning. Business Horizons, 30(3), 32-37.
  • Orwig, R., Chen, H., Vogel, D., & Nunamaker, J. F. (1997). A multi-agent view of strategic planning using group support systems and artificial intelligence. Group Decision and Negotiation, 6(1), 37-59.
  • Öztemel, E. (2009). Introduction to Industrial Engineering (Endüstri Mühendisliğine Giriş). Papatya Publishing Education.
  • Öztemel, E. (2010). Intelligent Manufacturing Systems. In: Benyoucef L., Grabot B. (eds) Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management. Springer Series in Advanced Manufacturing. Springer, London.
  • Öztemel, E. (2012). Artificial Neural Networks (Yapay Sinir Ağları). Papatya Publishing Education.
  • Papapostolou, A., Karakosta, C., Apostolidis, G., & Doukas, H. (2020). An AHP-SWOT-Fuzzy TOPSIS Approach for Achieving a Cross-Border RES Cooperation. Sustainability, 12(7), 2886, 2-28.
  • Piercy, N., & Giles, W. (1989). Making SWOT analysis work. Marketing Intelligence & Planning, 7(5/6), 5-7.
  • Pinson, S. D., Louçã, J. A., & Moraitis, P. (1997). A distributed decision support system for strategic planning. Decision Support Systems, 20(1), 35-51.
  • Pochampally, K. K., & Gupta, S. M. (2008). A multiphase fuzzy logic approach to strategic planning of a reverse supply chain network. IEEE Transactions on Electronics Packaging Manufacturing, 31(1), 72-82.
  • Prusty, S. K., Mohapatra, P. K., & Mukherjee, C. K. (2010). GOS tree (Goal–Objective–Strategy tree) approach to strategic planning using a fuzzy-Delphi process: An application to the Indian Shrimp Industry. Technological Forecasting and Social Change, 77(3), 442-456.
  • Raikov, A. (2020). Megapolis Tourism Development Strategic Planning with Cognitive Modelling Support. In Fourth International Congress on Information and Communication Technology (pp. 147-155). Springer, Singapore.
  • Reed, H. P. (1946). The development of the terrain model in the war. Geographical Review, 36(4), 632-652.
  • Shahanipour, S., Amindoust, A., Sahraian, K., & Beiranvand, S. (2020). Identification and prioritization of human resource strategies with employees’ creativity approach in administrative organizations using SWOT–ANP. OPSEARCH, 57(1), 119-143.
  • Smith, R., & Farmer, E. (1946). Housing, population and decentralisation. Strategic planning in action: the impact of the Clyde Valley Regional Plan, 1982, 41-72.
  • Spaatz, C. (1946). Strategic Air Power: Fulfillment of a Concept. Foreign Affairs, 24(3), 385-396.
  • Steiner, G. A. (2010). Strategic planning. Simon and Schuster. books.google.com
  • Şen, Z. (2009). Fuzzy Logic Principles and Modeling (Engineering and Social Sciences) (Bulanık Mantık İlkeleri ve Modelleme (Mühendislik ve Sosyal Bilimler)), Water Foundation Publications (Istanbul Su Vakfı Yayınları), İstanbul.
  • Ünal, A., Kılınç, İ. (2020). A Revıew on Relatıonshıp Between Artıfıcıal Intellıgence And Busıness Management (Yapay Zekâ İşletme Yönetimi İlişkisi Üzerine Bir Değerlendirme), Journal of Management Information Systems (Yönetim Bilişim Sistemleri Dergisi), 6(1), 51-78.
  • Valentin, E. K. (2001). SWOT analysis from a resource-based view. Journal of marketing theory and practice, 9(2), 54-69.
  • Von Krogh, G. (2018). Artifıcial intelligence in organizations: new opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409,
  • Wang, H. F., & Chang, W. Y. (2001). Fuzzy Scenario Analysis in Strategic Planning. Internatıonal Journal Of General System, 30(2), 193-207.
  • Wang, Y., Xu, L., & Solangi, Y. A. (2020). Strategic renewable energy resources selection for Pakistan: Based on SWOT-Fuzzy AHP approach. Sustainable Cities and Society, 52, 101861.
  • Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4(2), 65-85.
  • Yavuz, S., Deveci, M. (2011). The Effect of Statıstıcal Normalızatıon Technıques on The Performance of Artıfıcıal Neural Network (İstatiksel Normalizasyon Tekniklerinin Yapay Sinir Ağın Performansına Etkisi). Journal of Erciyes University Faculty of Economics and Administrative Sciences, 0(40), 167-187.
  • Yiğit, S, Yiğit, A. (2011). The External Envıronment Analysıs in Strategıc Management: A Comparısıon Between Small and Medıum Sıze Busınesses and Large Busınesses (Stratejik Yönetimde Dış Çevre Analizi: Kobi’ler ve Büyük İşletmeler Arasında Bir Karşılaştırma). Journal of Erciyes University Faculty of Economics and Administrative Sciences, 0(38), 119-136.
  • Zadeh, L. (1965). Fuzzy Sets, Information and Control, 8, 338-353.
  • Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4), 83-93.

Details

Primary Language English
Subjects Management
Journal Section Articles
Authors

Buket KARATOP (Primary Author)
İSTANBUL ÜNİVERSİTESİ CERRAHPAŞA
0000-0001-6053-1725
Türkiye

Early Pub Date December 21, 2021
Publication Date December 30, 2021
Application Date March 5, 2021
Acceptance Date November 18, 2021
Published in Issue Year 2021, Volume 10, Issue 4

Cite

APA Karatop, B. (2021). End-To-End Strategic Planning Model Proposal with Artificial Intelligence . İnsan ve Toplum Bilimleri Araştırmaları Dergisi , 10 (4) , 3526-3545 . DOI: 10.15869/itobiad.891561
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Turkey Journal of Theological Studies is under the İtobiad.