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Introductory Chapter: An Overview to the Analytic Principles with Business Practice in Decision Making

Written By

Fausto Pedro García Márquez, Alberto Pliego Marugán and Mayorkinos Papaelias

Published: 19 September 2018

DOI: 10.5772/intechopen.80220

From the Edited Volume

Decision Making

Edited by Fausto Pedro García Márquez, Alberto Pliego Marugán and Mayorkinos Papaelias

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1. Decision making

Decision-Making is a book based on contributions by different authors. The book synthesizes the analytic principles with business practice of decision-making [1]. The book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of decision-making. It is complementary to other subdisciplines such as economics, finance, marketing, decision and risk analysis, etc.

Decision-making can be understood as a method to select an option into a set. The method can be exact or not, quantitative/qualitative, and so on, and therefore, the option can be optimal or not [2]. These operations are done for anyone every day in anywhere. The decisions can be classified according to the period as politic (very long period), strategic (long period), and operational (short period).

The scientific advances, together with the competitiveness in the market, have led that this concept will be very important nowadays, generating a large number of research publications, new software, specific profiles in the human resources, etc., in every industry field.

Nowadays the industry is employing the new technologies and information system in decision-making. Business analytics employs data to build quantitative models to manage decisions due to the unknown future. The methods are based on statistical analysis, management science, operational research, etc. [3, 4].

It requires advanced methods for advanced analytics [5, 6, 7, 8]. Triantaphyllou showed a paradox on what decision-making method should be used to choose the best decision-making method [9]. A state-of-the-art survey of multiple attribute decision-making is discussed in Refs. [6, 10].

New methods are being presented [11, 12, 13], where the artificial intelligence is being one of the most important [14, 15, 16]. This book presents also several methods for different case studies.

Charnes et al. [17] presented a research work on measuring the efficiency of decision-making units. Linear and nonlinear programming methods were presented. This study also considered the engineering and economic connections to decision-making.

The chapters introduce and demonstrate a decision-making theory to practice case studies. It demonstrates key results for each sector with diverse real-world case studies. The theory is accompanied by relevant analysis techniques, with a progressional approach building from a simple theory to complex and dynamic decisions with multiple data points, including big data, lot of data, etc. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements.

The book is focused on graduate students and professionals in business administration, industrial organization, operations management, applied microeconomics, and the decisions sciences, either studying decision-making analysis or who are required to solve large, specific, and complex multi-criteria decision-making problems as a part of their jobs. The work will also be of interest to industrial engineers and engineering designers working with optimization problems, but this is not the main audience and finally researches from the academia.

References

  1. 1. Marugan AP, Marquez FPG. Decision-Making Management: A Tutorial and Applications. Elsevier; 2017
  2. 2. Marugán AP, Márquez FPG. Decision making approach for optimal business investments. In: García Márquez F, Lev B, editors. Advanced Business Analytics. Springer, Cham; 2015. pp. 1-20
  3. 3. Bose R. Advanced analytics: Opportunities and challenges. Industrial Management & Data Systems. 2009;109:155-172
  4. 4. Barton D, Court D. Making advanced analytics work for you. Harvard Business Review. 2012;90:78-83
  5. 5. Pliego Marugán A, García Márquez FP, Lev B. Optimal decision-making via binary decision diagrams for investments under a risky environment. International Journal of Production Research. 2017;55:5271-5286
  6. 6. Hwang C-L, Masud ASM. Multiple Objective Decision Making—Methods and Applications: A State-of-the-Art Survey. Vol. 164. Springer Science & Business Media; 2012
  7. 7. Pérez JMP, Asensio ES, Márquez FPG. Economic viability analytics for wind energy maintenance management. In: García Márquez F, Lev B, editors. Advanced Business Analytics. Springer, Cham; 2015. pp. 39-54
  8. 8. Márquez FPG, Lev B. Advanced Business Analytics. Springer; 2015
  9. 9. Triantaphyllou E. Multi-criteria decision making methods. In: Multi-Criteria Decision Making Methods: A Comparative Study. Springer; 2000. pp. 5-21
  10. 10. White D. Multiple attribute decision making—A state-of-the-art survey. Journal of the Operational Research Society. 1982;33:289-289
  11. 11. Marugán AP, Márquez FPG. Improving the efficiency on decision making process via BDD. In: Proceedings of the Ninth International Conference on Management Science and Engineering Management; 2015. pp. 1395-1405
  12. 12. Pliego Marugán A, García Márquez FP, Lorente J. Decision making process via binary decision diagram. International Journal of Management Science and Engineering Management. 2015;10:3-8
  13. 13. Marugán AP, Márquez FPG, Lavirgen JL. Decision making via binary decision diagrams: A real case study. In: Proceedings of the Eighth International Conference on Management Science and Engineering Management; 2014. pp. 215-222
  14. 14. Chen S-J, Hwang C-L. Fuzzy multiple attribute decision making methods. In: Fuzzy Multiple Attribute Decision Making. Springer; 1992. pp. 289-486
  15. 15. Jiménez AA, Muñoz CQG, Marquez FPG, Zhang L. Artificial intelligence for concentrated solar plant maintenance management. In: Proceedings of the Tenth International Conference on Management Science and Engineering Management; 2017. pp. 125-134
  16. 16. Marquez FPG, Pliego A, Lorente J, Trapero JR. A new ranking method approach for decision making in maintenance management. In: Proceedings of the Seventh International Conference on Management Science and Engineering Management Lecture Notes in Electrical Engineering; 2013. pp. 27-38
  17. 17. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research. 1978;2:429-444

Written By

Fausto Pedro García Márquez, Alberto Pliego Marugán and Mayorkinos Papaelias

Published: 19 September 2018