Application and Comparison of Simple Additive Weighting method, Fuzzy Analytic Hierarchy Process and Support Vector Machine in identifying the internal and external factors in SWOT’s analysis

Subject Areas : Strategic Management Researches

Ali  HaeriaAn Ardekani ¹ (Sadjad University of Technology)
Hamidreza  Koosha ² (Ferdowsi University of Mashhad)
fatemeh  mirsaeedi ³ (Department of industrial engineering, university of Torbat Heydarieh)

Received: 2015-06-27 Accepted : 2015-07-27 Published : 2016-05-21

Keywords: Support vector machine, strategic management, internal and external factors, Sample Additive Weighting method, Fuzzy Analytic Hierarchy Process,

Abstract :

All organizations, must determine their future path; in other words, they must understand where they stand and where they are heading to. Strategic management is one of the most recognized management approaches for this purpose. One of the most important steps in strategic management is recognizing organization’s internal and external factors. If these factors are recognized correctly, they can be used to establish correct and optimal strategies. So far, few researchers have used exact methods for identifying and prioritizing internal and external factors. In this article, we try to use multi criteria (Sample Additive Weighting and Fuzzy Analytic Hierarchy Process techniques) and data mining (support vector machine) for reorganization of internal and external factors. The case study in this research is Water and Sewerage Company of Mashhad. First, organization’s internal and external factors are identified and classified by organization’s higher managers and experts. For applying Sample Additive Weighting and Fuzzy Analytic Hierarchy Process, first, the criteria according to internal and external factor’s definition are determined and criteria’s weights are identified  by Fuzzy Analytic Hierarchy Process also Sample Additive Weighting. Then, by using these weights, the values for all factors are calculated and classified. Using these criteria (attributes) and WEKA software, after data preprocess, factors classified by Support Vector Machine that is one of the most accurate data mining approaches. The results show Support Vector Machine prediction more accurately compared to other techniques.

References:

 
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