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Swot Analysis For Decision Making
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Received: 2 March 2018 / Revised: 10 April 2018 / Accepted: 12 April 2018 / Published: 17 April 2018
Every organization seeks to establish strategies for its growth and development and for this it must take into account the factors that affect its success or failure. The most widely used method of strategic planning is the SWOT analysis. SWOT examines Strengths (S), Weaknesses (W), Opportunities (O) and Threats (T) to select and implement the best strategy to achieve the organization’s goals. The chosen strategy must exploit strengths and opportunities, manage weaknesses, and avoid or mitigate threats. SWOT analysis does not quantify factors (eg strengths, weaknesses, opportunities and threats) and does not rank available alternatives. To overcome this drawback, we incorporated it into the Analytic Hierarchy Process (AHP). AHP can identify quantitative and qualitative data, weight and rank them using comparison tables. Due to the inaccuracy and inconsistency of existing information in the real world, we applied the proposed model to the neurosophical setting. To validate our model, a real example from Starbucks was presented.
What Is A Personal Swot Analysis?
To achieve the goals of the organization, it is necessary to take into account the strategic factors that affect its activities. These strategic factors are classified as internal factors that are under your control and external factors that are not under your control.
The most popular method of strategic case analysis is the SWOT analysis. SWOT is considered a decision-making tool. The acronym SWOT stands for Strengths, Weaknesses, Opportunities and Threats [1]. Strengths and weaknesses are internal factors, while opportunities and threats are external factors. A successful organization’s strategic plan should focus on strengths and opportunities, try to manage weaknesses, and avoid or mitigate threats.
SWOT analysis can be used to create successful business strategies, but it does not provide estimates and metrics. Therefore, in the present study, we incorporated it into the Neuterosophic Analytic Hierarchy (ANH) process.
The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method (MCDM) used to solve and analyze complex problems. MCDM is an important branch of business research that seeks to create mathematics and software tools to select the best alternative among different options according to certain criteria.
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AHP consists of several steps. The first step is to structure the hierarchy of the problem to understand it more clearly. The AHP hierarchy consists of the objective (task), the decision criteria, the sub-criteria and finally all the available alternatives.
After building the AHP hierarchy, decision makers construct pairwise comparison tables to weight the criteria using the Saaty scale [2].
AHP can then evaluate both qualitative and quantitative data. For this reason, it is one of the most practical methods of making multi-criteria decisions [3].
In real-world applications, decision criteria are often vague, complex, and inconsistent in nature. Furthermore, the use of hard values in a comparison table is not always accurate due to the uncertainty and inaccuracy of the information available to decision makers. Many researchers started using fuzzy set theory [4]. However, fuzzy set theory only considers the degree of belonging to the truth. Atanasov introduced the intuitionistic theory of fuzzy sets [5], which takes into account both degrees of truth and degrees of falsity, but does not take uncertainty into account. To address the previous drawbacks of fuzzy and intuitionistic fuzzy sets, Smarandache introduced neutralosophical sets [6], which consider degrees of truth, uncertainty, and falsity in general to represent uncertain and inconsistent information. Thus, Neusophic sets are the best representation of reality. Therefore, in our study, we used AHP in a neutral-sophisticated environment.
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The structure of this article is as follows: a literature review on SWOT analysis and AHP is presented in section 2. Basic definitions of neutralosophical sets are presented in section 3. the proposed model is discussed in section 4. a real-world example illustrates the possibility applying the model proposed in section 5. and finally Section 6 concludes the paper, continuing the work.
In this section, we provide an overview of AHP and SWOT analysis techniques used in various fields.
SWOT analysis [7] is a practical methodology used by managers to build successful strategies through the analysis of strengths, weaknesses, opportunities and threats. SWOT analysis is a powerful methodology for making accurate decisions [8]. Develop organizational strategies to enhance your strengths, address weaknesses, seize opportunities, and avoid threats.
Kotler and others used SWOT analysis to gain a tactical approach to decision making [9, 10, 11]. Many researchers in various fields [4] use SWOT analysis. An overview of SWOT analysis applications is given by Helms and Nixon [8]. SWOT analysis was applied by Dyson in the field of education [12]. It has also been used in healthcare, government and non-profit organizations to address issues at the national level [13] and make decisions related to sustainable investments [14]. It is recommended for use in studying the relationship between countries [15]. SWOT analysis is mainly qualitative. This is the main disadvantage of SWOT because it cannot give the weight of strategic factors to alternatives. To overcome this drawback, many researchers have incorporated it into the Analytic Hierarchy Process (AHP).
Pdf] An Integrated Approach With Group Decision Making For Strategy Selection In Swot Analysis
Because AHP is user-friendly and easy to understand, some managers find it a very useful decision-making method. Vaidya and Kumar analyzed 150 papers published in international journals between 1983 and 2003 and concluded that the AHP method is useful for decision making, selection, evaluation and decision making [16]. Achieving a consensus decision despite a large number of decision makers is another advantage of AHP [17].
Several researchers have combined the SWOT analysis methodology with the Analytical Hierarchy Process (AHP). Leskinen and others combined SWOT with AHP in the field of environment [18, 19, 20], Kajanus used SWOT-AHP in tourism [21] and Setwart used SWOT-AHP in project management [22]. Competitive strength, environment and corporate strategy were brought together by Chan and Heide [23]. As the classical version of AHP does not deal with uncertainty, many researchers have integrated SWOT analysis with fuzzy AHP (FAHP). Demirtas et al used SWOT with fuzzy AHP to select a project management methodology [24]. Lumaxono used SWOT-FAHP to determine the best expansion strategy for a traditional shipyard [25]. Tavana et al., integrated SWOT analysis with intuitive fuzzy AHP for reverse logistics outsourcing [26].
Fuzzy sets only focus on the membership function (degree of truth) and do not consider non-membership (degree of falsehood) and degrees of uncertainty, so they cannot represent uncertainty and uncertainty. To overcome these weaknesses of fuzzy typing, we integrated SWOT analysis with the analytic hierarchy process in a neurosophical setting.
The Neusophic set is an extension of the classical set, fuzzy set, and intuitionistic fuzzy set and efficiently represents real-world problems by considering all aspects of the decision situation (i.e., truth, uncertainty, and falsity) [27, 28, 29]. . , 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]. In this study, an attempt was made for the first time to present a mathematical representation of SWOT analysis with AHP in a neurosophical setting. The Neusophic set acted as a symmetric tool in the proposed method, as belonging was the symmetric equivalent of not belonging with respect to uncertainty.
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[33, 34] The neutralosophical set N is characterized by three membership functions: true membership function T N e ( x ), uncertainty membership function I N e ( x ) and false membership function F N e ( x ), where x ∈ X and X is the space of points. Also, T N e ( x ) : X → [-0, 1+], I N e ( x ): X → [- 0, 1+] and F N e ( x ): X → [- 0, 1+] . There are no restrictions on the sum of T N e ( x ), I N e ( x ) and F N e ( x ), so 0− ≤ sup T N e ( x )
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