In order to outmaneuver
increasing competition, organizations must plan their strategies for managing
the future. Yet, planning is not solely adequate. It is important to ensure the
sustainability of feasible plans which are supported by performance
evaluations, and to focus on strategies that which pay attention to demands and
expectations of stakeholders.
The most prominent features of
the real-life problems are multiple criteria, complexity and uncertainty.
Benefiting from expert and stakeholder views help to reach optimal solutions of
those problems. Taking advantage of artificial intelligence techniques would
also contribute to the achievement of optimal results.
Strategy Focused Model (SFM)
aims to provide the most optimal possible solutions to real-life problems that
require multi-criteria decision making. In SFM, the views of stakeholders and
strategists are input into the decision making process. The main criteria of a
problem establish the mainframe of the model, and these criteria are utilized
in SWOT analysis. Hence, SWOT analysis is developed to Sectional SWOT analysis
(SSWOT) and become more effective in determining strategies. SFM suggests the
use of SSWOT analysis. The model requires to determine the weights of criteria
and sub-criteria as well as the weights of stakeholders’ and strategists’
views. Thereafter, those weights enter into the decision making process.
Although there are various methods to calculate those weights, Fuzzy Analytic
Hierarchy Process (FAHP) is the one used in this study. For Turkish branded
automobile production, SFM is applied and results are obtained and discussed.
Strategy Focused Model Sectional SWOT (SSWOT) FAHP Turkish automotive sector
Birincil Dil | İngilizce |
---|---|
Konular | Endüstri Mühendisliği |
Bölüm | Araştırma Makalesi |
Yazarlar | |
Yayımlanma Tarihi | 1 Ekim 2018 |
Gönderilme Tarihi | 20 Mart 2017 |
Kabul Tarihi | 22 Mart 2018 |
Yayımlandığı Sayı | Yıl 2018 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.