Open AccessANALYSIS OF MARKETING STRATEGIC PLANNING IN THE HOME FURNITURE INDUSTRY BY APPLYING ANP: A CASE STUDY OF THE IRANIAN HOME FURNITURE INDUSTRY
Author(s) -
Majid Azizi,
Hamzehali Mansouri
Publication year - 2021
Publication title -
international journal of the analytic hierarchy process
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 3
ISSN - 1936-6744
DOI - 10.13033/ijahp.v13i1.813
Subject(s) - analytic network process , portfolio , strategic planning , marketing , control (management) , business , process (computing) , process management , operations management , computer science , operations research , analytic hierarchy process , economics , mathematics , artificial intelligence , finance , operating system
This study was carried out because of the lack of research on strategic planning in Iran's home furniture market. Accordingly, in this study, we developed a decision-making model to select the best solution for strategic planning in the industry. After determining the strategic criteria or factors affecting the developed model, the control criteria of four merits or subsets of competencies were identified. To determine all the effective factors in the model, the sub-criteria of four control criteria and their connections were identified. The following four possibilities were considered as potential solutions: entrance to a foreign market (S1), increase portfolio (S2), emphasis on scientific management of mixing elements of marketing (S3), and generate research and development units (S4). The Analytic Network Process (ANP) and the Super Decisions software were used to synthesize and analyze the model. It was found that all calculated decisions were influenced by the strategic criteria. A value-weighted competency model was calculated in the first stage with the influence of strategic criteria on the competency model. Hierarchical design decisions were made for each of the competencies and their subsets (144 sub-criteria and 22 middle indices). Paired comparison matrices associated with the degree of the importance of each of the competencies were achieved in the second stage. In the final stage, subsets of the competency weights and their sub-options were identified with the combination of the competencies and the best solution was obtained.