This paper presents an empirical study to learn more about challenges in carpet industry using factor analysis. The proposed study of this paper designs a questionnaire consists of 51 questions in Likert scale and distribute it among experts in Carpet industry. We have analyzed the data using factor analysis and deleted 10 most redundant questions. Cronbach alpha was calculated as 0.88 for the remaining questions, which is well above the minimum desirable limit. We have also managed to remove six more questions using principal axis factoring leaving 8 basic components including 35 different questions. The proposed study of this paper has categorized eight factors including specialized relationships, knowledge coordinator, knowledge tool, knowledge organization, knowledge processes, knowledge chain, knowledge hardware and Knowledge feasibility study. Investigating details of the results of each eight items could help us build better strategies to help this industry grow faster and more reliable in today & apos; s business world.

Carpet is one of the most popular handcraft industries in Iran and it has created significant number of jobs in various villages and small towns in this country. There are literally many designs and plans introduced by Iranian carpet makers but during the past two decades, we have seen an increasing competition mostly from other countries such as China, India, Pakistan, etc. On the other hands, there are ongoing interests for membership of world trade organization (WTO) and it is necessary to take the necessary actions. The proposed study of this paper uses different mathematical techniques to consider different actions for changing threats to opportunities, reducing unnecessary costs, increasing revenue and market share, etc. The proposed model of this paper uses Fuzzy analytical hierarchy process (AHP), Benefits-Opportunities-Costs-Risks (BOCR) method to detect possible benefit, risk and cost components. We first setup all useful strategies and then using AHP prioritizes different actions.