1
Associate professor, Ferdowsi University of Mashhad.
2
Ph.D Student, Ferdowsi University of Mashhad.
Abstract
Agile manufacturing is a new productive model which is a result of changes in the environment of companies and it was shaped agility in two previous decades, it can also seek necessity of response to the increasingly intense competitive environment. Thus, in competitive era, our organization should implement this system quickly in order to succeed in production of high quality goods. This research, seeks suitable pattern in order to differentiate non-agile and agile companies by the most important factors of agility (speed, flexibility, competence and accountability) throughout multivariate discriminant function. Therefore, a questionnaire related to agility factors has been distributed among food companies of Toos Industries in Mashhad and then collected and analyzed by the software SPSS19. The results indicate that discriminant function obtained by the four variables have ability for differentiating of agility and non-agility companies. Moreover, it can be used in order to forecast agility, responsiveness, and flexibility that has had the most share of differentiation between two companies.
Pooya, A. R., & Khoobiyan, M. (2014). Design and Explain a Model Based Diagnostic Multivariate Analysis in Order to Forecasting Corporate Agility. Journal of Industrial Management Perspective, 4(2), 9-25.
MLA
Ali Reza Pooya; Mehdi Khoobiyan. "Design and Explain a Model Based Diagnostic Multivariate Analysis in Order to Forecasting Corporate Agility", Journal of Industrial Management Perspective, 4, 2, 2014, 9-25.
HARVARD
Pooya, A. R., Khoobiyan, M. (2014). 'Design and Explain a Model Based Diagnostic Multivariate Analysis in Order to Forecasting Corporate Agility', Journal of Industrial Management Perspective, 4(2), pp. 9-25.
VANCOUVER
Pooya, A. R., Khoobiyan, M. Design and Explain a Model Based Diagnostic Multivariate Analysis in Order to Forecasting Corporate Agility. Journal of Industrial Management Perspective, 2014; 4(2): 9-25.