Modelling and Enhancement of Organizational Resilience Potential in Process Industry SMEs

Sustainability

Slavko Arsovski³  Goran Putnik¹  Zora Arsovski²  Danijela Tadic³  Aleksandar Aleksic³  Aleksandar Djordjevic³  Slavisa Moljevic⁴ 

[1] Department of Production and Systems Engineering, Universidade do Minho, Braga 4705, Portugal;Faculty of Economics, University of Kragujevac, Djure Pucara Starog 3, Kragujevac 34000, Serbia;Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, Kragujevac 34000, Serbia;Faculty of Mechanical Engineering, University of East Sarajevo, Vuka Karadzica 30, Lukavica 71126, East Sarajevo, Bosnia and Herzegovina;

关键词: organizational resilience;    fuzzy sets;    genetic algorithm;    improvement strategy;   

DOI  :  10.3390/su71215828

来源: mdpi

【 摘 要 】

The business environment is rapidly changing and puts pressure on enterprises to find effective ways to survive and develop. Since it is almost impossible to identify the multitude of complex conditions and business risks, an organization has to build its resilience in order to be able to overcome issues and achieve long term sustainability. This paper contributes by establishing a two-step model for assessment and enhancement of organizational resilience potential oriented towards Small and Medium Enterprises (SMEs) in the process industry. Using a dynamic modelling technique and statistical tools, a sample of 120 SMEs in Serbia has been developed as a testing base, and one randomly selected enterprise was used for model testing and verification. Uncertainties regarding the relative importance of organizational resilience potential factors (ORPFs) and their value at each level of business are described by pre-defined linguistic expressions. The calculation of the relative importance of ORPFs for each business level is stated as a fuzzy group decision making problem. First, the weighted ORPFs’ values and resilience potential at each business level are determined. In the second step, near optimal enhancement of ORPFs’ values is achieved by applying a genetic algorithm (GA).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.