SUCCESS EVALUATION MODEL FOR PROJECT MANAGEMENT
The need for effective planning and management is on the increase along with the increasing complexity and laboriousness of making changes or creating new value. Project management is an ideal tool in this respect (Schwable,2011; Bočková et al., 2005). The risks associated with deadlines or budgets are extremely serious. Problems of project modelling and simulation are highly topical at the present time, because the application of project management principles is expanding into further areas – change management (Cummings et al., 2016; Yin-xiang, 2013), crisis management (Wimelius & Engberg, 2015), innovation management (Balkienė, 2013; Dudzevičiūtė & Tvaronavičienė, 2011; Grossmann, 2009), etc. – which have only very basic features of projects.
Jméno a příjmení autora:
Radek Doskočil, Stanislav Škapa, Petra Olšová
Project management, project success, evaluation model, fuzzy logic, decisionmaking
DOI (& full text):
The article presents an expert fuzzy model for evaluation of the project success rate. The model is implemented with the use of fuzzy logic. First, fundamental theoretical principles related to the…více
The article presents an expert fuzzy model for evaluation of the project success rate. The model is implemented with the use of fuzzy logic. First, fundamental theoretical principles related to the problems of project success rate, fuzzy sets and fuzzy logic are introduced, after which a fuzzy model for project success rate evaluation, including partial sub-models, is presented in the form of a case study which represents the main goal of the article.
The fuzzy model is implemented in the MATLAB software environment with the use of the Fuzzy Logic Toolbox application, where it is also veriﬁed and further speciﬁed. The fuzzy model consists of six input variables which are divided according to their character into three categories in each block (RB1, RB2, RB3) and are separately evaluated. Partial outputs from the blocks (RB1, RB2, RB3) are simultaneously inputs for block RB4, from which there is a single output variable – project success (PS). The RB1 rule block evaluates the situation from the point of view of the state of the project. The RB2 rule block evaluates the total value of project risk. The RB3 rule block evaluates project quality. The RB4 rule block evaluates the total project success rate.
Experimenting with the fuzzy model allows simulation of the uncertainty that is always involved in projects. The case study introduces an overall diagram of the fuzzy model, the input and output variables, including their attributes, and the evaluation rules of the four rule blocks.
The proposed fuzzy model is used to evaluate project success primarily in the implementation phase, then repeatedly after each phase of the project is completed. This provides project managers with a tool that allows relatively rapid evaluation of the success of the project and the opportunity of applying appropriate measures in good time if necessary.