Engineering, Technology & Applied Science Research Vol. 7, No. 6, 2017, 2194-2199 2194 www.etasr.com Ghaffari et al.: Assessing Market Development and Innovation Project Management Factors Using … Assessing Market Development and Innovation Project Management Factors Using the PICEA-g Hybrid Evolutionary Multi-Criteria Decision Technique The Calcimine Company Case Study Saeed Ghaffari Department of Industrial Engineering Payam-e-Noor University Tehran, Iran saeed.ghaffari68@gmail.com Amir Najafi Department of Industrial Engineering Zanjan Branch Islamic Azad University Zanjan, Iran asdnjf@gmail.com Meisam Jafari Eskandari Department of Industrial Engineering Payam-e-Noor University Tehran, Iran meisam_jafari@pnu.ac.ir Abstract—Project management includes the consideration of complex decision modes used in modern decision support techniques. The aim of this paper was to prioritize such factors and evaluate their effects on project management and optimal control. Their effect on management and optimal project control are evaluated in frame of a statistical hypothesis. A new algorithm, "IPICEA-g" is proposed for the assessment. A questionnaire is used for data collection distributed between 56 employees of the CALCIMINE Company. T-test, two-sentence test, ANP method, FUZZY SEAMATEL and the IPICEA-g hybrid algorithm, are employed for data analyzing. Results are further discussed and conclusions are drawn. Keywords-management; control; projects; hybrid; algorithm; IPICEA-g; DEAMATEL; fuzzy; ANP; marketing; development I. INTRODUCTION Project management uses two main strong arms: project planning and project control and it is widely accepted as an area of interdisciplinary nature. The central core of all project management activities is project control which is the main factor for distinguishing it from other areas of management [1]. A set of conditions and limitations are employed to make use of management knowledge in the field of project management while avoiding the creation of additional concepts or a new body of knowledge which would have little difference from their original version. The interference trend of project management can be investigated in three zones: production of project management knowledge, solutions for Enterprise Resource Planning and mobility of human resources [2]. The current paper investigates management elements for an optimal implementation in the case of Calcimine Company with the purpose of identifying and assessing such factors. Authors in [3] investigated the affecting factors in delays of development projects. Authors in [4] studied the affecting factors on the success of knowledge management projects. Authors in [5] provided a model of such factors in the construction industry and explained the relationship between them. Author in [6] discussed the dynamic modeling of projects execution time and explored influencing factors on the delay through a system dynamics approach in both micro and macro levels. In [7], the harmonization of knowledge and project management was studied. A model in which the success of change initiatives is explained by the quality of project management, which in turn is determined by the quality of the knowledge integration, was presented. In [8] the viewpoints of shareholders about cost estimates in project management are discussed. The aim of this study was to identify key issues and provide a conceptual model. In [9], the issue of comprehensive systematic thinking in the innovative project management was investigated. Authors in [10] examined the integration of project management office role in the forefront of innovation. The effect of communications management on construction project performance was investigated in [11]. Authors in [12] investigated the project risk management approach in small companies. In [13], authors provided a new method for controlling projects under uncertainty with regard to returning to first principles. Authors in [14] employed framework analysis to evaluate the success of the knowledge-based project approach. II. RESEARCH METHODOLOGY The method of study is descriptive and in terms of achieving results is focused on the explorations. Information is collected through questionnaires sent to experts and engineers of the Calcimine Company Local market and project Engineering, Technology & Applied Science Research Vol. 7, No. 6, 2017, 2194-2199 2195 www.etasr.com Ghaffari et al.: Assessing Market Development and Innovation Project Management Factors Using … characteristics were considered for the selection of factor for the conceptual model (Figure 1). Fig. 1. The conceptual model Two questionnaires were sent to each of the 56 managers of the Calcimine Company selected to participate in this study. Of these, 11 were senior managers and 45 R & D project managers. The first questionnaire follows a five-item Likert method that includes 11 questions in terms of organizational criteria, 12 questions in relation to performance criteria, 14 questions regarding personal standards and 8 questions regarding personal–group criteria. The second questionnaire contained 7 questions regarding organizational criteria, 10 questions in relation to performance metrics, 9 questions in relation to individual criteria, and 6 questions in relation to the criteria of the individual- groups and was designed considering the results from the first questionnaire. In terms of reliability and validity the Cranach’s alpha coefficient of the questionnaire is shown in Table I. To study and analyze questionnaire answers the t-test and binomial test were employed. Then through a combination of ANP and fuzzy methods the weights of the objective functions were determined and finally through the implementation of the IPICEA-g hybrid algorithm, the prioritization of factors was achieved. SPSS and Matlab were the software used for the analysis. TABLE I. THE RELIABILITY SCALE OF AFFECTING FACTORS ON PROJECT MANAGEMENT Row Subscale Cranach’s alpha coefficients 1 Organizational 0.82 2 Functional 0.79 3 Individual 0.77 III. COMPUTATIONAL RESULTS A. Descriptive Statistical Results 19.6% of the cases were female and 80.4% were male, 25% were under 30, 41.1% were from 30 to 35 years old, 10.7% from 36 to 40, 8.9% were from 41 to 45 and 14.3% from 46 and over. 32.1% owned a bachelor degree or less, 50% a master's and 17,9% owned a PhD. As for the work experience 35.7% of people had work experience of 5 years and less, 23.2% 6 to 10 years, 25% 11 to 15 years, and 16.1% of them had work experience longer than 16 years. B. Results of Criteria and Indicators Monitoring and Evaluation 0 1 : 0.5 : 0.5 H p H p        The proportion of people who agree with the standard X, less than the average. The proportion of people who agree with the standard X, more than the average. : : H H    0 1 According to the binomial test, the most important affecting factors on project management and optimal control are shown in Table II. According to the binomial test, the main criteria for the effective functioning on the management and optimization control of project are shown in Table III. According to the binomial test, the most affecting individual measures on the management and optimization control of project are shown in Table IV and the most important affecting individual - group measures on the management and optimization control of project are shown in Table V. TABLE II. ORGANIZATIONAL AFFECTING FACTORS ON PROJECT MANAGEMENT AND OPTIMAL CONTROL ID Knowledge Competence C1 Understanding of project characteristics C2 Understanding of the assessment process and the feasibility of the project C3 Understanding of project structures C4 Understanding the topic of project resources C5 Understanding the concept of project time C6 Understanding the budget issues and project costs C7 Understanding the topic of project resources TABLE III. PERFORMANCE CRITERIA FOR EFFECTIVE MANAGEMENT AND OPTIMAL CONTROL OF PROJECT ID Functional Competencies C8 Project Documentation C9 Determining the project scope, registration and approval C10 Choosing project structures C11 Resource planning, registration and approval C12 Select of qualified contractors and vendors C13 Estimating project costs C14 Directing, managing of registration process and executing contracts C15 Investigate, identify and implementation of necessary changes in project C16 Coordination between different units and issuing guidelines which related to projects C17 Funding, resources and equipment needed to start the project TABLE IV. AFFECTING INDIVIDUAL MEASURES ON THE MANAGEMENT AND OPTIMIZATION CONTROL OF PROJECT ID Behavioral Competencies C18 Hard work C19 The spirit of achievement C20 Initiative and innovation C21 Self Confidence C22 Result oriented C23 Being regulated C24 Having strategic perspectives C25 Spirit of competition C26 Having systematic approaches Engineering, Technology & Applied Science Research Vol. 7, No. 6, 2017, 2194-2199 2196 www.etasr.com Ghaffari et al.: Assessing Market Development and Innovation Project Management Factors Using … TABLE V. AFFECTING INDIVIDUAL - GROUP MEASURES ON THE MANAGEMENT AND OPTIMIZATION CONTROL OF PROJECT ID Behavioral Competencies C27 Sociability C28 Having the leadership and guidance behavior of the project team C29 Influence and impact on group projects C30 Spirit of unity and empathy C31 Motivate of project individuals C32 Communication and fair treatment with employees, customers and stakeholders C. Results The initial monitoring criteria were 45 which were later narrowed down to 32. The results of distribution variables in Kolmogorov-Smirnov test are shown in Table VI. As shown, the assumption of normality for all variables is confirmed. Therefore, t-test can be used to test the hypothesis and the results are shown in Table VII. Results show that individual factors, individual-group factors, performance factors, organizational factors are found to be effective. TABLE VI. RESULTS OF KOLMOGOROV-SMIRNOV TEST Variable Frequency Avera ge SD Statistic of z Level of signific ance Organizational Factors 56 3.02 0.506 0.954 0.322 Performance Factors 56 3.14 0.56 1.19 0.09 Individual factors 56 3.49 0.454 1.47 0.11 Individual- group factors 56 3.63 0.38 1.34 0.52 TABLE VII. RESULTS OF HYPOTHESES TESTING CI Number of items Degrees of freedom Signifi cance Quantity of T SD Avera ge Large scale and subscale Upper line Low line 3.507 3.170 9 55 0.000 39.748 0.628 3.339 individual 3.464 3.137 6 55 0.000 40.498 0.610 3.301 Individual- group 3.512 3.326 10 55 0.000 47.259 0.533 3.369 performance 3.529 3.164 7 55 0.000 36.663 0.683 3.346 organizational D. Hybrid Meta-Heuristic IPICEA-g Method Results The IPICEA-g hybrid meta-heuristic method is a multi- objective optimization algorithm based on non-dominated relationships and the brushing technique. To implement this algorithm, we need multiple objective functions and the algorithm searches the objective answers to achieve optimal solution space. If factors are defined as x1, x2, x3 and x4 we define objective functions as follows: max z1 = w1x1 max z2 = w2x2 max z3 = w3x3 maxz4 = w4x4 where, w1, w2, w3 and w4 are the weights or importance of individual, individual-group, performance and organizational. The objective is to determine the values of x1, x2, x3 and x4, in a range from 0 to 1. The values of x1, x2, x3 and x4 represent the final rank of the factors obtained by the proposed algorithm. To solve the functions with the proposed algorithm, the initial weights w1, w2, w3 and w4 were determined by combining fuzzy ANP-DEAMATEL and IPICEA-g. 1) The hybrid fuzzy ANP-DEAMATEL approach Performance evaluation steps are described below. a) Formation of decision Network The network consisted of 3 levels that are used because of computing limited super matrixes and rate of incompatibility. b) Implementation of dematel fuzzy method Results are shown in Table VIII. Language assessments are changed to corresponding triangular fuzzy numbers and converted to absolute numbers through CFCS (Converting Fuzzy data into Crisp Scores) and (1) to (9). The primary direct matrix is normalized by (10) and the overall relationship matrix T is calculated by (11). Afterwards, the matrix T, is placed in super matrixes as matrix W22. TABLE VIII. LINGUISTIC VARIABLES AND CORRESPONDING FUZZY NUMBERS Linguistic variable Final equivalent Fuzzy equivalent (a) Fuzzy equivalent (b) Very low 0 (0, 0.1,0.3) (0, 0, 0.25) little 1 (0.1, 0.3, 0.5) (0, 0.25, 0.5) Average 2 (0.3, 0.5, 0.7) (0.25, 0.5, 0.75) very 3 (0.5, 0.7, 0.9) (0.5, 0.75, 1) Very much 4 (0.7, 0.9, 1) (0.75, 1, 1) 1 max min min k k ij k K ijl ij l l xl      (1) 1 max min min k k ij k K ijk ij m l xm      (2) 1 max min mink kij k K ijk ij r l xr      (3) Engineering, Technology & Applied Science Research Vol. 7, No. 6, 2017, 2194-2199 2197 www.etasr.com Ghaffari et al.: Assessing Market Development and Innovation Project Management Factors Using … max min max min k k ij ijr l  (4) 1 k ijk ij k k ij ij xm xls xm xl    (5) 1 k ijk ij k k ij ij xr xls xr xm    (6) (1 ) 1 k k k k k ij ij ij ij ijk ij k k ij ij xls xls xrs xrs xrs x xrs xls       (7) 11 k K k ij ij k a BNP k     (8) max minmin k k k ij ij ijBNP l x   (9) X s A  (10a) 1 1 1 1 min , max \ \ max \ \ n n i ij j ijj j S a a            (10b) To obtain matrix T, the following formula is used: T=X+X2+…+Xk=X(i+X+X2+…+Xk-1)(1-X)(1-X)-1 =X(1-Xk)(1-X)-1 (11a) If limk→∞Xk=[0]nxn, the overall relationship matrix is obtained by the following equation T=X(1-X)-1 (11b) c) Fuzzy ANP process and its combination with fuzzy deamatel The data collection phase is based on paired comparison questionnaire. For example, a combination of expert opinions for paired comparison of finance criteria perspective is calculated and recorded by using (12). Then, by using the CFCS method, defuzzification was performed and by using (13) the weight of secondary criteria is obtained (incompatibility rate at this point is zero). For the other main criteria, there are similar calculations and the weights of these comparisons are recorded in a column titled local weight of minor factors. The weights as matrix W32 are in the initial super matrix. The main factors will pair. Thus, the initial matrix will form with calculation of W11, W22 and W23. 0 j ij i n c T     (12) 0 i ij j n r T     (13) d) Prioritization of key factors and sub-factors and identify of cause-effect factors To perform the required analysis, prioritization of factors is based on limited weights of super matrix. At the specific table the weight and prioritization of factors are recorded. To identify the causal factors, ri, cj, ri-cj are calculated by using (12) and (13) and their values are recorded. The first step is the formation of decision network and in next stage we described deamatel fuzzy technique for the forming of overall relationship matrix. The deamatel fuzzy relationship matrix obtained by the method is shown in Table IX. As can be seen in Table IX, individual and individual–group dimensions are the reasons and organizational and performance factors are the effects. As mentioned, this matrix w22 is used in initial super matrix of fuzzy ANP method. To the formation of the super matrix we need to form the W32 and W11 matrixes. The W32 matrix was obtained from the comparison of secondary factors. After CFCS defuzzification, the weight for each factor group is calculated with (13). Tables X-XIII show the calculated weight of sub-factors. The weight of calculated factors in Tables X and XIII is used to form the initial matrix w32. After preparing the matrices W22, W32 and W11, the super initial matrix is prepared and then the limited super matrix is formed. The weight of factors and their prioritization is shown in Table XIV. To identify causal relationships, ri, cj, ri-cj are calculated and the results are shown in the Table XV. TABLE IX. THE OVERALL RELATIONSHIP MATRIX (OUTPUT OF DEMATEL FUZZY) Individual Individual- group Perfor mance Organiza tional ri individual 0.0742 0.475 0.565 0.297 1.411 Individual- group 0.842 0.803 0.955 0.838 3.438 performance 0.142 0.384 0.088 0.082 0.695 organizational 0.172 0.332 0.379 0.936 1.818 cj 1.230 1.993 19999 .997 2.153 ri-cj 0.181 1.445 1.338- 0.335- Type of factor reason reason effect effect TABLE X. WEIGHT OF THE INDIVIDUAL SUB-INDICES Index name weight Hard work 0.222 The spirit of achievement 0.0401 Initiative and innovation 0.210 Self Confidence 0.0506 Result oriented 0.177 Being regulated 0.114 Having strategic perspectives 0.087 Spirit of competition 0.008 Having systematic approaches 0.0913 TABLE XI. WEIGHT OF INDIVIDUAL SUB-GROUPS INDICATORS Index name weight Sociability 0.103 Having the leadership and guidance behavior of the project team 0.223 Influence and impact on group projects 0.303 Spirit of unity and empathy 0.0809 Motivate of project individuals 0.109 Communication and fair treatment with employees, customers and stakeholders 0.1811 Engineering, Technology & Applied Science Research Vol. 7, No. 6, 2017, 2194-2199 2198 www.etasr.com Ghaffari et al.: Assessing Market Development and Innovation Project Management Factors Using … 2) The final results of IPICEA-g The IPICEA-g algorithm determines the optimal values of x1, x2, x3 and x4 ensuring they are less than 1. To implement the algorithm in this study, a structure for the presentation of the answers with an one-dimensional matrix and a row with 4 cells was used. The values of this matrix represent the values of model variables and they are between 0 and 1. The basic steps of the algorithm are as follows:  First a certain number of answers are produced as the first generation.  Secondly, with the use of non-dominated solutions they are arranged and ranked.  Third, using the combination operator, the solutions are combined and produce new solutions.  In the fourth step, solutions are leveled and according to the number of population size, we select next- generation solutions.  In the fifth step, the solutions identified to optimize border and then the algorithm returns to the second step. It should be noted that the above actions are repeated until the maximum number of iterations of the algorithm is reached. A single-point crossover operator is also employed. The IPICEA-g algorithm took 500 iterations and after its implementation, 75 responses were reported as optimal solutions. Mean values were calculated as the final weights factors shown in Table XVI TABLE XII. WEIGHT OF THE SUB-INDICES OF OPERATING PERFORMANCE Index name Weight Project Documentation 0.0701 Determining the project scope, registration and approval 0.0225 Choosing project structures 0.173 Resource planning, registration and approval 0.121 Select of qualified contractors and vendors 0.0205 Estimating project costs 0.193 Directing, managing of registration process and executing contracts 0.0419 Investigate, identify and implementation of necessary changes in project 0.112 Coordination between different units and issuing guidelines which related to projects 0.1675 Funding, resources and equipment needed to start the project 0.0785 TABLE XIII. WEIGHT OF THE SUB-INDICES OF ORGANIZATIONAL FACTORS Index name Weight Understanding of project characteristics 0.223 Understanding of the assessment process and the Feasibility of the project 0.0998 Understanding of project structures 0.104 Understanding the topic of project resources 0.0752 Understanding the concept of project time 0.298 Understanding the budget issues and Project costs 0.109 Understanding the topic of project resources 0.091 TABLE XIV. MAIN FACTOR WEIGHTS AND THEIR PRIORITIES Individual Individual - Group Performance Organizational Main factors 0.0013 0.0041 0.0009 0.0016 Main factors weight TABLE XV. DETERMINE THE CAUSAL FACTORS Type of factor Ri-cj cj ri Factor reason 0.0034 0.0066 0.01 individual reason 0.0186 0.0129 0.0315 Individual-group effect 0.0043- 0.0114 0.007 performance effect 0.0177- 0.0301 0.0125 organizational TABLE XVI. 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