This research provides a tool to select and prioritize new comers to work based on their preentry organizational commitment propensity through examining links between the big five personality factors: extroversion, agreeableness, conscientiousness, neuroticism, openness; and three component model of organizational commitment: affective commitment, continues commitment, normative commitment. Findings show that extroversion and openness respectively have positive and negative effects on all three components of organizational commitment. Results gained by Structured Equation Modelling (SEM) indicate neuroticism is negatively related to affective and continues commitment and positively to conscientiousness effects on continues commitment. In the second part of the study, the received results are applied to extract the general equations that enables to estimate new comer’s pre-entry organizational commitment and to rank them using TOPSIS and AHP. The AHP is used to determine the relative weights of commitment criteria and TOPSIS is employed for the final ranking of new comers based on these criteria’s.
The machinability and the process parameter optimization of turning operation for 15-5 Precipitation Hardening (PH) stainless steel have been investigated based on the Taguchi based grey approach and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). An L27 orthogonal array was selected for planning the experiment. Cutting speed, depth of cut and feed rate were considered as input process parameters. Cutting force (Fz) and surface roughness (Ra) were considered as the performance measures. These performance measures were optimized for the improvement of machinability quality of product. A comparison is made between the multi-criteria decision making tools. Grey Relational Analysis (GRA) and TOPSIS are used to confirm and prove the similarity. To determine the influence of process parameters, Analysis of Variance (ANOVA) is employed. The end results of experimental investigation proved that the machining performance can be enhanced effectively with the assistance of the proposed approaches.
One of the strategic decisions of any organization is decision making about manufacturing
strategy. Manufacturing strategy is a perspective distinguishing a company from other
present companies in that industry and creates a kind of stability in decisions and gives a special
direction to organizational activities. SIR (SUPERIORITY& INFERIORITY Ranking)
method and their applications have attracted much attention from academics and practitioners.
FSIR proves to be a very useful method for multiple criteria decision making in fuzzy
environments, which has found substantial applications in recent years. This paper proposes
a FSIR approach based methodology for TOPSIS, which using MILTENBURG Strategy
Worksheet in order to analyzing of the status of strategy of the Gas Company. Then formulates
the priorities of a fuzzy pair-wise comparison matrix as a linear programming and
derives crisp priorities from fuzzy pair-wise comparison matrices
Manufacturing levers (Alternatives) are examined and analyzed as the main elements of
manufacturing strategy. Also, manufacturing outputs (Criteria are identified that are competitive
priorities of production of any organization. Next, using a hybrid approach of FSIR
and TOPSIS, alternatives (manufacturing levers) are ranked. So dealing with the selected
manufacturing levers and promoting them, an organization makes customers satisfied with
the least cost and time.
Electrical Discharge Machining (EDM) process with copper tool electrode is used to investigate the machining characteristics of AISI D2 tool steel material. The multi-wall carbon nanotube is mixed with dielectric fluids and its end characteristics like surface roughness, fractal dimension and metal removal rate (MRR) are analysed. In this EDM process, regression model is developed to predict surface roughness. The collection of experimental data is by using L9 Orthogonal Array. This study investigates the optimization of EDM machining parameters for AISI D2 Tool steel using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Analysis of variance (ANOVA) and F-test are used to check the validity of the regression model and to determine the significant parameter affecting the surface roughness. Atomic Force Microscope (AFM) is used to capture the machined image at micro size and using spectroscopy software the surface roughness and fractal dimensions are analysed. Later, the parameters are optimized using MINITAB 15 software, and regression equation is compared with the actual measurements of machining process parameters. The developed mathematical model is further coupled with Genetic Algorithm (GA) to determine the optimum conditions leading to the minimum surface roughness value of the workpiece.