1. Problem Definition
Ms. XY has intention to enter next education level through a Master of Business and Administration degree. And she is strongly determined to choose MBA program which best fit with her preferences. And what she asked is advice on which one is the best MBA program for her.
2. Development of Feasible Alternative
In the previous blog post, I already created analysis using multi-attribute decision making using non-compensatory models. As advised by Dr. Paul (W5 Blog: https://simatupangaace2014.wordpress.com/2013/10/04/w5_tom_-choosing-mba-program-by-using-multi-attribute-decision-making-non-compensatory-models/#comments) in order to create deeper and sharper analysis, we extend to use multi-attribute decision making compensatory models, which are non-dimensional scaling and additive weighting technique models.
3. Development Outcomes for Alternatives
Based on Ms. XY’s personal preferences, she would like to choose one of four possible alternatives, which are MBA programs at University of Melbourne, INSEAD, Boston University, or Erasmus University.
4. Selection of Criteria
The selection of the best MBA for Ms. XY should have analysis that considers attributes of alternatives which shown below:
5. Analysis of the Alternative
The basic principle behind all compensatory models, which involve a single dimension, is that the values for all attributes must be converted into a common measurement. And the other thing about compensatory model is that good performance in one attribute can compensate for poor performance in another attribute of alternative.
The equations for converting attribute values to its dimensionless rating is:
Therefore we are able to generate attributes’ dimensionless ratings which shown as follow:
Afterward, we can see the ratings and accumulate total dimensionless rating value of alternatives:
Using this model, Erasmus University is considered to be the best choice, with the highest rating value of 7.27.
Additive Weighting Technique
Additive Weighting Technique utilizes information derived from non-dimensional scaling and lexicography model, which we previously done. The objective is to combine dimensionless rating value with relative rank in a calculation.
Based on information above we are able to see relative rank of each attribute:
In this case, we are able to see that INSEAD has the highest value, followed by Erasmus University with only 0.01 point of value difference.
6. Selection of Preferred Alternative
Based on analysis above and considering results derived from non-compensatory models, (dominance model, satisficing model & disjunctive resolution model and lexicography model), the best MBA program that best fit with Ms. XY’s preferences would be MBA at Erasmus University.
7. Performance Monitoring & Post Evaluation Result.
Even though the analysis selects Erasmus University as the result, however on the other hand INSEAD seems very competitive and actually can turn out to be the second alternative. It has 0.01 point of value higher than Erasmus University in additive weighting technique model and very competitive in the non-compensatory models.
I hope this blog and all my previous blogs will be good references for anyone and I am very pleased to share it.
Sullivan, W. G., Wicks, E. M., & Koelling, C. P. (2012). Engineering Economy. (15th ed.).Chapter 14 Decision Making Considering Multiattributes. (pp. 555-560). Boston, MA: Prentice Hall.
- AACE Course Jakarta Blog (2013). Choosing MBA program Using Multiattribute Decision Making Non-Compensatory models. Retrieved on 10 October 2013 from https://simatupangaace2014.wordpress.com/2013/10/04/w5_tom_-choosing-mba-program-by-using-multi-attribute-decision-making-non-compensatory-models/
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Retrieved on 27 September 2013 from:http://management.bu.edu/graduate/graduate-admissions/tuition/full-time/
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Retrieved on 27 September 2013 from:http://rankings.ft.com/businessschoolrankings/boston-university-school-of-management/global-mba-ranking-2013#global-mba-ranking-2013
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Retrieved on 27 September 2013 from:http://www.bloomberg.com/quote/EURUSD:CUR