Tender Miner

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  • 1. Tender Analysis Module
    Paras Deshpande

2. Tenders
The tenders have various attributes like country, document type, CPV codes.
These attributes can have different values depending upon the particular tender in question.
Also the tender can have same attribute with different values e.g. CPV Codes.
3. Methodology: Analysis of Tenders
The attributes of the tenders have been formulated as Categories. These are therefore general representation of the attributes.
The various specific values that the attributes takes are captured into criteria. The tender thus has a dual level one with a broader scope while the other encapsulates the specific information.
4. Methodology: Analysis of Tenders cont
The more categories a tender has the more attributes it will be evaluated upon.
The more criteria's a category has the category will become more comprehensive and general.
The ranking given to a criteria is normalised over other criteria within the category.
5. Advantages of Weighted Criteria Algorithm
The Algorithm offers various advantages enumerated below:
Firstly, the Algorithm is simple and intuitive yet very effective in case of Tender Analysis.
The Algorithm is scalable and can be robustly implemented for very large number of Tenders.
6. AHP vis--vis Weighted Criteria
AHP is effective when decisions involve many intangibles to be measured along side tangibles whose measurements must also be evaluated by the decision maker
Therefore when the measurement of the criterias assumes subjectivity then AHP is the method of choice.
In case of Tender Analysis the measurement of the criterias is tangible and hence the Weighted Criteria method preferred apart from the scalability issues.
7. Mathematical formulation
8. Notations
9. Weightages

  • We then Normalise the weightages to the scale of -1 to +1

Calculation of Tender Score
10. i
Preference
11. Need foriPreference
iPreference stands for inferred preferences.
iPreference is the logical extension of the Tender Miner because of its unique capability to infer, map and store user preferences.
12. Features of iPreference
User can now store previously won tenders as favorites which will subsequently be used to infer their preferences and hence rank tenders according to inferred preferences.
This will greatly improve the user experience by saving him from cumbersome task of filling the forms and also address changing preferences.
13. Methodology
The User will be required to fill in the categories he wishes to have the tenders judged upon along with their weightages.
He then has a option of either judging some tenders provided by the service OR can add a list of previously won tenders to his favorites.
Now the iPreference uses these tenders to infer
user preference and adds his inferred profile to the knowledge Base.
14. Methodology cont
The logic used for inferring is described next:
Ranking Tenders requires categories and their weightages, criteria and their weightages.
In this case instead of criteria we have tenders with their weightages.
Therefore a simple correlation logic would suggest: More favorable a tender is More favorable its criteria's are expected to be.
15. Advantages
This is the first* time a reverse approach is being taken for decision making.
Its reverse in a sense it presents the reference alternatives and then ranks candidate alternatives on the basis of knowledge gained rather than taking down the preferences first.
* To best of my knowledge
16. Finer Aspects
There are some issues which need further attention. For e.g. if a favorable tender might have some favorable and some unfavorable criteria's, and presently iPreference cannot differentiate between them.
It may be agued though as more tenders and fairly representative tenders are added to the list the unfavorable criterias will slowly get neglected as happens with heuristics.
Eventually the better criteria will prevail !!!
17. Finer Aspects cont
This method is similar to Neural Networks in many cases and has similar advantages and limitations.
This method requires less effort on learning routines.
Under learnt profile might result in substandard results and a minimum of 10-15 tenders should be included in favorites.
18. Mathematical Formulations
19. Notations
20. Weightages

  • Normalized weightages :

Algorithm
21. Ending Remarks
The Algorithm shows that with different types of tenders being added to the favorites the favorable criteria will occur more frequently and thus grow stronger in weightages.
There can be improvements on finding ways to promote favorable criteria's and eliminating the unfavorable ones.
The main contribution thuswill be proposing a intuitive and simple inference mechanism.
22. References
http://people.revoledu.com/kardi/tutorial/AHP/Multi-Criteria-Decision-Making.htm
Saaty, T.L. (2008) Decision making with the analytic hierarchy process, Int. J. Services Sciences, Vol. 1, No. 1, pp.8398.
23. End of Slides