C-FARm

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Decision support system

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“Aggregation and Ranking method” (C-FARm), use an innovative

approach to create composite indicators allowing ranking various

items (countries, firms, consumers, etc.).

C-FARm is a helpful tool to aggregate multi-dimensional information

and extract knowledge for decision-making in many areas.

Why Composite indicators?

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Composite indicators are used to summarise complex information and constitute a precious instrument of action and decision.

These indicators facilitate the task of ranking on complex issues in benchmarking exercise in different areas such :

Economy and Finance: rank countries, regions, banks, businesses, academic institutions, etc. Marketing: rank products, strategies, customers, etc.

Medicine: rank diseases, hospitals, patients, etc.

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The demand in composite indicators is rapidly growing for at least two main reasons:

• Complexity of modern economics.

• Enormous amount of information has to be processed.

Growth in use of Composite indicators

Google searches on composite indicators

Oct:2005 june:2006 june:2007

Google scholar Google

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Robust and Objective Decision Maker

• Objectivity : No empirical manipulation of weights

• Specificity : A specific equation for each item

• Decision support : Ability to perform simulations and propose action

plans and optimal sequence of reforms to decision-makers.

C-FARm is based on an new approach using neural networks insuring

a high level of robustness – Its main features are:

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Three steps to aggregate complex information

First, C-FARm realizes self-organization of items into homogeneous

subsets (clustering), through a learning process that takes into account

the positive and negative interactions.

Second, an appropriate weights vector is determined for each item.

Finally, the weighting vectors are applied to original data to calculate

the composite indicator and make the overall ranking.

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Breakthrough

C-FARm solves a major concern of aggregation problems whereby the

question of the importance of each variable is still valid.

The weighting system can be characterized as objective since it

emanates from the informational content of the variables themselves and

their internal dynamics.

This last feature of C-FARm represents a valuable step forward and a going-

beyond what is currently practiced in terms of classification / aggregation.

These advances are based on benefits of Artificial Neural Networks model.

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C-FARm references and Success

The model has attracted interest from several organizations:

World Bank: • Construction of an indicator of governance and country rankings.• Developing a plan of reforms to improve standards of governance in Algeria.

Other international organizations have also expressed interest in it.

• International Labor Organisation (OIT)

• African Development Bank (ADB)

• Ministry of Economy, Finance and Industry in France (MINEFI)

• African Union (AU)

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Target market

• Banks and Insurance Companies

• Rating Agencies

• The Office of Research and Investment advice

• Large Enterprises

• Research Institutions

• International Organizations

•Etc.

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Thank You