PRODUCTION QUALITY

Description : It analyzes historical production data involving hundreds of parameters to find the most significant parameters affecting the quality of the finished output. It can model the hierarchy of the production process and analyze impact of parameters in a process on the output of the same process and also subsequent processes. It can also specify desired UCL and LCL for the significant parameters.
It also generates RYG process control charts to give alerts for significant factors tending to go out or going out of the safe zone.

Quantitative Techniques : Step-wise regression (linear and non-linear), confidence limits

Environment : MS SQL Server. 2000, MS .NET Framework. 1.1., IIS 6.0, Microsoft Windows Server 2000

PROPOSAL ESTIMATION

Description : It analyzes historical proposal estimation data obtained from a legacy software application involving a large number of parameters spread over a dozen departments to find the critical parameters in each department to predict the proposal effort and material cost estimates.

Quantitative Techniques : Step-wise regression (linear and non-linear), confidence limits, Neural Network model, model back-test

Environment : Excel VBA s

MATERIAL FORECASTING

Description : It analyzes historical material sent out and received data obtained from several existing applications to make the material forecasts.

Quantitative Techniques : Trend, decomposition and correlation analysis, ARIMA, growth curve fitting, cluster analysis, partition analysis, regression analysis (linear and non-linear), normal curve fitting, confidence limits, Neural Network, model back-test

Environment : Excel VBA, MS-Access

LOSS DISTRIBUTION

Description : Loss assessments provide an estimate of the loss severity in the event of default. These are assigned to individual instruments - loans, bonds, and preferred stock.

Quantitative Techniques : Used modified probability distribution, simulation and developed distribution solver algorithm to estimate parameters of the distribution

Environment : MS SQL Server. 2000, l MS .NET Framework. 1.1., Internet Information Services (IIS 6.0)., MS Office XP/2003, Microsoft Windows Server 2003

SURVIVAL MODEL

Description : The objective of this application is to know the probability of an issuer defaulting in a given time horizon. In fact it is able to give the individual probabilities of the issuer being in each of the different ratings and the exit states like Default in each of the perids during the forecasting horizon.

Quantitative Techniques : Proportional Hazards model using Transition matrices, Baseline transition Parameters, Covariates Parameters.

Environment : MS SQL Server. 2000, MS .NET Framework. 1.1., Internet Information Services (IIS 6.0)., MS Office XP/2003, Microsoft Windows Server 2003

SCENARIO ANALYSIS

Description : Scenario Analysis (SA) estimates the performance of CDO tranches for different Default and Prepayment scenarios. SA aims at providing quick insights about the level of tranche protection, as well as a means of comparing different deals that share or are expected to have similar default and prepayment characteristics.

Quantitative Techniques : Generate sub portfolios, scenario analysis and distribution loss using waterfall and scenario building methods based on several factors including coupon rates and spreads.

Environment : Excel VBA, C# .NET, API DLL in C++

ADVANCED RATES GENERATOR

Description : This is a model for calculating Advance Rates in market value CDOs collateralized with assets of different asset types and/or industries. This model permits the full benefit of diversification by explicitly including intra-industry and inter-industry correlation in the option-based formula used to calculate the Advance Rates.

Quantitative Techniques : Using a correlation matrix for inter industry and intra industry to calculate portfolio volatility. Using specific factors in the application for calculation of the Portfolio Advance Rate. These were done at the security level instead of at just the portfolio level.

Environment : Excel VBA

The purpose of Business Analytics is to identify, analyze and create value from the significant business processes and data of an organization Apart from the quantitative benefits that accrue from a focused application of Business Analytics, the key qualitative benefits are:

Data Mining : Clementine, SAS (with whom we are in dialogue to become an alliance partner)
Statistical : Minitab, JMP
Development : .NET, J2EE
DBMS : SQL-Server, ORACLE
Configuration Management : VSS