optimization & risk analytics service offering

© 2010-13 OptiRisk India (P) Ltd, All rights reserved

Ph: +91 98406 18472/ +91 44 4501 7482

Web: http://www.optiriskindia.comEmail : [email protected]

Optimization & Risk Analytics

Bala. PadmakumarDirector & CEOOptiRisk India

mailto:[email protected]

http://www.optiriskindia.com/

http://optiriskindia.com/


http://in.linkedin.com/in/padmakumarpillai?trk=pub-pbmap

Value Proposition

2© 2010-13 OptiRisk India (P) Ltd, All rights reserved

Maximizing Utility Minimizing Cost & Risk Improving ROI

for our Customers

With Custom Optimization

Planning Optimization

R & D of Optimisation

models, covering

Deterministic problems

stochastic problems

R & D of Risk Analytics

frameworks

OptiRisk Service Offerings


Training in Optimization and Risk Analytics

OptiRisk Undertake

OptiRisk Service Offerings


Financ

e

• Portfolio Optimization• Asset Liability Management• Risk Analytics

Industrial

• Transport Optimization• Supply Chain Management• Operations Planning

Defens

e

• Resource Planning• Resource Allocation• Resource Scheduling

Portfolio Planning for Investment Banks

Asset and Liability Management

Integrating Market Risk with Credit Risk

Quantify News Analytics

Executed Projects (Finance)


UBS Equity Research

HBOS

BP Oil

Raven Pack


Executed Projects (Industrial)

Natural oil purchase policy

Residual risk of industrial explosion protection system

Supply Chain Network Design under uncertainty

UNILEVER

Kidde PLC (part of United Technologies)

Daimler AG


Executed Projects (Defense)

Resource Scheduling

Resource Planning

Resource Planning & Allocation

US Coast Guard

Singapore Defense

NATO


Case Study #1 – Transport Optimization

Operational Planning

Customer: A Leading LPG Company

Optimal Route Selection

Sector: Energy

Case Study


Key Benefits:

23%+ reduction in total out-bound delivery transport cost.

Total planning time is reduced to minutes (Planning Automation)

Payback of investment was less than a month

Improved service level and increased customer satisfaction.

Problem Statement:

The aim was to develop a automated planning tool which

would reduce total cost incurred on out-bound logistics

Software and What It Does for YOU

USE OPTIRISK / IBM SOFTWARE to get Optimized Vehicle Routes

Software used custom OR models + CPLEX solver.Software is customized to YOUR CLIENT+BUSINESS needs.

Software helps client to achieve :Reduced fleet travel distance / time / cost (5 to 30% saving)Faster Customer-Response-Times (Optional)Extra Carriage Capacity with same fleet (3 to 10% saving)Improved Stakeholder Satisfaction LevelsSignificant Planning Man-Hours Saving (70% to 90% saving)Helps in long Term Planning

© 2010-12 OptiRisk India (P) Ltd, All rights reserved 10

Proposed Solution

Benefits – How?


Reduced Stock-outs Improved Service Level Satisfied Customers Increased Business

On-Time Delivery

Greater visibility and control Increased Planning Productivity Enabled Employees

Increased Visibility

Fewer Fleet requirements Lesser driving distance / time. Lower cost & Investment Increased ROI

Optimized Delivery Routes

Visual Displays Ready to print customized reports

Visual Performance dash boards for KPI tracking

Ease of Use

Less than six months ROI 1: 30 to 70 times (in 5 years)

Payback in months

Impact on Company Performance


Shareholder Value

ROI

Profit Investment

Cost Revenue # Trucks

Business Less mileage (10 to 30% )

Service Level .

Lead time (5 to 10%)

PlanningErrors

ORPSS – User Interface


OptiRisk Route Planner & Scheduler Studio (ORPSS)

Manufacturing

Suppliers Customers

Agencies

Inbound Logistics

Outbound Logistics

Transportation in Manufacturing

Spare Parts Logistics

ORPSS (OptiRisk Route Planner & Scheduler) can be used for in-bound, out-bound, and spare parts logistics transport planning and scheduling.

Case Study #2 - ALM


Asset Liability Management

Customer: HBOS

Pension Fund - ALM

Sector: Finance



Pension funds wish to make integrated financial decisions to match and outperform liabilities.

FixedMix

Strategy

Dynamic Asset Only

Strategy



with Uncertainty

ALM Models


Inflows

Outflows

WEALTH WEALTH WEALTH

t=1 t=2...T-1 t=T

Outflows Outflows

Inflows Inflows

Carry Carry

ALM Models


Deterministic Linear Programming

Two Stage Stochastic Programming

Integrated Chance Constrained

Programming

Minimise Total A&L PV01 Deviations vs. Initial Injected Cash

Minimise Total A&L PV Deviations vs.

Initial Injected Cash

Minimise Total A&L PV Deviations vs.

Initial Injected Cash

-

-

Probabilistic Constraints

restricting Deficit Events

Model Objective Risk

ALM Constraints Classes


Institution Specific Constraints

• Asset Classes• Planning Horizon• Threshold

Constraints• Cardinality

Constraints• Transaction Cost• Etc.

Country Specific Constraints

• Tax• Regulatory

Requirements• Minimum Asset

Reserve• Etc.

Risk Measures Constraints

• Downside• VaR• CVaR• Variance• MAD• Etc.

ALM and SP Integration


Models of (parameter) randomness

Two-stage SP with recourse

Multistage SP with recourse

Chance-constrained SP with recourse

Expected value LP

Integrated chance constraints SP with recourse

Scenario Generator (Asset)

Scenario Generator (Liability)

Simulationand

Decision evaluation

Performance and decision measuresStatistical measures: mean, variance, skewness, kurtosis

Stochastic measures: EVPI, VSS`

Risk measures: VaR, CVaR, standard deviation

Performance measures: Solvency ratio, Sharpe ratio, Sorting ratio

Ex-ante decision models

ALM Models


All models have two objective functions: Minimise

o Initial injected cash o Total present value (or PV01) deviations between assets

and liabilities

Pension fund needs to trade off these two objective functions: How much risk to accept of not matching the liabilities (measured by deviations) versus how much money to raise from the sponsoring company and members to guarantee a close A&L match

ALM Models


• Solved using Integrated chance constraint programming (a variant of SP)

• Not only the probability of underfunding is important, but also the amount of underfunding (conceptually close to conditional surplus-at-risk CSaR) is important.

011 st

stt

st shortageLA

t

S

s

st Lshortage ˆ

1

ts,

t

Where λ is the shortfall parameter

ALM Models


• Solved using Integrated chance constraint programming (a variant of SP)

• Not only the probability of underfunding is important, but also the amount of underfunding (conceptually close to conditional surplus-at-risk CSaR) is important.

011 st

stt

st shortageLA

t

S

s

st Lshortage ˆ

1

ts,

t

Where is the shortfall parameter

ALM – LDI Prototype


Case Study #3 – Portfolio Optimization


Portfolio Optimization

Customer: UBS Equity Research

Investment Portfolio Optimization

Sector: Finance



Modelling Paradigmo Markowitz M-V modelo Risk and return…two objectiveso Efficient frontier…Pareto optimalo Utility function…risk aversion Role of Information Systems (IS) Risk Metrics Computational Solution



Transactional Database

Information Analysis Models

Portfolio Models

Data Mart

Decision Database

Analytical Database


Data MartProduction Database

Internal Data:Portfolios, Cashflows...

Market Data:Historical Prices

Analytical Models

Optimisation Engine

Solver

Modelling System

Portfolio Optimisation ModelContinuous or Discrete

User Input:Risk Aversion,

Target Portfolio Return ..

Pre-Analytical Database

Pre Analytics:Styles, Risk Statistics, Financial Ratios ..

Model Data Parameters:Average Return Var/Cov Matrix ...

Decision Database

Optimisation Results:Portfolio Returns, Potfolio Risk,

Optimum Asset Mix

Post-Analytical Database

Results Analytics:What if, Different objectives...

Post Analytics:Backtesting, Risk Analysis...

Analytical Models




Excel/VBA- data storage

- driving application

MPL/AMPL

Calls

Reads Data

Sends to Solver

FortMP/QP/QMIP

ResultsSolution file

Reads solution

Adjusts MPL/AMPLModel file Calls


Case Study #4 – Purchase Optimization

Operation Planning

Customer: UNILEVER

Optimal Purchasing Policy

Sector: FMCG

Unilever – Operational Planning - DSS


Project Scope

Project Implementation

Implemented using two stage stochastic optimization DSS with SP Model, scenario generators, solution algorithms,

and risk/return view of policies.

Decide on when to purchase the raw materialo Maximize margino Minimize risk given the uncertainties in raw material o price & product demand


Unilever – Operational Planning - DSS

India

Malaysia

USA

Finished goods exported

Oils are processed

Asia

Europe

Natural Oils imported

Volatile buying price Volatile selling price

A math modelling framework that maximises the margin and balances the risks dues to the uncertainties in the oil prices, the sales margin and sales price revision for each oil type and product.

DSS – Natural Oil Buying


Supply Side Depends on1. Monsoon in India,2. Yield of Soya crop in the U.S,3. Output of palm oil world wide,4. Production of rape-seed oil world-wide (other than

India and China). Decision: Buy now (spot) or later (future)?

Demand SideDepends on: Retail demand, inflation, promotion, competitionDecision: Pricing of oil, and its revision interval.


Processing in the factory and storage of reserves.

Raw material supply (uncertain).

Demand for finished product (uncertain).

reserve reserve

• Supply side uncertainty can be hedged through contracts in the financial market.

• Need for a quantitative DSS to maximise margin at an acceptable risk for each product and sales market via setting the financial cover for the various oils.

DSS – Natural Oil Buying

Unilever – DSS – Result Analysis


Profile of the cover policies.


The cost distribution on buying from the spot price.

Unilever – DSS – Result Analysis

The cost distribution for a futures contract of 3 weeks.

Unilever – DSS – Back / Stress Testing


Back testing • Collect and analyze the historical (transactional) data.

• Obtain past reports from the domain expert in respect of various events which affected the spot and futures prices for the natural oils and the selling price for the end products.

• Run the model against historical data, verify that the decisions made through the model are indeed best hedged.

• Quantify and analyze the different Risk metrics such as VAR, CVAR, downside for the implementation of the cover policy decisions.

Stress Testing• Integrate stress testing within general risk management framework.

• Test the robustness of the Stochastic programming (hedged) solution by looking at the extreme events.

• Stress technique is a mixture of quantitative techniques, expert judgment, imaginative flair and market intuition.


Case Study #5 – Portfolio Optimization

Resource Scheduling

Customer: US Coast Guard

Fleet Scheduling

Sector: Defense

US Coast Guard – Fleet Scheduling




Project Scope:

Sea Vessels and aircrafts are used for • Search & rescue• Law enforcement• Response to environment incidents• Fishery & custom regulation • Vessel safety

Come up with operational schedule for these vessels taking various factors into consideration including those that affect crew morale.



Large Scale constraint satisfaction problem Generate a set of possible schedule for each vessel Come up with the “fleet” schedule by selecting

one of the possible schedule for each vessel. Solved by “Extended set partitioning model” (Integer Goal Programming)

Custom DSS Development


Steps Involved: Determining high level business requirements Approximate budget range Proof-of-concept, if requested. Detailed business requirements and model design Implementation, testing and debugging Deployment and training Post deployment support

OptiRisk Value Promise


Exceed client expectations by creating models and DSS that deliver superior return on client’s investments.UK team has more than 20 years of experience.Customers of OptiRisk in Asia get both cost advantage of development in India and the support from the UK team with more than 20 years modeling experience.

- Optimization & Risk Management

Boutique consulting organization

Caters to Industrials & Financial sectors

20 years old in UK; 4 years old in India

A campus company of Brunel University, UK

Served some of the Fortune 500 companies, and

Defense Establishments.

A certified partner of IBM

___________________________________________________________________________________________________________________________Who we are?


Contact:


Bala. PadmakumarPh: +91 98406 18472 / +91 44 4501 8472

Email: [email protected]: http://www.optiriskindia.com/

OptiRisk R&D House One Oxford Road,Uxbridge Middlesex, UB9 4DA,United Kingdom.

Europe & America :

No 12, Ground Floor, 25th Cross StreetThiruvalluvar Nagar, Thiruvanmiyur,Chennai - 600041, India.

Asia Pacific, Africa, Australia & Middle East :

Thank you

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