Optirisk corporate presentation

Ph: +91 98406 18472/ +91 44 4501 7482 Bala. Padmakumar
Email : optimize@optiriskindia.com Director & CEO
Web: http://www.optiriskindia.com OptiRisk India
© 2010-13 OptiRisk India (P) Ltd, All rights reserved

With Custom Optimization

Maximizing Utility
Minimizing Cost & Risk
Improving ROI

for our Customers

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

Planning
Optimization

R & D of R & D of Risk
Optimisation Analytics
models, covering frameworks

Deterministic problems

stochastic problems

Training in Optimization and Risk Analytics


• Transport Optimization
• Supply Chain Management
Industrial • Operations Planning

• Portfolio Optimization
• Asset Liability Management
Finance • Risk Analytics

• Resource Planning
• Resource Allocation
Defense • Resource Scheduling


Portfolio Planning for Investment Banks

Asset and Liability Management
HBOS

Integrating Market Risk with Credit Risk
BP Oil

Quantify News Analytics
Raven Pack


Natural oil purchase policy

Residual risk of industrial
explosion protection system
Kidde PLC (part of United Technologies)

Supply Chain Network Design
under uncertainty
Daimler AG


Resource Scheduling

Resource Planning
Singapore Defense

Resource Planning & Allocation
NATO


Operational Planning

Optimal Route Selection

Sector: Energy

Customer: A Leading LPG Company


The aim was to develop a automated planning tool which
would reduce total cost incurred on out-bound logistics

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.


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 Levels
Significant Planning Man-Hours Saving (70% to 90% saving)
Helps in long Term Planning


On-Time Delivery Increased Visibility
Reduced Stock-outs Greater visibility and control
Improved Service Level Increased Planning Productivity
Satisfied Customers Enabled Employees
Increased Business

Optimized Delivery Routes Ease of Use
Fewer Fleet requirements Visual Displays
Lesser driving distance / time. Ready to print customized reports
Lower cost & Investment Visual Performance dash boards
Increased ROI for KPI tracking

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


Shareholder Value

ROI

Profit Investment

Cost Revenue # Trucks

Less mileage Business
(10 to 30% )

Service Level .

Planning Lead time
Errors (5 to 10%)


OptiRisk Route Planner & Scheduler Studio (ORPSS)


Customers

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.

Asset Liability Management

Pension Fund - ALM

Sector: Finance

Customer: HBOS


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

Fixed Dynamic Asset Liability
Asset Liability
Mix Asset Only Management
Management
Strategy Strategy with Uncertainty


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

In flo ws In flo ws In flo ws

C arry C arry
W EAL T H W EAL T H W EAL T H

O u tflo ws O u tflo ws O u tflo ws


Model Objective Risk
Minimise Total A&L
Deterministic Linear
PV01 Deviations vs. -
Programming
Initial Injected Cash

Minimise Total A&L
Two Stage Stochastic
PV Deviations vs. -
Programming
Initial Injected Cash

Integrated Chance Minimise Total A&L Probabilistic
Constrained PV Deviations vs. Constraints restricting
Programming Initial Injected Cash Deficit Events


Institution Specific Country Specific Risk Measures
Constraints Constraints Constraints
• Asset Classes • Tax • Downside
• Planning Horizon • Regulatory • VaR
• Threshold Requirements • CVaR
Constraints • Minimum Asset • Variance
• Cardinality Reserve • MAD
Constraints • Etc. • Etc.
• Transaction Cost
• Etc.


Models of (parameter) randomness
Scenario Generator Scenario Generator
(Asset) (Liability)

Ex-ante decision models

Expected value LP

Two-stage SP with recourse
Simulation
Multistage SP with recourse
and
Chance-constrained SP with recourse
Decision evaluation
Integrated chance constraints SP with recourse

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

Stochastic measures: EVPI, VSS`

Risk measures: VaR, CVaR, standard deviation

Performance measures: Solvency ratio, Sharpe ratio,
Sorting ratio


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

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


• 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.

At
s
1 t
s
Lt 1
shortage t
s
0 s, t

S

shortage
s ˆ
Lt t
t
s 1

Where λ is the shortfall parameter


• 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.

At
s
1 t
Lt
s
1
shortage t
s
0 s, t
S

shortage
s ˆ
Lt t
t
s 1

Where is the shortfall parameter


Portfolio Optimization

Investment Portfolio Optimization

Sector: Finance

Customer: UBS Equity Research


Modelling Paradigm
o Markowitz M-V model
o Risk and return…two objectives
o Efficient frontier…Pareto optimal
o Utility function…risk aversion
Role of Information Systems (IS)
Risk Metrics
Computational Solution


Transactional Information Analytical
Portfolio Models
Database Analysis Models Database

Decision
Database

Data Mart


Production Database
Data Mart
Market Data: Pre-Analytical Database
Historical Prices

Model Data Parameters:
Internal Data:Portfolios, Average Return Var/Cov Matrix ... Optimisation Engine
Cashflows...
User Input:
Pre Analytics: Risk Aversion,
Styles, Risk Statistics, Financial Ratios .. Target Portfolio Return ..
Analytical
Models Portfolio Optimisation Model
Continuous or Discrete

Decision Database
Modelling System
Optimisation Results:
Portfolio Returns, Potfolio Risk,
Optimum Asset Mix
Solver

Post-Analytical Database
Analytical
Models Results Analytics:
What if, Different objectives...

Post Analytics:
Backtesting, Risk Analysis...


Adjusts MPL/AMPL
Model file Calls
Calls

Excel/VBA
- data storage MPL/AMPL
- driving application
Reads Data

Reads Sends to
solution Solver

Results FortMP/QP/
Solution file
QMIP


Operation Planning

Optimal Purchasing Policy

Sector: FMCG

Customer: UNILEVER


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

Implemented using two stage stochastic optimization
DSS with SP Model, scenario generators, solution algorithms,
and risk/return view of policies.


Volatile buying price Volatile selling price

India
Asia

Malaysia Oils are
processed

USA Europe

Natural Oils Finished goods
imported exported

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.


Depends on
1. 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)?

Depends on: Retail demand, inflation, promotion,
competition
Decision: Pricing of oil, and its revision interval.


Processing in the factory and
storage of reserves.

Demand for finished product
(uncertain).
Raw material supply
(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.


Profile of the cover policies.


The cost distribution on buying from the spot price.

The cost distribution for a futures contract of 3 weeks.


• 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.

• 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.


Resource Scheduling

Fleet Scheduling

Sector: Defense

Customer: US Coast Guard


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)


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


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.


___________________________________________________________________________________________________________________________


Bala. Padmakumar
Ph: +91 98406 18472 / +91 44 4501 8472
Email: optimize@optiriskindia.com
Web: http://www.optiriskindia.com/

Asia Pacific, Africa, Australia & Middle East : Europe & America :

No 12, Ground Floor, 25th Cross Street OptiRisk R&D House One Oxford Road,
Thiruvalluvar Nagar, Thiruvanmiyur, Uxbridge Middlesex, UB9 4DA,
Chennai - 600041, India. United Kingdom.


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