optimization & risk analytics service offering
DESCRIPTION
Specializing in computational Optimization and Risk Analytics, OptiRisk offers custom-built solutions to businesses to increase revenue, productivity and reduce cost; thus improving bottom line and ROI.TRANSCRIPT
© 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
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
3© 2010-13 OptiRisk India (P) Ltd, All rights reserved
Training in Optimization and Risk Analytics
OptiRisk Undertake
OptiRisk Service Offerings
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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)
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UBS Equity Research
HBOS
BP Oil
Raven Pack
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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
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Executed Projects (Defense)
Resource Scheduling
Resource Planning
Resource Planning & Allocation
US Coast Guard
Singapore Defense
NATO
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Case Study #1 – Transport Optimization
Operational Planning
Customer: A Leading LPG Company
Optimal Route Selection
Sector: Energy
Case Study
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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?
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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
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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
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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
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Asset Liability Management
Customer: HBOS
Pension Fund - ALM
Sector: Finance
Asset Liability Management
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Pension funds wish to make integrated financial decisions to match and outperform liabilities.
FixedMix
Strategy
Dynamic Asset Only
Strategy
Asset Liability Management
Asset Liability Management
with Uncertainty
ALM Models
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Inflows
Outflows
WEALTH WEALTH WEALTH
t=1 t=2...T-1 t=T
Outflows Outflows
Inflows Inflows
Carry Carry
ALM Models
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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
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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
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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
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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
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• 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
23© 2010-13 OptiRisk India (P) Ltd, All rights reserved
• 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
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Case Study #3 – Portfolio Optimization
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Portfolio Optimization
Customer: UBS Equity Research
Investment Portfolio Optimization
Sector: Finance
Portfolio Optimization
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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
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Portfolio Optimization
Transactional Database
Information Analysis Models
Portfolio Models
Data Mart
Decision Database
Analytical Database
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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
Portfolio Optimization
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Portfolio Optimization
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
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Case Study #4 – Purchase Optimization
Operation Planning
Customer: UNILEVER
Optimal Purchasing Policy
Sector: FMCG
Unilever – Operational Planning - DSS
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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
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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
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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.
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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
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Profile of the cover policies.
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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
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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.
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Case Study #5 – Portfolio Optimization
Resource Scheduling
Customer: US Coast Guard
Fleet Scheduling
Sector: Defense
US Coast Guard – Fleet Scheduling
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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.
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US Coast Guard – Fleet Scheduling
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
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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
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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?
44© 2010-13 OptiRisk India (P) Ltd, All rights reserved
Contact:
45© 2010-13 OptiRisk India (P) Ltd, All rights reserved
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