Optimization & Risk Analytics Service Offering

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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.

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Optimization & Risk Analytics Service Offering

  1. 1. 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
  2. 2. With Custom Optimization Maximizing Utility Minimizing Cost & Risk Improving ROI for our Customers © 2010-13 OptiRisk India (P) Ltd, All rights reserved 2
  3. 3. Planning Optimization R & D of R & D of Risk Optimisation Analytics models, covering frameworks Deterministic problems stochastic problemsTraining in Optimization and Risk Analytics © 2010-13 OptiRisk India (P) Ltd, All rights reserved 3
  4. 4. • Transport Optimization • Supply Chain ManagementIndustrial • Operations Planning • Portfolio Optimization • Asset Liability ManagementFinance • Risk Analytics • Resource Planning • Resource AllocationDefense • Resource Scheduling © 2010-13 OptiRisk India (P) Ltd, All rights reserved 4
  5. 5. Portfolio Planning for Investment Banks Asset and Liability Management HBOSIntegrating Market Risk with Credit Risk BP Oil Quantify News Analytics Raven Pack © 2010-13 OptiRisk India (P) Ltd, All rights reserved 5
  6. 6. 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 © 2010-13 OptiRisk India (P) Ltd, All rights reserved 6
  7. 7. Resource Scheduling Resource Planning Singapore DefenseResource Planning & Allocation NATO © 2010-13 OptiRisk India (P) Ltd, All rights reserved 7
  8. 8. Operational Planning Optimal Route Selection Sector: EnergyCustomer: A Leading LPG Company © 2010-13 OptiRisk India (P) Ltd, All rights reserved 8
  9. 9. The aim was to develop a automated planning tool whichwould 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. © 2010-12 OptiRisk India (P) Ltd, All rights reserved 9
  10. 10. Software and What It Does for YOUUSE 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 © 2010-12 OptiRisk India (P) Ltd, All rights reserved 10
  11. 11. On-Time Delivery Increased VisibilityReduced Stock-outs Greater visibility and control Improved Service Level Increased Planning Productivity Satisfied Customers Enabled Employees Increased BusinessOptimized Delivery Routes Ease of UseFewer 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) © 2010-12 OptiRisk India (P) Ltd, All rights reserved 11
  12. 12. Shareholder Value ROI Profit Investment Cost Revenue # TrucksLess mileage Business (10 to 30% ) Service Level . Planning Lead time Errors (5 to 10%) © 2010-12 OptiRisk India (P) Ltd, All rights reserved 12
  13. 13. OptiRisk Route Planner & Scheduler Studio (ORPSS) © 2010-12 OptiRisk India (P) Ltd, All rights reserved 13
  14. 14. Customers Manufacturing Spare Parts LogisticsORPSS (OptiRisk Route Planner & Scheduler) can be used for in-bound, out-bound, and spare parts logistics transport planning and scheduling.
  15. 15. Asset Liability Management Pension Fund - ALM Sector: Finance Customer: HBOS © 2010-13 OptiRisk India (P) Ltd, All rights reserved 15
  16. 16. Pension funds wish to make integrated financial decisions tomatch and outperform liabilities. Fixed Dynamic Asset Liability Asset Liability Mix Asset Only Management Management Strategy Strategy with Uncertainty © 2010-13 OptiRisk India (P) Ltd, All rights reserved 16
  17. 17. t=1 t=2 ...T -1 t=TIn 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 © 2010-13 OptiRisk India (P) Ltd, All rights reserved 17
  18. 18. Model Objective Risk Minimise Total A&LDeterministic Linear PV01 Deviations vs. - Programming Initial Injected Cash Minimise Total A&LTwo 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 © 2010-13 OptiRisk India (P) Ltd, All rights reserved 18
  19. 19. 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. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 19
  20. 20. 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 evaluationIntegrated 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 © 2010-13 OptiRisk India (P) Ltd, All rights reserved 20
  21. 21. 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 © 2010-13 OptiRisk India (P) Ltd, All rights reserved 21
  22. 22. • 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. s s A t 1 t L t 1 shortage t s 0 s, t S shortage s ˆ Lt t t s 1 Where λ is the shortfall parameter © 2010-13 OptiRisk India (P) Ltd, All rights reserved 22
  23. 23. • 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 © 2010-13 OptiRisk India (P) Ltd, All rights reserved 23
  24. 24. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 24
  25. 25. Portfolio OptimizationInvestment Portfolio Optimization Sector: Finance Customer: UBS Equity Research © 2010-13 OptiRisk India (P) Ltd, All rights reserved 25
  26. 26. Modelling Paradigmo Markowitz M-V modelo Risk and return…two objectiveso Efficient frontier…Pareto optimalo Utility function…risk aversionRole of Information Systems (IS)Risk MetricsComputational Solution © 2010-13 OptiRisk India (P) Ltd, All rights reserved 26
  27. 27. Transactional Information Analytical Portfolio Models Database Analysis Models Database Decision Database Data Mart © 2010-13 OptiRisk India (P) Ltd, All rights reserved 27
  28. 28. 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... © 2010-13 OptiRisk India (P) Ltd, All rights reserved 28
  29. 29. 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 © 2010-13 OptiRisk India (P) Ltd, All rights reserved 29
  30. 30. Operation PlanningOptimal Purchasing Policy Sector: FMCG Customer: UNILEVER © 2010-13 OptiRisk India (P) Ltd, All rights reserved 30
  31. 31. Decide on when to purchase the raw material o Maximize margin o Minimize risk given the uncertainties in raw material o price & product demandImplemented using two stage stochastic optimizationDSS with SP Model, scenario generators, solution algorithms,and risk/return view of policies. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 31
  32. 32. Volatile buying price Volatile selling price India Asia Malaysia Oils are processed USA Europe Natural Oils Finished goods imported exportedA math modelling framework that maximises the margin and balances therisks dues to the uncertainties in the oil prices, the sales margin and salesprice revision for each oil type and product. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 32
  33. 33. 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)?Depends on: Retail demand, inflation, promotion,competitionDecision: Pricing of oil, and its revision interval. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 33
  34. 34. 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. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 34
  35. 35. Profile of the cover policies. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 35
  36. 36. The cost distribution on buying from the spot price. The cost distribution for a futures contract of 3 weeks. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 36
  37. 37. • 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. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 37
  38. 38. Resource Scheduling Fleet Scheduling Sector: DefenseCustomer: US Coast Guard © 2010-13 OptiRisk India (P) Ltd, All rights reserved 38
  39. 39. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 39
  40. 40. Sea Vessels and aircrafts are used for • Search & rescue • Law enforcement • Response to environment incidents • Fishery & custom regulation • Vessel safetyCome up with operational schedule for these vesselstaking various factors into consideration including thosethat affect crew morale. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 40
  41. 41. Large Scale constraint satisfaction problemGenerate a set of possible schedule for each vesselCome up with the “fleet” schedule by selectingone of the possible schedule for each vessel.Solved by “Extended set partitioning model” (Integer Goal Programming) © 2010-13 OptiRisk India (P) Ltd, All rights reserved 41
  42. 42. Determining high level business requirementsApproximate budget rangeProof-of-concept, if requested.Detailed business requirements and model designImplementation, testing and debuggingDeployment and trainingPost deployment support © 2010-13 OptiRisk India (P) Ltd, All rights reserved 42
  43. 43. Exceed client expectations by creating models andDSS that deliver superior return on client’sinvestments.UK team has more than 20 years of experience.Customers of OptiRisk in Asia get both costadvantage of development in India and the supportfrom the UK team with more than 20 years modelingexperience. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 43
  44. 44. ___________________________________________________________________________________________________________________________ © 2010-13 OptiRisk India (P) Ltd, All rights reserved 44
  45. 45. 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. © 2010-13 OptiRisk India (P) Ltd, All rights reserved 45

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