Portfolio Management An Inexact Science. Mark Lawry Portfolio Management Dir, GSK Milan, September 2010

Transcription

1 Portfolio Management An Inexact Science Mark Lawry Portfolio Management Dir, GSK Milan, September

2 Portfolio Management Vision Optimizing the value delivered from the portfolio in line with strategy Understand the current situation and predict future state on: Sales/Value Risks Time (Schedule) Cost/Resource Strategic Objectives Key value drivers Key sensitivities Profitability measures... Regulatory Differentiation Scientific Operational... Quality of estimates Schedule & budget Critical path Right content... Transparency of resource allocation Strategic Resourcing Demand forecasts... Portfolio strategies Desired risk/return profile Strategic investment focus... Develop consistent & adaptable processes and practices to integrate all components Portfolio level scenario planning & transparent, objective information to support governance decisions aimed at optimizing portfolio value.

3 Drivers for Portfolio Management Resource Constraints The typical driver for portfolio decisions Risk Forecast Decisions Time Cost Critical Mass If no critical mass, effective project management sufficient Portfolio Management Capability Need to ensure portfolio management capability is not wasted overhead 3

4 Limitations of Data Key Risks Time/Effort to Discharge Risk Probability of Success Project & Portfolio Context Time Risk Forecast Emerging Data Assumptions Target Profile External Environment Cost 6 12Months > 12Months 6 12Months > 12Months Decreasing Accuracy Decreasing Accuracy 4

5 Asset Quality Time R is k Cost Forecast Quality Scientific Rationale Emerging Data Assumptions Target Profile Innovation Differentiation Quality Ensure objective assessment of asset quality & value 5

6 The Context Filter for Data What Parameters? Complex Systems Bottom Up Build of Data Frequency of updates? Budget Milestone Project Context the filter Monitor progress of portfolio Meaningful Analyses Comparison with other organisations Opportunity for Learning 6

7 Analysis is not Management Forecast Estimates Cost Estimates Time Estimates Risk Evaluation Options & Recommendations Priority Strategy & Plan Resource & Budget allocation Decisions Priority Strategy & Plan Resource & Budget allocation 7

8 Do High Hurdles Lead to Easier Decisions? Regulatory standards Access to formularies Pricing constraints Crowded market Generic competition etc. Larger effects to achieve differentiation and re imbursement leads to higher hurdles 8

9 Who Makes Decisions and How? Decisions driven by scientific interest/knowledge/experience Difficult decisions avoided unless: Lack of resource or budget Project termination is hard unless: Clear lack of efficacy or Clear sign of toxicity Tendency to resource to another decision point Senior level decision making can be improved, informed by objective portfolio management 9

10 Meaningful Analysis Forecast Estimates Profile Assumptions on market Timing to launch and market penetration Pricing assumptions Patent situation Etc. Time Estimates Assumptions driving plan Quality & detail of plan Issues affecting plan 3 Point estimates Likelihood of staying on track Next key milestone Cost Estimates Assumptions driving budget Near term accuracy v.s. long term estimates Fixed package v.s. variable package costs Internal costs v.s. external contract costs Risk Evaluation Objective evaluation of key risks Clarity on work required to discharge risk Timing & cost to decision point Overall evaluation of risk POS Consider limitations of data Frame the recommendation through analysis What do decision makers want? What do they need? What do they understand? What do they use? Are they asking the right questions? 10

11 Balancing Needs Complex Systems Bottom Up Build of Data Is it the right data? How accurate is the data? Management at Gross Level Major Decisions at Senior Level e.g. cut budget by 10% How accurate does the data need to be to drive the necessary decision? More accurate picture: Cost & Resource Strategy & Plans Risk & opportunities Better Prediction of Future Returns 11

12 Simple Presentation Complex Data Meaningful Analysis Jul 2010 Aug Sep Oct Nov Dec Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q Plan OPTION1 Activity 1 Activity 2 Data RISKS Decision Activity 3 Phase I Study Phase II Study 1 Study 2 Decision Phase III Study 1 Study 2 Submission Budget Committed Flexible 12

13 Improve the Ease of Input Those that input the data.rarely benefit from analyses of it Easier data entry Standard planning tools Standard budgeting tools Data derived from algorithms Appropriate parameters Appropriate precision Improved compliance. More focus on project management. 13

14 Same Portfolio Management Capabilities Different Focus (1) Trends Focus on NCEs Focus on Individual Assets Early Stage Large Portfolio Highly Dynamic Focus on Science & Desired Profile Focus on Label Lead/NCE Product/PLE Portfolio Complexity Asset Complexity Higher Project Volume Lower Project Volume Higher Project Turnover Lower Project Turnover Less Commercial Focus More Commercial Focus Lower POS Higher POS Lower Project Budget Higher Project Budget Late stage Smaller expensive portfolio Higher stability but project failure has major impact 14

15 Same Portfolio Management Capabilities Different Focus (2) Operational Portfolio Management:: Strategic Portfolio Management: Execution of strategy Development of strategy Tactical allocation of short term resources High level resource allocation & prioritization Medium to long term Line Resource Owners Business Unit/Matrix Unit Senior Management Committee Resource Allocation Therapeutic Area Strategies Resource & Budget Allocation Therapeutic Area Balancing Stage Gate Decisions Short Term Focus Short Medium Term Focus Medium Long Term Focus 15

16 Conclusion Achievable Vision: Optimizing the value delivered from the portfolio Understand the current state, predict the future Data is essential but understand its limitations Improving the ease of data entry improves quality Context is vital: Communication is key Portfolio Analysis isn t Portfolio Management Understand the needs of decision makers Influence decisions and target analyses Senior level decision making can be improved, informed by objective portfolio management 16