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Relative Return on Investment

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Stephen Bounds discusses a better way to guide decision-making through RROI ranking of scenarios using propensity scoring of relevant events and Monte Carlo simulations.

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Relative Return on Investment

  1. 1. Relative Return on Investment Having the right conversations about information value
  2. 2. Stephen Bounds Executive, Information Management stephen.bounds@cordelta.com 28 January 2020 2
  3. 3. Do any of these sound familiar? 28 January 2020 3 We have a great IM plan, but my manager always thinks that something else is more important… No one cares about anything until a crisis hits! We educate people, but they nod their heads and then revert to their old habits Why don’t people take IM seriously? Licensed under CC BY-NC-ND
  4. 4. Psychological distance 28 January 2020 4 We think more concretely about things that are nearer to us, and more abstractly about things that are further away Temporal distance Whether an event is a long time in the future or the past Spatial distance Whether you are separated by a large geographic distance Social distance Whether you feel you know someone well or not Hypothetical distance Whether you think an event is likely to occur or not
  5. 5. Proximate and ultimate goals Starting point Proximate goal Proximate goal … Ultimate goal 28 January 2020 5
  6. 6. Typical Information Management goals 28 January 2020 6  Right information to the right person at the right time  Faster information discovery  Better organisational governance  Fewer decision making errors  Better knowledge retention
  7. 7. Proximate and ultimate goals Starting point Proximate goal Proximate goal … Ultimate goal 28 January 2020 7 Most information projects sit here
  8. 8. 6 ways to optimise outputs Can we use information better to change … ? 28 January 2020 8 Category Description Fixed cost the cost of turning the lights on Incremental cost the cost of one more item Cycle time time for one item start to finish Throughput items per unit of time Quality absence of errors and defects Excellence going above and beyond to meet individual needs or do “cool stuff”
  9. 9. 8 ways to reduce waste (Lean) Category Description Transport Moving people, products & information Inventory Storing parts, pieces, documentation without need Motion Bending, turning, reaching, lifting Waiting For information, instructions, equipment, or parts Over production Making or doing more than is immediately required Over processing Completing to a higher standard than is necessary Defects Needing to rework, scrap, or redo (products or information) Skills Capabilities present but not used, tasking inadequately trained staff 28 January 2020 9 Can we use information better to reduce waste in … ?
  10. 10. RROI = Relative Return on Investment  Industrial re-engineering focuses on predictable outcomes  Time & motion studies = do X, result isY, cost is Z  Return on Investment (ROI) is known per event  RROI provides a way to quantitively assess and compare indirect outcomes from projects 28 January 2020 10
  11. 11. How it works 28 January 2020 11 Identify potential initiatives Target ultimate goals Quantify assumptions Perform benefits modelling Rank RROI and agree on action
  12. 12. What can be done vs What will it do 28 January 2020 12
  13. 13. Ultimate goal measurements  Any benefit to be quantified as “takes X less seconds to doY” should be treated with extreme caution unless it leads to:  lower fixed or incremental costs (eg  assigned staff)  decreased cycle time / increased throughput (eg  service levels)  Increased “excellence” should be aligned to the principles of the organisation, and still quantified, eg  higher customer satisfaction scores  lower staff absenteeism 28 January 2020 13
  14. 14. How will the needle move? NB: measures can be financial or non-financial. For global interventions, separately model business outcomes across multiple teams / sections. 28 January 2020 14
  15. 15. Caution: Power law  Normal distribution (bell curve) only describes products of true randomness  Power law distribution (80/20 curve) applies for most social / economic patterns, especially where past state influences future 28 January 2020 15
  16. 16. Propensity and variability  Understanding propensity (likelihood of occurrence) is key  Power law environments with low transaction volumes can lead to highly variable outcomes  eg if you have 200 transactions a year, and only 1 in 1000 leads to catastrophic failure, there is a strong temptation to act as if the possibility for catastrophic failure does not exist  Variability must be explicitly flagged with management to set expectations  A “good year” doesn’t mean things are working  A “bad year” doesn’t mean that the process is broken  If your model is sound, it is correct to measure behaviours, not outcomes in the short to medium term 28 January 2020 16
  17. 17. MonteCarlo modelling  Monte Carlo simulations build a model of the world and use this to simulate outcomes.  Re-running the model multiple times shows both variability and expected (average) results.  Can approximate real-world results without a known algorithm, eg calculating area of a circle: 28 January 2020 17
  18. 18. Recap So far we have: 1. Proposed and costed our initiatives 2. Identified the business benefits 3. Quantified expected benefits through modelling 28 January 2020 18
  19. 19. RROI ranking (finally!) 28 January 2020 19 Financial RROI evaluation Non-financial RROI evaluation
  20. 20. Now what?  RROI provides a powerful tool to link indirect benefits to real outcomes  Leads to better conversations about assumptions and expectations  Relies upon a model of all futures, not a prediction of the future to make smarter choices  After model is agreed, the shift can focus to immediate measures and accountability  Once armed with actual results – review outcomes and if necessary, refine your organisation’s model of the world 28 January 2020 20
  21. 21. Questions? 28 January 2020 21

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