>> FAQs

  1. Doesn’t decision analysis slow down decision-making?
  2. How to know the best decision is being made?
  3. How will the use of decision analysis impact my organization’s performance?
  4. How is intuition considered in decision analysis?
  5. Which decision-support software do you recommend?
  6. How complex is the mathematics involved in decision analysis?
  7. Mid and long term strategies should have inbuilt flexibility. How does DA treat this subject?
    1. Doesn’t decision analysis slow down decision-making?
      The discipline of decision analysis (DA) can be an effective way of tackling excessive analysis, dithering and rework, which are common problems in big decisions. By allowing complex decisions to be made with more confidence the first time, DA diminishes the incidence of dithering and rework. Excessive analysis is avoided due to DA’s identification of, and focus on the relevant drivers of value and risk. It accordingly only seeks to analyse information that matters. Also, DA’s scalability (it can be scaled up or down), allows time constraints to be taken into consideration.
    2. How to know the best decision is being made?
      When decision-outcomes cannot be well predicted, the quality of our choices must rely on the quality of our decision-making process. This is exactly what the discipline of Decision Analysis (DA) is about: designed for complicated decision contexts, it comprises processes more likely to bring us the results we want. DA methodically assesses the potential of each decision alternative and compares them; in contexts carrying substantial uncertainties, DA allows us to take into account the effects of uncertainty on each of the alternative’s value. With a sound, clearer comparison of alternatives, we increase our chances of achieving the best decision results.
    3. How will the use of decision analysis impact my organization’s performance?
      In a study of 760 organizations of diverse industries and sizes (most headquartered in the U.S., U.K., Germany, France, China and Japan) Blenko et al* concluded there was a potential for the average organization to more than double its decision effectiveness. Their measurements were based on 4 factors they named: decision quality, i.e., in hindsight, if fully and timely implemented, how well would decisions taken achieve their objectives (note: strictly, this sufficiently defines decision effectiveness, but the authors’ inclusion of more factors is well explained); decision speed, i.e., whether decisions are made in time for opportunities to be properly acted on, and how that speed compares to competitors’; decision yield, i.e., how well decisions were converted into action; and decision effort, i.e., the time, trouble, and expense required for each decision. By benchmarking these factors, the authors demonstrate how an organization can gauge the potential impact of improved decision-making methods and processes. *(Blenko et al, Decide and Deliver, 2010, Harvard Business Review Press) Use of DA directly allows the improvement of these 4 factors in a balanced way.
    4. How is intuition considered in decision analysis?
      The analysis tests our intuitions and employs them in a more considered, justifiable way.  Seymour Eptstein’s definition of intuition as “knowing without knowing how you know”, suggests why a simplistic reliance on intuition can be especially dangerous when high stakes are at play in complex decision contexts. Counterintuitive decisions have often been the best.
    5. Which decision-support software do you recommend?
      Good analysis, irrespective of level of sophistication,  can only be conducted by those who know which methodologies best apply to specific decision situations, and who understand the axioms, assumptions and limitations underlying these methodologies. Otherwise, it may happen that apparently elegant and informative, but in reality flawed modeling is conducted, misleading insights are drawn and poor decisions are made. MS Excel with some add-ons can be used very effectively.
    6. How complex is the mathematics involved in decision analysis?
      It is often possible to go very far in managerial DA by just using addition, multiplication and division. When more sophisticated mathematics has to be used by the analyst, he/she should be able to explain, in ordinary language, what the results mean and which assumptions were made. In the end, since much of the number-crunching is carried out by software, analysts and decision-makers gain time to focus on the insights generated by modeling and by the model outputs.
    7. Long term strategies should have inbuilt flexibility. How does DA treat this subject?
      Strategic decisions typically involve assumptions about unknown values, about how the future may evolve. Proper decision analysis carefully examines these assumptions in order to improve speculations about the future. In spite of that, assumptions may prove wrong. Given the unavoidable uncertainties in decisions whose implementation and consequences will extend over the long term, it is wise to commit to a strategy that can be fine-tuned or even adapted as uncertainties resolve themselves. Decision analysis guides the construction of such flexible, robust strategies. Examples of important tools in the generation of such strategies are scenario analysis and range forecasts.

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