>> FAQs

  1. Doesn’t decision analysis slow down decision-making?
  2. What is a good decision?
  3. How will the use of decision analysis impact my organization’s performance?
  4. How is intuition considered in decision analysis?
  5. Use of decision analysis software is explained by their vendors. How knowledgeable in decision analysis must one be, before choosing and using such software?
  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?
  8. How is information confidentiality dealt with?

 

    1. Doesn’t decision analysis slow down decision-making?
      This is a falce notion. In reality decision analysis can be an effective way of tackling excessive analysis, dithering and rework, which are common problems in complicated decisions involving high stakes. 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, and because it accordingly only seeks to analyse information that matters. Also, DA’s scalability (DA can be scaled up or down), allows time constraints to be rigorously taken into account. The key is to balance the necessity for analysis with existing constraints. Besides these benefits to decision speed, agility in the employment of DA significantly increases with experience, and with the employment of models that can be repeatedly used or adapted.
    2. What is a good decision?
      Decision effectiveness, i.e., how well a decision reaches its objectives, can only be evaluated retrospectively. There is an element of luck in it. Nevertheless, while this element of luck makes it impossible for decision effectiveness to be directly controlled, it can be indirectly managed via decision-making quality. This discussion may seem trivial, but it deals with an important missunderstanding. A decision is good or bad before the outcome happens, and this outcome may be good in a bad decision or bad in a good decision. Luck interferes, and while learning good decision-making won’t eliminate the possibility of getting bad outcomes, it will minimize this possibility whilst boosting the possibility of getting excellent results. And so, what is a good decision? Put simply, it is one in which the best alternative is chosen as a result of sound decision-making skills (not by luck) that allow the choice to accurately reflect the decision-makers’ preferences. The development of these “sound decision-making skills” can methodically be achieved with the discipline of decision analysis.
    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?
      Decision analysis is a complement to intuitive judgment. The analysis challenges and tests intuition, and produces more considered, justifiable and explainable decisions. Seymour Eptstein’s definition of intuition as “knowing without knowing how you know”, suggests why a heavy reliance on intuition is especially dangerous when complex decision situations are addressed. Counterintuitive decisions have often been the best.
    5. Use of decision analysis software is explained by their vendors. How knowledgeable in decision analysis must one be, before choosing and using such software?
      Decision analysis software is a tool that supports good analysis, but good analysis 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. It is therefore advisable that adequate knowledge of decision analysis be acquired before, not after, any investment in such software. Otherwise there is a risk that apparently elegant and informative, but in reality flawed models are built, misleading insights are drawn and poor decisions are made. To avoid bad investment and potentially grave consequences, it is worth consulting an independent decision analyst. Using only Excel for less sophisticated but flawless modeling is certainly wiser than using sophisticated software in an improper way.
    6. How complex is the mathematics involved in decision analysis?
      DA has evolved from a rather abstract mathematical subject—decision theory—into a practical discipline increasingly adopted by busy managers, most of whom although experts in their fields, are not mathematicians or statisticians. Also, since the number-crunching (which in some cases can be sophisticated) is carried out by software, what becomes important is that good model construction and use are understood. By using software as Excel the analyst gains time to focus on the insights generated by modeling and by the model outputs.
    7. Mid and 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 such uncertainties, especially in decisions to be implemented in the medium and long terms, it is wiser 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.
    8. How is information confidentiality dealt with?
      All confidential information is strictly kept confidential, as stipulated in clauses 5.2, 5.3 and 5.4 of the General Terms & Conditions for Consultancy Services provided by Arthur J. Drucker.

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