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Decision-making can be especially complicated when aimed at different simultaneous objectives, or when affected by fundamental uncertainties on how the future may evolve, or by factors that cannot be directly quantified. Modeling and simulation, informed by decision science, can address these and other typical difficulties in complex decisions, thus allowing the decision-maker to test and compare his choices with more clarity and effectiveness.

As a specialist in decision modeling and simulation, I enhance people’s ability to

  • conceive, test and improve decision alternatives; besides improving the use of knowledge and information, modeling also improves the employment of intuition by allowing assumptions to be tested in an inexpensive virtual world;
  • forecast the value of alternative strategies taking into account the effect of uncertainty;
  • choose the best alternative or prioritize a portfolio of alternatives;
  • recognize important trends as these emerge, and accordingly adjust strategies in a timely manner;
  • convince decision stakeholders and implementers.

 (view an expanded version of the above list)

Contact me to discuss how your organization can achieve more value in a coming big decision. Read further to see why my decision support allows you to better capitalize on existing opportunities and to avoid unnecessary losses that commonly afflict organizations.

 Enhanced decision-making, more value achieved

A lack of focus on the quality of decision-making is responsible for significant losses in organizations of all sorts (see below). Suffered as actual costs and as opportunity costs that often go unnoticed, these losses are to a great extent avoidable with the discipline of decision analysis (DA). Arthur’s modeling and simulation uses the methodologies and tools comprised in DA. Informed by Decision Science, these techniques allow decision-makers to better explore, improve and compare their strategic alternatives with respect to costs, benefits and risks.

Many studies have examined the value of DA in practice. As an example, experts at the London School of Economics examined resource allocation methodologies and concluded there was an average 25% gain in efficiency when a decision-analytic approach was employed in place of prevailing approaches (Phillips, L. D.  and Bana e Costa, C. A., Transparent prioritization, budgeting and resource allocation with multi-criteria decision analysis and decision conferencing, Springer Science+Business Media, LLC 2007). Another indication of DA’s potential is given by a study of 1048 business decisions, mostly on M&A, organizational change, or expansion into new geographies, products, and services, which revealed that there was an up to 7 % increase in returns when judgmental bias was successfully addressed in decision-making (Lovallo and Sibony, The case for behavioral strategy,  McKinsey Quarterly, March 2010).

In a study of 760 organizations of diverse industries and sizes, most headquartered in the U.S., U.K., Germany, France, China and Japan, the authors concluded there was a potential for the average organization to more than double its decision effectiveness (Blenko et al, Decide and Deliver, 2010, Harvard Business Review Press).

Further reading: read about the significant loss of value that prevails in managerial decision making, and that is largely avoidable with the use of DA. (Some of these texts became best-sellers in business literature)

View video: Chevron’s vice-chairman testimonial of the importance of DA to manage risk and guide decision-making.

DA methodologies with which these and additional benefits can be obtained are described in the Services section.