Stites & Associates, LLC
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Evidence-Based Decision-Making

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Stites & Associates, LLC Approach to Evaluating Technology:

Successful Technology Development begins with an objective evaluation of existing technology. Frequently the data supporting technology decisions are inconclusive or have not been effectively translated into financial performance. It is crucial that all the assumptions supporting technology decision are:

  1. Well defined and understood,
  2. Meet minimum criteria for statistical reliability and
  3. Are combined in a financial model that indicates economic sustainability with a reasonable level of confidence

Such evaluations require management and decision skills that are not very common. It first requires a knowledge of the science of gathering and evaluating data (scientific, marketing, engineering, safety and financial), and then combining those data with operational processes. Realistic models must be created so that variability in the data can be “propagated” throughout the entire business process. That is the only objective way to evaluate the financial risks associated with a technology development program.

A first approximation of the validity of the business model can frequently be achieved through a straightforward “propagation of error” calculation. When the relationships between revenues, costs and risks are fairly simple (especially linear relationships of normally distributed variables), a straightforward summing of weighted variances will give a reasonable estimate of the overall risk.

As the Technology Development Process continues and financial consequences become more serious (e.g. thinking about building a plant), the financial models need to be improved. This often involves the addition of variables that are not normally distributed and including relationships between factors and results that are not linear. Combining disparate factors such as product price projections, yields, cost of money, costs of raw materials and failure rates, along with the associated variances, quickly becomes a model too complex to evaluate with simple propagation of error calculations. This frequently requires the use of simulation techniques to achieve useful and reliable results.

Even small errors in predictions can result in huge losses, especially for long-term, high capital projects. SALLC brings together a wide variety of statistical, scientific, engineering, database and financial skills to build useful models that give managers, owners and stakeholders in depth insight into the payouts and risks associated with a Technology Development Project.

The SALLC approach to Technology Evaluation is another example application of the SALLC commitment to Evidence-Based Decision-Making.