Summer 2017


    1. Misconceptions, Missteps, and Bad Practices in S&OP by Chris Gray and John Dougherty
      In this first of a multipart article, Chris Gray and John Dougherty— authors of the pathbreaking book Sales & Operations Planning – Best Practices— review their firsthand experience with S&OP practices in organizations, attempting to flush out what they’ve found to be among the most blatant misconceptions and missteps, the sort that do real harm to the potential of S&OP.

    2. The Theta Method by Fotios Petropoulos, and Kostas Nikolopoulos
      In this, Foresight’s newest forecasting-methods tutorial, we offer a step-by-step description of Theta, the top-performing method in the M3 Competition. Foresight’s Forecasting Support Systems Editor, Fotios Petropoulos, and Kostas Nikolopoulos—one of the creators of Theta—demonstrate how the methodology can be applied in practice.

    3. Longevity: Blessing or Curse? by Ira Sohn
      Motivated by the publication of The 100-Year Life by Lynda Gratton and Andrew Scott, Foresight Strategic Forecasting Editor Ira Sohn questions the sacred tenet of economics that “more is preferred to less,” at least in terms of longevity. Here, Ira provides an overview of the factors that make it challenging to deal with longevity risk from our rapidly growing proportion of elderly citizenry. What seems clear is that current public policies are not up to the challenges.
    4. Communicating Forecasts to the C-Suite: A Six-Step Survival Guide by Todd Tomalak
      Todd Tomalak, Vice President for Research and Head of Building Products at John Burns Real Estate Consulting, writes that most forecasting practitioners have extensive training in technical skills but end up having to “learn the hard way” about discussing forecasts with CEO/CFOs. Here, he offers guidance on how to talk forecasts with the C-Suite.
    5. The Quest for a Better Forecast Error Metric: Measuring More than the Average Error by Stefan de Kok
      In an article in the Summer 2006 issue of Foresight, Tom Willemain presented the argument that

While most forecast-error metrics are averages of forecast errors, for intermittent demand series we should focus on the demand distribution and assess forecast error at each distinct level of demand. Accordingly, the appropriate accuracy metric will assess the difference between the actual and forecasted distributions of demand.

The issue has not had much traction over the years since—except perhaps in energy studies —and we do not find error metrics based on full distributions present in forecasting support systems. Now Stefan de Kok picks up the argument and extends it to develop an error metric— Total Percentage Error—that measures the full range of uncertainty in our forecasts and, in doing so, both enables better inventory-planning and provides a more comprehensive way to gauge the quality of the forecast.

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