Note from the Editor, Spring 2017 issue of Foresight

Since our first issue in 2005, Foresight has strived to serve up articles that unite the scholarship of our field’s academic researchers with the perspectives of experienced organizational practitioners, all the time emphasizing lessons learned—and sometimes those lessons are learned the hard way.

The buzz in this issue begins with a couple of “b” words. Bias is the first. Many business forecasters and researchers consider that forecast bias is not only a serious problem but one that can be significantly mitigated. One source of bias is statistical, resulting from models that haven’t adequately reflected reality. More serious perhaps is the organizational bias emerging from conflicts between component units (silos), frequently the result of misplaced incentives.

And that, says Roy Batchelor in our lead article, Earnings Forecasts: The Bias Is Back, is precisely the problem:

Financial analysts consistently overestimate the annual earnings of U.S. companies, a dramatic forecasting bias that is only partly corrected later in the year.… Both inside their organisations and in the market for their forecasts, these analysts are subject to many incentives to publish figures that do not represent the statistically best guess at company earnings.

The second “b” is big data.

Our feature section this issue, Big Data and Supply Chain Forecasting, deconstructs the argument that big data will fundamentally change the way we forecast by shifting the focus from the product to the consumer, as well as from time-series projections to causal models. Here we print six commentaries from noted forecasting, S&OP, and supply-chain thought leaders.

  • Shaun Snapp asks Is Big Data the Silver Bullet for Supply-Chain Forecasting?
  • Mike Gilliland probes into Customer vs. Item Forecasting.
  • Chris Gray writes on Becoming Responsible Consumers … of Big Data.
  • Stephan Kolassa wonders whether it’s Big Data or Big Hype?
  • Niels van Hove goes into industry specifics in Big Data, A Big Decision.
  • Peter Catt links Big Data and the Internet of Things.

By now, we all know that predictions about the 2016 U.S. presidential election missed the mark, as many did earlier in the year over the Brexit vote. A previously reliable source of election forecasts had come from prediction markets, where bets are placed on individual candidates. But not in 2016. Foresight Prediction Markets Editor Andreas Graefe looks at what went wrong, and why, in Prediction Market Performance in the 2016 U.S. Presidential Election. He raises the possibilities of market manipulation, participant misunderstanding, and bettors’ systematic bias.

Niels van Hove has spent many years studying S&OP performance and its intimate bond with corporate culture. In a prior Foresight article (Summer 2016), Niels wrote about the importance of proper modes of communication for S&OP success. Now he puts the microscope on individual mind-sets and behaviors in his article How to Shape a Company Culture with S&OP:

While effective S&OP can thrive in the right company culture, S&OP itself can influence and shape that culture. S&OP leaders need to articulate goals that include clear expectations on behaviors. Doing so will not only improve S&OP effectiveness but also enable S&OP to play an active role in improving employee attitudes and satisfaction.

Trustworthiness is universally recognized as a key behavior in S&OP success. In a Commentary on Niels’ article, M. Sinan Gonul discusses the key drivers of trust between parties in an organization and emphasizes that trust is “fragile and brittle.”

Handle trust as you would a crystal champagne glass: once it’s broken, there may be no putting it back together!

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