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Spring 2017

Special Feature: Big Data and Supply Chain Forecasting

Is Big Data the Silver Bullet for Supply-Chain Forecasting? by Shaun Snapp
Shaun Snapp takes on the frequently heard argument these days that big data will alter the nature of supply chain forecasting for the better. Here he lays out the reasons for his dissent. Shaun’s points are supported and extended in the six commentaries that follow his article. While the bottom line seems to be don’t fall for big hype, the discussion illuminates critical issues in forecasting for the supply chain, including the roles of product vs. customer forecasting, the nature of causal forecasting models, and whether these models can supersede traditional time series forecasts.

How to Shape a Company Culture with S&OP by Niels van Hove
Niels van Hove argues that while effective S&OP can thrive in the right company culture, the process itself can influence and shape that culture. He calls for S&OP leaders to articulate goals that include clear expectations on behaviors. Doing so will not only improve effectiveness but also enable S&OP to play an active role in improving employee attitudes and satisfaction.

Commentary on “How to Shape a Company Culture with S&OP”: Building and Maintaining Trust by M. Sinan Gonul
In “How to Shape a Company Culture with S&OP,” Niels van Hove asserts that, within organizations, the feeling of “trust” is paramount for fostering a prosperous company culture and driving effective S&OP. I think his arguments on the benefits of trust in achieving positive organizational outcomes and feelings of psychological well-being/safety are predominantly true. However, reading about all these desirable aspects may give the false impression that, once attained in an organization, trust is almost automatically built up and effortlessly maintained.


  1. Earnings Forecasts: The Bias Is Back by Roy Batchelor
    A recent study reported in The Economist reveals that, early in each calendar year, financial analysts consistently overestimate the annual earnings of U.S. companies, a dramatic forecasting bias that is only partly corrected later in the year. In this article, Foresight’s Financial Forecasting Editor, Roy Batchelor, probes the sources of these “earnings surprises” and concludes that it all has to do with misplaced incentives. It’s a problem not very different from the kind every business forecaster faces when “the boss” implicitly (or explicitly) requests a more favorable result from the forecast than the data can permit.
  2. Prediction Market Performance in the 2016 U.S. Presidential Election by Andreas Graefe
    The 2016 U.S. presidential election was a particularly bad case for prediction markets, as was the Brexit vote in the UK. In theory, these markets should be very effective in aggregating the information of individual forecasters into an overall market forecast. Because the individual participants must put “skin in the game,” they are expected to be more diligent about making use of relevant information than participants in surveys who are simply asked what they think will happen.
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