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Forecasting: Let’s Start by Asking the Right Questions

Supply Chain Shaman

This is especially true for forecasting. I find that too many companies try to buy forecasting software through a Request For Proposal (RFP). How forecastable is your product set? What is your Forecast Value Add (FVA) by product segment? E.G. 2015 and 2016 would be used to try to forecast 2017.).

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Why CPG Demand Forecasting Has Hit a Ceiling

ToolsGroup

Most CPG companies have hit a demand forecasting ceiling. And complexity creates a challenge of how to forecast accurately when faced with new items, new channels and demand shaping. Recent evidence strongly suggests that traditional forecasting techniques in this environment have reached their limits and hit a ceiling.

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A New Decade: Give Science A Chance

Supply Chain Shaman

In the orbit chart in Figure 2, you first will notice that both VF is less resilient than Nike and that from 2015 to 2019, Nike outperformed VF. Their supply chain results in 2019 are worse than in 2015, and this was before the pandemic. The SAS forecasting system implemented in 2019 was not tested for model accuracy.

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Why the Forecast for Supply Chain Planning has been Only Partly Cloudy

ToolsGroup

in Cloud-Computing Promises Fall Short (The Wall Street Journal, October 2015). Systems of Engagement (SOEs) are systems that depend on collaboration, gathering and orchestrating information outside the enterprise.

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New Year’s Resolution #3 for Supply Chain Planners: Find the Causes of Forecast Errors

Demand Solutions

Forecast error: so many companies complain about it. Most companies just accept forecast error as a given in their supply chain processes. They figure any forecast is bound to be wrong, but at least it will give them a ballpark idea of what to expect in the months to come. We made a bad forecast. Here’s an example: 1.

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Get Your COV On….

Supply Chain Shaman

It is useful to analyze demand data to understand “forecastability” and randomness. Not all data is forecastable, and not all demand optimization engines are equal. The more forecastable the data set, the easier it is to find an optimizer. In Figure 1, I share data from the risk management study of 2015.

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Illusively Complex – Effective Approach to Mixing Judgment and Statistics in Forecasting

Arkieva

I was fortunate to be an original member and had the opportunity to work extensively on all key components including DM (1996-97) – created an estimate of demand – a forecast. One of the critical challenges was finding the “best way” to mix and match judgment and statistical forecasts, where there are typically multiple judgments.