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Note: An upcoming webcast will feature this topic as well as an actual demand sensing project between one of the world’s largest CPG companies at the world’s largest retailer. Reserve your seat here!
This is part two in a series on demand sensing. Part one is here.
At some point most forecasting methods will hit the law of diminishing returns where the forecast accuracy will tend to flatten out, regardless of the formulae or analytics that are applied.
Mathematicians have traditionally approached forecasting based on various time series and curve fitting techniques which create a forecast based on data such as prior sales history, drawing on several years of data to provide insights into predictable seasonal patterns.
Supply chain professionals have also come to the realization that past sales can be a poor predictor of future sales, especially when considering the variations associated with capabilities like distributed order management.
Yet with today’s rapid pace of new product introduction and shortened product life cycles, having more than an 18 month sales history might be considered a luxury. Supply chain professionals have also come to the realization that past sales can be a poor predictor of future sales, especially when considering the variations associated with capabilities like distributed order management.
Couple those challenges with all the promotional activities in today’s markets, as well as the availability of increasingly precise and frequent data, and you can begin to understand why yesterday’s math is having difficulty solving today’s problems.
Recently, forward thinking companies have begun employing improved mathematical approaches that can leverage the movement toward real time and network-based business processes along with their increased data volumes to create a completely new ceiling for forecast accuracy.
I’ll discuss these points further in future posts. If you’re impatient though, I suggest you read the new white paper, What is Demand Sensing? Much More than a Demand Management Revolution.