For consumer packaged goods companies serving retailers, forecasting the true demand for perishable products is the key to maximizing revenue while reducing the costs associated with salvaged goods. This can translate into millions of dollars of savings and additional revenue annually for some companies.
There are two extreme ends of the retail business model, each with its pros and cons. At one end all stores are virtually identical (think chains like Starbucks, Gap, Subway, etc.), which makes for simpler operations and a consistent, predictable customer experience. On the flip side are chains where the owner operator has a great deal of discretion on assortment, pricing and promotions. For example, retailers like Aeon Group in Japan, ICA in Sweden and Metro in Canada give significant control to store operators.
What is a reasonable forecast accuracy for my business?” I get asked that question all the time. People generally understand that forecasts cannot be 100% accurate, but they’re seeking that elusive magic number that represents a good forecast—is it 90%, 85%, 80%? The truth is that a single number cannot describe the quality of the forecast for every situation. The example that I like to use is predicting the number of days in a given week versus predicting the total rainfall in a given week.
What makes me an analyst and not just a prognosticator? I verify that my predictions can be trusted—that the number of bananas in my forecast for next month, for example, is a number that can be used to positively influence business decisions. But due to the nature of forecasting, we’re forced to wait for the future to evaluate my present forecasts. So how can we validate the effectiveness of current forecasting methods? It’s a simple matter of traveling back in time.
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