Forecast Accuracy: What's the magic number?

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.

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The Supply Chain of the Future is Here. Are you ready?

You’re sitting on a balcony, scanning the news on your mobile. You tap the app of your favorite local coffee house to order your caffeinated beverage of choice. Based on your preferences, the app recommends an exotic coffee sourced from Indonesia, and you decide to try it. Ten minutes later, you hear a distinctive buzz coming towards you. You take your double latte from the drone hovering at your elbow, sip and sigh. Life is good.

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Markdown Optimization Kicks Off Savings for Athletic Apparel Factory Outlets

Aging inventory and shrinking clearance margins don’t just affect traditional retail stores—factory outlets are equally susceptible. We recently completed an engagement for an international athletic apparel company that demonstrates the significant revenue lift that factory outlets can realize through pricing analytics and markdown optimization.

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Forecast Validation & Time Travel

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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Lowering the Barriers to Understanding Analytics

Without question, the worst analytical results are those that are never acted upon. Imagine a company that invests significant capital (both human and financial) into an analytics project. Afterwards, the insights are ignored and never used to drive a decision. Why does waste like this occur regularly?

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