Ah, MAPE. Or Mean Absolute Percentage Error. The metric that supply chain professionals love to hate. The villain of the forecasting world, blamed for everything from poor accuracy to global warming (okay, maybe not that last one). But is MAPE really the problem, or are we just looking for a scapegoat?
Team I-Plan recently attended an industry event in Utrecht. Jonathan Karelson, a member of the Harvard Business Review Advisory Council, gave a great talk on Forecast Value Add, throwing in a “No one ever got fired for using MAPE!” – an amusing nod to the heated online debates about its shortcomings. He’s probably right, of course, and MAPE remains one of the most widely understood and utilised metrics in industry. So what’s the issue?
Why All the Hate?
MAPE (short for Mean Absolute Percentage Error) has a reputation for being “unfair” when volumes are low, “misleading” when demand is intermittent, and “failing” when actuals are zero. And yes, it’s true, MAPE isn’t perfect. But here’s the kicker: neither is any other metric. Every accuracy measure has its quirks, its blind spots, its awkward moments at the supply chain party.
The Reality Check
MAPE is no more or less accurate than its cousins, MAE, RMSE, or the ever-so-sophisticated Weighted MAPE. They all tell a story, but none of them tells the whole story. If you’re relying on one metric to judge forecast performance, you’re basically trying to understand Shakespeare by reading a single quote. (Spoiler: it won’t end well.)
MAPE’s Secret Superpower
Despite its flaws, MAPE is simple. It’s easy to explain, easy to calculate, and easy to compare across products and time periods. That simplicity is why it’s survived decades of supply chain evolution. It’s not the enemy; it’s a tool. And like any tool, it works best when used correctly and in context.
So, what’s the Takeaway?
Stop the MAPE hate. Embrace a balanced approach. Use multiple metrics. Understand the limitations. And most importantly, remember that the goal isn’t to win a metric beauty contest, it’s to make better decisions.
MAPE isn’t perfect, but neither are we. And that’s okay.
At I-Plan, we take forecasting accuracy seriously. MAPE is part of the picture, but it’s never the whole story. That’s why we present MAPE with a suite of other metrics to give a more complete view of performance. Each metric highlights different aspects of forecast behaviour, helping us understand not just how far off we were, but why.
And that’s only looking backwards! Our platform offers Expert Selection, a machine learning and AI-driven tool that evaluates patterns, weighs the strengths of different predictive models, and recommends the best forecast approach for your business. The result? Smarter decisions, better accuracy, and a forecasting process that’s built for real-world complexity.
Because in the end, it’s not about defending a metric… It’s about delivering results.
Ready to talk forecasting?
Reach out to us to start a conversation, even if it’s just to have a good moan about MAPE (we never judge!). We’d love to explore how I-Plan can help you turn those frustrations into actionable insights.
MAPE (Mean Absolute Percentage Error) refers to a key accuracy metric used to evaluate the performance of forecasting models. It measures the average absolute percentage difference between forecasted values and actual outcomes, providing a straightforward way to understand forecast accuracy across different scales.
This metric is widely used because it expresses error as a percentage, making it easy to interpret and compare across products, regions, or time periods.
