Case study
Their Excel-based forecasting methodology resulted in low forecast accuracy and an inability to detect trend shifts. This subsequently hindered them from optimizing capacity planning
There was a requirement to extend forecasting coverage to more markets for both demand and production needs. Due to a heavy reliance on a scarce team covering multiple markets globally, this was not possible.
They needed to have the data to adapt production capacity efficiently and ahead of time to meet market turns and shifts, and to date, this was not possible.
What was implemented?
1. Identification of leading indicators
2. Model benchmarking
3. Utilized a smart weighting scheme
4. Took seasonal patterns into consideration
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What was the impact?
Using Indicio, they forecasted the number of registered trucks in Europe weighing over 6 tons, and they achieved a Mean Absolute Percentage Error (MAPE) forecast accuracy improvement,
In comparison to their internal forecasted data, this was a 57.7% forecast accuracy improvement.
They were now able to detect trend shifts on the market1-2 months earlier than before. This gave the manufacturer enough time to adjust production before a trend shift, resulting in significant savings when the market went down and the ability to meet demand when the market went up.
Whether your goal is to increase market share or safeguard against volatility,
the road to making decisions confidently lies in generating accurate forecasts you can trust.
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