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Case study
Forecast accuracy was low and this impacted their forecasting capacity for the global commercial vehicles market. There was a dependency on building models in Excel and this resulted in suboptimal decisions being made.
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 now also had the capacity to interpret the impact of new regulations, and were able to detect market trends earlier now. due to ability to identify new and stronger market drivers.
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.