Case study

Here's how a tier-1 market leader in the heavy duty truck industry, serving well over 100 international markets, successfully improved their forecast accuracy by 57%.

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Their challenges

Lack of accurate forecasting

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.

Aligning forecasting globally

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.

Meet market shifts

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?

An improved forecast process
where each subprocess is intentional towards improving long-term capacity planning

1. Identification of leading indicators

2. Model benchmarking

3. Utilized a smart weighting scheme

4. Took seasonal patterns into consideration

What was the impact?

Achieved a double-digit
MAPE forecast accuracy improvement

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.

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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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