Case study

How a market leader in the commercial trucks industry improved their forecast accuracy by 57%.

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Their challenges

Lack of accurate forecasting

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.

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.

Key results

Saw a +57.72% forecast accuracy improvement

Continuous forecasting minimized error margin over a year.

Identified new & stronger leading indicators

They were now able to identify the indicators that were relevant to their production numbers.

Achieved a structured process for all markets

Established process that enabled and aligned all teams with one source of truth.

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.

They were now able to detect trend shifts on the market 1-2 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.

How was this done?

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

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