Increasing data availability enables the use of advanced data science techniques, such as machine learning and process mining to make more accurate statistical forecasts. However, for some types of products, human intervention is still needed. In these cases, the planner has the essential task to provide his or her expertise to create a more accurate forecast. But, how can you define which of your products need human intervention?
How to decide which products need human intervention
At EyeOn, we use the ABC/XYZ classification as guidance for planners to decide which products to focus on. Products are classified based on their volatility, from ‘stable demand’ to ‘high volatile demand’, and on their importance for the total turnover, for example ‘A products’ are important, whereas ‘C products’ are less important.
This ABC/XYZ classification results in nine quadrants, which are classified in terms of the human involvement needed.
- For those products that are important in terms of turnover and relatively easy to forecast (light green), a human planner can review the statistical forecast provided.
- For products that are not very important in terms of turnover, or are stable and medium important (dark green), we recommend using statistical forecasting only. Human involvement is not recommended for these products.
- Lastly, for products that are important for the turnover and hard to forecast (dark purple), we recommend performing manual forecasting. Human planers can focus on these products to increase forecasting accuracy. The same holds for the products that are new to the market (NPI) or in their end of life (EOL).
Enable planners to focus and make future adjustments smarter
By automating the forecast for most of your products, you enable the planners to focus on key products where their expertise is most needed and has the highest impact. These focused adjustments performed by the planners can be used to see where planners add value and can be analyzed and used to make future adjustments even smarter!
If you would like to know more about human intervention in forecasting and increase your forecasting accuracy, feel free to reach out to Bregje van der Staak!
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