Hi Nicolas, to calculate the bias in case of many unpredictable new products introduction and phase out, the latter are not considered because the time series are not available in the forecast period while the former are considered and demand is greatly overestimated, as the 2 cases do not compensate, the overall bias on the product portfolio is always positive. How do you recommend managing this case? Should I consider as a forecast error also the forecast 0 on NPI even if their time series were not available at the time of the forecast?
Hello, this is quite a complicated case. You could compute bias in two different flavors, with and without NPIs. The idea is that you don't want to bring the message that bias is close to 0% whereas obviously, you missed 10% of NPIs. But the responsibility for these NPIs might lie with another team.
Thank you Nicolas. Actually what happens is that if I include NPIs I get an unbiased forecast overall because NPIs compensate unpredictable phase out products, while if I don’t include NPIs I get a positive bias (globally on the product portfolio). Maybe global metrics in this case are not meaningful and I should look at the distribution of Bias/MAE of product time series.
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We are struggling to get forecast accuracy to reach 50%. Wondering what other companies are at.
Hi Nicolas, to calculate the bias in case of many unpredictable new products introduction and phase out, the latter are not considered because the time series are not available in the forecast period while the former are considered and demand is greatly overestimated, as the 2 cases do not compensate, the overall bias on the product portfolio is always positive. How do you recommend managing this case? Should I consider as a forecast error also the forecast 0 on NPI even if their time series were not available at the time of the forecast?
Hello, this is quite a complicated case.
You could compute bias in two different flavors, with and without NPIs.
The idea is that you don't want to bring the message that bias is close to 0% whereas obviously, you missed 10% of NPIs.
But the responsibility for these NPIs might lie with another team.
Thank you Nicolas. Actually what happens is that if I include NPIs I get an unbiased forecast overall because NPIs compensate unpredictable phase out products, while if I don’t include NPIs I get a positive bias (globally on the product portfolio). Maybe global metrics in this case are not meaningful and I should look at the distribution of Bias/MAE of product time series.
Thanks Nicolas for amazing content.. how can i join your live sessions?
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