In one of my job as Supply Chain practicioner we build a process where we would look at differences between forecasts (one week vs previous week), trying to understand: * why did the forecast change for this product or product category or country or... * decide if we need to change our plans based on this change much more efficient than recalculating a full distribution plan every week
Absolutely. Yet, not all numerical recipes are equal in their capacity to generate results that make sense - even for the data scientists who understand the algorithms. This is why we have a whole process referred to as 'white boxing' at Lokad to address this. Best regards, Joannes
In one of my job as Supply Chain practicioner we build a process where we would look at differences between forecasts (one week vs previous week), trying to understand:
* why did the forecast change for this product or product category or country or...
* decide if we need to change our plans based on this change
much more efficient than recalculating a full distribution plan every week
Absolutely. Yet, not all numerical recipes are equal in their capacity to generate results that make sense - even for the data scientists who understand the algorithms. This is why we have a whole process referred to as 'white boxing' at Lokad to address this. Best regards, Joannes