# Finding Outliers in Resource Forecast Data

Unsure if this is the correct place to place this, please close if so.

I'm a workforce analyst at a large retail company, I own and maintain all the forecasting for our retail stores. This is based on Product Sales Forecast which I run through a model which has various tasks (such as changing items on a shelve or selling a car alarm for example) binned into timed categories, e.g

Item A takes 30 seconds, so 500 product forecasts will allow you (0.5 * 500) or 4.1 hours which are then further binned into Weekly allocations.

Now, as, for each iteration of this model I've run, there are always outliers (read mistakes), such as Store Y has no product volume to Product Group X in Week 22, which is a mistake from the finance team.

My senior has said this has always been the case for the 8 or so years he's been with the company.

Now, in my head, I assume I would be able to find outliers based on historical data using some sort of statistical method, however, I have no idea where to start, my data is as follows and is around 10 million rows of data.

import pandas as pd
import numpy as np

data =  [19,21,24,18,3]
pg = ['PG','ZF','AA','GG','ZF']
location = ['AA_1','AA_1','AA_2','AA_2','AA_2']
weeks = [1,1,2,2,2]

df = pd.DataFrame({'Location' : location,
'productGroup' : pg,
'Week' : weeks,
'productVolumes' : data })
print(df)

Location  productGroup    Week    productVolumes
0   AA_1    PG  1   19
1   AA_1    ZF  1   21
2   AA_2    AA  2   24
3   AA_2    GG  2   18
4   AA_2    ZF  2   3


in your expert opinions, what would be the best method to use?