Data analytics helps warehouse management | by Yefeng Xia | Dec, 2020

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symbol by Author: Total annual shipped items in rolls and general weight within the 12 months 2018 and 2019

(annual general shipping in 2018+ annual general shipping in 2019 -initial stock+ ultimate stock) = (quantity of machines in 2018* workdays in 2018+ quantity of machines in 2019* workdays in 2019)* every device’s productiveness

The handiest unknown worth is every device’s productiveness “X”.

Daily productiveness in 2018 is 109.27 rolls/ day and Daily productiveness in 2019 is 127.49 rolls/ day

Based at the above-mentioned cases, we will ascertain that within the 12 months 2018 the manufacturing unit produced items 129.27 rolls professional day and in 2019 produced items 127.49 rolls professional day and all through Chinese new 12 months manufacturing unit holiday 2019.1.20- 2019.2.22 (except for 2 days ahead of the primary cargo after each and every lengthy holiday, as above stated).

symbol by Author: a work of outbound shipping paperwork of the brand new workshop in 2019. the place a inexperienced sprint line lies there’s a massive time hole, ahead of and after manufacturing unit holiday.
symbol by Author: knowledge body transformed from excel document
symbol by Author: outbound logistics knowledge body
import datetime
for i,day in enumerate(day_list):
if day.start_time == datetime.datetime(2019,1,20):
vacation_start= i
if day.12 months == 2018:
warehouse_df.iloc[i,0]=sum(outbound_df.iloc[0:i+1,0])+109.27*(i+3)
elif day.12 months == 2019 and day.start_time < datetime.datetime(2019,1,20):
warehouse_df.iloc[i,0]=sum(outbound_df.iloc[0:i+1,0])+109.27*309+(i-307+3)*127.49
elif day.start_time > datetime.datetime(2019,1,20) and day.start_time < datetime.datetime(2019,2,22):
warehouse_df.iloc[i,0]=sum(outbound_df.iloc[0:i+1,0])+109.27*309+(vacation_start-307+3)*127.49
elif day.start_time > datetime.datetime(2019,2,22):
warehouse_df.iloc[i,0]=sum(outbound_df.iloc[0:i+1,0])+109.27*309+(i-307+3-34)*127.49

round_warehouse_df= warehouse_df.astype('int32')
print(round_warehouse_df)

symbol by Author: warehouse stock knowledge body, presentations what number of items stayed within the warehouse after an afternoon of manufacturing and shipping.

Similarly, we will plot the consequences with a well-designed taste, the place two immediately traces point out to us how is the present warehouse loading standing. Below the fairway line implies that the warehouse house is considerable, above the purple line warns of the overloaded warehouse.

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