From the bar plot above, we will conclude that the brand new workshop’s outbound quantity has higher after pursuing a brand new manufacturing device, each on roll quantity and general material weight.
Since virtually all of the knitting machines run day and night time with out prevent except for manufacturing unit holiday, which happens yearly as soon as and lasts about one month (now not in 2018 for the reason that new workshop opened after Spring Festival). We can apply the day by day productiveness of the entire workshop as consistent. To simplify the issue additional, the potency of every manufacturing device might be thought to be the similar. So we want to know the way many workdays in 2018 and 2019, respectively.
Excluding new 12 months manufacturing unit holiday that lasts normally a couple of month, however together with 2 days ahead of the primary cargo after each and every lengthy holiday, the brand new workshop has run by conference, there are nonetheless 309 workdays in 2018 and 333 workdays in 2019.
Besides, we nonetheless want to to find out the preliminary stock and ultimate stock within the warehouse. Unluckily, the chief of the warehouse hasn’t ever recorded the precise selection of the stocked textile rolls after an afternoon of labor. He may just handiest more or less keep in mind that on the finish of the remaining day in December the warehouse just about part complete. The same old capability of the warehouse is round 1000 gray textile rolls. For emergency, it could possibly cling 2000 gray textile rolls however it’ll affect the day by day operations of the manufacturing unit and build up further prices. When the warehouse stock reaches 2500, the warehouse is extremely overloaded and the overfull stacked items may even compete with other people for “oxygen”. So right here I’ve to make a daring assumption that at the night time of 2019.12.31 after the remaining truck left, the overall stocked completed items have been 500 rolls.
productiveness method derivation:
approximative Daily productiveness= annual productiveness/ workdays
annual productiveness= ultimate stock+ annual general delivery- preliminary stock
every device’s productiveness in 2018= every device’s productiveness in 2019
approximative Daily productiveness= every device’s productiveness* quantity of machines
Therewith, we will calculate every device’s productiveness like the next:
(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”.
So, the complicated query is transformed right into a easy algebraic equation(a Basic Linear Equation).
32609+43109–0+500= (6*309+7*333)*X
X=18.2122 manner every device’s productiveness is 18.2122 rolls/ day.
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).
It’s simple that we get an information body recording the day by day sum of the delivered items with Pandas (the comparable steps are detailed within the Story): https://towardsdatascience.com/view-a-chinese-factory-from-the-data-aspect-with-python-ad698461f40a
Here I skip the rationale of this phase and get the desired knowledge body without delay.
The got knowledge body from the excel document is known as new_df, by multiplying -1, we get the outbound_df, the place minus stands for outbound logistics.
now, we will have to upload in every date the day by day completed items amount, the calculation result’s if truth be told stock within the warehouse.
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)*127.49round_warehouse_df= warehouse_df.astype('int32')
print(round_warehouse_df)
After printing the brand new knowledge body “round_warehouse_df”, we see the desired effects on our display.
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.
