def calculateTurn(inventory):
'''
Parameters
----------
inventory : TYPE list
DESCRIPTION. list of inventory values
Returns
-------
turn : TYPE float
DESCRIPTION. daily TURN index
'''
#define inventory from movements
movements = movementfunctionfromInventory(inventory)
movements = movements.dropna()
#calculate the average outbound quantity per day
out_qty_day = -np.sum(
movements[movements['QUANTITY'] < 0]['QUANTITY']) / len(movements)
#calculate average inventory quantity
I_t_avg = np.nanmean(inventory)
if I_t_avg > 0:
turn = out_qty_day / I_t_avg
else:
turn = np.nan
return turn
def calculateCOI(inventory):
'''
Parameters
----------
inventory : TYPE list
DESCRIPTION. list of inventory values
Returns
-------
COI_in : TYPE float
DESCRIPTION. daily COI index IN
COI_out : TYPE float
DESCRIPTION. daily COI index OUT
'''
#define inventory from movements
movements = movementfunctionfromInventory(inventory)
movements = movements.dropna()
pop_in, pop_out, _, _ = calculatePopularity(movements['QUANTITY'])
#calculate daily COI
I_t_avg = np.nanmean(inventory)
if I_t_avg > 0:
COI_in = pop_in / I_t_avg
COI_out = pop_out / I_t_avg
else:
COI_in = COI_out = np.nan
return COI_in, COI_out
def updateTURN(D_SKUs):
#create result columns
D_SKUs['TURN'] = np.nan
for index, row in D_SKUs.iterrows():
#select inventory curve
I_t = D_SKUs.loc[index]['INVENTORY_QTY']
#calculate the popularity
movements = movementfunctionfromInventory(I_t)
movements = movements.dropna()
if len(movements) > 0:
TURN = calculateTurn(I_t)
#update the dataframe
D_SKUs.at[index, 'TURN'] = TURN
return D_SKUs
def updateFourieranalysis(D_SKUs):
#create result columns
D_SKUs['FOURIER_CARRIER'] = np.nan
D_SKUs['FOURIER_PERIOD'] = np.nan
for index, row in D_SKUs.iterrows():
#select inventory curve
I_t = D_SKUs.loc[index]['INVENTORY_QTY']
#calculate the popularity
movements = movementfunctionfromInventory(I_t)
movements = movements.dropna()
if len(movements) > 0:
carrier, period = fourierAnalysisInventory(I_t)
#update the dataframe
D_SKUs.at[index, 'FOURIER_CARRIER'] = carrier
D_SKUs.at[index, 'FOURIER_PERIOD'] = period
return D_SKUs
def updatePopularity(D_SKUs):
#create result columns
D_SKUs['POP_IN'] = np.nan
D_SKUs['POP_OUT'] = np.nan
D_SKUs['POP_IN_TOT'] = np.nan
D_SKUs['POP_OUT_TOT'] = np.nan
for index, row in D_SKUs.iterrows():
#select inventory curve
I_t = D_SKUs.loc[index]['INVENTORY_QTY']
#calculate the popularity
movements = movementfunctionfromInventory(I_t)
movements = movements.dropna()
if len(movements) > 0:
POP_IN, POP_OUT, POP_IN_TOT, POP_OUT_TOT = calculatePopularity(
movements['QUANTITY'])
#update the dataframe
D_SKUs.at[index, 'POP_IN'] = POP_IN
D_SKUs.at[index, 'POP_OUT'] = POP_OUT
D_SKUs.at[index, 'POP_IN_TOT'] = POP_IN_TOT
D_SKUs.at[index, 'POP_OUT_TOT'] = POP_OUT_TOT
return D_SKUs