def holts_trend_corrected_exponential_smoothing_forecast_from_file(file_path: str, file_type: str, length: int,
alpha: float, gamma: float, **kwargs):
item_list = {}
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
item_list = (_data_cleansing.clean_orders_data_row(raw_data, length))
elif file_type == FileFormats.csv.name:
with open(file_path) as raw_data:
item_list = _data_cleansing.clean_orders_data_row_csv(raw_data, length=length)
else:
incorrect_file = "Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter."
raise Exception(incorrect_file)
for sku in item_list:
sku_id, unit_cost, lead_time, retail_price, quantity_on_hand = sku.get("sku_id"), sku.get("unit_cost"), sku.get(
"lead_time"), sku.get("retail_price"), sku.get("quantity_on_hand")
orders = [int(i) for i in sku.get("demand")]
if kwargs['optimise']:
log.log(logging.WARNING,
"An OPTIMISED Holts trend exponential smoothing forecast has been completed for SKU {}.".format(sku_id))
yield {sku_id: holts_trend_corrected_exponential_smoothing_forecast(demand=orders, alpha=alpha, gamma=gamma,
forecast_length=4, initial_period=18,
optimise=True)}
else:
log.log(logging.WARNING,
"A STANDARD Holts trend exponential smoothing forecast has been complete for SKU {}.".format(sku_id))
yield {sku_id: holts_trend_corrected_exponential_smoothing_forecast(demand=orders, alpha=alpha, gamma=gamma,
forecast_length=4, initial_period=18,
optimise=False)}
def _clean_file(file_type: str, file_path: str, interval_length: int) -> dict:
""" Cleans-up csv and txt files and validates the format.
Args:
file_type (str): 'csv' or 'txt' file type.
file_path (str) : Absolute path to file.
interval_length (int): The number of periods included in raw data.
Returns:
dict: Sanitised source data.
"""
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
item_list = (_data_cleansing.clean_orders_data_row(
raw_data, interval_length))
return item_list
elif file_type == FileFormats.csv.name:
with open(file_path, 'r') as raw_data:
item_list = _data_cleansing.clean_orders_data_row_csv(
raw_data, length=interval_length)
return item_list
else:
incorrect_file = "Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter."
raise Exception(incorrect_file)
def simple_exponential_smoothing_forecast_from_file(file_path: str, file_type: str, length: int,
smoothing_level_constant: float, forecast_length=5,
**kwargs) -> dict:
"""
Args:
file_path (str):
file_type (str):
length (int):
smoothing_level_constant (int):
forecast_length (int):
**optimise (bool) Optimisation flag for exponential smoothing forecast.
Returns:
"""
item_list = {}
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
item_list = (_data_cleansing.clean_orders_data_row(file=raw_data, length=length))
elif file_type == FileFormats.csv.name:
with open(file_path) as raw_data:
item_list = _data_cleansing.clean_orders_data_row_csv(file=raw_data, length=length)
else:
incorrect_file = "Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter."
raise Exception(incorrect_file)
for sku in item_list:
sku_id, unit_cost, lead_time, retail_price, quantity_on_hand = sku.get("sku_id"), sku.get("unit_cost"), sku.get(
"lead_time"), sku.get("retail_price"), sku.get("quantity_on_hand")
orders = [int(i) for i in sku.get("demand")]
if kwargs['optimise']:
log.log(logging.WARNING,
"An OPTIMISED simple exponential smoothing forecast has been completed for SKU {}.".format(sku_id))
yield {sku_id: simple_exponential_smoothing_forecast(demand=orders,
forecast_length=forecast_length,
smoothing_level_constant=smoothing_level_constant,
optimise=True)}
else:
log.log(logging.WARNING,
"A STANDARD simple exponential smoothing forecast has been completed for SKU {}.".format(sku_id))
yield {sku_id: simple_exponential_smoothing_forecast(demand=orders,
forecast_length=forecast_length,
smoothing_level_constant=smoothing_level_constant)}
def _clean_file(file_type: str, file_path: str, interval_length: int) -> dict:
""" Cleans-up csv and txt files and validates the format.
Args:
file_type (str): 'csv' or 'txt' file type.
file_path (str) : Absolute path to file.
interval_length (int): The number of periods included in raw data.
Returns:
dict: Sanitised source data.
"""
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
item_list = (_data_cleansing.clean_orders_data_row(raw_data, interval_length))
return item_list
elif file_type == FileFormats.csv.name:
with open(file_path, 'r') as raw_data:
item_list = _data_cleansing.clean_orders_data_row_csv(raw_data, length=interval_length)
return item_list
else:
incorrect_file = "Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter."
raise Exception(incorrect_file)
def holts_trend_corrected_exponential_smoothing_forecast_from_file(
file_path: str, file_type: str, length: int, alpha: float,
gamma: float, **kwargs) -> dict:
"""
Args:
file_path: Path to source file.
file_type: File type.
length: Number of periods in data source (Jan..Dec = 12).
alpha:
gamma:
**kwargs:
Returns:
"""
item_list = {}
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
item_list = (_data_cleansing.clean_orders_data_row(
raw_data, length))
elif file_type == FileFormats.csv.name:
with open(file_path) as raw_data:
item_list = _data_cleansing.clean_orders_data_row_csv(
raw_data, length=length)
else:
incorrect_file = "Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter."
raise Exception(incorrect_file)
for sku in item_list:
sku_id, unit_cost, lead_time, retail_price, quantity_on_hand = sku.get(
"sku_id"), sku.get("unit_cost"), sku.get("lead_time"), sku.get(
"retail_price"), sku.get("quantity_on_hand")
orders = [int(i) for i in sku.get("demand")]
if kwargs['optimise']:
log.log(
logging.WARNING,
"An OPTIMISED Holts trend exponential smoothing forecast has been completed for SKU {}."
.format(sku_id))
yield {
sku_id:
holts_trend_corrected_exponential_smoothing_forecast(
demand=orders,
alpha=alpha,
gamma=gamma,
forecast_length=4,
initial_period=18,
optimise=True)
}
else:
log.log(
logging.WARNING,
"A STANDARD Holts trend exponential smoothing forecast has been complete for SKU {}."
.format(sku_id))
yield {
sku_id:
holts_trend_corrected_exponential_smoothing_forecast(
demand=orders,
alpha=alpha,
gamma=gamma,
forecast_length=4,
initial_period=18,
optimise=False)
}
def simple_exponential_smoothing_forecast_from_file(
file_path: str,
file_type: str,
length: int,
smoothing_level_constant: float = 0.5,
forecast_length=5,
optimise: bool = True) -> dict:
"""Performs a simple exponential smoothing forecast on historical demand from file using a generator.
Args:
file_path (str): Path to source file.
file_type (str): File type.
length (int): Number of periods in data source (Jan..Dec = 12).
smoothing_level_constant (int): Alpha value for forecast.
forecast_length (int): Number of periods to extend the forecast.
optimise (bool): Optimisation flag for exponential smoothing forecast.
Returns:
dict: Simple exponential forecast.
"""
item_list = {}
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
item_list = (_data_cleansing.clean_orders_data_row(
file=raw_data, length=length))
elif file_type == FileFormats.csv.name:
with open(file_path) as raw_data:
item_list = _data_cleansing.clean_orders_data_row_csv(
file=raw_data, length=length)
else:
incorrect_file = "Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter."
raise Exception(incorrect_file)
for sku in item_list:
sku_id, unit_cost, lead_time, retail_price, quantity_on_hand = sku.get(
"sku_id"), sku.get("unit_cost"), sku.get("lead_time"), sku.get(
"retail_price"), sku.get("quantity_on_hand")
orders = [int(i) for i in sku.get("demand")]
if optimise:
log.log(
logging.WARNING,
"An OPTIMISED simple exponential smoothing forecast has been completed for SKU {}."
.format(sku_id))
yield {
sku_id:
simple_exponential_smoothing_forecast(
demand=orders,
forecast_length=forecast_length,
smoothing_level_constant=smoothing_level_constant,
optimise=True)
}
else:
log.log(
logging.WARNING,
"A STANDARD simple exponential smoothing forecast has been completed for SKU {}."
.format(sku_id))
yield {
sku_id:
simple_exponential_smoothing_forecast(
demand=orders,
forecast_length=forecast_length,
smoothing_level_constant=smoothing_level_constant)
}
def analyse_orders_abcxyz_from_file(file_path: str,
z_value: Decimal,
reorder_cost: Decimal,
file_type: str = FileFormats.text.name,
period: str = "month",
length: int = 12,
currency: str = 'USD') -> list:
"""
Analyse orders data from file and returns ABCXYZ analysis
Analyses orders data for a single sku, using the values from a file arranged in columns.The data should be arranged
in two columns, 1 for the period and the other for the corresponding data-point.
Args:
file_path (str): The path to the file containing two columns of data, 1 period and 1 data-point for 1 sku.
reorder_cost (Decimal): The average lead-time for the sku over the period represented by the data,
in the same unit.
length (int): The number of periods in the data-ser referenced from the second column of the row
onwards.
reorder_cost (Decimal): The cost to place a reorder. This is usually the cost of the operation divided by number
of purchase orders placed in the previous period.
z_value (Decimal): The service level required to calculate the safety stock
file_type (str): Type of 'file csv' or 'text'
period (str): The period of time the data points are bucketed into.
currency (str):
Returns:
list: An AbcXyz class object is returned.
Raises:
Exception: Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter.
Exception: Unspecified file type, Please specify 'csv' or 'text' for file_type parameter.
Examples:
>>> from decimal import Decimal
>>> from supplychainpy.model_inventory import analyse_orders_from_file_col
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> abc = analyse_orders_abcxyz_from_file(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
... z_value=Decimal(1.28),
... reorder_cost=Decimal(5000),
... file_type="csv")
"""
warnings.warn(
'Analyse orders function has been deprecated. Please use the analyse function',
DeprecationWarning)
analysed_orders_collection = []
item_list = {}
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
item_list = (_data_cleansing.clean_orders_data_row(
raw_data, length))
elif file_type == FileFormats.csv.name:
with open(file_path) as raw_data:
item_list = _data_cleansing.clean_orders_data_row_csv(
raw_data, length=length)
else:
incorrect_file = "Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter."
raise Exception(incorrect_file)
for sku in item_list:
orders = {}
sku_id, unit_cost, lead_time, retail_price, quantity_on_hand = sku.get(
"sku_id", "UNKNOWN_SKU"), sku.get("unit_cost"), sku.get(
"lead_time"), sku.get("retail_price"), sku.get(
"quantity_on_hand")
orders['demand'] = sku.get("demand")
total_orders = 0
if quantity_on_hand is None:
quantity_on_hand = 0.0
for order in orders['demand']:
total_orders += int(order)
analysed_orders = analyse_uncertain_demand.UncertainDemand(
orders=orders,
sku=sku_id,
lead_time=lead_time,
unit_cost=unit_cost,
reorder_cost=Decimal(reorder_cost),
z_value=Decimal(z_value),
retail_price=retail_price,
quantity_on_hand=quantity_on_hand,
currency=currency)
average_orders = analysed_orders.average_orders
reorder_quantity = analysed_orders.fixed_order_quantity
eoq = economic_order_quantity.EconomicOrderQuantity(
total_orders=float(total_orders),
reorder_quantity=float(reorder_quantity),
holding_cost=float(0.25),
reorder_cost=float(reorder_cost),
average_orders=average_orders,
unit_cost=float(unit_cost))
analysed_orders.economic_order_qty = eoq.economic_order_quantity
analysed_orders.economic_order_variable_cost = eoq.minimum_variable_cost
analysed_orders_collection.append(analysed_orders)
AbcXyz(analysed_orders_collection)
return analysed_orders_collection
def analyse_orders_from_file_row(file_path: str,
z_value: Decimal,
reorder_cost: Decimal,
retail_price: Decimal,
file_type: str = FileFormats.text.name,
period: str = "month",
length: int = 12,
currency: str = 'USD') -> list:
""" Analyse multiple SKUs from a file with data arranged by row.
Args:
file_path (file): The path to the file containing two columns of data, 1 period and 1 data-point for 1 sku.
reorder_cost (Decimal): The cost to place a reorder. This is usually the cost of the operation divided by number
of purchase orders placed in the previous period.
z_value (Decimal): The service level required to calculate the safety stock
file_type (str): Type of 'file csv' or 'text'
period (str): The period of time the data points are bucketed into.
length (int): The length of the period.
Returns:
list: A list of summaries containint
Raises:
Exception: Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter.
Exception: Unspecified file type, Please specify 'csv' or 'text' for file_type parameter.
Examples:
>>> from supplychainpy.model_inventory import analyse_orders_from_file_row
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> analysed_data = analyse_orders_from_file_row(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
... reorder_cost=Decimal(45),
... z_value=Decimal(1.28),
... file_type="csv",
... retail_price=Decimal(30),
... currency='USD')
"""
warnings.warn(
'Analyse orders function has been deprecated. Please use the analyse function',
DeprecationWarning)
orders = {}
analysed_orders_summary = []
analysed_orders_collection = []
item_list = {}
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
item_list = (_data_cleansing.clean_orders_data_row(
raw_data, length=length))
elif file_type == FileFormats.csv.name:
with open(file_path, 'r') as raw_data:
item_list = _data_cleansing.clean_orders_data_row_csv(
raw_data, length=length)
else:
unspecified_file = "Unspecified file type, Please specify 'csv' or 'text' for file_type parameter."
raise Exception(unspecified_file)
# maybe use an iterable instead of unpacking for constructor
try:
for sku in item_list:
sku_id = sku.get("sku_id")
unit_cost = sku.get("unit_cost")
lead_time = sku.get("lead_time")
quantity_on_hand = sku.get("quantity_on_hand")
if quantity_on_hand is None:
quantity_on_hand = 0.0
orders['demand'] = sku.get("demand")
analysed_orders = analyse_uncertain_demand.UncertainDemand(
orders=orders,
sku=sku_id,
lead_time=lead_time,
unit_cost=unit_cost,
reorder_cost=reorder_cost,
z_value=z_value,
retail_price=retail_price,
quantity_on_hand=quantity_on_hand,
currency=currency)
analysed_orders_collection.append(analysed_orders)
analysed_orders_summary.append(analysed_orders.orders_summary())
orders = {}
del analysed_orders
except KeyError:
print("The csv file is incorrectly formatted")
return analysed_orders_summary
def analyse_orders_from_file_col(file_path,
sku_id: str,
lead_time: Decimal,
unit_cost: Decimal,
reorder_cost: Decimal,
z_value: Decimal,
retail_price: Decimal,
file_type: str = FileFormats.text.name,
period: str = PeriodFormats.months.name,
currency='USD') -> dict:
""" Analyse orders from file arranged in a single column.
Analyses orders data for a single sku, using the values
from a file arranged in columns.The data should be arranged in two columns, 1 for the period and the other for the
corresponding data-point.
Args:
file_path (str): The path to the file containing two columns of data, 1 period and 1 data-point for 1 sku.
sku_id (str): The unique id of the sku.
lead_time (Decimal): The average lead-time for the sku over the period represented by the data, in the same unit.
unit_cost (Decimal): The unit cost of the sku to the organisation.
reorder_cost (Decimal): The cost to place a reorder. This is usually the cost of the operation divided by number of purchase orders placed in the previous period.
z_value (Decimal): The service level required to calculate the safety stock
retail_price (Decimal): The price at which the sku is retailed.
file_type (str): Type of 'file csv' or 'text'
period (int): The period of time the data points are bucketed into.
currency (str): The currency of the source data.
Returns:
dict: The summary of the analysis.
`{'ABC_XYZ_Classification': 'AX', 'reorder_quantity': '258', 'revenue': '2090910.44',
'average_order': '539', 'reorder_level': '813', 'economic_order_quantity': '277', 'sku': 'RR381-33',
'demand_variability': '0.052', 'economic_order_variable_cost': '29557.61',
'standard_deviation': '28', 'safety_stock': '51'}`
Raises:
Exception: Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter.
Exception: Unspecified file type, Please specify 'csv' or 'text' for file_type parameter.
Examples:
>>> from decimal import Decimal
>>> from supplychainpy.model_inventory import analyse_orders_from_file_col
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> # text file
>>> RX9304_43_analysis_txt = analyse_orders_from_file_col(file_path=ABS_FILE_PATH['PARTIAL_COL_TXT_SM'],
... sku_id='RX9304-43',
... lead_time=Decimal(2),
... unit_cost=Decimal(400),
... reorder_cost=Decimal(45),
... z_value=Decimal(1.28),
... file_type='text',
... retail_price=Decimal(30))
>>> #csv file
>>> RX9304_43_analysis_csv = analyse_orders_from_file_col(ABS_FILE_PATH['PARTIAL_COL_CSV_SM'], 'RX9304-43',
... reorder_cost=Decimal(45),
... unit_cost=Decimal(400),
... lead_time=Decimal(45),
... z_value=Decimal(1.28),
... file_type="csv",
... retail_price=Decimal(30))
"""
orders = {}
file_type_processed = ''
# warnings.warn('Analyse orders function has been deprecated. Please use the analyse function', DeprecationWarning)
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
orders = _data_cleansing.clean_orders_data_col_txt(raw_data)
file_type_processed = 'txt'
elif file_type == FileFormats.csv.name:
with open(file_path) as raw_data:
orders = _data_cleansing.clean_orders_data_col_csv(raw_data)
file_type_processed = 'csv'
else:
raise Exception(
'Unspecified file type, Please specify \'csv\' or \'text\' for file_type parameter.'
)
d = analyse_uncertain_demand.UncertainDemand(orders=orders,
sku=sku_id,
lead_time=lead_time,
unit_cost=unit_cost,
reorder_cost=reorder_cost,
z_value=z_value,
period=period,
retail_price=retail_price,
currency=currency)
log.debug('Raw data columnar from {} analysed'.format(file_type_processed))
return d.orders_summary()
def analyse_orders_abcxyz_from_file(file_path: str, z_value: Decimal, reorder_cost: Decimal,
file_type: str = FileFormats.text.name,
period: str = "month", length: int = 12, currency: str = 'USD') -> list:
"""
Analyse orders data from file and returns ABCXYZ analysis
Analyses orders data for a single sku, using the values from a file arranged in columns.The data should be arranged
in two columns, 1 for the period and the other for the corresponding data-point.
Args:
file_path (str): The path to the file containing two columns of data, 1 period and 1 data-point for 1 sku.
reorder_cost (Decimal): The average lead-time for the sku over the period represented by the data,
in the same unit.
length (int): The number of periods in the data-ser referenced from the second column of the row
onwards.
reorder_cost (Decimal): The cost to place a reorder. This is usually the cost of the operation divided by number
of purchase orders placed in the previous period.
z_value (Decimal): The service level required to calculate the safety stock
file_type (str): Type of 'file csv' or 'text'
period (str): The period of time the data points are bucketed into.
currency (str):
Returns:
list: An AbcXyz class object is returned.
Raises:
Exception: Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter.
Exception: Unspecified file type, Please specify 'csv' or 'text' for file_type parameter.
Examples:
>>> from decimal import Decimal
>>> from supplychainpy.model_inventory import analyse_orders_from_file_col
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> abc = analyse_orders_abcxyz_from_file(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
... z_value=Decimal(1.28),
... reorder_cost=Decimal(5000),
... file_type="csv")
"""
warnings.warn('Analyse orders function has been deprecated. Please use the analyse function', DeprecationWarning)
analysed_orders_collection = []
item_list = {}
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
f = open(file_path, 'r')
item_list = (_data_cleansing.clean_orders_data_row(f, length))
elif file_type == FileFormats.csv.name:
f = open(file_path)
item_list = _data_cleansing.clean_orders_data_row_csv(f, length=length)
else:
incorrect_file = "Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter."
raise Exception(incorrect_file)
for sku in item_list:
orders = {}
sku_id, unit_cost, lead_time, retail_price, quantity_on_hand = sku.get("sku_id", "UNKNOWN_SKU"), sku.get(
"unit_cost"), sku.get(
"lead_time"), sku.get("retail_price"), sku.get("quantity_on_hand")
orders['demand'] = sku.get("demand")
total_orders = 0
if quantity_on_hand is None:
quantity_on_hand = 0.0
for order in orders['demand']:
total_orders += int(order)
analysed_orders = analyse_uncertain_demand.UncertainDemand(orders=orders,
sku=sku_id,
lead_time=lead_time,
unit_cost=unit_cost,
reorder_cost=Decimal(reorder_cost),
z_value=Decimal(z_value),
retail_price=retail_price,
quantity_on_hand=quantity_on_hand, currency=currency)
average_orders = analysed_orders.average_orders
reorder_quantity = analysed_orders.fixed_order_quantity
eoq = economic_order_quantity.EconomicOrderQuantity(total_orders=float(total_orders),
reorder_quantity=float(reorder_quantity),
holding_cost=float(0.25),
reorder_cost=float(reorder_cost),
average_orders=average_orders,
unit_cost=float(unit_cost))
analysed_orders.economic_order_qty = eoq.economic_order_quantity
analysed_orders.economic_order_variable_cost = eoq.minimum_variable_cost
analysed_orders_collection.append(analysed_orders)
AbcXyz(analysed_orders_collection)
return analysed_orders_collection
def analyse_orders_from_file_row(file_path: str, z_value: Decimal, reorder_cost: Decimal, retail_price: Decimal,
file_type: str = FileFormats.text.name,
period: str = "month", length: int = 12, currency: str = 'USD') -> list:
""" Analyse multiple SKUs from a file with data arranged by row.
Args:
file_path (file): The path to the file containing two columns of data, 1 period and 1 data-point for 1 sku.
reorder_cost (Decimal): The cost to place a reorder. This is usually the cost of the operation divided by number
of purchase orders placed in the previous period.
z_value (Decimal): The service level required to calculate the safety stock
file_type (str): Type of 'file csv' or 'text'
period (str): The period of time the data points are bucketed into.
length (int): The length of the period.
Returns:
list: A list of summaries containint
Raises:
Exception: Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter.
Exception: Unspecified file type, Please specify 'csv' or 'text' for file_type parameter.
Examples:
>>> from supplychainpy.model_inventory import analyse_orders_from_file_row
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> analysed_data = analyse_orders_from_file_row(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
... reorder_cost=Decimal(45),
... z_value=Decimal(1.28),
... file_type="csv",
... retail_price=Decimal(30),
... currency='USD')
"""
warnings.warn('Analyse orders function has been deprecated. Please use the analyse function', DeprecationWarning)
orders = {}
analysed_orders_summary = []
analysed_orders_collection = []
item_list = {}
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
item_list = (_data_cleansing.clean_orders_data_row(raw_data, length=length))
elif file_type == FileFormats.csv.name:
with open(file_path, 'r') as raw_data:
item_list = _data_cleansing.clean_orders_data_row_csv(raw_data, length=length)
else:
unspecified_file = "Unspecified file type, Please specify 'csv' or 'text' for file_type parameter."
raise Exception(unspecified_file)
# maybe use an iterable instead of unpacking for constructor
try:
for sku in item_list:
sku_id = sku.get("sku_id")
unit_cost = sku.get("unit_cost")
lead_time = sku.get("lead_time")
quantity_on_hand = sku.get("quantity_on_hand")
if quantity_on_hand is None:
quantity_on_hand = 0.0
orders['demand'] = sku.get("demand")
analysed_orders = analyse_uncertain_demand.UncertainDemand(orders=orders, sku=sku_id,
lead_time=lead_time,
unit_cost=unit_cost,
reorder_cost=reorder_cost, z_value=z_value,
retail_price=retail_price,
quantity_on_hand=quantity_on_hand,
currency=currency)
analysed_orders_collection.append(analysed_orders)
analysed_orders_summary.append(analysed_orders.orders_summary())
orders = {}
del analysed_orders
except KeyError:
print("The csv file is incorrectly formatted")
return analysed_orders_summary
def analyse_orders_from_file_col(file_path, sku_id: str, lead_time: Decimal, unit_cost: Decimal,
reorder_cost: Decimal, z_value: Decimal, retail_price: Decimal,
file_type: str = FileFormats.text.name,
period: str = PeriodFormats.months.name, currency='USD') -> dict:
""" Analyse orders from file arranged in a single column.
Analyses orders data for a single sku, using the values
from a file arranged in columns.The data should be arranged in two columns, 1 for the period and the other for the
corresponding data-point.
Args:
file_path (str): The path to the file containing two columns of data, 1 period and 1 data-point for 1 sku.
sku_id (str): The unique id of the sku.
lead_time (Decimal): The average lead-time for the sku over the period represented by the data, in the same unit.
unit_cost (Decimal): The unit cost of the sku to the organisation.
reorder_cost (Decimal): The cost to place a reorder. This is usually the cost of the operation divided by number of purchase orders placed in the previous period.
z_value (Decimal): The service level required to calculate the safety stock
retail_price (Decimal): The price at which the sku is retailed.
file_type (str): Type of 'file csv' or 'text'
period (int): The period of time the data points are bucketed into.
currency (str): The currency of the source data.
Returns:
dict: The summary of the analysis.
`{'ABC_XYZ_Classification': 'AX', 'reorder_quantity': '258', 'revenue': '2090910.44',
'average_order': '539', 'reorder_level': '813', 'economic_order_quantity': '277', 'sku': 'RR381-33',
'demand_variability': '0.052', 'economic_order_variable_cost': '29557.61',
'standard_deviation': '28', 'safety_stock': '51'}`
Raises:
Exception: Incorrect file type specified. Please specify 'csv' or 'text' for the file_type parameter.
Exception: Unspecified file type, Please specify 'csv' or 'text' for file_type parameter.
Examples:
>>> from decimal import Decimal
>>> from supplychainpy.model_inventory import analyse_orders_from_file_col
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> # text file
>>> RX9304_43_analysis_txt = analyse_orders_from_file_col(file_path=ABS_FILE_PATH['PARTIAL_COL_TXT_SM'],
... sku_id='RX9304-43',
... lead_time=Decimal(2),
... unit_cost=Decimal(400),
... reorder_cost=Decimal(45),
... z_value=Decimal(1.28),
... file_type='text',
... retail_price=Decimal(30))
>>> #csv file
>>> RX9304_43_analysis_csv = analyse_orders_from_file_col(ABS_FILE_PATH['PARTIAL_COL_CSV_SM'], 'RX9304-43',
... reorder_cost=Decimal(45),
... unit_cost=Decimal(400),
... lead_time=Decimal(45),
... z_value=Decimal(1.28),
... file_type="csv",
... retail_price=Decimal(30))
"""
orders = {}
file_type_processed = ''
# warnings.warn('Analyse orders function has been deprecated. Please use the analyse function', DeprecationWarning)
if check_extension(file_path=file_path, file_type=file_type):
if file_type == FileFormats.text.name:
with open(file_path, 'r') as raw_data:
orders = _data_cleansing.clean_orders_data_col_txt(raw_data)
file_type_processed = 'txt'
elif file_type == FileFormats.csv.name:
with open(file_path) as raw_data:
orders = _data_cleansing.clean_orders_data_col_csv(raw_data)
file_type_processed = 'csv'
else:
raise Exception('Unspecified file type, Please specify \'csv\' or \'text\' for file_type parameter.')
d = analyse_uncertain_demand.UncertainDemand(orders=orders, sku=sku_id, lead_time=lead_time,
unit_cost=unit_cost, reorder_cost=reorder_cost, z_value=z_value,
period=period, retail_price=retail_price, currency=currency)
log.debug('Raw data columnar from {} analysed'.format(file_type_processed))
return d.orders_summary()