def analyse_orders(data_set: dict, sku_id: str, lead_time: Decimal, unit_cost: Decimal, reorder_cost: Decimal,
z_value: Decimal, retail_price: Decimal, quantity_on_hand: Decimal, currency: str = 'USD') -> dict:
"""Analyse orders data for one sku using a dictionary.
Analyses orders data for a single sku using the values in the data_set dict.
Args:
data_set (dict): The orders data for a specified period.
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 retail or standard price of the sku.
quantity_on_hand (Decimal): The quantity currently on hand as of analysis or retrieving data set.
currency (str): Currency of source raw 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:
ValueError: Dataset too small. Please use a minimum of 3 entries.
Examples:
>>> from supplychainpy.model_inventory import analyse_orders
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> from decimal import Decimal
>>> yearly_demand = {'jan': 75, 'feb': 75, 'mar': 75, 'apr': 75, 'may': 75, 'jun': 75, 'jul': 25,
... 'aug': 25, 'sep': 25, 'oct': 25, 'nov': 25, 'dec': 25}
>>>
>>> summary = analyse_orders(yearly_demand,
... sku_id='RX983-90',
... lead_time=Decimal(3),
... unit_cost=Decimal(50.99),
... reorder_cost=Decimal(400),
... z_value=Decimal(1.28),
... retail_price=Decimal(600),
... quantity_on_hand=Decimal(390))
"""
if len(data_set) > 2:
warnings.warn('The \'analyse_orders\' function has been deprecated. Please use the \'analyse\' function',
DeprecationWarning)
d = analyse_uncertain_demand.UncertainDemand(orders=data_set, 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)
else:
raise ValueError('Data set too small. Please use a minimum of 3 entries.')
return d.orders_summary_simple()
def test_order_constraint(self):
"""Test the constraint for performing the calculation with less than five data points"""
orders_placed = [25, 25, 25]
with self.assertRaises(Exception):
analyse_uncertain_demand.UncertainDemand(orders=orders_placed,
sku='Rx493-90',
lead_time=Decimal(4),
unit_cost=Decimal(40),
reorder_cost=Decimal(400),
retail_price=Decimal(600),
currency='USD')
def _analyse_orders(orders: dict, sku_id: str, lead_time: Decimal,
unit_cost: Decimal, reorder_cost: Decimal,
z_value: Decimal, retail_price: Decimal,
quantity_on_hand: Decimal, currency: str,
total_orders: float, backlog: Decimal) -> UncertainDemand:
""" Performs several types of common inventory analysis on the raw demand data. Including safety stock, reorder
levels.
Args:
orders (dict):
sku_id (str):
lead_time (Decimal):
unit_cost (Decimal):
reorder_cost (Decimal):
z_value (Decimal):
retail_price (Decimal):
quantity_on_hand (Decimal):
currency (str):
total_orders (float):
Returns:
UncertainDemand: UncertainDemand objects.
"""
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,
backlog=backlog)
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
return analysed_orders
def setUp(self):
self._z_value = Decimal(1.28)
self._lead_time = Decimal(4)
self._holding_cost_percentge = Decimal(0.25)
self._retail_price = Decimal(5000)
self._unit_cost = Decimal(55)
self._reorder_cost = Decimal(450)
self._quantity_on_hand = 1000
self._backlog = 0
self._data_set = {
'jan': 25,
'feb': 25,
'mar': 25,
'apr': 25,
'may': 25,
'jun': 25,
'jul': 75,
'aug': 75,
'sep': 75,
'oct': 75,
'nov': 75,
'dec': 75
}
self._orders_analysis = model_inventory.analyse(
file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
z_value=Decimal(1.28),
reorder_cost=Decimal(5000),
file_type="csv",
length=12,
currency='USD')
self._uncertain_demand = analyse_uncertain_demand.UncertainDemand(
self._data_set,
sku='Rx493-90',
lead_time=self._lead_time,
reorder_cost=self._reorder_cost,
z_value=self._z_value,
holding_cost=self._holding_cost_percentge,
retail_price=self._retail_price,
unit_cost=self._unit_cost,
currency='USD',
quantity_on_hand=self._quantity_on_hand,
backlog=0)
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(currency: str,
z_value: Decimal = 1.28,
reorder_cost: Decimal = 10,
interval_length: int = 12,
interval_type: str = 'month',
**kwargs):
""" Performs several types of common inventory analysis on the raw demand data. Including safety stock, reorder
levels.
Args:
currency (Decimal): Currency the raw data is stored in.
z_value (Decimal): Service level requirement
reorder_cost (Decimal): Cost to place a reorder based on the cost of operations over the period
interval_length (Decimal): The number of periods the demand data is grouped into e.g. 12 (months)
interval_type (Decimal): months, weeks,days, years, quarters.
Keyword Args:
**df(pd.DataFrame): Pandas DataFrame containing the raw data in the correct format.
**file_path (str) : Path to csv or txt file containing formatted data.
Returns:
dict/pd.DataFrame: Analysis of inventory profile.
Examples:
>>> from supplychainpy.model_inventory import analyse
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> from decimal import Decimal
>>> analysed_data = analyse(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
... z_value=Decimal(1.28),
... reorder_cost=Decimal(400),
... retail_price=Decimal(455),
... file_type='csv',
... currency='USD')
>>> analysis = [demand.orders_summary() for demand in analysed_data]
>>> # Example using pandas DataFrame.
>>> import pandas as pd
>>> raw_df = pd.read_csv(ABS_FILE_PATH['COMPLETE_CSV_SM'])
>>> analyse_kv = dict(
... df=raw_df,
... start=1,
... interval_length=12,
... interval_type='months',
... z_value=Decimal(1.28),
... reorder_cost=Decimal(400),
... retail_price=Decimal(455),
... file_type='csv',
... currency='USD'
... )
>>> analysis_df = analyse(**analyse_kv)
"""
analysed_orders = UncertainDemand
analysed_orders_collection = []
pool = multiprocessing.Pool(4)
collect_kwargs = []
try:
if kwargs is not None:
if kwargs.get('file_path') == UNKNOWN:
converted_data = list(np.array(kwargs['df']))
for i in converted_data:
sku_id, orders, unit_cost, lead_time, retail_price, quantity_on_hand, backlog = \
_unpack_row(row=i, interval_length=interval_length)
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_orders(
orders=orders,
sku_id=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,
total_orders=total_orders,
backlog=backlog)
analysed_orders_collection.append(analysed_orders)
AbcXyz(analysed_orders_collection)
analysis_df = _convert_to_pandas_df(analysed_orders_collection)
return analysis_df
elif kwargs['file_path'] is not UNKNOWN:
analysed_orders_collection = []
item_list = _clean_file(file_type=kwargs['file_type'],
file_path=kwargs['file_path'],
interval_length=interval_length)
for sku in item_list:
orders = {}
sku_id, unit_cost, lead_time, retail_price, quantity_on_hand, backlog = sku.get("sku_id","UNKNOWN_SKU"), \
sku.get("unit_cost"), \
sku.get("lead_time"), \
sku.get("retail_price"), \
sku.get("quantity_on_hand"), \
sku.get("backlog")
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)
kwargs = {
'orders': orders,
'sku_id': 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,
'total_orders': total_orders,
'backlog': float(backlog)
}
collect_kwargs.append(kwargs)
analysed_orders = [
pool.apply(_analyse_orders,
args=(x['orders'], x['sku_id'], x['lead_time'],
x['unit_cost'], x['reorder_cost'],
x['z_value'], x['retail_price'],
x['quantity_on_hand'], x['currency'],
x['total_orders'], x['backlog']))
for x in collect_kwargs
]
#mapfunc = partial(_analyse_orders, **kwargs)
#analysed_orders = pool.map(mapfunc, 0)
for x in analysed_orders:
analysed_orders_collection.append(x)
AbcXyz(analysed_orders_collection)
# analysis = [i.orders_summary() for i in analysed_orders_collection]
return analysed_orders_collection
elif kwargs['raw_data']:
if kwargs['raw_data'] > 2:
d = analyse_uncertain_demand.UncertainDemand(
orders=kwargs['raw_data'],
sku=kwargs['sku_id'],
lead_time=kwargs['lead_time'],
unit_cost=kwargs['unit_cost'],
reorder_cost=reorder_cost,
z_value=z_value,
retail_price=kwargs['raw_data'],
quantity_on_hand=kwargs['quantity_on_hand'],
currency=currency)
else:
raise ValueError(
"Data set too small. Please use a minimum of 3 entries."
)
return d.orders_summary_simple()
except KeyError as e:
print(e)
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()