def test_normal_distribution_mean(self):
""" Verifies mean and variance for random normal distribution.
"""
# arrange
app_dir = os.path.dirname(__file__, )
rel_path = 'supplychainpy/data.csv'
abs_file_path = os.path.abspath(os.path.join(app_dir, '..', rel_path))
# act
orders_analysis = model_inventory.analyse_orders_abcxyz_from_file(
file_path=abs_file_path,
z_value=Decimal(1.28),
reorder_cost=Decimal(5000),
file_type="csv")
sim = monte_carlo.SetupMonteCarlo(
analysed_orders=orders_analysis.orders, period_length=1)
for sku in orders_analysis.orders:
item = sku.orders_summary()
if item['sku'] == 'KR202-209':
# assertd
diff = abs(
float(item['average_order']) - float(
np.mean(sim.normal_random_distribution[0]['KR202-209']
[0][0])))
self.assertLess(diff, float(item['standard_deviation']))
def test_build_window(self):
"""Test building simulation window, a transactioin block
for the discrete events simulation."""
sim_dict = {}
period_length = 12
sim_collection = []
simulation = monte_carlo.SetupMonteCarlo(
analysed_orders=self.__orders_analysis)
random_demand = simulation.generate_normal_random_distribution(
period_length=period_length)
for sim_window in simulation.build_window(
random_normal_demand=random_demand,
period_length=period_length):
sim_dict = {
"index": "{:.0f}".format(sim_window.index),
"period": "{:.0f}".format(sim_window.position),
"sku_id": sim_window.sku_id,
"opening_stock": "{:.0f}".format(sim_window.opening_stock),
"demand": "{:.0f}".format(sim_window.demand),
"closing_stock": "{:.0f}".format(sim_window.closing_stock),
"delivery":
"{:.0f}".format(sim_window.purchase_order_receipt_qty),
"backlog": "{:.0f}".format(sim_window.backlog)
}
sim_collection.append([sim_dict])
self.assertEqual(len(sim_collection), 1)
def test_variance(self):
""" Verifies mean and variance for random normal distribution."""
sim = monte_carlo.SetupMonteCarlo(analysed_orders=self.__orders_analysis, period_length=1)
for sku in self.__orders_analysis:
item = sku.orders_summary()
if item['sku'] == 'KR202-209':
self.assertLess((float(item['standard_deviation']) - \
float(sim.normal_random_distribution[0]['KR202-209'][0][0])),
Decimal(item['standard_deviation']))
def setUp(self):
self.__skus = ['KR202-209', 'KR202-210', 'KR202-211']
self.__orders_analysis = 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",
length=12)
self.__sim = monte_carlo.SetupMonteCarlo(
analysed_orders=self.__orders_analysis, period_length=10)
def test_build_window(self):
app_dir = os.path.dirname(__file__, )
rel_path = 'supplychainpy/data.csv'
abs_file_path = os.path.abspath(os.path.join(app_dir, '..', rel_path))
orders_analysis = model_inventory.analyse_orders_abcxyz_from_file(
file_path=abs_file_path,
z_value=Decimal(1.28),
reorder_cost=Decimal(5000),
file_type="csv")
period_length = 12
sim_collection = []
for k in range(0, 1):
simulation = monte_carlo.SetupMonteCarlo(
analysed_orders=orders_analysis.orders)
random_demand = simulation.generate_normal_random_distribution(
period_length=period_length)
for sim_window in simulation.build_window(
random_normal_demand=random_demand,
period_length=period_length):
sim_dict = {
"index":
"{:.0f}".format(sim_window.index),
"period":
"{:.0f}".format(sim_window.position),
"sku_id":
sim_window.sku_id,
"opening_stock":
"{:.0f}".format(sim_window.opening_stock),
"demand":
"{:.0f}".format(sim_window.demand),
"closing_stock":
"{:.0f}".format(sim_window.closing_stock),
"delivery":
"{:.0f}".format(sim_window.purchase_order_receipt_qty),
"backlog":
"{:.0f}".format(sim_window.backlog)
}
sim_collection.append([sim_dict])
self.assertEqual(len(sim_collection), 1)
def run_monte_carlo(orders_analysis: list,
runs: int,
period_length: int = 12) -> list:
"""Runs monte carlo simulation.
Generates random distribution for demand over period specified and creates a simulation window for opening_stock,
demand, closing_stock, delivery and backlog for each sku in the data set. Creates a transaction summary window
for inventory movements.
Args:
orders_analysis (list): list of UncertainDemand objects, containing the results of inventory analysis.
period_length (int): The number of periods define the simulation window e.g. 12 weeks, months etc.
runs (int): The number of times to run the simulation.
Returns:
list: A list containing the transaction summary for each period.
[[{'closing_stock': '0', 'backlog': '240', 'po_quantity': '6009', 'po_raised': 'PO 49', 'revenue':
'1145799', 'opening_stock': '1700', 'index': '9', 'period': '1', 'shortage_units': '240.4612',
'po_received': '', 'delivery': '0', 'shortage_cost': '48621', 'demand': '1940',
'quantity_sold': '1699', 'sku_id': 'KR202-217'}]
[{'closing_stock': '240', 'backlog': '852', 'po_quantity': '6380', 'po_raised':
'PO 59', 'revenue': '0', 'opening_stock': '0', 'index': '9', 'period': '2',
'shortage_units': '611.5989', 'po_received': '', 'delivery': '0', 'shortage_cost': '123665',
'demand': '612', 'quantity_sold': '0', 'sku_id': 'KR202-217'}]
[{'closing_stock': '0', 'backlog': '383', 'po_quantity': '6152', 'po_raised':
'PO 69', 'revenue': '162070', 'opening_stock': '240', 'index': '9', 'period': '3',
'shortage_units': '383.4993', 'po_received': '', 'delivery': '0', 'shortage_cost': '77543',
'demand': '624', 'quantity_sold': '240', 'sku_id': 'KR202-217'}]
[{'closing_stock': '4463', 'backlog': '383', 'po_quantity': '1689', 'po_raised': 'PO 79',
'revenue': '2749508', 'opening_stock': '0', 'index': '9', 'period': '4',
'shortage_units': '0.000000', 'po_received': 'PO 49', 'delivery': '6010',
'shortage_cost': '0', 'demand': '1547', 'quantity_sold': '4079', 'sku_id': 'KR202-217'}]
[{'closing_stock': '8002', 'backlog': '0', 'po_quantity': '0', 'po_raised': '',
'revenue': '5393458', 'opening_stock': '4463', 'index': '9', 'period': '5',
'shortage_units': '0.000000', 'po_received': 'PO 59', 'delivery': '6381',
'shortage_cost': '0', 'demand': '2841', 'quantity_sold': '8002', 'sku_id': 'KR202-217'}]
[{'closing_stock': '13594', 'backlog': '0', 'po_quantity': '0', 'po_raised': '',
'revenue': '9162457', 'opening_stock': '8002', 'index': '9', 'period': '6',
'shortage_units': '0.000000', 'po_received': 'PO 69', 'delivery': '6153',
'shortage_cost': '0', 'demand': '561', 'quantity_sold': '13594', 'sku_id': 'KR202-217'}]
[{'closing_stock': '13144', 'backlog': '0', 'po_quantity': '0', 'po_raised': '',
'revenue': '8859056', 'opening_stock': '13594', 'index': '9', 'period': '7',
'shortage_units': '0.000000', 'po_received': 'PO 79', 'delivery': '1690',
'shortage_cost': '0', 'demand': '2140', 'quantity_sold': '13144', 'sku_id': 'KR202-217'}]
[{'closing_stock': '11666', 'backlog': '0', 'po_quantity': '0', 'po_raised': '',
'revenue': '7862688', 'opening_stock': '13144', 'index': '9', 'period': '8',
'shortage_units': '0.000000', 'po_received': '', 'delivery': '0', 'shortage_cost': '0',
'demand': '1478', 'quantity_sold': '11665', 'sku_id': 'KR202-217'}]
[{'closing_stock': '8542', 'backlog': '0', 'po_quantity': '0', 'po_raised': '',
'revenue': '5757540', 'opening_stock': '11666', 'index': '9', 'period': '9',
'shortage_units': '0.000000', 'po_received': '', 'delivery': '0', 'shortage_cost': '0',
'demand': '3123', 'quantity_sold': '8542', 'sku_id': 'KR202-217'}]
[{'closing_stock': '7832', 'backlog': '0', 'po_quantity': '0', 'po_raised': '',
'revenue': '5278665', 'opening_stock': '8542', 'index': '9', 'period': '10',
'shortage_units': '0.000000', 'po_received': '', 'delivery': '0', 'shortage_cost': '0',
'demand': '710', 'quantity_sold': '7831', 'sku_id': 'KR202-217'}]
[{'closing_stock': '5379', 'backlog': '0', 'po_quantity': '389', 'po_raised': 'PO 149',
'revenue': '3625634', 'opening_stock': '7832', 'index': '9', 'period': '11', 'shortage_units':
'0.000000', 'po_received': '', 'delivery': '0', 'shortage_cost': '0', 'demand': '2453',
'quantity_sold': '5379', 'sku_id': 'KR202-217'}]
[{'closing_stock': '3799', 'backlog': '0', 'po_quantity': '1969', 'po_raised': 'PO 159',
'revenue': '2560610', 'opening_stock': '5379', 'index': '9', 'period': '12',
'shortage_units': '0.000000', 'po_received': '', 'delivery': '0', 'shortage_cost': '0',
'demand': '1580', 'quantity_sold': '3799', 'sku_id': 'KR202-217'}],...]
"""
Transaction_report = []
# add shortage cost,
for k in range(0, runs):
simulation = monte_carlo.SetupMonteCarlo(
analysed_orders=orders_analysis)
random_demand = simulation.generate_normal_random_distribution(
period_length=period_length)
for sim_window in simulation.build_window(
random_normal_demand=random_demand,
period_length=period_length):
sim_dict = {
"index":
"{}".format(sim_window.index),
"period":
"{}".format(sim_window.position),
"sku_id":
sim_window.sku_id,
"opening_stock":
"{}".format(round(sim_window.opening_stock)),
"demand":
"{}".format(round(sim_window.demand)),
"closing_stock":
"{}".format(round(sim_window.closing_stock)),
"delivery":
"{}".format(round(sim_window.purchase_order_receipt_qty)),
"backlog":
"{:.0f}".format(sim_window.backlog),
"po_raised":
"{}".format(sim_window.po_number_raised),
"po_received":
"{}".format(sim_window.po_number_received),
"po_quantity":
"{:.0f}".format(int(sim_window.purchase_order_raised_qty)),
"shortage_cost":
"{:.0f}".format(Decimal(sim_window.shortage_cost)),
"revenue":
"{:.0f}".format(sim_window.revenue),
"quantity_sold":
"{:0.0f}".format(sim_window.sold),
"shortage_units":
"{:.0f}".format(sim_window.shortage_units)
}
Transaction_report.append([sim_dict])
return Transaction_report
def random_demand(orders_analysis=None, period_length=None)->list:
""""""
simulation = monte_carlo.SetupMonteCarlo(analysed_orders=orders_analysis)
r_demand = simulation.generate_normal_random_distribution(period_length=period_length)
return r_demand