Inventory simulation using Pandas DataFrame

I've been learning Python like for a year and started learning a little about pandas DataFrame. I made this little program to practice all the concepts and would like to hear your suggestions for improvement.

GOAL

This program should plot how the inventory levels behave according some initials conditions: number of periods, demand distribution, lead time distribution and the chosen control policy (see types of policy).

DEFINITIONS AND FORMULAS

inventory position = on hand inventory + inventory on order

lead time = the amount of time between the placing of an order and the receipt of the goods ordered.

NOTATION

s reorder point, Q order quantity, R review period, S order-up-to level

TYPES OF POLICY

We use four different base policy types:

1. (s, Q)-policy. Whenever the inventory position reaches the reorder point s or drops below this point, an order of size Q is triggered.
2. (s, S)-policy. Under this policy, the size of the order – triggered whenever the inventory position reaches or drops below the reorder point s – equals the difference between the order-up-to level S and the current inventory position.
3. (R, S)-policy. The review period R indicates the length of the interval in-between two reviews. At these points of time, the inventory position is observed and the difference between the inventory position and the order-up-to level S is ordered.
4. (R, s, S)-policy. Every R intervals the inventory position is checked. An order is triggered only if the inventory position has reached or dropped below the reorder point s. Then the order size equals the difference between the order-up-to level S and the current inventory position.

import matplotlib.pyplot as plt
import numpy as np
from pandas import DataFrame

# The distribution factory
def make_distribution(function,*pars):
def distribution():
return function(*pars)
return distribution

def make_data(periods=52,
initial_inventory = 10,
demand_dist = make_distribution(np.random.normal,2,1),
policy = {'method':'Qs', 'arguments': {'Q':3,'s':5}}):
""" Return a Pandas dataFrame that contains the details of the inventory simulation.

Keyword arguments:
periods           -- numbers of periods of the simulation (default 52 weeks)
initial_inventory -- initial inventory for the simulation
demand_dist       -- distribution of the demand (default triangular min=1, mode=2, max=3)
lead_time_dist    -- distribution of the lead time (default triangular min=1, mode=2, max=3)
policy            -- dict that contains the policy specs (default = {'method':'Qs', 'arguments': {'Q':3,'s':5}})
"""

# Create zero-filled Dataframe
period_lst = np.arange(periods) # index
header = ['initial_inv_pos', 'initial_net_inv', 'demand', 'final_inv_pos',
'final_net_inv', 'lost_sales', 'avg_inv', 'order', 'lead_time'] # columns
df = DataFrame(index = period_lst, columns = header).fillna(0)

# Create a list that will store each period order
order_list = [Order(quantity=0, lead_time=0, sent=False) for x in range(periods)]

# Fill DataFrame
for period in period_lst:
if period == 0:
df['initial_inv_pos'][period] = initial_inventory
df['initial_net_inv'][period] = initial_inventory
else:
df['initial_inv_pos'][period] = df['final_inv_pos'][period-1] + order_list[period - 1].quantity
df['initial_net_inv'][period] = df['final_net_inv'][period-1] + pending_order(order_list, period)
df['demand'][period] = int(demand_dist())
df['final_inv_pos'][period] = df['initial_inv_pos'][period] - df['demand'][period]
df['final_net_inv'][period] = df['initial_net_inv'][period] - df['demand'][period]
if df['final_net_inv'][period] < 0:
df['lost_sales'][period] = abs(df['final_net_inv'][period])
df['final_net_inv'][period] = 0
else:
df['lost_sales'][period] = 0
df['avg_inv'][period] = 0
df['order'][period] = order_list[period].quantity

return df

"""Place the order acording the inventory policy:

Keywords arguments:
final_inv_pos    -- final inventory position of period
policy           -- chosen policy Reorder point (Qs, Ss) or Periodic Review (RS, Rss)
period           -- actual period
"""

# Qs = if we hit the reorder point s, order Q units
if policy['method'] == 'Qs' and \
final_inv_pos <= policy['arguments']['s']:
# Ss = if we hit the reorder point s, order S - final inventory pos
elif policy['method'] == 'Ss' and \
final_inv_pos <= policy['arguments']['s']:
return policy['arguments']['S'] - final_inv_pos, lead_time, True
# RS = if we hit the review period and the reorder point S, order S - final inventory pos
elif policy['method'] == 'RS' and \
period%policy['arguments']['R'] == 0 and \
final_inv_pos <= policy['arguments']['S']:
return policy['arguments']['S'] - final_inv_pos, lead_time, True
# RSs = if we hit the review period and the reorder point s, order S - final inventory pos
elif policy['method'] == 'RSs' and \
period%policy['arguments']['R'] == 0 and \
final_inv_pos <= policy['arguments']['s']:
return policy['arguments']['S'] - final_inv_pos, lead_time, True
# If the conditions arent satisfied, do not order
else:
return 0, 0, False

def pending_order(order_list, period):
"""Return the order that arrives in actual period"""
indices = [i for i, order in enumerate(order_list) if order.sent == True]
sum = 0
for i in indices:
if period - (i + order_list[i].lead_time +1) == 0:
sum += order_list[i].quantity

return sum

class Order(object):
"""Object that stores basic data of an order"""
self.quantity = quantity
self.sent = sent # True if the order is already sent

def make_plot(df, policy, period):
#Plot
plt.rcParams['figure.figsize'] = 15,4 #define the fig size
fig = plt.figure()

y1 = df['final_inv_pos']
l1, = plt.plot(y1, 'k', linewidth=1.2, drawstyle='steps', label='Final Inv')

if policy['method'] == 'Qs':
title = 'Simulation Policy = (Q: {Q}, s: {s})'.format(**policy['arguments'])
y2 = policy['arguments']['s']*np.ones(period)
l2, = plt.plot(y2, 'r:', label='Reorder point')
elif policy['method'] == 'Ss':
#TODO
pass

t = ax.set_title(title)

ax.tick_params(axis='both', which='major', labelsize=8)
plt.xticks(np.arange(period))
plt.ylim(bottom=0)
plt.legend(loc='best', prop={'size':10})
plt.xlabel("Periods")
plt.ylabel("Inventory Level")
plt.show()

def simulate():
#parameters of simulation
Qs_policy   = {'method':'Qs', 'arguments': {'Q':3,'s':5}}
demand_dist = make_distribution(np.random.normal,3,1)
period = 52

#df.to_csv("out.csv")

make_plot(df, Qs_policy, period)

if __name__ == '__main__':
simulate()