Inventory simulation using Pandas DataFrame

Question

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),
 lead_time_dist = make_distribution(np.random.triangular,1,2,3),
 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]
 order_list[period].quantity, order_list[period].lead_time, order_list[period].sent = placeorder(df['final_inv_pos'][period], policy, lead_time_dist, 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
 df['lead_time'][period] = order_list[period].lead_time 
 return df
def placeorder(final_inv_pos, policy, lead_time_dist, period):
 """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)
 lead_time_dist -- distribution of lead time
 period -- actual period
 """
 lead_time = int(lead_time_dist())
 # Qs = if we hit the reorder point s, order Q units
 if policy['method'] == 'Qs' and \
 final_inv_pos <= policy['arguments']['s']:
 return policy['arguments']['Q'], lead_time, True
 # 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"""
 def __init__(self, quantity, lead_time, sent):
 self.quantity = quantity
 self.lead_time = lead_time
 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()
 ax = fig.add_subplot(111)
 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)
 lead_time_dist = make_distribution(np.random.triangular,1,4,5)
 period = 52
 df = make_data(period,10,demand_dist,lead_time_dist,Qs_policy)
 #df.to_csv("out.csv")
 make_plot(df, Qs_policy, period)
if __name__ == '__main__':
 simulate()

Answer 1

Nice piece of code. Are you calculating safety stock based on the std dev of the demand distribution? Couldn't find in the code - sorry if it is there.

If not not, that would be the natural next step.

The second step would be a simulation of inventory levels based on the policy you created in step one and two.

But you can go further and calculate the lost sales - that should involve bayesian calculations - and, given the cost of holding inventory, the money the company is saving by following your suggested policy: - reduced avg inventory (free working capital: one time only) - increased margin by selling more (every period)

Do a basic NPV calculation and you have a very solid number to show to any client.

• Javier Cárdenas: This was a long time ago. I made a toy web app later, you could play with it here. The code is here