Demand Forecasting means estimating future customer demand for a product or service based on historical data and relevant factors. Inventory Optimization is the strategic management of inventory levels to ensure that the right amount of goods is available at the right time to meet customer demand while minimizing costs. If you want to learn how to forecast demand and optimize inventory according to the forecasted demand, this article is for you. In this article, I’ll take you through the task of Demand Forecasting and Inventory Optimization using Python.
Demand Forecasting and Inventory Optimization: Process We Can Follow
Demand Forecasting involves predicting the quantity and pattern of customer orders, which is crucial for businesses to efficiently allocate resources, manage inventory, and plan production. Accurate demand forecasting enables companies to meet customer needs, avoid overstocking or understocking, and optimize their supply chain operations.
Inventory Optimization aims to strike a balance between having sufficient stock to meet demand without carrying excess inventory that ties up capital and storage space. Effective inventory optimization helps businesses reduce carrying costs, improve cash flow, and enhance customer satisfaction.
These concepts are especially relevant in retail, manufacturing, and distribution, where managing supply and demand dynamics is essential for profitability and customer satisfaction.
For the task of Demand Forecasting and Inventory Optimization, we can start by gathering historical sales data, customer orders, and relevant external factors like seasonality, promotions, and economic trends. Then, we can use appropriate forecasting models like ARIMA, SARIMA, or exponential smoothing for demand forecasting. Then, we can use the demand forecasts to optimize inventory levels by implementing strategies like reorder points, safety stock, and economic order quantity (EOQ) calculations.
So, the process starts with gathering appropriate data. I found an ideal dataset for the task of Demand Forecasting and Inventory Optimization. You can download the data from here.
Demand Forecasting and Inventory Optimization using Python
Now, let’s get started with the task of Demand Forecasting and Inventory Optimization by importing the necessary Python libraries and the dataset:
import pandas as pd
import numpy as np
import plotly.express as px
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
import matplotlib.pyplot as plt
from statsmodels.tsa.statespace.sarimax import SARIMAX
data = pd.read_csv("demand_inventory.csv")
print(data.head())Unnamed: 0 Date Product_ID Demand Inventory 0 0 2023-06-01 P1 51 5500 1 1 2023-06-02 P1 141 5449 2 2 2023-06-03 P1 172 5308 3 3 2023-06-04 P1 91 5136 4 4 2023-06-05 P1 198 5045
There’s an unnamed column in the dataset. I’ll drop it and move forward:
data = data.drop(columns=['Unnamed: 0'])
Now, let’s visualize the demand over time:
fig_demand = px.line(data, x='Date',
y='Demand',
title='Demand Over Time')
fig_demand.show()
Now, let’s visualize the inventory over time:
fig_inventory = px.line(data, x='Date',
y='Inventory',
title='Inventory Over Time')
fig_inventory.show()
Demand Forecasting:
I can see seasonal patterns in the demand. We can forecast the demand using SARIMA. Let’s first calculate the value of p and q using ACF and PACF plots:
data['Date'] = pd.to_datetime(data['Date'],
format='%Y/%m/%d')
time_series = data.set_index('Date')['Demand']
differenced_series = time_series.diff().dropna()
# Plot ACF and PACF of differenced time series
fig, axes = plt.subplots(1, 2, figsize=(12, 4))
plot_acf(differenced_series, ax=axes[0])
plot_pacf(differenced_series, ax=axes[1])
plt.show()
The value of p, d, and q will be 1, 1, 1 here. You can learn more about calculating these values and time series forecasting here. Now, let’s train the model and forecast demand for the next ten days:
order = (1, 1, 1) seasonal_order = (1, 1, 1, 2) #2 because the data contains a time period of 2 months only model = SARIMAX(time_series, order=order, seasonal_order=seasonal_order) model_fit = model.fit(disp=False) future_steps = 10 predictions = model_fit.predict(len(time_series), len(time_series) + future_steps - 1) predictions = predictions.astype(int) print(predictions)
2023-08-02 117 2023-08-03 116 2023-08-04 130 2023-08-05 114 2023-08-06 128 2023-08-07 115 2023-08-08 129 2023-08-09 115 2023-08-10 129 2023-08-11 115 Freq: D, Name: predicted_mean, dtype: int64
Inventory Optimization:
Now let’s see how we can optimize inventory according to the forecasted demand for the next ten days:
# Create date indices for the future predictions
future_dates = pd.date_range(start=time_series.index[-1] + pd.DateOffset(days=1), periods=future_steps, freq='D')
# Create a pandas Series with the predicted values and date indices
forecasted_demand = pd.Series(predictions, index=future_dates)
# Initial inventory level
initial_inventory = 5500
# Lead time (number of days it takes to replenish inventory)
lead_time = 1 # it's different for every business, 1 is an example
# Service level (probability of not stocking out)
service_level = 0.95 # it's different for every business, 0.95 is an example
# Calculate the optimal order quantity using the Newsvendor formula
z = np.abs(np.percentile(forecasted_demand, 100 * (1 - service_level)))
order_quantity = np.ceil(forecasted_demand.mean() + z).astype(int)
# Calculate the reorder point
reorder_point = forecasted_demand.mean() * lead_time + z
# Calculate the optimal safety stock
safety_stock = reorder_point - forecasted_demand.mean() * lead_time
# Calculate the total cost (holding cost + stockout cost)
holding_cost = 0.1 # it's different for every business, 0.1 is an example
stockout_cost = 10 # # it's different for every business, 10 is an example
total_holding_cost = holding_cost * (initial_inventory + 0.5 * order_quantity)
total_stockout_cost = stockout_cost * np.maximum(0, forecasted_demand.mean() * lead_time - initial_inventory)
# Calculate the total cost
total_cost = total_holding_cost + total_stockout_cost
print("Optimal Order Quantity:", order_quantity)
print("Reorder Point:", reorder_point)
print("Safety Stock:", safety_stock)
print("Total Cost:", total_cost)Optimal Order Quantity: 236 Reorder Point: 235.25 Safety Stock: 114.45 Total Cost: 561.8000000000001
Now let’s understand this output one by one:
- Optimal Order Quantity: 236 – The optimal order quantity refers to the quantity of a product that should be ordered from suppliers when the inventory level reaches a certain point. In this case, an optimal order quantity of 236 units has been calculated.
- Reorder Point: 235.25 – The reorder point is the inventory level at which a new order should be placed to replenish stock before it runs out. In this case, a reorder point of 235.25 units has been calculated, which means that when the inventory reaches or falls below this level, an order should be placed to replenish stock.
- Safety Stock: 114.45 – Safety stock is the additional inventory kept on hand to account for uncertainties in demand and supply. It acts as a buffer against unexpected variations in demand or lead time. In this case, a safety stock of 114.45 units has been calculated, which helps ensure that there’s enough inventory to cover potential fluctuations in demand or lead time.
- Total Cost: 561.80 – The total cost represents the combined costs associated with inventory management. In this case, the total cost has been calculated as approximately 561.80 units based on the order quantity, reorder point, safety stock, and associated costs.
By analyzing these values, you can make informed decisions about how much inventory to order and when to place orders to ensure a smooth supply chain and customer satisfaction while minimizing costs.
Summary
So this is how you can perform Demand Forecasting and Inventory Optimization using Python. Demand Forecasting involves predicting the quantity and pattern of customer orders, which is crucial for businesses to efficiently allocate resources, manage inventory, and plan production. Inventory Optimization aims to strike a balance between having sufficient stock to meet demand without carrying excess inventory that ties up capital and storage space. I hope you liked this article on Demand Forecasting and Inventory Optimization using Python. Feel free to ask valuable questions in the comments section below.
