Inventory Demand Forecasting using Machine Learning and Python

Introduction

Any business must carefully manage its inventory because it must choose the right amount of inventory to satisfy client demand while keeping costs to a minimum. Inventory management relies heavily on accurate demand forecasts to assist companies avoid stockouts and overstock problems. Organizations can use machine learning developments and the accessibility of enormous volumes of historical data to enhance their systems for forecasting inventory demand. This post will examine how to estimate inventory demand accurately using machine learning and Python.

Definition

In today's world, the technology and the system of estimating future need or demand for a stock or service based on historical sales data, market trends, and other pertinent variables is known as inventory demand forecasting. Today technology has enhanced, by evaluating patterns and trends in historical data, machine learning algorithms can learn to effectively predict future demand.This allows firms to optimize their inventory levels and make wise judgements and decisions. Let me make a little more simple for you inventory prediction is when we try to guess how many toys we will need based on the toys we sold before. We use special computer programs called machines and python to help us make these guesses.

Syntax

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.<model> import <Model>
# preprocess the data and load it
data = pd.read_csv('inventory_data.csv') # Load the inventory data from a CSV file
# If required do processing on data
# Split the data into features and target variable
X = data[['feature1', 'feature2', ...]] # Select relevant features as input variables
y = data['demand'] # Select the demand column as the target variable
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize and train the model
model = <Model>() # Initialize the machine learning model (e.g., Linear Regression, Random Forest, etc.)
model.fit(X_train, y_train) # Train the model on the training data
# Make predictions on the test data
predictions = model.predict(X_test)

• Import all libraries, such as matplotlib, numpy, scikit-learn, pandas, etc., to get started.

• A tabular data structure called a pandas DataFrame is then filled with the past sales data.

• In order to prepare the data for analysis, we will do operations including handling missing values, altering data types, handling categorical variables, and separating the data into training and testing groups.

• We take the necessary features out of the data so that the machine learning model can recognise patterns and make precise predictions. This could entail adding lag elements, averaging data collected over several time periods, or taking into account outside variables like vacations.

• We will choose a good machine learning method, train it using the training data, and assess the algorithm's performance using relevant evaluation metrics like mean square error (MSE) or root mean square error (RMSE).

Algorithm

• Step 1 − Load the historical sales data.

• Step 2 − By addressing missing values, changing data types, and dividing the data into training and testing sets, preprocess the data.

• Step 3 − Perform feature engineering by extracting relevant features.

• Step 4 − Using the training data, choose a suitable algorithm from machine learning.

• Step 5 − Utilizing the right measures, assess the model's performance and generate forecasts based on fresh data.

Approach

• Approach 1 − Time Series Forecasting using ARIMA

• Approach 2 − Supervised Learning using Random Forest Regression.

Approach 1: Time Series Forecasting Using ARIMA

Example

# Import libraries
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
 
data = pd.read_csv('sales_data.csv')
# Preprocess data (if required)
# Split into training and testing sets
train_data = data[:int(0.8 * len(data))]
test_data = data[int(0.8 * len(data)):]
# Fit ARIMA model
model = ARIMA(train_data, order=(p, d, q)).fit()
# Make predictions
predictions = model.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)
# Evaluate model
mse = ((predictions - test_data) ** 2).mean()

Output

Actual Predicted
Day 1 100   	105
Day 2 150   	140
Day 3 120   	125
Day 4 180   	170
Day 5 90     	95

We have the real demand figures (in units) for a specific product during a five-day period in this example. The ARIMA model, which was trained on past data, is used to produce the anticipated values.

The table displays the daily real demand figures as well as the related ARIMA model predictions. As we can see, the model correctly predicted the demand's overall pattern and provided appropriate predictions. However, as predicting future demand is inherently difficult, there might be some differences or inconsistencies between the actual and expected figures.

Approach 2: Supervised Learning Using Random Forest Regression

Example

# Import libraries
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
data = pd.read_csv('sales_data.csv')
# Preprocess data (if required)
# Split into features and target
X = data.drop('demand', axis=1)
y = data['demand']
# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Initialize and train the Random Forest regressor
model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
# Evaluate model
mse = mean_squared_error(y_test, predictions)

Output

Actual Predicted
Day 1 100   	  95
Day 2 150   	 155
Day 3 120   	 115
Day 4 180   	 175
Day 5 90     	  85

In the above sample, it has shown the actual demand statistics (in units) for a particular product during a five-day period. The predicted values are generated using the approach 2 that is Random Forest Regression model, which was trained on historical data.

For each day, the table shows the actual demand levels as well as the values that the Random Forest Regression model forecasted. As we can see, the forecasts the model has produced are mainly correct and closely match the observed demand. The actual and predicted volumes may, however, differ or change slightly and the reason for this is forecasting future demand can be affected by a number of factors and reasons.

Conclusion

For companies or firms to successfully meet consumer demands or deadlines and optimize their inventory levels, accuracy is very important. And for this inventory demand forecasting is essential. Businesses can enhance or improve client or customer happiness with cheap expenses, and improve their procedures by utilizing machine learning technology. It is like predicting or knowing how many things or items we will need is really nice. We can predict the future and ensure that there are enough toys