Engineeering & IT Projects and Resources: Retail Inventory Demand Forecast

Retail Inventory Demand Forecast

1. Introduction

Objective: Predict retail inventory demand at the multi-store and SKU level using historical sales data and machine learning models.
Purpose: Enable efficient inventory management, reduce stockouts, and optimize resource allocation for retail businesses.

2. Project Workflow

1. Problem Definition:
   - Forecast inventory demand for multiple stores and SKUs.
   - Key questions:
     - Which stores/SKUs have the highest demand variability?
     - How can future demand be accurately predicted?
2. Data Collection:
   - Source: Point-of-sale (POS) systems, inventory logs, or Kaggle datasets.
3. Data Preprocessing:
   - Clean and structure sales data at store-SKU granularity.
4. Exploratory Data Analysis:
   - Analyze demand trends, seasonality, and SKU-level patterns.
5. Modeling:
   - Apply regression and time series forecasting models.
6. Evaluation and Insights:
   - Assess model performance and derive actionable insights.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn, Plotly
- Machine Learning: scikit-learn, XGBoost, LightGBM
- Time Series: statsmodels, Prophet

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn xgboost lightgbm statsmodels prophet
```

Step 2: Load and Explore Data

Load historical sales data:
```
import pandas as pd

data = pd.read_csv("retail_sales.csv")
print(data.head())
```
Inspect the dataset:
```
print(data.info())
print(data.describe())
```

Step 3: Preprocess Data

Clean and structure the dataset:
```
data['Date'] = pd.to_datetime(data['Date'])
data = data.sort_values(by=['Store', 'SKU', 'Date'])
data.fillna(0, inplace=True)
```
Aggregate sales data:
```
store_sku_sales = data.groupby(['Store', 'SKU', 'Date'])['Sales'].sum().reset_index()
```

Step 4: Feature Engineering

Create additional features:
```
data['Month'] = data['Date'].dt.month
data['Year'] = data['Date'].dt.year
data['DayOfWeek'] = data['Date'].dt.dayofweek
data['Rolling_Mean'] = data['Sales'].rolling(window=7).mean()
data.dropna(inplace=True)
```

Step 5: Modeling

Split data into training and testing sets:
```
from sklearn.model_selection import train_test_split

X = data[['Store', 'SKU', 'Month', 'Year', 'DayOfWeek', 'Rolling_Mean']]
y = data['Sales']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
Train an XGBoost regression model:
```
from xgboost import XGBRegressor

model = XGBRegressor()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
```

Step 6: Evaluate Model

Evaluate model performance using Mean Absolute Error (MAE):
```
from sklearn.metrics import mean_absolute_error

mae = mean_absolute_error(y_test, predictions)
print(f'MAE: {mae}')
```

Step 7: Forecast

Generate future forecasts for each store and SKU:
```
future_data = ... # Prepare future input data for prediction
future_predictions = model.predict(future_data)
```

5. Expected Outcomes

1. SKU and store-level demand predictions with high accuracy.
2. Visualizations showing historical and forecasted demand trends.
3. Actionable insights for inventory management.

6. Additional Suggestions

- Experiment with advanced forecasting models like LightGBM or Prophet.
- Integrate external factors (e.g., holidays, promotions) for better predictions.
- Build a dashboard for real-time monitoring and visualization of forecasts.

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