Вивчайте Demand Forecasting with Python | Optimizing Operations with Python

Demand forecasting is a critical process in operations management, enabling you to anticipate future customer demand and plan resources efficiently. Accurate forecasts help you avoid stockouts, minimize excess inventory, and optimize staffing, leading to improved service levels and reduced costs. By leveraging Python, you can analyze historical sales data, identify demand patterns, and automate the forecasting process, making your operations more responsive and data-driven.


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import pandas as pd

# Sample historical sales data
data = {
    "date": [
        "2024-06-01", "2024-06-02", "2024-06-03", "2024-06-04", "2024-06-05",
        "2024-06-06", "2024-06-07"
    ],
    "sales": [120, 135, 128, 140, 130, 125, 138]
}

df = pd.DataFrame(data)
df["date"] = pd.to_datetime(df["date"])

# Calculate average daily demand
average_daily_demand = df["sales"].mean()
print(f"Average daily demand: {average_daily_demand:.2f}")

Simple forecasting techniques, such as moving averages, are commonly used to predict future demand based on historical trends. A moving average smooths out fluctuations by calculating the average of sales over a specific window, such as the past three or four weeks. In Python, you can easily implement moving averages using pandas, allowing you to generate rolling forecasts and adjust your operations proactively.


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import pandas as pd

# Sample weekly sales data for the past 6 weeks
data = {
    "week": [
        "2024-05-05", "2024-05-12", "2024-05-19", "2024-05-26", "2024-06-02", "2024-06-09"
    ],
    "sales": [820, 860, 810, 890, 870, 840]
}

df = pd.DataFrame(data)
df["week"] = pd.to_datetime(df["week"])

# Calculate 3-week moving average
df["3_week_moving_avg"] = df["sales"].rolling(window=3).mean()

# Predict next week's demand using the latest moving average
predicted_next_week = df["3_week_moving_avg"].iloc[-1]
print(f"Predicted demand for next week: {predicted_next_week:.2f}")

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              1234567891011121314151617
            
import pandas as pd

# Sample historical sales data
data = {
    "date": [
        "2024-06-01", "2024-06-02", "2024-06-03", "2024-06-04", "2024-06-05",
        "2024-06-06", "2024-06-07"
    ],
    "sales": [120, 135, 128, 140, 130, 125, 138]
}

df = pd.DataFrame(data)
df["date"] = pd.to_datetime(df["date"])

# Calculate average daily demand
average_daily_demand = df["sales"].mean()
print(f"Average daily demand: {average_daily_demand:.2f}")


              12345678910111213141516171819
            
import pandas as pd

# Sample weekly sales data for the past 6 weeks
data = {
    "week": [
        "2024-05-05", "2024-05-12", "2024-05-19", "2024-05-26", "2024-06-02", "2024-06-09"
    ],
    "sales": [820, 860, 810, 890, 870, 840]
}

df = pd.DataFrame(data)
df["week"] = pd.to_datetime(df["week"])

# Calculate 3-week moving average
df["3_week_moving_avg"] = df["sales"].rolling(window=3).mean()

# Predict next week's demand using the latest moving average
predicted_next_week = df["3_week_moving_avg"].iloc[-1]
print(f"Predicted demand for next week: {predicted_next_week:.2f}")

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 2

Demand Forecasting with Python

1. Why is demand forecasting important for operations managers?

2. What is a moving average and how is it used in forecasting?

3. How can Python automate the forecasting process?

Demand Forecasting with Python

1. Why is demand forecasting important for operations managers?

2. What is a moving average and how is it used in forecasting?

3. How can Python automate the forecasting process?