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Python for Operations Managers

bookDemand Forecasting 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}")
copy

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}")
copy

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?

question mark

Why is demand forecasting important for operations managers?

Select the correct answer

question mark

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

Select the correct answer

question mark

How can Python automate the forecasting process?

Select the correct answer

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 2

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bookDemand Forecasting with Python

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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.


              
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}")
copy

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.


              
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}")
copy

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?

question mark

Why is demand forecasting important for operations managers?

Select the correct answer

question mark

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

Select the correct answer

question mark

How can Python automate the forecasting process?

Select the correct answer

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 2
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