Impara Making Data-Driven Decisions | Data Analysis for Operations Decisions

In modern operations management, data analysis is essential for making informed decisions that drive efficiency and improve outcomes. By examining trends in sales, inventory levels, or resource utilization, you can proactively adjust processes to meet changing demands. For instance:

When data shows a spike in product sales, you might decide to increase inventory or speed up production;
If analysis reveals underutilized resources, you can reassign them to areas experiencing higher demand.

These data-driven decisions help you respond quickly to real-world changes, reduce waste, and better meet customer needs.


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# Example: Triggering a stock reorder based on recent sales data
recent_sales = 120  # Number of units sold this week
reorder_threshold = 100  # Threshold for triggering reorder

if recent_sales > reorder_threshold:
    print("Sales exceeded threshold. Initiating stock reorder.")
else:
    print("Sales below threshold. No action required.")

To automate operational responses, you need to set actionable thresholds—specific numeric values that signal when action is needed. For example, you might decide to reorder stock if weekly sales surpass a certain number, or send a notification if a product's popularity is rising. In Python, you can use if statements to compare data against these thresholds and trigger the appropriate response. This approach ensures that your operations remain agile, as actions are taken automatically when data trends reach critical points.


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# Script: Notify if an item's sales trend is increasing
sales_last_week = 80
sales_this_week = 110

if sales_this_week > sales_last_week:
    print("Alert: Item is trending upward in sales!")
else:
    print("No upward trend detected.")

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Sezione 2. Capitolo 6

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When data shows a spike in product sales, you might decide to increase inventory or speed up production;
If analysis reveals underutilized resources, you can reassign them to areas experiencing higher demand.

These data-driven decisions help you respond quickly to real-world changes, reduce waste, and better meet customer needs.


              12345678
            
# Example: Triggering a stock reorder based on recent sales data
recent_sales = 120  # Number of units sold this week
reorder_threshold = 100  # Threshold for triggering reorder

if recent_sales > reorder_threshold:
    print("Sales exceeded threshold. Initiating stock reorder.")
else:
    print("Sales below threshold. No action required.")


              12345678
            
# Script: Notify if an item's sales trend is increasing
sales_last_week = 80
sales_this_week = 110

if sales_this_week > sales_last_week:
    print("Alert: Item is trending upward in sales!")
else:
    print("No upward trend detected.")

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 2. Capitolo 6

Making Data-Driven Decisions

1. How can data analysis improve operational efficiency?

2. What is an actionable threshold in the context of operations?

3. How can Python automate responses to data trends?

Making Data-Driven Decisions

1. How can data analysis improve operational efficiency?

2. What is an actionable threshold in the context of operations?

3. How can Python automate responses to data trends?