What is prescriptive analytics?

Prescriptive analytics tools like mathematical optimization help you make decisions based on your real-world business goals (“objectives”) and limitations (“constraints.”) This can be especially useful when you’re facing a business problem with multiple, conflicting goals (such as cutting spending while increasing production) and multiple constraints (such as time, distance, product availability).

Learn more about prescriptive analytics in our article, “What is Prescriptive Analytics?”

What is the difference between predictive analytics and prescriptive analytics?

Predictive analytics seeks to identify patterns in data to forecast future events, such as predicting cyberattacks or imminent machine failures. Prescriptive analytics, on the other hand, utilizes mathematical modeling to guide decisions based on real-world objectives and constraints, such as minimizing costs or managing raw material inventory.
While predictive analytics tells you what might happen, prescriptive analytics provides actionable recommendations on how to achieve specific goals, given certain limitations.

Learn more about the difference in our article, “Predictive Analytics vs. Prescriptive Analytics.”

What are some examples of prescriptive analytics in the real world?

In the real world, prescriptive analytics has diverse applications, including transportation providers like Air France and Uber using it to create optimal routing, staffing, and maintenance plans. Professional sports leagues, such as the National Football League, plan their game schedules using prescriptive analytics. Additionally, manufacturers utilize prescriptive analytics to plan and manage the procurement, production, and distribution of their products, aligning decisions with real-world goals and constraints.

Learn more about examples in our article, “Examples of Prescriptive Analytics.”

Can I improve my machine learning applications by applying optimization?

Yes! By using machine learning predictions as valuable input for mathematical optimization solutions, or conversely, using mathematical optimization to inform machine learning predictions, you can leverage the problem-solving power of mathematical optimization to enhance machine-learning applications.
Learn more in our article, “Improving Machine Learning Applications with Prescriptive Analytics.”

How can prescriptive and predictive analytics work together?

Say you were planning a trip. Predictive analytics can predict what you may encounter along your journey (weather, traffic, engine trouble), and prescriptive analytics can, given those predictions, identify the route that best helps you achieve your goals (fastest, cheapest, safest route), given your constraints (time, budget, speed limits).
Here are some additional examples:

  • Use predictive analytics to predict supply chain issues, and use prescriptive analytics to identify the least costly way to reroute shipments.
  • Use predictive analytics to predict cyberattacks before they happen, and use prescriptive analytics to identify the right investigators based on cost and skill.
  • Use predictive analytics to predict imminent machine failure, and use prescriptive analytics to identify the best time to shut down the production line.
  • Use predictive analytics to predict customer likelihood to buy more with targeted offers, and use prescriptive analytics to identify how many discount coupons to offer, in order to maximize revenue.

Learn more in our article, “How Can Prescriptive and Predictive Analytics Work Together?”

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