Prescriptive analytics uses advanced modeling techniques, data, and knowledge to find the best course of action for a given situation. It builds on descriptive and diagnostic analytics by going beyond the “what” and “why” questions and providing recommendations on “what should be done” to achieve specific goals.
A prescriptive analytics process can typically include the following steps:
- Defining the problem and objectives
- Collecting and preparing the data
- Developing and testing models
- Analyzing the results and evaluating different scenarios
- Generating recommendations and selecting the best course of action
- Implementing the chosen course of action
The models used in prescriptive analytics can be based on various techniques such as mathematical optimization, simulation, and machine learning. The results are used to make recommendations on actions to achieve specific goals.
Prescriptive analytics can be applied to many areas, like:
- supply chain optimization,
- financial planning,
- predictive maintenance,
- healthcare delivery,
- workforce planning and scheduling,
- retail management,
- energy management.
In summary, Prescriptive analytics is the branch of analytics that uses advanced modeling techniques, data, and knowledge to recommend the best course of action for a given situation. It goes beyond the “what” and “why” and provides recommendations on “what should be done” to achieve specific goals. It considers the performance of different scenarios and suggests the best course of action, it’s based on mathematical optimization, simulation, and machine learning, and it’s applied to a wide range of areas.
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