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Predictive, descriptive and prescriptive models: what they are and how to apply them in business

Manuela Bazzarelli

Predictive, descriptive and prescriptive models are key elements in data science and artificial intelligence: they are analytical models that provide a comprehensive framework for predicting, understanding and making data-driven decisions

AI, today, offers many opportunities for improving the efficiency and effectiveness of business operations, optimizing decision-making processes and industrial automations, and enabling companies to remain competitive in the marketplace, across industries. 

Let’s go through them individually; you can also access our What We Do section to find out how we apply them on a daily basis.


Predictive models

Predictive models are designed to make predictions or estimates about future events. Using advanced machine learning algorithms, these models analyze historical data to identify patterns and trends, and provide intuitive data-driven information and estimates about the likelihood of a future outcome. For example, a predictive model could be used to estimate future sales of a product based on historical sales data.

Predictive analyses are then used when one wants to understand the future.


Descriptive models

Descriptive models are designed to describe and understand ongoing phenomena. These models use statistical analysis techniques to extract meaningful information from the data, such as mean, standard deviation, distribution, and correlations. Descriptive models provide an overview of the past and present, helping to identify patterns and trends in data behavior. They are often used for exploratory analysis and to gain an in-depth understanding of the characteristics of the data themselves; in this sense, they provide a knowledge base on which to build nowcasting models.

Descriptive analyses are then used when one wants to understand, at an aggregate level, what is happening in an organization.


Prescriptive models

Prescriptive models, on the other hand, are designed to provide recommendations or guidance on what to do based on the available data and drive activity toward a solution. These models use complex algorithms to process data and generate suggestions about possible actions to take, attempting to quantify the effect of future decisions to recommend possible outcomes before they are actually taken. For example, a prescriptive model can be used to suggest the best pricing strategy to maximize a company’s profits, considering various factors such as cost, demand and competition. 

Thus, prescriptive analyses are used when one wants to provide recommendations regarding actions.


Applications of mathematical models in management and industrial processes

Predictive, descriptive, and prescriptive mathematical models have various practical applications both in the management of “office” procedures – such as HR, document area, budget allocation etc. – as well as in industrial and manufacturing processes.

Here are some real-world applications examples: 

  • Demand forecasting – a company can use predictive models to estimate future demand for a product or service. This can help in optimizing production strategies, inventory planning and supply chain management;
  • Market data analysis – descriptive models can be used to analyze market data and understand customer behavior, buying preferences, and market trends. This information can be leveraged to adapt marketing strategies and product positioning;
  • Manufacturing process efficiency – prescriptive models can be used to optimize industrial production processes. For example, a prescriptive model can suggest the most efficient processing sequence, minimizing production time and reducing waste;
  • Maintenance management – predictive models can be used to predict failures or malfunctions in industrial equipment. This allows preventive maintenance to be scheduled to minimize unplanned shutdowns and optimize operational efficiency; 
  • Rationalization of human resources – prescriptive models can be applied to HR management, for example, to optimize staff scheduling, resource allocation, and prediction of skills needed for future projects;
  • Optimization of workflow and business decisions – prescriptive models can help to make optimal decisions regarding resource allocation, activity planning and monitoring of business KPIs. For example, they can be used to determine pricing strategies, procurement strategies and risk management policies.