Prescriptive analytics and prescriptive systems

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Prescriptive analytics and prescriptive systems

Introduction

In the rapidly evolving world of data and technology, prescriptive analytics has become an increasingly important tool for businesses and organizations. In simple terms, prescriptive analytics is a branch of data analytics that uses algorithms and mathematical models to make recommendations and suggest specific actions to achieve specific goals. In this use case, we'll explore what prescriptive analytics is, how it differs from other forms of analytics, and its various applications in the modern world.

Prescriptive analytics is a type of advanced analytics that uses algorithms and mathematical models to provide recommendations and suggest specific actions to achieve specific goals. It is the final stage of the analytics process, after descriptive and predictive analytics, and goes beyond simply summarizing data and making predictions.

In today's data-driven world, businesses and organizations are faced with a growing amount of data and the need to make informed decisions quickly and accurately. Prescriptive analytics helps to meet this need by providing recommendations and specific actions to achieve specific goals, thereby enabling organizations to make better decisions and achieve their desired outcomes.

Here we will explore the concept of prescriptive analytics in depth, discussing its definition, key features, and differences from other forms of analytics, such as predictive and descriptive analytics. We will also discuss the various applications of prescriptive analytics, including asset analytics and asset management analytics, and the ultimate goal of prescriptive analytics, which is to help organizations make better decisions.

What is Prescriptive Analytics?

Prescriptive analytics is a type of advanced analytics that uses algorithms and mathematical models to provide recommendations and suggest specific actions to achieve specific goals. It is the final stage of the analytics process, after descriptive and predictive analytics, and goes beyond simply summarizing data and making predictions.

Prescriptive analytics uses mathematical models and algorithms to analyze data and make recommendations based on that analysis. The models take into account various constraints and objectives to determine the best course of action. The algorithms then analyze the data and generate a set of recommendations and specific actions to achieve the desired goals.

Key Benefits:

  • Provides recommendations and  specific actions to achieve specific goals
  • Utilizes mathematical models and algorithms to analyze data
  • Takes into account various constraints and objectives to determine the best course of action
  • Goes beyond simply summarizing data and making predictions
  • Enables organizations to make better decisions and achieve their desired outcomes.

How is Prescriptive Analytics Different from Predictive Analytics?

Predictive analytics is a type of analytics that uses historical data, statistical algorithms, and machine learning techniques to make predictions about future events. It is the stage of analytics prior to prescriptive analytics and is focused on understanding what will happen in the future based on past trends and patterns.

Predictive Analytics
Predictive Analytics Example

While both prescriptive and predictive analytics are concerned with using data to inform decision-making, there are key differences between the two. Predictive analytics focuses on understanding what will happen in the future, while prescriptive analytics provides recommendations and specific actions to achieve specific goals. Predictive analytics relies on historical data and patterns to make predictions, while prescriptive analytics takes into account a wider range of constraints and objectives to determine the best course of action.

Advantages of using prescriptive analytics over predictive analytics:

  • Provides specific actions to achieve specific goals, rather than just predictions
  • Takes into account a wider range of constraints and objectives to determine the best course of action
  • Enables organizations to make more informed and effective decisions
  • Can help organizations stay ahead of the competition by providing actionable insights into how to achieve their goals
Prescriptive Analytics
Prescriptive Analytics Example

How is Prescriptive Analytics Different from Descriptive Analytics?

Descriptive analytics is the stage of analytics prior to predictive and prescriptive analytics. It is focused on summarizing data and understanding what has happened in the past. Descriptive analytics uses tools such as graphs, charts, and tables to represent data and help identify patterns and trends.

While both descriptive and prescriptive analytics are concerned with understanding and making decisions based on data, there are key differences between the two. Descriptive analytics focuses on summarizing data and identifying patterns and trends, while prescriptive analytics provides specific recommendations and actions to achieve specific goals. Descriptive analytics provides an understanding of what has happened in the past, while prescriptive analytics takes that understanding and applies it to decision-making in the present and future.

Advantages of using prescriptive analytics over descriptive analytics:

  • Provides specific recommendations and actions to achieve specific goals, rather than just summarizing data
  • Enables organizations to make better, data-driven decisions
  • Can help organizations stay ahead of the competition by providing actionable insights into how to achieve their goals
  • Goes beyond simply understanding what has happened in the past and applies that understanding to decision-making in the present and future.

Applications of Prescriptive Analytics

Prescriptive analytics can be applied in a wide range of industries, including manufacturing, logistics, healthcare, and finance. In manufacturing, prescriptive analytics can be used to optimize production processes, reduce waste, and improve overall efficiency. In logistics, prescriptive analytics can be used to optimize delivery routes and reduce transportation costs. In healthcare, prescriptive analytics can be used to improve patient outcomes and reduce healthcare costs. In finance, prescriptive analytics can be used to make more informed investment decisions and optimize portfolio management.

Asset analytics and asset management analytics are specific applications of prescriptive analytics in the realm of asset management. Asset analytics uses data and advanced analytics techniques to optimize the performance and utilization of physical assets, such as machinery, equipment, and real estate. Asset management analytics uses data and analytics to inform investment and portfolio management decisions, helping organizations to optimize the performance and value of their assets over time.

Modla isa leading provider of prescriptive analytics solutions, offering a range of tools and services to help organizations make more informed, data-driven decisions. With a focus on asset analytics and asset management analytics, Modla helps organizations optimize the performance and utilization of their physical assets and make better investment decisions. As a leading provider of prescriptive analytics solutions, Modla is dedicated to helping organizations leverage the power of data to drive success.

The Goal of Prescriptive Analytics

The ultimate goal of prescriptive analytics is to help organizations make better, more informed decisions. By analyzing data and applying advanced analytics techniques, prescriptive analytics provides actionable insights and recommendations that organizations can use to optimize processes, reduce costs, and improve outcomes.

Prescriptive analytics can help businesses make better decisions by providing them with amore complete understanding of their operations and the factors that influence their performance. By analyzing data and using advanced analytics techniques, prescriptive analytics provides organizations with a clear and detailed picture of their operations, helping them to identify opportunities for improvement and make more informed decisions.

There are numerous benefits to using prescriptive analytics, including improved decision-making, increased operational efficiency, reduced costs, and improved outcomes. By leveraging the power of data and advanced analytics techniques, prescriptive analytics helps organizations to make better decisions and achieve better results, leading to improved performance and success. Additionally, by providing actionable insights and recommendations, prescriptive analytics helps organizations to optimize their processes and improve the overall performance of their operations.

What is Prescriptive Maintenance?

The term "Prescriptive maintenance" doesn't really make sense. The maintenance does not prescribe anything. It is generally accepted however, that this term refers to the execution of maintenance, that is “prescribed” or“ recommended” by a system (i.e. Prescriptive System), and that follows a repeatable method or process (Prescriptive Analytics).

Prescriptive Maintenance
Typical Prescriptive Maintenance Pattern

Prescriptive systems run prescriptive analytics, which inform prescriptive maintenance

Contrary to popular belief, prescriptive maintenance does not need a high organisational maturity to attain. Any system that makes a recommendation, is by definition a prescriptive system. If Maintenance is then performed on the basis of that recommendation, this is classified as "Prescriptive Maintenance".

Let’s start with the building blocks.

Asset Modelling

An asset modelling is foundational to a prescriptive maintenance system. An asset model contains all of the relevant information, knowledge and data required to understand an asset’s performance. The assumptions, data quality, and expertise contained within this model will have a significant effect on the quality of outputs from a prescriptive maintenance system.

The Prediction(s)

An asset model can be fed information or data to inform a statistical prediction. This is the domain of predictive maintenance, and a typical use case would be to make use of sensor data as an input, to inform the current predictions. While use of sensor data is currently a "trending" field in asset management, It is worthwhile to note that you don’t need sensor data to be “predictive”. Up to date visual inspection information, ages, operational information, or any other available data can be used to alter the model predictions.

Understanding the current state of the asset, and then the future predictions (Performance, reliability, or otherwise), allows a prescriptive system to recommend appropriate actions. The determination of what is "appropriate", is typically its own defined method, such as cost/benefit, NPV of options or otherwise.

Analysis of the asset model, input data and causal relationships can be used to determine various paths forward and potential outcomes. These can be used to make decisions around the future operation, maintenance, and interventions at an asset level. Prescriptive analytics, and thus prescriptive maintenance, is an extension of the asset model and inputs configuration above. It now includes an element of analysis as shown below.

Since the analysis component is a method or process that can be coded and automated, it means that the analysis can be performed live or in a continuous manner, to produce recommendations and options. Any time an input to a an asset model updated, then the analysis can be re-run, and the recommendations and outputs updated.

Prescribing Maintenance

A prescriptive maintenance approach ensures that an organization is acting on the latest "intelligence", and adapting their strategies to suit. Prescriptive maintenance is enabled by technology and software services such as modla.

In a prescriptive maintenance system, the prescriptive analytics is usually automated. The asset model becomes the area of focus for reliability engineers and subject matter experts, ensuring their current decision making and logic is captured correctly within the model structure.

Modla's platform is built with the prescriptive analytics and a prescriptive maintenance end goal in mind, however you do not need an abundance of data or high levels of organizational maturity to leverage the above approach.

Conclusion

Prescriptive analytics is a powerful tool that can help organizations make better, more informed decisions by providing them with a more complete understanding of their operations and the factors that influence their performance. As businesses and organizations become increasingly data-driven, prescriptive analytics will become increasingly important, helping organizations to optimize their operations and achieve better results.

In today's data-driven world, the importance of using prescriptive analytics for businesses and organizations cannot be overstated. By leveraging the power of data and advanced analytics techniques, prescriptive analytics provides organizations with the insights and recommendations they need to make better, more informed decisions and achieve better outcomes. As a prescriptive analytics solution provider, "Modla" is well-positioned to help organizations of all sizes and across all industries to make the most of this powerful technology.