In this article, we introduce the broad field of predictive analytics, its connection with machine learning, and how simulation works as a predictive analytics technology.
Why use predictive analytics?
Predictive analytics is about making forecasts based on historical data. Practitioners analyze past events with statistical algorithms and machine learning techniques to produce probabilities and predictions for systems in the future. Almost everyone is subject to and may benefit from predictive analytics.
In a consumer credit system, for example, each of us is given a credit score that represents the probability we will repay a loan. Or, in another example, modern Android phones have an adaptive battery system that prioritizes battery power for the most-used applications. In general, the insights provided by predictive analytics help optimize processes and manage risks.
Predictive analytics is valuable wherever there is data. Business sector use cases include:
- Manufacturing – to optimize production processes, improve maintenance scheduling, plan inventory, and more.
- Healthcare – in clinical trials, predictive scheduling systems, pharmaceutical market analysis, and more.
- Oil & Gas – for operations planning, field production optimization, storage management, and more.
- Business Processes – for optimization, investment analysis, impact analysis, and more.
- Supply Chain – for design, planning, sourcing optimization, inventory management, transportation planning, risk management — see anyLogistix.
Two trends in predictive analytics
Predictive analytics is increasingly common thanks to two trends in computing:
Rapidly growing data sets — Data collection is widespread and growing because storage costs are low, and the activities of people and devices are increasingly online. Due to data management advances, the increasing size and complexity of data sets is not a limiting factor.
Ease-of-use — Continuing advances in data storage and processing reduce costs and increase access to quick and complex analytics. User interfaces are also improving and making predictive analytics tools more accessible and understandable. Furthermore, complex data analysis using machine learning techniques is becoming simpler thanks to platforms such as Microsoft’s Project Bonsai, H2O.ai’s automatic machine learning, and Pathmind AI.
As predictive analytics software increases in popularity, the more stored data is analyzed, and the more its value is realized.
Simulation and Predictive Analytics
Simulation and predictive analytics are related because both require models. Simulations model the behavior of a system, while predictive analytics uses models for insights into the future.
In predictive analytics, it is possible to model straightforward systems with decision trees. For large data sets and complex systems, regression or neural-network-based machine learning may be better options. Decision trees will indicate if something will or will not happen depending on inputs. In contrast, systems based on machine learning can specify a value, such as when to schedule maintenance. Another difference between the two approaches is their data requirements. Modelers can construct decision trees from limited historical data, while machine learning requires large amounts of training data. For machine learning, this data usually comes from historical data sets or continuous feedback but may also be synthesized using a simulation model.
Simulation can help when systems are not easy to describe mathematically or when historical data is not adequate for training or testing machine learning techniques. Instead of representing a complete system as a statistical algorithm or generating a fixed data set, simulation captures the characteristics and relationships of system components to provide a dynamic model. System behavior appears from running a simulation, and the data produced by a simulation differs depending on its configuration. Simulation helps predictive analytics in two ways:
- For machine learning, when data is scarce, perhaps due to costs or risks, a simulation model can provide synthetic training data.
- For complex systems, it is often easier to describe a system’s components and their relationships than the behavior of a whole system.
Due to advancing data collection and ever more powerful computers, machine learning provides a powerful method for making predictions and is increasingly popular. Learn more in our blog about machine learning and simulation.
Learn how Pfizer uses AnyLogic as predictive modeling software in this clinical trials case study.
Prescriptive Analytics – responding to predictions
Prescriptive analytics goes beyond predictions by telling us the actions necessary to achieve our goals and what the wider effects of achieving them may be. After creating a predictive analytics model based on simulation, you can experiment with process configurations and discover how to achieve your goals. From the simulation model, it is clear which steps are required to reach your goals and what impact they will have.
Use AnyLogic for Predictive Analytics Simulations
For companies looking to optimize processes and manage risk in complex systems, simulation modeling helps apply predictive analytics. Furthermore, with a simulation model, it is possible to conduct tests, analyze the impacts of future states, and create plans based on these insights. Simulation modeling lets you foresee the impact of changes and determine the courses of action necessary to meet your goals.
Companies around the world use AnyLogic for predicting how scenarios will develop and to create responses that ensure optimal future operations. AnyLogic makes it easy to connect simulation models with databases, integrate with existing business systems, and implement custom user interfaces. With statistics and visualizations, and experiments to predict outcomes and test ideas – such as Reinforcement Learning, Monte Carlo, and Parameter Variation – AnyLogic is a powerful and flexible tool for predictive analytics.