Predictive Analytics

Production solutions offered by ITPS experts are not only aimed at technical maintenance and repair of industrial equipment, but also work with industrial monitoring systems, helping to reduce the customer’s costs for production downtime and maintenance. Such systems require a whole arsenal of analytical programs designed to track a large amount of data generated by the enterprise, to predict further developments and to influence management decisions. Together, these services are called predictive or forecasting analytics.

A typical process of implementing a predictive analytics system in an enterprise consists of three stages:

  1. Preparation:
  • Evaluation of available data;
  • Determination of requirements, quality indicators, success criteria;
  • Data transfer and dataset preparation;
  • Forecast of economic impact.
  1. The pilot project:
  • Model development and training;
  • Development of a system fully-integrated with the model;
  • Testing the model and the system;
  • Checking the success criteria, evaluating model performance.
  1. Industrial operation of the service:
  • Launch of system with integrated model within the customer’s perimeter or cloud service;
  • QA of the service (A/B testing of metrics), including validation of economic impact;
  • Support of the service, including regular after-training.

The ultimate goal of predictive analytics is not to analyse the activities of the enterprise and its production processes, but to develop a strategy for taking into account possible risks, finding growth potential, identifying new opportunities and predicting future events. For this purpose, statistical methods of economic analysis, mathematical models, data mining techniques, game theory, sustainable behaviour patterns and many more are used.

The use of predictive analytics, in the first place, is necessary for:

  • Analysis and forecasting of factors impacting production parameters;
  • Forecast of equipment failures – transition from maintenance by regulations to maintenance by condition;
  • Forecast of production volumes and energy and resources consumption;
  • Proactive online alerts on future emergency situations.

How does predictive analytics work?

The intelligent system performs an analysis of a huge amount of available data (through a predictive mathematical model). It reviews business and production processes in real time, integrates with an enterprise’s Computer-Aided Process Control System, MES and ERP-systems, systematizes the received volume of data and predicts further events. Based on the received data, the system determines the optimum level of current activities (technological processes) and suggests steps for improvement.

For equipment maintenance and repair, machine learning and artificial intelligence are used. In contrast to the basic control mechanisms installed by the manufacturers, they are better equipped to analyse and visualize additional factors affecting the condition of equipment. All this makes it possible to predict equipment failure scenarios, to prevent fatal errors and, as a result, additional costs being borne.

The analytical capabilities of modern intelligent systems are so complex that they make it possible to produce highly accurate forecasts and to apply data insights to technological processes in order to refine technical and economic production indicators. The use of predictive analytics is the best way to identify latent opportunities for an enterprise, manage risks, impact the business development strategy and make optimal business decisions.