The application of the Operation Intelligence to modern processes provides a layer of real-time data visibility over diverse operational and business systems, which allows companies to achieve better operation decisions due to a continuously updated analytics view on aggregated & contextualized process data, reducing direct and indirect costs related with unforeseen process deviations.
From Business Inteligence
- Analysis of historical data over defined periods of time
- implementation of reactive optimization measures
…to Operation Intelligence
- Continuous analysis of process data
- Continuously optimized and predictive operations with cost effective maintenance services
Value:
Achieving better operations decisions due to an integrated view on aggregated & contextualized data
IIoT Cloud Platform
Some of the research and development areas addressed by this project have as their object, the optimization of processes and predictive maintenance, based on analysis and machine learning algorithms.
To ensure that these developments effectively result in practical improvements on the shop floor, it is imperative that all the data and sources required from both shop floor and digital twin systems is properly collected and aggregated to ensure effective algorithms. To respond to this challenge, a platform based on the “Mindsphere – IoT operating system” was considered, to process the application responsible for calculating the OEE (Overall Equipment Effectiveness) indicators, and aggregate and buffer all the data from different sources at the factory level, to ensure greater confidence in terms of data reliability, but also security, which is a very sensitive topic for industries.
The OEE is one of the most important KPIs for the management of Production and Maintenance on the shop floor, because it reliably provides a clear and unambiguous image of the performance of the production system. Based on this indicator, strategic maintenance decisions are made and production forecasts are indicated that influence both customers and suppliers.
The application responsible for OEE calculation runs in a MindApp in the MindSphere operating system. It is intended that this MindApp is capable of communicating with the real production cell and with the Digital Twin to extract the required inputs for OEE calculation, to return to the MLStudio and real process, the previously calculated indicators for a determined time interval. This bi-directional application will be used to automatically compare production sequences and validate the improvements introduced by Smart Recipes to the optimized virtual production sequences, and therefore to the real production process.
Technological/ scientific innovation
Development of a technology to enable process optimization, predictive maintenance management and continuous improvement of equipment and processes, with support for simulation and machine learning mechanisms.
The development of this aspect within the framework of this project results in the development of the following tasks:
T1.5 MindSphere Application Setup Model – This task aims to define the specific scope of the application to be developed in the project based on the MindSphere solution – MindApp – and the respective generic functional requirements, based on the state of the art and the objectives to be achieved.
T2.5 MindSphere Application Architecture – In this task, the development of the model created in Task T1.5 will begin, and the application architecture design in terms of infrastructure, hardware, communications and software that will allow the processing of the data.
T3.3 MindSphere Applications Development – During this task, we intend to develop a MindApp capable of calculating the main equipment efficiency indicators such as: TEE (Total Equipment Effectiveness), OEE (Overall Equipment Effectiveness), and EE (Equipment Effectiveness) . As a result, the design of the solution is defined according to the contents of the “MindApp – Architecture and Specifications” Report.
T4.2 MindSphere Application Tests – Conducting operation and performance/stress tests of the application (MindApp) on the MindSphere platform.
