Manufacturing Analytics

In manufacturing, controller machines generate a huge amounts of information that is not stored, we loss the opportunity to analyze and take advantage of data.

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In production insights for industry 4.0, Data Science plays an important role for operation and production plant control.

Data Analytics for Manufacturing industry is a tool developed by ITEIN, it is capable to generate reports in record time response helping CEOs and managers of Manufacturing companies to obtain valuable information about their processes. Its modular implementation enables scalability according to the requirements of companies such as OEMS, Tier 1 and Tier 2 depending on the amount of information to be considered for the analysis.
Data analytics for Manufacturing has a secure environment against intrusions, and allows all kind of users to use it in an easy way, from staff to managers. Besides, it enables an easy access from mobile devices with Android or iOS operating systems.

Big Data is a huge collection of data whose characteristics of size, variety and location imply a big challenge for the analysis and management using traditional tools and databases. All areas face significant challenges since data must be gathered, stored, cleaned, analyzed and finally presented using modern tools.

The influence of many variables on Manual or Automated Manufacturing processes could affect in many ways the final result. Large amount of data generated during the process has to be stored, gathered and analyzed so that we can anticipate possible failures and mistakes in the analyzed process in advance. Interconnected machines and processes providing data for later analysis under the concept of a “smart factory”.

About opportunities for industry, data driven failure forecasting is a good example, in this case, it requires previous phases which enable a systematization and refining in the prediction, optimizing data collection and generating useful information for decision making.

The phases involved in the development include defining data collection policy, problem understanding phase, development of descriptive analytics and predictive analytics. All those phases must follow a continuous cycle of data exploitation.

Data Science starts with establishing data policy and defining the objectives of analysis, in this case, there is a question that it is needed to be answered from data sources, why do the valves not open at carrying out the process of brake fluid filling?” and it is necessary first to determine if there is a data source that conducts to objective.