Energy Intelligence Dashboard

Technology Reply realizes Business Intelligence solutions for the analysis and monitoring of Plant emissions.

Scenario

In recent years, for all our customers, and in particular for customers in the Manufacturing sector, the assessment of the emissions produced by the various plants has become increasingly important. The scope of reducing emissions caused by the production process meets global needs for pollution reduction, energy saving and government constraints imposed by different nations.

Technology Reply supports its customers in the study of indicators that allow to define how green a plant is, in the definition of company data that responds appropriately to monitoring needs and in the creation of Business Intelligence solutions that allow punctual and proactive interventions.

The customer, world leader in the Tire & Manufacturing sector, expressed the need for a data analysis and data discovery tool which, for each site, allows the analysis of different types of indicators:

  • CO2 emissions

  • Energy consumption

  • Cost of production

Solution

Technology Reply took care of the identification of the indicators and calculation logics, the development of the entire pipeline for the reception and integration of data on the Data Platform, the creation of the Business Intelligence and Self-Selvice Analytics application and finally of the automation of the whole end-to-end process.

The Data Integration process within the Cloud Data Platform was implemented through a set of configurable ETLs which, supported by a Data Quality process, allowed the integration and rationalization of heterogeneous data.

The data, in fact, have been cleaned up, normalized, filtered and reorganized in order to make them homogeneous and as suitable as possible for the Business Intelligence analyses.

The QlikSense platform was adopted as a Business Intelligence and Data Visualization tool, integrating the standard functions with the Vizlib extension, allowing the construction of an interactive dashboard that simplified the consultation of the KPIs by end users.

A Data Entry tool was also created, which allowed business users to manage autonomously all data not present in company systems or that cannot be integrated within the Data Platform.

The Data Integration component was structured according to the customer's guidelines which provide for the implementation of a three-level data architecture: Staging Layer, Layer 1, Layer 2.

  • On the Staging Layer all the daily photographs of the data are saved, in order to have all the history available

  • On the first level the data updated to the last available photograph is saved

  • On the second level (Business layer) the Business calculation logics are applied, creating the data structure which can then be queried by users via Qlik Sense

The design of the frontend solution was shared with the end users step by step, creating an intuitive layout that could meet every business need.

The application is user oriented and consists of a guided navigation that allows you to move intuitively between the various pages in order to consult the KPIs of interest; there are different types of reports, from simple table views to interactive maps that guide the analysis of the data starting from the geographical location of the plants.

The Self-Service Analysis section allows users to autonomously create new types of reports starting from the set of information available to them; all indicators and attributes of interest have been modeled to simplify consultation in Self-Service mode.

Access to the use of the data is managed through a profiling mechanism which allows only the information necessary for the user to be displayed, guaranteeing its confidentiality.

Advantages

  • Intuitive and user friendly graphic interface, combined with efficient data management, totally delegated to the software

  • Centralization of information and of historical information within the Enterprise Data Platform

  • Data modeling which allows future integration with additional information sets

  • Self Service Analytics: ability to perform analyzes by building Qlik Sense objects autonomously

  • Accurate data profiling per plant