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Capgemini_Client-stories_Daimler-Business-critical-data
Client story

Business-critical data available on the go

Client: Daimler AG
Region: Germany
Industry: Automotive

Working with Capgemini, a premium vehicle manufacturer created an SAP BW/4HANA®-based solution to unify its data landscape, simplify the overall reporting process, and enable cutting-edge data analytics

Client Challenge: A need for higher standardisation within the reporting process and desire to gain more value from its data

Solution: Implementation of an SAP BW/4HANA®-based business warehouse for data harmonisation in addition to the development of an interactive dashboard based on Qlik and web technologies.

Benefits:
-Harmonised KPIs and analytical dimensions to improve quality control
-Automated data load and provision
-Defined and documented business logic applied along the data pipeline
-Greater flexibility and higher ability for users to interact with digital tools
-Easy to use and harmonised dashboard available for all devices

The very best in design, production, and service are represented at one of the world‘s leading automotive manufacturers. Quality assurance throughout the organisation guarantees that all vehicles produced are held to the exceptionally high standards customers expect. As part of its ongoing digital transformation, the quality assurance process was identified as a key driver for improved data insights.

Achieving this kind of data-driven approach would necessarily involve the optimisation of the existing quality and production reporting process, which was inhomogeneous in quality and content at the time. The monthly preparation of consolidated reports required substantial manual effort from a variety of departments to create a clear picture of current QA standards. In addition, the data related to these processes was distributed across several departments, leading to redundancies.

Consolidation of an extensive digital warehouse

Capgemini analysed the existing data and identified relevant elements, enabling the definition and implementation of 16 KPIs. Capgemini then built a three-tier solution based on SAP BW/4HANA®, Qlik, and the Angular JS framework to establish a fully automated data integration and reporting platform.

Data is now ingested from 10 different source systems on a regular basis. Cleansing, harmonising, and consolidation of data is also automatically carried out. The Qlik-based interactive reporting engine was set up to define dimensions and key performance calculations. Capgemini also implemented an appealing and interactive web application based on the Angular JS framework.

Following the project, the business community and board members can now gain access to all quality-related data via an easy-to-use web application that can be accessed anytime, anywhere. As a result, fact-based decisions for changes in design and production can be met more easily and quickly.

Quality insights at users’ disposal

Information on the quality reporting is now truly available to the right people at the right time. The solution has ensured absolute consistency in the current reporting landscape and allows the automatisation of the data preparation and integration process. As a result, the overall process now takes hours instead of days and requires minimal business attention.

State-of-the-art front-end data has now become readable and clear. The ability to combine various filters on the dashboard, such as country, product group, and vehicle issue, provides completely new insights and has improved the visibility of the overall quality data. The mobile-first approach of the dashboard also makes it possible to access high-quality information on the go.

Based on the data baseline created in the solution, it is now possible to apply AI to quality data from the past years to anticipate future patterns or customer feedback. The applied algorithm will examine insights from previous production years to help further fine-tune manufacturing and customer service processes. Additionally, Capgemini is currently integrating unstructured data into the quality data. This will enable additional contextual information such as customer comments on top of the KPI results.