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Data and Analytics Benefits in Manufacturing

Data and analytics offer numerous advantages in the manufacturing sector, improving product quality by monitoring data during the production process; this way, deviations can be detected and corrected before they lead to defects in the final product.

Analysing data from machine and production processes enhances process efficiency by identifying inefficient methods and optimising them. Additionally, patterns and trends in operational failures can be identified to predict equipment or machine failures, reducing production downtime. Lead times for various processes can also be shortened by optimising processes and automating operations. By optimising inventory management, overstocking can be avoided, ensuring sufficient material availability to meet demand.

The abundance of use cases of data and analytics in the manufacturing industry demonstrates their utmost importance in this sector. We can help you identify the most relevant use cases and increase your company's productivity through clever data management and thoughtful analysis. Benefit from our skilled application of this technology to achieve streamlined processes, improved product quality, and enhanced efficiency.

Well prepared

Expert knowledge in Manufacturing

D&A Use Cases

Here are some of the many use cases we’ve worked on:


  • Standard use cases of data and analytics in manufacturing include reporting solutions for controlling, contribution margin accounting, income, balance, and cash flows.
  • Solutions for Balanced Scorecard, planning, forecasting and consolidation.
  • Integration of CRM with sales and contract reporting.
  • Identification of types of car with near-human accuracy.
  • Reliable error prevention through automated, efficient forecasts.
  • Enabling large-scale, systematic analysis of repair data.
  • Reporting for individual factories (efficiency metrics, failure rates, production output) on a central DWH.
  • Prediction modelling for maintenance data.
  • Reporting for used car administration.
  • Tool evaluation for a planning system for production, sales, and marketing.

Preparation and Tools

Some of the tools and techniques we employed:


  • Apache Solr and Spark, with Banana Framework.
  • Keras with a TensorFlow backend, Databricks, Azure Machine Learning  and REST web service.
  • Kafka, Spark, Oracle, GoldenGate.
  • Migration to IBM Cognos and DataStage.
  • Integration of SAP with non-SAP Systems.
  • IBM DB and Ab Initio hub.
  • Ab Initio, Toad and Oracle.
  • Microsoft SQL Server, SSIS, SSAS.
  • IBM Cognos TM1.
  • Oracle, SAP Business Objects, IBM Cognos.
  • IBM DB2, DataStage and MicroStrategy.

Standards and Regulation

  • In compliance with the European Emission Laws, advertisers are required to label car models in any photo shared on social media. Our team has developed a sophisticated solution to automate this labelling process when car models are copied from personal accounts to the company account for advertising purposes.


  • We developed a technical enterprise standard for semiconductor KPIs.


Our customers in Man­u­fac­tur­ing


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