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Business Intelligence & Data Analysis | Industry & Mechanical Engineering

Master Data Management as the Foundation—An Agricultural Equipment Manufacturer Achieves Systematic Cost Transparency Through a Multidimensional Data Cube

An international manufacturer of agricultural machinery lacked fact-based cost transparency for production and development: product costs could not be analyzed in a structured manner across relevant dimensions, there was no integrated tool for linking cost elements and process steps, and decisions had previously been made without a reliable data foundation. KBC built a multidimensional data cube that transformed the client’s complex master data—which was distributed across multiple systems—into a clear three-dimensional structure. This structure made further analysis possible and thus forms the core of the client’s Disruptive Cost Optimization (DCO) program.

Master Data Management by the Numbers

Data Dimensions 
Data Sources
as a consolidated database
Models

The Customer

An international manufacturer of agricultural machinery

The customer is an internationally active manufacturer of agricultural machinery with a broad product portfolio and complex, multi-stage manufacturing processes. Given the growing demands for product cost optimization and efficiency within the company’s Disruptive Cost Optimization (DCO) program, there had previously been no integrated tool for analyzing product costs in a structured manner across relevant dimensions. Cost elements and process steps were not systematically linked, meaning that decisions in production and development often had to be made without a reliable data foundation. A key prerequisite for reliable cost analysis was therefore robust master data management, as only consistent and complete master data made in-depth analysis possible in the first place.

The Project

Multidimensional data cube based on sound master data management

KBC built a multidimensional data cube for the client to ensure systematic cost transparency. The cube was based on SAP extracts and Excel spreadsheets, from which the relevant cost and master data were consolidated. To ensure the quality and validity of the data cube, KBC developed an AI-driven approach to master data analysis that identified inconsistencies and gaps in master data management at an early stage and validated the data foundation for further modeling. Based on this, the data cube was modeled across three dimensions: product class, cost types, and value-added steps. To make the results usable for business units, KBC also developed a Power BI application for interactive cost visualization, which allows cost drivers along the product hierarchy to be identified at a glance. The data cube was finally integrated into the company-wide DCO program, where it serves as the central foundation for product cost optimization. 

Portrait photo of Philipp Berger, Lead Software Engineer at Kemény Boehme Consultants (KBC). A man with very short dark brown hair, smiling broadly and looking directly at the camera. He is wearing a dark navy blue suit over a white shirt without a tie, a brown leather belt, and has both hands relaxed in his pants pockets.
Portrait photo of Philipp Berger, Lead Software Engineer at Kemény Boehme Consultants (KBC). A man with very short dark brown hair, smiling broadly and looking directly at the camera. He is wearing a dark navy blue suit over a white shirt without a tie, a brown leather belt, and has both hands relaxed in his pants pockets.

This Is How We View Master Data Management

Even the most impressive data cube is useless if it is based on inconsistent master data. That is why we made a conscious decision to invest in proper master data management before we began the actual cost analysis. This is the only way to create a database that the business units can truly trust.

Philipp Berger, Lead Software Engineer at KBC

The result

Fact-Based Cost Transparency Based on Reliable Data

Thanks to the implementation of the Data Cube, the customer now has fact-based cost transparency for production and development for the first time. Cost analyses can now be conducted in a structured manner across product hierarchies, cost categories, and process steps—something that was previously impossible due to incomplete and scattered data sources. The underlying master data management system, validated by AI, ensures that the analyses are based on a reliable and consistent data foundation. 

Beyond providing immediate cost transparency, the project established the Data Cube as a key success factor for product cost optimization within the client’s DCO program. The interactive Power BI application enables business units to independently identify cost drivers and make informed decisions in production and development. The master data management system that was implemented also provides the foundation for conducting future data analyses with comparably high data quality and for expanding the AI-supported approach to additional use cases. 

Other Projects That Make a Difference

Portrait photo of Philipp Berger, Lead Software Engineer at Kemény Boehme Consultants (KBC). A man with short, dark brown hair is smiling broadly at the camera. He is wearing a dark blue suit with a matching jacket and pants over a white shirt (no tie), as well as a cognac-colored leather belt; both hands are in his pockets, and he has a relaxed and open posture.
Portrait photo of Philipp Berger, Lead Software Engineer at Kemény Boehme Consultants (KBC). A man with short, dark brown hair is smiling broadly at the camera. He is wearing a dark blue suit with a matching jacket and pants over a white shirt (no tie), as well as a cognac-colored leather belt; both hands are in his pockets, and he has a relaxed and open posture.
Philipp Berger
Lead Software Engineer

Facing a similar challenge? Talk to us

Whether it’s master data management, systematic cost transparency, or AI-powered data analysis: In a brief discussion, we’ll determine which steps make sense and how KBC— an international boutique consulting firm based in Munich —can help you move forward. 

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