Usecase FI & S

FI&S operates in the food processing industry. They develop, produce and sell tailor-made blends to add more value to food products. By offering food processors functionality, FI&S is able to make its customers' products more valuable in terms of economy, customer experience, quantity, product quality, process quality, consistency, (food) safety, reliability and appearance.

Challenge

FI&S wanted to gain more insight into their business data and make data driven business decisions. As with most medium-sized companies, the data processing and data analytics were performed in Excel. Multiple Excel files were used instead of one organized dashboard. This resulted in a lot of repetitive work and slow operations because the data could not be viewed live and decisions could not be made immediately based on the data. The ERP system's database, called CSB, contained more than 3,500 tables with approximately 200 columns per table. Little documentation was publicly available about the ERP system and the column names had German abbreviations. 

The goal was to filter out the usable data in this abundance of data and convert it into a data warehouse, so that it could be used for data analytics.

Process

The ERP system CSB runs on a SQL Server database, in which all data is registered: from customer orders and warehouse stock to production data and delivery times. For example, by searching SQL in the CSB database for amounts that matched the packing slips and invoices, the correct tables could be created in the data warehouse.

After detecting the correct data in the CSB system, we advised FI&S to use PowerBI for the data visualisations. We then made the link between PowerBi and the CSB system and further expanded the data warehouse in PowerBi, where we then constructed the visuals.

Solution

In our collaboration it became clear that a data warehouse was necessary for a good foundation of the data structure and that PowerBI visuals are a good solution to visualize the KPIs of FI&S.

Becoming a data-driven organization is, as CEO Noud van Stekelenburg pointed out, a multi-year process that can be divided into different phases: preprocess the data, construct a data warehouse, visualize the data in dashboards, and make descriptive and predictive analyses.

We look forward to expanding these steps further in the coming years in collaboration with the innovative FI&S.