How Agristo tackles data diversity with SAP BW/4HANA

Every year, Agristo processes around 800,000 tons of potatoes into a wide variety of frozen foods. Although there’s only one base product, a huge volume of different data points can be collected from field to supermarket, from sales figures and stock amounts to demand predictions. Furthermore, to comply with food regulations, every Agristo product needs to be traced back to its origin, making traceability an absolute must.

From Excel labyrinth to automated data warehouse

“Because of our rapid growth, the need to centralize our data and automate reporting became more pressing,” explains Martijn Mennen, CIO at Agristo. “At the same time, we also wanted to start exploring data analytics. To free up time for this, we had to thoroughly change the way we worked. We often found ourselves lost in a labyrinth of Excel spreadsheets, which could lead to arguments and misunderstandings.”

After a thorough assessment of the situation, delaware experts proposed implementing SAP BW/4HANA: an SAP data warehouse platform equipped with SAP tooling. “You can think of a data warehouse as a traditional warehouse, filled with boxes of data,” says Bruno Windels, data governance manager at Agristo. But there is more to it. “During the construction of this warehouse, you’ll already have to decide how you are going to organize your data and how it will find its way to the right location. In a data warehouse, all the ‘goods’ are stored in a standardized way. As a result, there is a ‘single source of truth’ for everyone to rely on.”

5 advantages of SAP BW4/HANA

Apart from increased standardization and unparalleled data clarity, the implementation of a data warehouse has numerous other benefits for Agristo. These include:

• Increased speed. Information is rapidly made available, enabling the business to adjust faster as well. “In the past, monthly sales reports could only be delivered by the 5^th day of the month because of the amount of manual work involved. Now, the reports are ready on the first day of each month,” explains Bart Van Peteghem, SAP Analytics solution lead at delaware.
• User friendliness and precision. “Our data end users don’t need to put together spreadsheet reports on their ‘islands’. With a data warehouse, you can also get rid of data exports and the use of Excel as a reporting tool,” Bruno continues. “What’s more, the tools allow colleagues to make ad hoc analyses with close-to-real-time information.”
• Find out ‘why’. “Because of cleaner data and the existence of a single source of truth, Agristo gained more insights into its own business processes,” explains Bart. “Not only do they get timely answers on what, how, when and how much, but also on why. ‘Why does a certain production line have a lower output’, for example. Or ‘why does maintenance take longer in production line B’. These insights help Agristo optimize its planning and correct course where needed.”
• Transparency. These insights also enable Agristo to respond faster to questions from customers or regulatory bodies. “Some clients, like supermarket chains, need our stock reports to be able to anticipate changing customer behavior,” Bart adds.
• Talent development. “We’ve noticed a shift in our organization,” Bruno continues. “Employees who used to make reports have evolved into proactive team members who take actions based on insights generated by the reports. In addition, end users are a lot less dependent on internal IT services.”

3 tips to get started with a modern data warehouse

• Free up an employee or even a team to bridge the gap between end users and your external IT partner. This will help to assure that the needs of end users and the implemented solutions are aligned.
• Transitioning to central reporting and automation can’t be done overnight. Setting up a data warehouse is only one part of the equation; training people how to work with it is just as important.
• Using a data warehouse will expose all the imperfections in company data. This provides you with the opportunity to clean up your data pipelines and improve data quality. Also, don’t underestimate the importance of data definitions, and make sure everyone in the organization is aligned on them.

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