Embedded BI in Trade ERP

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In most trading companies, a BI project begins with the same task:

‍Spending days or even weeks extracting data from the ERP, cleaning it, and laboriously transferring it into a data warehouse. Only then can reports be built, usually with a great deal of effort. Anyone in the HVAC, steel, or industrial supply trade looking for an ERP BI solution usually encounters this exact problem first, along with the question of who is actually supposed to handle the setup: the internal IT team, an external consultant, or no one at all.

This is exactly where the embedded BI solution from enventa comes in.

‍The data pools for sales, purchasing, inventory, and financial accounting are already set up and connected to the enventa Trade ERP data structure. Anyone working with Trade ERP can therefore start generating reports immediately instead of having to build a data model first.

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"Normally, 70% of the work in a BI project goes into data management, leaving only 30% for the actual analysis. With the starter set in enventa Trade ERP, that is reversed right from the start of the project. Standard reports and dashboards are available immediately; the bulk of the work is already done." Lukas Motzer, BI Sales Expert for Trade ERP

What is already included in the starter set for Trade ERP

The Trade starter set is a proven BI starter set in the enventa portfolio and covers four areas with pre-built reports:

1. Sales: Margin by representative, revenue by warehouse and product group, quote conversion rate by year, order backlog by warehouse, delivery reliability by month
2. Purchasing: Lead times, supplier evaluation, open invoice audits
3. Financial Accounting: Balance sheet, P&L, open items, cash flow statement, provision schedule
4. Inventory: Inventory reach, turnover rate, inventory movements, inventory movement forecast

These reports access live data from your ongoing ERP operations. A purchasing manager who wants to know which supplier's lead times have recently worsened can open the purchasing dashboard and see the year-over-year variance by supplier. Reliable data instead of gut feeling.

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From report back to ERP

The difference between a BI solution that sits alongside the ERPand one that is embedded within it shows in the details: every report allows for a drill-down from the high-level overview to the individual customer line, and from that line, a right-click takes you directly back to the customer record in the ERP. If you spot an unusual customer in an ABC analysis, you don't have to search for them in a second system.

The reports themselves can be freely customized in terms of row and column structure, analyzed across multiple clients, and expanded ad hoc without the need for a development request.

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Where the data comes from

The analyses are based on ERP data. What is recorded in the ERP during the day is processed overnight and is available in the BI portal the next day . For most retail KPIs, from revenue to inventory reach, this is sufficient, as these are viewed on a daily basis anyway.

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The no-code approach: finally, BI without your own developers

Another difference compared to a classic BI project is evident right from the setup phase: there is no need for someone from your internal IT team or an external consultant to build a data model for the ERP database, with all the complexities that entails regarding units of measure, currencies, and purchase price levels. This model is already included in the starter package.

For the business departments, this means self-service BI in the truest sense: Power users can customize reports themselves, change filters, add columns, or build their own analyses from existing data pools without having to submit a development request every time. This frees up internal IT, and a project that would otherwise tie up months of development capacity starts instead with training and configuration.

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Expansion via drag & drop:
When one data source is no longer enough

The starter set covers ERP and finance data. As soon as you want to add further sources, such as a CRM system, HR data, or external spreadsheets, the Enterprise Query Manager (EQM) handles the connection via self-service. Again, without needing to involve IT or an external consultant. This marks the transition from a single data source (embedded BI) to multiple data sources (engineered BI solution), turning the starter set into a company-wide single source of truth whenever needed.

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BARC study confirms enventa BI:
1st place for short project duration

In the BARC BI & Analytics Survey 26 enventa BI ranked 1st place for project duration compared to other BI providers. The BI solution also received recognition in several other categories.

Read more about the study and the results here: BARC BI & Analytics Survey 26

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Success story from the field: Fischer Kälte-Klima-Gruppe

The Fischer Kälte-Klima-Gruppe, a technical wholesaler for refrigeration and air conditioning technology with over 500 employees and approximately 250 million euros in annual revenue, has been a customer since 2021 and has rolled out enventa trade ERP and BI across all its corporate structures.

According to the employee responsible for BI at the company, the deciding factor was precisely this point: having a robust data model with pre-built standard pools, rather than having to build one from scratch for the complex ERP data structure. As they put it within the company:

"For a company of our size and structure, it is a huge advantage that there is almost nothing in the system that we cannot do ourselves—completely independently and at our own pace, without having to rely on technical services or major consulting firms." Christian Brachvogel, Fischer Kälte-Klima Technik

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Today, around 35 to 40 people work in the BI portal, with the majority actively using drill-downs and customizing reports themselves, right down to the individual document level. Beyond the standard pools, the company has added its own reports, for example for field sales, bonus forecasts, and returns, and now also automates recurring external reports via the task manager that were previously created manually.

Read the full case study here: Reference Christoph Fischer

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