Case studies
Data that turns into results.
This is what changes in a company when its data is organised, reliable and on time. Area by area, case by case.
One case, in one image
Monday's meeting: from three numbers to one.
Move the slider and compare. It's what people ask us for most and what you notice most: stop arguing about where the number comes from and start deciding what to do with it.
- BeforeThree files, three different margins and half the meeting spent agreeing.
- AfterA single model in Microsoft Fabric and a Power BI dashboard, up to date every morning.
- ResultThe meeting starts by deciding, not by reconciling.
Typical cases
From problem to result, area by area.
01General ManagementIndustry · 400 employees
Problem
Every month, five people manually pulled together Excel files from six sources for the committee. Figures arrived late and never matched.
Solution
A platform on Microsoft Fabric with a single business model and a management dashboard in Power BI.
- Microsoft Fabric
- Power BI
Result
The committee sees the same number, refreshed every morning. Closing the books stops being a monthly project.
Sources: ERP, Excel
02Human ResourcesServices · 900 employees
Problem
Absenteeism, turnover and headcount lived in different systems. HR answered management with reports made by hand.
Solution
We connected payroll and people management in a single model, where each manager sees only their own team's data.
- Power BI
- Data Governance
Result
HR answers questions on the spot, and each manager looks up their team's data without requesting reports.
Sources: Payroll, time and attendance
03QualityIndustry · production plant
Problem
Non-conformities were logged in one system and analysed months later, when there was nothing left to do.
Solution
A quality data flow with alerts and root-cause analysis by line, shift and supplier.
- Data Engineering
- Power BI
Result
Quality spots deviations as they happen and focuses on the causes that cost the most.
Sources: Quality system, ERP
04SalesDistribution · network of 1,200 customers
Problem
Sales, margins and stock were looked at separately. Nobody knew for sure which customers were really profitable.
Solution
A sales and margin model on Fabric, with views by sales rep, customer and product family.
- Microsoft Fabric
- Power BI
Result
The sales team negotiates with the real margin in front of them and spots at-risk customers sooner.
Sources: Business Central, CRM
05ProductionIndustry · 3 plants
Problem
Machine data wasn't linked to order and cost data. Stoppages were explained by gut feeling.
Solution
We combined plant data with ERP data and added an AI agent that answers questions about efficiency and stoppages.
- AI agents
- Microsoft Fabric
Result
Production asks in natural language and checks every stoppage against reliable data.
Sources: Plant data, ERP
06LogisticsDistribution · 4 warehouses
Problem
Orders left the warehouse with no way of knowing which would arrive late. Customers were the ones who found out about incidents.
Solution
We connected SAP and carriers in Fabric, tracking on-time and in-full deliveries for each warehouse.
- Microsoft Fabric
- Power BI
Result
Logistics sees every morning which orders are at risk and acts before the customer notices.
Sources: SAP, carriers
07Management controlServices · several sites
Problem
Targets lived in Excel and deviations were spotted when it was already too late to correct them.
Solution
A targets and billing model with Business Central, and deviation alerts by business line.
- Power BI
- Automation
Result
Management sees each line's performance at any time and corrects course.
Sources: Business Central, Excel
08Stores and retailRetail · store network
Problem
Sales were in Odoo and targets in various files: nobody saw each store's real margin.
Solution
We brought Odoo and the files together on one platform, with a sales and margin model by store.
- Microsoft Fabric
- Power BI
Result
Each store sees its margin and deviation the next day, without waiting for month-end close.
Sources: Odoo, Excel