Hello!
📦 Do you really know what's inside your internal warehouse?
In this new project, I’ve developed an inventory dashboard focused on monitoring and analyzing internal consumption in an industrial warehouse. This is not your traditional inventory management setup — it's a system where technicians request materials for daily tasks, and stock must remain available to ensure uninterrupted operations.
🧪 Dataset Generation
Since this isn't a typical inventory solution but one tailored to internal consumption, I created the dataset using a Python script. This allowed me to simulate product movements and restocking in a controlled way — with randomized outputs and automatic replenishments triggered by safety stock levels, along with variable delivery lead times. While the data doesn’t reflect real-world complexity, it provides a solid base for analysis.
🧱 Data Model
From a technical perspective, the model follows a star schema, with two fact tables (movements and orders) and several dimension tables. I used a custom date table instead of Power BI’s built-in one to gain better control over time relationships and calculations.
DAX measures include current stock calculations, time-based KPIs, trends, conditional formatting for visual alerts, and comparative analysis. I also implemented bookmarks for alternate views and parameters for adjustable filters.
📄 Dashboard Structure
🔸 Summary – A high-level overview with KPIs comparing current consumption with last year, items below safety stock, daily output charts, and product trend visuals.
🔸 Products – Product-level analysis by category, showing stock levels, time until safety stock breach, and year-over-year consumption. Also highlights items at risk of stockout.
🔸 IN-OUT – Log of product entries and withdrawals, with weekly consumption trends and technician-based analysis.
🔸 Suppliers and Orders – Insights on pending orders, order history, and supplier-level consumption tracking.
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