IKEA Breda Client

POS Terminal Deployment Analytics

Turned 1,300+ raw POS-terminal deployment events into a Power BI dashboard that surfaced a 92% compliance rate, flagged anomalous logging across 67 terminals, and gave store operations a clear month-over-month view of where deployments were slipping.

Client

IKEA Breda

Year

2024

Domain

Analytics, Data Visualization

Stack

Power BI · Power Query · DAX

Context

IKEA Breda runs a large network of point-of-sale terminals spread across retail floors, kiosks and food-service areas. Each terminal moves through a lifecycle of deployment, re-deployment and management — but that activity lived as raw event logs, with no easy way to see patterns, catch compliance gaps, or tell whether operations were improving.

I was asked to turn three months of that operational data (June–August 2024) into something the team could actually act on.

What I built

A five-page Power BI dashboard covering the full terminal lifecycle:

  • Overview — headline KPIs across 1,337 deployment events and 67 tracked terminals.
  • Deployment frequency — monthly trends and per-terminal activity, showing deployments settling as the rollout matured.
  • Time-gap analysis — distribution and trend of intervals between deployments, with automatic flags for the negative gaps that signalled back-dated or mis-logged entries.
  • Location performance — comparison across retail, kiosk and food-service zones, which turned out to follow distinctly different operational rhythms.
  • Compliance — weekly compliance tracking that isolated the specific terminals behind the 7.85% non-compliance rate.

Outcome

The dashboard gave operations a single, trustworthy view of terminal management: a 92.15% compliance rate, clear evidence that time-gap performance was improving month over month, and a short, specific list of terminals and locations worth investigating — replacing manual log-reading with something a manager could open and understand in seconds.

What I took from it

This was where I learned to translate a messy operational dataset into decisions a non-technical stakeholder could trust — choosing the few metrics that mattered, and making the anomalies impossible to miss rather than burying them in a table.