🏭 REAL-TIME OPS VISIBILITY

Warehouse KPI
Dashboard

📦 Inventory Power BI SQL

Warehouse managers lack a unified view of pick rates, order accuracy, capacity utilisation, and throughput across shifts. Data sits in WMS exports that require manual Excel manipulation — causing reporting delays, missed inefficiencies, and shift-level blind spots worth thousands in lost productivity per month.

// live metrics

12 Warehouse KPIs

// shift performance

Shift-Level Drill-Down

// analytics dashboard

Performance Charts

Pick Rate by Shift (Units/hr)

Average hourly pick rate across Morning, Afternoon, Night shifts over 8 weeks

Order Accuracy Rate (%)

Daily order accuracy trend — target 99.5% accuracy with week-over-week view

Capacity Utilisation by Zone (%)

Current storage utilisation per warehouse zone — threshold at 85%

Dock-to-Stock Time (hrs)

Average inbound processing time from dock arrival to put-away completion

Outbound On-Time Rate (%)

Daily outbound despatch on-time performance against committed cut-off times

Returns Processing Time (hrs)

Average time to process and restock or quarantine returned units per shift

// dataset

📊 Raw Dataset

Real WMS-style data used in this analysis — 50 rows shown. Download full dataset below.

#	Date	Shift	Zone	Operative ID	Units Picked	Orders	Accuracy %	Dock→Stock (hrs)	Capacity %	Returns	OTD %	Overtime hrs

// methodology

Problem → Solution → Findings

🔴▶ PROBLEM

Warehouse managers had no real-time view of pick rates or order accuracy — KPI reports required 2–3 hours of manual Excel extraction from the WMS every morning
Shift-level performance differences were invisible, meaning low-performing shifts were never identified or coached in time to recover within the same working day
Capacity utilisation was tracked by zone in separate spreadsheets, causing bottlenecks in high-density zones that weren't flagged until overflow occurred
Returns processing backlogs built up undetected across shifts, creating stock accuracy issues that fed downstream fulfilment errors

🔵▶ SOLUTION

SQL data pipeline aggregating WMS operative-level pick data, dock events, and despatch records into a single warehouse_kpi_daily table refreshed every 15 minutes
Power BI dashboard with 12 KPI cards, shift-level drill-down, and zone-level capacity heat-map — visible on floor-mounted screens and manager tablets
Automated daily PDF report generated at 08:00 summarising previous day's performance by shift, emailed to warehouse manager and ops director automatically
Threshold-based alerts: pick rate drops below 85 units/hr or accuracy falls below 99.2% trigger a Slack/Teams notification to the shift supervisor within 5 minutes

🟢▶ FINDINGS

Night shift consistently underperformed on pick rate (avg 79 units/hr vs 98 for Morning) — root cause: inadequate RF scanner provisioning and single zone access after 22:00
Zone C operated at 91% capacity on average — 6 percentage points above the safety threshold, causing 23% of pick delays due to congestion in narrow aisles
Dock-to-stock time averaged 3.8 hours on Monday and Friday (inbound peaks) vs 2.1 hours mid-week — staffing didn't align with inbound schedule
Returns processing time improved 31% after dashboard go-live as supervisors could see backlog building in real time and redeploy operatives proactively
Order accuracy improved from 98.7% to 99.4% within 6 weeks as shift-level visibility created accountability and catch-and-correct behaviours

// outlook

📈 Forecast & Projections

Weekly Throughput Forecast — Units Picked (Next 12 Weeks)

Historical throughput (solid) vs projected (dashed) based on order volume trends and seasonal uplift modelling

Base Case: Weekly throughput is projected to grow from approximately 42,000 units/week to 51,000 units/week over the next 12 weeks, driven by Q2 seasonal volume uplift (+8%) and planned headcount addition of 4 operatives in Week 7. Capacity utilisation is forecast to reach 88% in peak weeks, requiring a zone rebalancing exercise before Week 9.

// strategic guidance

💡 Advice for Companies with Similar Challenges

Warehouse Operations Visibility — Key Recommendations

Start with just 5 KPIs on your warehouse dashboard — pick rate, order accuracy, capacity utilisation, on-time outbound, and returns rate. Adding more metrics without acting on the basics creates noise rather than insight. Master the core 5 first, then layer in complexity.
Shift-level granularity is where the real value lies. Aggregate daily data masks the variance between morning, afternoon, and night shifts that accounts for 15–20% productivity gaps in most warehouses. You cannot manage what you cannot see at shift level.
Connect your dashboard to your WMS's raw SQL tables, not Excel exports. Manual extraction creates a 12–24 hour reporting lag that makes your dashboard a historical artefact rather than an operational tool. SQL queries running on a 15-minute refresh cost nothing but deliver enormous value.
Set threshold-based alerts before your dashboard goes live. A dashboard no one looks at is worthless — but a dashboard that sends an alert when pick rate drops below target forces attention at exactly the right moment.

// KEY TAKEAWAY

A well-built warehouse KPI dashboard doesn't just report what happened yesterday — it enables same-shift intervention. The goal is to get management response time from 24 hours (morning report) down to under 15 minutes (real-time alert). That single change, in a 50-person warehouse operating 3 shifts, is worth €80,000–€150,000 per year in recovered productivity.

Warehouse KPIDashboard

Warehouse KPI
Dashboard