🏭 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