This integration ensured high-performance querying, near real-time analysis, and the ability to drill into granular warehouse operations data.

The primary goal was to empower warehouse leaders with intuitive, data-driven insights

To enhance operational visibility within warehouse environments, the business required a consolidated view of staff activity and efficiency. The challenge was to transform raw operational data into actionable insights that would help warehouse managers and operations teams make informed decisions on workforce planning, resource allocation, and process optimisation. The solution was built on Azure Synapse Analytics (dedicated SQL pool) as the scalable backend, with a dimensional data model and Power BI reports. This integration ensured high-performance querying, near real-time analysis, and the ability to drill into granular warehouse operations data.

Objective

The primary goal was to empower warehouse leaders with intuitive, data-driven insights to:

• Monitor and evaluate staff performance at multiple levels.
• Understand workforce activeness and activity distribution across shifts.
• Identify performance and process trends that inform workforce planning.
• Surface inefficiencies and bottlenecks to drive continuous process improvement.

Approach

A structured data and analytics approach was taken:

• Data Modeling in Azure Synapse
• Designed a dimensional model optimised for performance and scalability.
• Modeled fact tables around pallet movements, case picking, staff activity, and equipment usage.
• Integrated conformed dimensions for operatives, time, and material handling equipment (MHE).

Power BI Report Design:

• Built interactive reports highlighting productivity, activeness, case picking and pallet movement trends.
• Implemented drill-through capabilities for detailed investigation on staff and MHE levels.
• Created KPI cards, heatmaps, and trend visuals for at-a-glance performance monitoring.
• Enabled flexible analysis with multi-level filters (time of day, staff, equipment, and activity type).
• Advanced Analytics Enablement
• Leveraged time-series insights for identifying peak/low productivity windows.
• Built in comparative analysis for cross-shift and cross-staff benchmarking.
• Ensured performance monitoring with close to real-time refreshes, powered by Synapse pipelines.

Solution

The final solution was a Power BI reporting suite underpinned by Azure Synapse dedicated SQL pool, featuring:

• Operational KPIs: Visualised metrics such as throughput per staff, pallets moved per hour, and MHE utilisation rates.
• Interactive Exploration: Drill-through navigation from warehouse-level overviews down to per-user or per-equipment insights.
• Trend & Heat Analysis: Identified temporal activity spikes, peak demand windows, and inefficiency patterns.
• Configurable Filters: Allowed stakeholders to view performance by operative, shift, activity type, or MHE category.
• Scalability & Transparency: Designed to handle growing warehouse datasets while maintaining responsiveness and accessibility.

Outcome

The business realised significant value across multiple dimensions:

• Operational Visibility: Warehouse managers gained real-time transparency into staff activity, pallet movement and case picking, enabling faster responses to inefficiencies.
• Performance Management: Identified underperforming staff and shifts while spotlighting peak productivity periods, helping to optimise shift structures.
• Workforce Planning: Provided clear evidence for resource allocation, informing when and where additional staffing was required.
• Process Improvement: Surfaced recurring bottlenecks, driving initiatives to streamline workflows and enhance material handling.
• Decision-Making Agility: Equipped leadership with data-backed insights to support both tactical daily operations and strategic planning.
• Accountability & Continuous Improvement: Delivered clear per-user and per-MHE performance tracking, promoting accountability and enabling ongoing optimisation.