Dulux
Optimising Inventory Management for Dulux: Leveraging Data Analytics for Efficient Stock Control
The Opportunity
An Australian paint maker carried $60 million in excess stock, faced inconsistent forecasting, and relied on high-level reporting with no plant, product, or material detail. Their KNIME proof of concept added manual work and reduced agility.
The Delivery
Agile Insights delivered a unified inventory solution using Power BI Premium and Azure Synapse, consolidating SAP, KNIME, and IBP data in one place. Databricks APIs streamlined ingestion, KNIME views were mirrored in Synapse, and automated workflows replaced manual reporting. Stakeholders gained real‑time, granular visibility into inventory performance.
The Outcome
The company cut excess stock, freed capital, and improved profitability. Forecasting became more accurate, decisions more data-driven, and teams reacted faster to demand shifts. A scalable framework now drives continuous improvement, reducing waste, strengthening planning, and boosting confidence in inventory control.
About Dulux
Dulux is a leading Australian manufacturer known for innovation, premium product quality, and strong customer trust. Serving both professional and consumer markets, the company is recognised for its commitment to performance, reliability, and market-leading product development.
An Australian paint manufacturer was burdened with $60 million in excess inventory, driving up storage costs, increasing the risk of expired stock, and tying up capital needed for growth.
Forecasting methods varied across teams, making it difficult to plan accurately or benchmark performance against pre-COVID levels.
Reporting lacked the detail required for effective decision-making; only high-level summaries were available, with no visibility by plant, product, or material.
Their KNIME proof-of-concept added further strain, requiring labour-intensive manual reporting and limiting agility.
Our Solution
Agile Insights delivered a consolidated, analytics-driven inventory management solution built on Power BI Premium and Azure Synapse.
Data from SAP, KNIME Inventory Models, and IBP sources was unified into a single reporting environment. APIs developed in Databricks streamlined ingestion, while KNIME model views were replicated in Synapse to ensure accuracy and accessibility.
Automated workflows replaced manual reporting, and stakeholders gained real-time, granular visibility into inventory drivers, forecasting accuracy, and operational performance.
Reporting
Before: High-level summaries only; no visibility by plant, product, or material; manual KNIME-based reporting
After: Detailed, automated reporting with consistent, trusted data
Decision making
Before: Inconsistent forecasting methods and limited insight made accurate planning difficult
After: Standardised, reliable data enabling confident, statewide decision making
Before: Slow, labour-intensive reporting processes reduced agility
After: Faster, more responsive analytics supporting quicker operational action
Before: Siloed systems, manual processes, and slow progress
After: Streamlined, unified reporting environment improving speed and efficiency
Agile Insights delivered the solution over a structured multi-month program focused on:
Infrastructure design aligned with Power BI best practices
Data integration across SAP, KNIME, and IBP sources
Automation of manual reporting processes
Knowledge transfer to embed capability within internal teams
Conclusion
The company significantly reduced excess inventory, freeing capital for strategic initiatives and improving profitability. Forecasting accuracy improved through deeper insight into inventory drivers, and decision-making became more data-driven across the organisation. Teams responded faster to market changes, identifying demand shifts and risks earlier. The scalable framework now supports continuous improvement, strengthening planning processes, reducing waste, and improving delivery performance. Internal stakeholders gained confidence through clearer reporting and more predictable inventory control.
Download the full case study here.
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