Real results from real organisations across Western Australia's mining, energy, and government sectors. Every engagement measured. Every outcome documented.
A leading iron ore producer was managing financial consolidation across 12 entities using Excel workbooks emailed between teams. Version control was non-existent, and the monthly close took 10 business days.
Solution Minds implemented IBM Planning Analytics to automate the full consolidation process — including intercompany eliminations, currency translation, and multi-entity P&L and balance sheet. The monthly close now completes in under 3 days, with a full audit trail and real-time variance commentary.
An integrated energy utility needed to align operational demand forecasting with financial planning across its generation, transmission, and retail divisions. Previously, each division planned independently, with finance consolidating the results manually in Excel.
Anaplan's connected platform was deployed across all three divisions, enabling finance to run integrated scenarios across the full value chain in real time — a capability that previously required weeks of manual reconciliation.
A WA State Government agency engaged Solution Minds to consolidate data from 9 disparate systems into a unified Databricks Lakehouse. Legacy reporting relied on manual data extracts and overnight batch jobs, with no real-time visibility into workforce costs or service delivery.
The Databricks implementation enabled predictive workforce analytics, real-time budget monitoring, and service demand forecasting across 4,000+ employees — identifying $4M in budget savings opportunities in the first year of operation.
A mid-tier gold mining company was spending 12 weeks on its annual budget process, with Finance manually consolidating inputs from 8 mine sites and 3 processing facilities. IBM Planning Analytics replaced the process with a structured web-based input workflow and automated consolidation — cutting the budget cycle to under 5 weeks.
A WA power generation company was experiencing unplanned outages averaging 4 per quarter, each costing $500K+ in lost generation and emergency maintenance. Solution Minds built a Databricks ML model ingesting SCADA sensor data to predict failures 48–72 hours in advance — reducing unplanned outages by 30% in the first year.
A government health services agency was consistently unable to forecast demand with sufficient accuracy, leading to staffing mismatches and service quality issues. Solution Minds built an AI forecasting model on Databricks that ingested 5 years of demand history, demographic data, and seasonal patterns — achieving 98% accuracy on 30-day demand forecasts.
Let's talk about what a successful implementation looks like for your organisation.