On remote Australian mine sites the reliability of their spares is make or break. A single unavailable bearing or pump seal can shut down a shovel or conveyor, while a rushed charter flight to airlift parts erodes margins and adds risk. The foundation of a dependable supply chain is a single source of truth, your ERP, where catalogues, locations, suppliers and usage are monitored consistently across sites. Having solid information and accurate data available in your Pronto Xi ERP enables you to make more informed inventory decisions, reducing the risk of stockouts and excessive freight costs.
Here are some steps you can take to optimise your inventory management with the help of your ERP system to reduce stockouts and costs in 2026.
Enrich and Clean Your ERP Data
In a world where data is king, it is important to have enriched data within your ERP system. The first step to having clean data is to begin with cleansing your item masters, de-duplicating SKUs, and aligning attributes and units of measure so that forecasts and purchasing logic behave consistently. Map critical assets and their BOMs to parts so maintenance plans drive demand signals.
Next, enrich your ERP with context that enhances forecast accuracy. Data such as actual supplier lead times per part, consumption variability, shutdown calendars, and environmental factors like wet-season constraints are all essential for seasonal forecasts. Having a warehouse management tool, such as barcode/RF scanning, also helps provide accurate data to your ERP system.
Set Smarter Min/Max Levels With Advanced Forecasting
Often static min/max or ABC classification is used in forecasting, but they miss the volatility of remote operations such as weather windows, contractor schedules and maintenance. A better approach is to layer policy with probabilistic forecasting. The ABC classification should be reviewed in consideration of actual usage variability and supplier lead time, rather than calendar frequency.
On forecasting, shift from seasonal demand to models that handle intermittent demand and long, uncertain lead times. AI forecasting tools utilise intermittent demand algorithms and can change maintenance plans into time-phased part requirements. When integrated with Pronto Xi’s purchasing and warehouse functions, teams can simulate stockout risk against on-hand, in-transit and supplier assumptions to automatically adjust order quantities, safety stock, and reorder points.
Improve Vendor Lead-Time Accuracy and Visibility
To capture the true cost of stockouts, companies should model not only the purchase price but also expediting premiums, charter flights for breakdowns, and production losses. Supplier lead times with actual receipt history should be monitored, and exceptions should be flagged when variance spikes. Vendor scorecards should be used in conjunction with contracts to ensure forecasts account for supplier capacity constraints.
Strengthen Operational Processes Across Sites
Operationally, your ERP should have a standard catalogue structure: unique IDs for assemblies and components, harmonised units, and vendor alternatives. Use RF scanners integrated into your ERP to close the loop on receipts, bin movements and issues, eliminating incorrect data that degrades forecasts. For multi-site miners, enable transfer suggestions to rebalance slow-moving assets and prevent duplicate spares across nearby sites.
Establish a cross-functional spares, inventory and operations planning that aligns maintenance shutdowns and production campaigns with purchasing and logistics capacity.
Industry insights, such as the Minerals Council’s Digital Mine 2022 report and CSIRO’s TraNSIT research, reinforce the value of data integration and freight optimisation.
Summary
Remote Australian mine sites rely on dependable access to spare parts, making accurate ERP data essential for avoiding stockouts, costly freight, and operational downtime. By cleansing and enriching item data in systems like Pronto Xi, aligning asset structures, and incorporating real-world factors such as supplier lead times, seasonal constraints, and consumption variability, miners can improve forecast accuracy and inventory decisions. Modern approaches such as probabilistic and AI-driven forecasting, dynamic min/max settings, vendor performance monitoring, and integrated warehouse tools help sites better manage uncertainty and optimise stock levels. With strong catalogue standards, multi-site visibility, and cross-functional planning, ERP-enabled workflows can significantly reduce risk, spend, and inefficiencies in 2026.
