Operational excellence has become a key source of competitive advantage for many businesses. The tough economic environment demands a relentless focus on the smooth running of the business. Demand volatility, depressed commodity prices and the divergence between rapid-growth and developed markets require a high degree of flexibility, agility and efficiency from corporate operations.
Ideal conditions for Lean mining operations are created when machines, facilities, software and people work together to develop systems with the aim of reducing losses throughout the mining process with its focus on the reduction and elimination of inefficiencies. The types of inefficiencies that are addressed, are inefficiencies of production, time, transportation, processing itself, stock at hand, movement, and making defective products.
Big data analytics
On a broad scale, data analytics technologies and techniques provide a means of analyzing data sets and drawing conclusions about them to help organizations make informed business decisions. Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive, statistical algorithms and what-if analyses powered by high-performance analytics systems.
When big data analytics are applied to mine- and mechanical engineering processes with the objective to reduce and eliminate inefficiencies and improve operational performance, the management of physical assets such as equipment, process plants, machinery and vehicles, focuses at a strategic level on establishing, operating and maintaining an asset portfolio that is aligned with strategic mine engineering objectives within the context of the regulatory and broader organisational environment.
JIT works best with one-piece flow operations where one activity is completed at a time instead of performing each step of a process in batches. This is the production method that best aligns with Lean because it improves efficiencies and makes workers and equipment operate more effectively.
Instead of using traditional push production where scheduling is done based on historical data, since 1996 Tacmin's Lean mining and performance improvement approach which features a clear set of objectives for the delivery process, is aimed at maximizing performance for the customer at the project level, concurrent design, mining, and the application of project controls throughout the life cycle of the project from design to delivery.
Look-ahead scheduling when applied during big data analytics enable us to analyse, predict, implement, monitor, control and optimize mining processes from design-to-pit-to-plant-to-port. In the context of mining, this imply that look-ahead mine production scheduling, should not only be validated for sequence, time, production and cost against historical data, but bench-marked against life-cycle asset performance in the context of OEM and mine site specification, capabilities and projected life-cycle cost controls.
Consequently you will create a cycle of continuous improvement in your mine’s performance, based on collecting data, analysing where it needs to be optimized, predicting where it will need to be applied, implementing relevant action plans in the field, measuring their impact, and engaging all your mining personnel.
To learn more on how we apply processes as referred to here-in visit us at www.tacminmadini.com.au