Inventory management Credit: RPMGlobal[/caption]
RPMGlobal has expanded its optimization and artificial intelligence (AI) suite of technology solutions with the acquisition of Canada-headquartered IMAFS, an inventory optimization management software company,.
With the closing of the acquisition, the company has added a cloud-delivered, inventory management and forecasting software solution to its suite of technology solutions that uses proprietary AI algorithms to improve inventory management.
IMAFS analyzes inventory data from the corporate Enterprise Resource Planning (ERP) systems to deduce the optimal timeframe for inventory orders, costs and order frequency. IMAFS not only supports several forecasting methods, but also takes advantage of AI and machine learning to optimize the calculation of the key variables in inventory management.
The ‘forward looking’ predictive algorithms become particularly powerful when used in conjunction with RPMGlobal’s asset management system, AMT. AMT uses a Dynamic Life Cycle Costing engine that forecasts, in real-time, every maintenance event for a piece of equipment, at a component level, until the end of its economic life.
The future demand of an organization’s assets, combined with the optimization algorithms within IMAFS, are critical pieces of the puzzle to optimize procurement and management of critical parts and components.
The delivery of an integrated AMT and IMAFS offering delivers an industry-first, predictive solution with more certainty of the future and less reliance on historical data.
ERP systems typically use historical consumption to predict future requirements, which has its limitations in a highly volatile maintenance and repair focused industry, such as mining. This future demand knowledge will further improve the accuracy of parts availability, reducing inventories, decreasing stockouts and reducing equipment downtimes.
IMAFS is a proven industry solution with one operation reducing 78% of stockouts for items with high usage, while concurrently reducing stock levels by 14% for the items controlled by IMAFS. Another operation was able to reduce global inventory by 15% within the first 10 months without any diminution of service levels.
“The company is leading the way in the practical application of innovative technology that increased miners’ operational competitiveness,” Richard Mathews, RPMGlobal CEO, said in a release. “With our growing suite of optimization products, RPMGlobal has built a capacity to help global operations extract more value where it counts. We are excited to be utilizing AI technologies to improve the efficiency of mining operations across the asset management function. We intend to build on this AI capability by including additional variables in the models that may impact availability for our customers operating in remote locations. This experience will also assist us as we look to use these same advanced techniques to further optimize mine planning and scheduling.”
The acquisition of IMAFS further strengthens RPMGlobal’s Asset Management optimization suite, adding to AMT’s maintenance strategy optimization used by hundreds of customers globally. The introduction of another optimization engine into the portfolio builds on RPMGlobal’s established optimization tools that are already being used in mine design, scheduling, and simulation.
Mathews added that RPMGlobal’s investment in its growing suite of optimization and AI tools reflected the evolving requirements and advances in the various ways operations could extract value.
“The ability to optimize production and identify opportunities for improvement across the mining value chain is proving a key competitive advantage in this dynamic operating landscape, and we are proud to offer innovative solutions that are able to optimize operations in ways that would have been unthinkable in the past. Alongside the continued migration of our mining software and data to the cloud, we are looking forward to delivering enhanced value to our global customer base.”
For more information, visit www.RPMGlobal.com.
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