Abstract
In today’s competitive business environment, even small improvements in productivity and efficiency can have a huge impact on company’s profitability. Whether it is through reduced lead time/waste, minimized downtime or improved quality, operation managers are looking for any advantage at every stage. Lean management is a long-term operational discipline that methodically seeks to enhance efficiency and quality by eliminating wastage. The concept is being used in varied industries and helped by improved productivity, safety & better management. Lean in being used by few mining companies also at a small scale, which has helped them in limited way. AI/ML is another important technology used by many organizations. Many global mining companies have also used it for improving operational efficiency, which have helped miners in a limited ways in improving the productivity and safety. Mining companies have to deal with tons of data set generated from heavy machinery and plants, which have to be stored, processed, and stored. There are many challenges in the data set generated/used by AI/ML-application for mining operations. One of the key challenges in AI/ML is accessibility and availability of accurate & consistent data. To ensure consistency of data there is a need for a governance structure which will guarantee the availability of required accurate data set for AI/ML system. One possible solution could be, CMMI’s metrics processes used by IT companies and have helped in improving quality. CMMI’s Metrics related processes are being used extensively by the organizations for better monitoring & control of key measures. This paper explores the possibility of usage of CMMI’s metrics practices with lean implementation in mining for ensuring availability of accurate data sets for AI/ML applications. The proposed framework will put a governance model in place to ensure accuracy & consistency of data to be used for AI/ML-applications in Mining.
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More From: International Journal of Research In Science & Engineering
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