Mining Intelligence Tools: How to Consolidate Information and Drive Business Decisions
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Presented at the 2013 Society of Mining, Metallurgy and Exploration Annual Meeting (SME 2013). Operations managers are under constant pressure to optimize plant production processes. To manage this goal, they need to maximize plant effectiveness and reliability, which requires a steady stream of detailed information. This presents a significant challenge as mining operations need not only to collect this valuable data but then be able to understand and drive business and operation decisions from it.
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Mining Intelligence Tools: How to Consolidate Information and Drive Business Decisions
- 1. MINING YOUR OWN BUSINESS HOW TO CONSOLIDATE INFORMATION AND DRIVE BUSINESS DECISIONS Richard Witucki Schneider Electric SME Annual Meeting and Exhibition 2013
- 4. Aggregation – 10 sec. sample (15 min.) 120 100 80 60 40 20 0
- 5. Aggregation – 1 day – 15 minute avg 200 150 100 90 Samples 50 0 -50 -100
- 8. Contextualization How should we characterize the data? Production Downtime Metrics Consumption Over Quality Maintenance Consumption Planning Knowledge Inventory Recipe Safety Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 10. Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 11. Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 12. Pareto Analysis Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 13. Pareto Analysis Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 14. Pareto Analysis Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 15. Gantt Analysis Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 16. Gantt Analysis Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 17. Production Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 18. Production Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 19. Production Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 21. Our methodology we focus on people Our philosophy we care about visual design Our technology our methodology and design philosophy, integrated into a purpose-built platform Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 22. Dashboard – Site Energy Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 23. Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 24. Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 25. Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
- 27. Thank You! Schneider Electric - Industry Business – MINING YOUR OWN BUSINESS – 2013
Editor's Notes
- Once we have automated our data gathering as much as possible we run into problems such as aligning the data, aggregating the data and classifying the data to add further value and give it useful meaning. Aligning the data is important for several reasons:We might have multiple control systems that are networked together into a DCS or SCADA system, or data arriving late from a LIMS or ERP system. All of these systems operate on their own and make aligning data a necessity. Fortunately we can use a technique called Historical Data Processing. Historical Data Processing or HDP is a method of reconstructing the data sequentially by timestamp. Each calculation is dependent on a set of values. As the values arrive from the different sources the HDP engine aligns the values based on their timestamps and an accurate calculation is made. The HDP awaits on any outstanding or late arriving data that the calculation is dependent on before it finalizes the calculations.Instead of one instantaneous sample we have a stream of samples.This is so important that a numberof vendors are starting to incorporating bits and pieces into their offering and is exactly what the industry needs to move towards more reliable data.Historical Connectors ProsLow poll-rateCan back-fill lost data Real-time connectorsCan connect to any system that has dataUses a real-time interface or protocol to obtain ‘current’ values.No concept of getting values from a time other than ‘now’Requires the connector to be permanently online to avoid missing changesHigh poll/update rate required to avoid missing changesWe have made great strides in industry in gathering data: Obviously automatic data gathering has taken care of many of the problems that have plagued us in the past. Problems such as manually entering data that is outof bounds, duplicating data, entering data with the wrong units (not converting the data), missing decimal points, missing data. These types of problems get exacerbated when we start handling the data more than once such as when we write it on a clipboard and then enter the data into a spreadsheet.
- 90 SamplesAnalisysParalisys
- TraditionalDundasData graphics are designed to make data flashyTake the numbers and make them sizzle with charts and speedometersDesign is how it looks and feelsUsers want the latest gadgets and widgetsReduce detail by increasing the number drill-downs and interactivityVisualization helps people understand data in ways that numbers alone cannotFind the problem and use visualization and design to solve itDesign is how it worksPeople want to solve problems better and fasterReduce work by making detail easier and simpler to understand