{"id":2557,"date":"2026-05-20T18:04:23","date_gmt":"2026-05-20T18:04:23","guid":{"rendered":"https:\/\/mrmr2026.cim.org\/?page_id=2557"},"modified":"2026-05-20T19:39:03","modified_gmt":"2026-05-20T19:39:03","slug":"uncertainty-in-the-metal-supply","status":"publish","type":"page","link":"https:\/\/mrmr2026.cim.org\/fr\/uncertainty-in-the-metal-supply\/","title":{"rendered":"Uncertainty in the Metal Supply"},"content":{"rendered":"
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Incertitude dans l\u2019approvisionnement en m\u00e9taux et gestion des risques dans la planification strat\u00e9gique mini\u00e8re et les r\u00e9serves min\u00e9rales <\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t\t
\n\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Short Course Level:<\/strong> Intermediate<\/span><\/span>
Facilitators: <\/strong>David Francisco Machuca Mory, SRK Consulting (Canada) Inc.<\/span><\/span> & <\/span><\/span>Roussos Dimitrakopoulos, McGill University\u202f<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Short Course Objectives<\/strong><\/p>

  • Discover how and why risk-based models create value and opportunities.<\/li>
  • Explore various applications of geostatistical simulation techniques beyond Mineral Resource estimation, including Value of Information studies and probabilistic Mineral Resource classification.<\/li>
  • Learn how geostatistical simulation can be used for modelling vein, skarn, layered, and other orebody geometries.<\/li>
  • Gain insight into workflows for honouring complex multivariate relationships in simulated models. Understand how geostatistical simulation reduces the biases introduced by linear interpolation methods in Mineral Resource estimates.<\/li>
  • Appreciate why the correct characterization of grade and geological variability is key to accurate production forecasting.<\/li>
  • Learn about efficient simulation methods for modelling orebodies and how to utilize the results in pertinent mining applications.<\/li>
  • Understand how to quantify and utilize grade, tonnage, and metal uncertainty and variability.<\/li>
  • Learn how the confidence metrics provided by geostatistical simulations can be used as robust input for Mineral Resource classification.<\/li>
  • Understand how to use quantified orebody risk in ore reserve estimation, mine planning and design, and mineral project valuation.<\/li>
  • Understand the limitations of conventional pit optimization and its effects on reserve estimation, with examples.<\/li>
  • Learn about the new stochastic mine planning framework for life-of-mine optimization.<\/li>
  • Learn about the simultaneous stochastic optimization of mining complexes and mineral value chains with supply and demand uncertainty.<\/li>
  • Be exposed to actual industry examples and comparisons of stochastic mine planning and production scheduling in diverse applications (gold, copper, iron ore, and nickel laterite mines), including effects on reserve estimation.<\/li>
  • Be introduced to uncertainty in recoverable reserves, pit design, and production scheduling using simulated orebodies (e.g., disseminated gold deposit).<\/li>
  • Be exposed to a new cut-off grade optimization framework for mineral value chains, affecting ore reserve estimation and reporting.<\/li>
  • Understand why the stochastic mine planning framework generates substantially better mine plans and production forecasts, representing an important paradigm shift.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t

    Target Audience<\/strong><\/p>

    Mineral Resource and Mineral Reserves Managers, Mine Geologists, Mine Planners.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

    \n\t\t\t\t
    \n\t\t\t\t\t

    Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t

    Geological uncertainty affects the mining material supply (material types and grades), and it is quantified with geostatistical simulations. It can be integrated with demand uncertainty (markets) into the new digital technologies for life-of-mine planning, as part of strategic risk management. This one-day course is divided into two components: (1) Mineral Resources Uncertainty Characterization and (2) New Digital Technologies and Risk Management in Strategic Mine Planning.<\/p>

    The first part will cover modern geostatistical simulation techniques for the stochastic characterization of geological, grade, and geometallurgical uncertainties affecting material supply. Categorical simulation techniques can be used to characterize the tonnage and geometric uncertainty of different types of deposits, while continuous simulation techniques can address the complex multivariate relationships between grade and geometallurgical variables. The use of stochastic approaches for modelling mineral resources makes it possible to identify opportunities to increase confidence in mineral resource estimates. Mineral resource classification can benefit from the quantitative confidence metrics provided by simulations.<\/p>

    The second part of the course presents a new generation of applied technologies that take mine planning and production scheduling optimization\u2014as well as asset valuation, including reserve estimation and reporting\u2014to a new level:\u202fSimultaneous optimization of mining complexes \u2013 mineral value chains with uncertainty.<\/p>

    A\u202fmining complex \u2013 mineral value chain\u202frefers to the integration of mining and processing operations with multiple pits and\/or underground mines, multiple metals or minerals, stockpiles, blending options, and alternative processing streams to yield sellable products delivered to various customers and\/or the spot market.<\/p>

    Simultaneous optimization of mining complexes\u202faims to generate a production schedule for the various mines and processing streams that maximizes the economic value of the enterprise as a whole, in terms of the market value of metal product(s). This approach provides an advanced framework and methodologies for reserve estimation and classification.<\/p>

    Emphasis is placed on downstream applications pertinent to the feasibility, development, and planning stages of mining ventures, as well as on the\u202ffinancial optimization\u202fof relevant aspects of operations and production, with particular focus on\u202fmineral reserve estimation and classification.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

    \n\t\t\t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t

    About the instructors<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
    \n\t\t\t\t\t
    \n\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t

    David F. Machuca,\u202fPh.D., P.Eng. is a Principal Consultant in Geostatistics at SRK Consulting Canada. <\/h4>\t\t\t\t<\/div>\n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t

    David F. Machuca, Ph.D., P.Eng. is a Principal Consultant in Geostatistics at SRK Consulting Canada. David has 20 years of experience in mining operations, academic research and consulting related to the evaluation of various types of mineral and energy resources.<\/p>

    His areas of expertise include the application of standard and advanced geostatistical methods for mineral resource modelling, grade and geological uncertainty and risk assessment and value of information studies. His consulting activities also include the auditing of mineral resource estimation processes, due diligences of mining projects in various stages, as well as the preparation of technical reports and training.<\/p>

    He holds a PhD from the University of Alberta and a Postgraduate Diploma in Mining Geostatistics from MINES ParisTech. Prior to joining SRK, David was a research associate at COSMO \u2013 Stochastic Mine Planning Laboratory where he conducted research on advanced geostatistical simulation methods. He has taught basic and advanced geostatistics and sampling theory courses in English, Spanish and French for universities and the industry in Canada, Africa, and Latin America.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

    \n\t\t\t\t
    \n\t\t\t\t\t

    Roussos Dimitrakopoulos\u202fis a professor of the Department of Mining and Materials Engineering at McGill University<\/h4>\t\t\t\t<\/div>\n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t

    Roussos Dimitrakopoulos is a Professor and Canada Research Chair (Tier I) in Sustainable Mineral Resource Development and Optimization under Uncertainty, and Director of the COSMO – Stochastic Mine Planning Laboratory.\u00a0<\/p>

    He holds a PhD from \u00c9cole Polytechnique de Montr\u00e9al and an MSc from the University of Alberta. He works on risk- based simulation and stochastic optimization, as well as on artificial intelligence applications in mine planning and production scheduling, along with the simultaneous optimization of mining complexes and mineral value chains under uncertainty.<\/p>

    \u00a0He has taught short courses and worked in Australia, North America, South America, Europe, the Middle East, South Africa and Japan. He received the Synergy Award of Innovation in 2012 by the Governor General of Canada for research contributions to mining science and engineering and his long-standing partnership with AngloGold Ashanti, Anglo American, Agnico Eagle, BHP, De Beers, IAMGOLD, Kinross Gold, Newmont and Vale.\u00a0<\/p>

    In 2013, he received AIME\u2019s Mineral Economics Award, was a CIM Distinguished Lecturer in 2015-2016, became a CIM Fellow in 2018 and a RSC Fellow in 2022.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

    Uncertainty in the Metal Supply and Risk Management in Strategic Mine Planning and Ore Reserves Short Course Level: IntermediateFacilitators: David Francisco Machuca Mory, SRK Consulting (Canada) Inc. & Roussos Dimitrakopoulos, McGill University\u202f Short Course Objectives Discover how and why risk-based models create value and opportunities. Explore various applications of geostatistical simulation techniques beyond Mineral Resource estimation, including Value of Information studies and probabilistic Mineral Resource classification. Learn how geostatistical simulation can be used for modelling vein, skarn, layered, and other orebody geometries. Gain insight into workflows for honouring complex multivariate relationships in simulated models. Understand how geostatistical simulation reduces the biases introduced by linear interpolation methods in Mineral Resource estimates. Appreciate why the correct characterization of grade and geological variability is key to accurate production forecasting. Learn about efficient simulation methods for modelling orebodies and how to utilize the results in pertinent mining applications. Understand how to quantify and utilize grade, tonnage, and metal uncertainty and variability. Learn how the confidence metrics provided by geostatistical simulations can be used as robust input for Mineral Resource classification. Understand how to use quantified orebody risk in ore reserve estimation, mine planning and design, and mineral project valuation. Understand the limitations of conventional pit optimization and its effects on reserve estimation, with examples. Learn about the new stochastic mine planning framework for life-of-mine optimization. Learn about the simultaneous stochastic optimization of mining complexes and mineral value chains with supply and demand uncertainty. Be exposed to actual industry examples and comparisons of stochastic mine planning and production scheduling in diverse applications (gold, copper, iron ore, and nickel laterite mines), including effects on reserve estimation. Be introduced to uncertainty in recoverable reserves, pit design, and production scheduling using simulated orebodies (e.g., disseminated gold deposit). Be exposed to a new cut-off grade optimization framework for mineral value chains, affecting ore reserve estimation and reporting. Understand why the stochastic mine planning framework generates substantially better mine plans and production forecasts, representing an important paradigm shift. Target Audience Mineral Resource and Mineral Reserves Managers, Mine Geologists, Mine Planners. Abstract Geological uncertainty affects the mining material supply (material types and grades), and it is quantified with geostatistical simulations. It can be integrated with demand uncertainty (markets) into the new digital technologies for life-of-mine planning, as part of strategic risk management. This one-day course is divided into two components: (1) Mineral Resources Uncertainty Characterization and (2) New Digital Technologies and Risk Management in Strategic Mine Planning. The first part will cover modern geostatistical simulation techniques for the stochastic characterization of geological, grade, and geometallurgical uncertainties affecting material supply. Categorical simulation techniques can be used to characterize the tonnage and geometric uncertainty of different types of deposits, while continuous simulation techniques can address the complex multivariate relationships between grade and geometallurgical variables. The use of stochastic approaches for modelling mineral resources makes it possible to identify opportunities to increase confidence in mineral resource estimates. Mineral resource classification can benefit from the quantitative confidence metrics provided by simulations. The second part of the course presents a new generation of applied technologies that take mine planning and production scheduling optimization\u2014as well as asset valuation, including reserve estimation and reporting\u2014to a new level:\u202fSimultaneous optimization of mining complexes \u2013 mineral value chains with uncertainty. A\u202fmining complex \u2013 mineral value chain\u202frefers to the integration of mining and processing operations with multiple pits and\/or underground mines, multiple metals or minerals, stockpiles, blending options, and alternative processing streams to yield sellable products delivered to various customers and\/or the spot market. Simultaneous optimization of mining complexes\u202faims to generate a production schedule for the various mines and processing streams that maximizes the economic value of the enterprise as a whole, in terms of the market value of metal product(s). This approach provides an advanced framework and methodologies for reserve estimation and classification. Emphasis is placed on downstream applications pertinent to the feasibility, development, and planning stages of mining ventures, as well as on the\u202ffinancial optimization\u202fof relevant aspects of operations and production, with particular focus on\u202fmineral reserve estimation and classification. About the instructors David F. Machuca,\u202fPh.D., P.Eng. is a Principal Consultant in Geostatistics at SRK Consulting Canada. David F. Machuca, Ph.D., P.Eng. is a Principal Consultant in Geostatistics at SRK Consulting Canada. David has 20 years of experience in mining operations, academic research and consulting related to the evaluation of various types of mineral and energy resources. His areas of expertise include the application of standard and advanced geostatistical methods for mineral resource modelling, grade and geological uncertainty and risk assessment and value of information studies. His consulting activities also include the auditing of mineral resource estimation processes, due diligences of mining projects in various stages, as well as the preparation of technical reports and training. He holds a PhD from the University of Alberta and a Postgraduate Diploma in Mining Geostatistics from MINES ParisTech. Prior to joining SRK, David was a research associate at COSMO \u2013 Stochastic Mine Planning Laboratory where he conducted research on advanced geostatistical simulation methods. He has taught basic and advanced geostatistics and sampling theory courses in English, Spanish and French for universities and the industry in Canada, Africa, and Latin America. Roussos Dimitrakopoulos\u202fis a professor of the Department of Mining and Materials Engineering at McGill University Roussos Dimitrakopoulos is a Professor and Canada Research Chair (Tier I) in Sustainable Mineral Resource Development and Optimization under Uncertainty, and Director of the COSMO – Stochastic Mine Planning Laboratory.\u00a0 He holds a PhD from \u00c9cole Polytechnique de Montr\u00e9al and an MSc from the University of Alberta. He works on risk- based simulation and stochastic optimization, as well as on artificial intelligence applications in mine planning and production scheduling, along with the simultaneous optimization of mining complexes and mineral value chains under uncertainty. \u00a0He has taught short courses and worked in Australia, North America, South America, Europe, the Middle East, South Africa and Japan. He received the Synergy Award of Innovation in 2012 by the Governor General of Canada for research contributions to mining science and engineering and…<\/p>","protected":false},"author":6,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_kad_post_transparent":"","_kad_post_title":"hide","_kad_post_layout":"fullwidth","_kad_post_sidebar_id":"","_kad_post_content_style":"unboxed","_kad_post_vertical_padding":"hide","_kad_post_feature":"hide","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"class_list":["post-2557","page","type-page","status-publish","hentry"],"yoast_head":"\nUncertainty in the Metal Supply - MRMR 2026<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mrmr2026.cim.org\/fr\/uncertainty-in-the-metal-supply\/\" \/>\n<meta property=\"og:locale\" content=\"fr_CA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uncertainty in the Metal Supply - MRMR 2026\" \/>\n<meta property=\"og:description\" content=\"Uncertainty in the Metal Supply and Risk Management in Strategic Mine Planning and Ore Reserves Short Course Level: IntermediateFacilitators: David Francisco Machuca Mory, SRK Consulting (Canada) Inc. & Roussos Dimitrakopoulos, McGill University\u202f Short Course Objectives Discover how and why risk-based models create value and opportunities. Explore various applications of geostatistical simulation techniques beyond Mineral Resource estimation, including Value of Information studies and probabilistic Mineral Resource classification. Learn how geostatistical simulation can be used for modelling vein, skarn, layered, and other orebody geometries. Gain insight into workflows for honouring complex multivariate relationships in simulated models. Understand how geostatistical simulation reduces the biases introduced by linear interpolation methods in Mineral Resource estimates. Appreciate why the correct characterization of grade and geological variability is key to accurate production forecasting. Learn about efficient simulation methods for modelling orebodies and how to utilize the results in pertinent mining applications. Understand how to quantify and utilize grade, tonnage, and metal uncertainty and variability. Learn how the confidence metrics provided by geostatistical simulations can be used as robust input for Mineral Resource classification. Understand how to use quantified orebody risk in ore reserve estimation, mine planning and design, and mineral project valuation. Understand the limitations of conventional pit optimization and its effects on reserve estimation, with examples. Learn about the new stochastic mine planning framework for life-of-mine optimization. Learn about the simultaneous stochastic optimization of mining complexes and mineral value chains with supply and demand uncertainty. Be exposed to actual industry examples and comparisons of stochastic mine planning and production scheduling in diverse applications (gold, copper, iron ore, and nickel laterite mines), including effects on reserve estimation. Be introduced to uncertainty in recoverable reserves, pit design, and production scheduling using simulated orebodies (e.g., disseminated gold deposit). Be exposed to a new cut-off grade optimization framework for mineral value chains, affecting ore reserve estimation and reporting. Understand why the stochastic mine planning framework generates substantially better mine plans and production forecasts, representing an important paradigm shift. Target Audience Mineral Resource and Mineral Reserves Managers, Mine Geologists, Mine Planners. Abstract Geological uncertainty affects the mining material supply (material types and grades), and it is quantified with geostatistical simulations. It can be integrated with demand uncertainty (markets) into the new digital technologies for life-of-mine planning, as part of strategic risk management. This one-day course is divided into two components: (1) Mineral Resources Uncertainty Characterization and (2) New Digital Technologies and Risk Management in Strategic Mine Planning. The first part will cover modern geostatistical simulation techniques for the stochastic characterization of geological, grade, and geometallurgical uncertainties affecting material supply. Categorical simulation techniques can be used to characterize the tonnage and geometric uncertainty of different types of deposits, while continuous simulation techniques can address the complex multivariate relationships between grade and geometallurgical variables. The use of stochastic approaches for modelling mineral resources makes it possible to identify opportunities to increase confidence in mineral resource estimates. Mineral resource classification can benefit from the quantitative confidence metrics provided by simulations. The second part of the course presents a new generation of applied technologies that take mine planning and production scheduling optimization\u2014as well as asset valuation, including reserve estimation and reporting\u2014to a new level:\u202fSimultaneous optimization of mining complexes \u2013 mineral value chains with uncertainty. A\u202fmining complex \u2013 mineral value chain\u202frefers to the integration of mining and processing operations with multiple pits and\/or underground mines, multiple metals or minerals, stockpiles, blending options, and alternative processing streams to yield sellable products delivered to various customers and\/or the spot market. Simultaneous optimization of mining complexes\u202faims to generate a production schedule for the various mines and processing streams that maximizes the economic value of the enterprise as a whole, in terms of the market value of metal product(s). This approach provides an advanced framework and methodologies for reserve estimation and classification. Emphasis is placed on downstream applications pertinent to the feasibility, development, and planning stages of mining ventures, as well as on the\u202ffinancial optimization\u202fof relevant aspects of operations and production, with particular focus on\u202fmineral reserve estimation and classification. About the instructors David F. Machuca,\u202fPh.D., P.Eng. is a Principal Consultant in Geostatistics at SRK Consulting Canada. David F. Machuca, Ph.D., P.Eng. is a Principal Consultant in Geostatistics at SRK Consulting Canada. David has 20 years of experience in mining operations, academic research and consulting related to the evaluation of various types of mineral and energy resources. His areas of expertise include the application of standard and advanced geostatistical methods for mineral resource modelling, grade and geological uncertainty and risk assessment and value of information studies. His consulting activities also include the auditing of mineral resource estimation processes, due diligences of mining projects in various stages, as well as the preparation of technical reports and training. He holds a PhD from the University of Alberta and a Postgraduate Diploma in Mining Geostatistics from MINES ParisTech. Prior to joining SRK, David was a research associate at COSMO \u2013 Stochastic Mine Planning Laboratory where he conducted research on advanced geostatistical simulation methods. He has taught basic and advanced geostatistics and sampling theory courses in English, Spanish and French for universities and the industry in Canada, Africa, and Latin America. Roussos Dimitrakopoulos\u202fis a professor of the Department of Mining and Materials Engineering at McGill University Roussos Dimitrakopoulos is a Professor and Canada Research Chair (Tier I) in Sustainable Mineral Resource Development and Optimization under Uncertainty, and Director of the COSMO – Stochastic Mine Planning Laboratory.\u00a0 He holds a PhD from \u00c9cole Polytechnique de Montr\u00e9al and an MSc from the University of Alberta. He works on risk- based simulation and stochastic optimization, as well as on artificial intelligence applications in mine planning and production scheduling, along with the simultaneous optimization of mining complexes and mineral value chains under uncertainty. \u00a0He has taught short courses and worked in Australia, North America, South America, Europe, the Middle East, South Africa and Japan. He received the Synergy Award of Innovation in 2012 by the Governor General of Canada for research contributions to mining science and engineering and...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/mrmr2026.cim.org\/fr\/uncertainty-in-the-metal-supply\/\" \/>\n<meta property=\"og:site_name\" content=\"MRMR 2026\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/CIM.ICM\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-20T19:39:03+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mrmr2026.cim.org\/wp-content\/uploads\/2026\/05\/Toronto_Canada_David_Machuca.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"220\" \/>\n\t<meta property=\"og:image:height\" content=\"296\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@CIM_ICM\" \/>\n<meta name=\"twitter:label1\" content=\"Estimation du temps de lecture\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/uncertainty-in-the-metal-supply\\\/\",\"url\":\"https:\\\/\\\/mrmr2026.cim.org\\\/uncertainty-in-the-metal-supply\\\/\",\"name\":\"Uncertainty in the Metal Supply - MRMR 2026\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/uncertainty-in-the-metal-supply\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/uncertainty-in-the-metal-supply\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/mrmr2026.cim.org\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Toronto_Canada_David_Machuca.webp\",\"datePublished\":\"2026-05-20T18:04:23+00:00\",\"dateModified\":\"2026-05-20T19:39:03+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/uncertainty-in-the-metal-supply\\\/#breadcrumb\"},\"inLanguage\":\"fr-CA\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/mrmr2026.cim.org\\\/uncertainty-in-the-metal-supply\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-CA\",\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/uncertainty-in-the-metal-supply\\\/#primaryimage\",\"url\":\"https:\\\/\\\/mrmr2026.cim.org\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Toronto_Canada_David_Machuca.webp\",\"contentUrl\":\"https:\\\/\\\/mrmr2026.cim.org\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Toronto_Canada_David_Machuca.webp\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/uncertainty-in-the-metal-supply\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/mrmr2026.cim.org\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Uncertainty in the Metal Supply\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/#website\",\"url\":\"https:\\\/\\\/mrmr2026.cim.org\\\/\",\"name\":\"MRMR 2026\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/mrmr2026.cim.org\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"fr-CA\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/#organization\",\"name\":\"CIM\",\"url\":\"https:\\\/\\\/mrmr2026.cim.org\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-CA\",\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/mrmr2026.cim.org\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/cropped-MRMR-Conference-Logo-with-dates-1.png\",\"contentUrl\":\"https:\\\/\\\/mrmr2026.cim.org\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/cropped-MRMR-Conference-Logo-with-dates-1.png\",\"width\":1920,\"height\":705,\"caption\":\"CIM\"},\"image\":{\"@id\":\"https:\\\/\\\/mrmr2026.cim.org\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/CIM.ICM\",\"https:\\\/\\\/x.com\\\/CIM_ICM\",\"https:\\\/\\\/www.instagram.com\\\/cim.icm\\\/\",\"https:\\\/\\\/www.youtube.com\\\/c\\\/CanadianInstituteofMiningMetallurgyandPetroleum\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/cim-icm\\\/\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Uncertainty in the Metal Supply - MRMR 2026","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/mrmr2026.cim.org\/fr\/uncertainty-in-the-metal-supply\/","og_locale":"fr_CA","og_type":"article","og_title":"Uncertainty in the Metal Supply - MRMR 2026","og_description":"Uncertainty in the Metal Supply and Risk Management in Strategic Mine Planning and Ore Reserves Short Course Level: IntermediateFacilitators: David Francisco Machuca Mory, SRK Consulting (Canada) Inc. & Roussos Dimitrakopoulos, McGill University\u202f Short Course Objectives Discover how and why risk-based models create value and opportunities. Explore various applications of geostatistical simulation techniques beyond Mineral Resource estimation, including Value of Information studies and probabilistic Mineral Resource classification. Learn how geostatistical simulation can be used for modelling vein, skarn, layered, and other orebody geometries. Gain insight into workflows for honouring complex multivariate relationships in simulated models. Understand how geostatistical simulation reduces the biases introduced by linear interpolation methods in Mineral Resource estimates. Appreciate why the correct characterization of grade and geological variability is key to accurate production forecasting. Learn about efficient simulation methods for modelling orebodies and how to utilize the results in pertinent mining applications. Understand how to quantify and utilize grade, tonnage, and metal uncertainty and variability. Learn how the confidence metrics provided by geostatistical simulations can be used as robust input for Mineral Resource classification. Understand how to use quantified orebody risk in ore reserve estimation, mine planning and design, and mineral project valuation. Understand the limitations of conventional pit optimization and its effects on reserve estimation, with examples. Learn about the new stochastic mine planning framework for life-of-mine optimization. Learn about the simultaneous stochastic optimization of mining complexes and mineral value chains with supply and demand uncertainty. Be exposed to actual industry examples and comparisons of stochastic mine planning and production scheduling in diverse applications (gold, copper, iron ore, and nickel laterite mines), including effects on reserve estimation. Be introduced to uncertainty in recoverable reserves, pit design, and production scheduling using simulated orebodies (e.g., disseminated gold deposit). Be exposed to a new cut-off grade optimization framework for mineral value chains, affecting ore reserve estimation and reporting. Understand why the stochastic mine planning framework generates substantially better mine plans and production forecasts, representing an important paradigm shift. Target Audience Mineral Resource and Mineral Reserves Managers, Mine Geologists, Mine Planners. Abstract Geological uncertainty affects the mining material supply (material types and grades), and it is quantified with geostatistical simulations. It can be integrated with demand uncertainty (markets) into the new digital technologies for life-of-mine planning, as part of strategic risk management. This one-day course is divided into two components: (1) Mineral Resources Uncertainty Characterization and (2) New Digital Technologies and Risk Management in Strategic Mine Planning. The first part will cover modern geostatistical simulation techniques for the stochastic characterization of geological, grade, and geometallurgical uncertainties affecting material supply. Categorical simulation techniques can be used to characterize the tonnage and geometric uncertainty of different types of deposits, while continuous simulation techniques can address the complex multivariate relationships between grade and geometallurgical variables. The use of stochastic approaches for modelling mineral resources makes it possible to identify opportunities to increase confidence in mineral resource estimates. Mineral resource classification can benefit from the quantitative confidence metrics provided by simulations. The second part of the course presents a new generation of applied technologies that take mine planning and production scheduling optimization\u2014as well as asset valuation, including reserve estimation and reporting\u2014to a new level:\u202fSimultaneous optimization of mining complexes \u2013 mineral value chains with uncertainty. A\u202fmining complex \u2013 mineral value chain\u202frefers to the integration of mining and processing operations with multiple pits and\/or underground mines, multiple metals or minerals, stockpiles, blending options, and alternative processing streams to yield sellable products delivered to various customers and\/or the spot market. Simultaneous optimization of mining complexes\u202faims to generate a production schedule for the various mines and processing streams that maximizes the economic value of the enterprise as a whole, in terms of the market value of metal product(s). This approach provides an advanced framework and methodologies for reserve estimation and classification. Emphasis is placed on downstream applications pertinent to the feasibility, development, and planning stages of mining ventures, as well as on the\u202ffinancial optimization\u202fof relevant aspects of operations and production, with particular focus on\u202fmineral reserve estimation and classification. About the instructors David F. Machuca,\u202fPh.D., P.Eng. is a Principal Consultant in Geostatistics at SRK Consulting Canada. David F. Machuca, Ph.D., P.Eng. is a Principal Consultant in Geostatistics at SRK Consulting Canada. David has 20 years of experience in mining operations, academic research and consulting related to the evaluation of various types of mineral and energy resources. His areas of expertise include the application of standard and advanced geostatistical methods for mineral resource modelling, grade and geological uncertainty and risk assessment and value of information studies. His consulting activities also include the auditing of mineral resource estimation processes, due diligences of mining projects in various stages, as well as the preparation of technical reports and training. He holds a PhD from the University of Alberta and a Postgraduate Diploma in Mining Geostatistics from MINES ParisTech. Prior to joining SRK, David was a research associate at COSMO \u2013 Stochastic Mine Planning Laboratory where he conducted research on advanced geostatistical simulation methods. He has taught basic and advanced geostatistics and sampling theory courses in English, Spanish and French for universities and the industry in Canada, Africa, and Latin America. Roussos Dimitrakopoulos\u202fis a professor of the Department of Mining and Materials Engineering at McGill University Roussos Dimitrakopoulos is a Professor and Canada Research Chair (Tier I) in Sustainable Mineral Resource Development and Optimization under Uncertainty, and Director of the COSMO – Stochastic Mine Planning Laboratory.\u00a0 He holds a PhD from \u00c9cole Polytechnique de Montr\u00e9al and an MSc from the University of Alberta. He works on risk- based simulation and stochastic optimization, as well as on artificial intelligence applications in mine planning and production scheduling, along with the simultaneous optimization of mining complexes and mineral value chains under uncertainty. \u00a0He has taught short courses and worked in Australia, North America, South America, Europe, the Middle East, South Africa and Japan. He received the Synergy Award of Innovation in 2012 by the Governor General of Canada for research contributions to mining science and engineering and...","og_url":"https:\/\/mrmr2026.cim.org\/fr\/uncertainty-in-the-metal-supply\/","og_site_name":"MRMR 2026","article_publisher":"https:\/\/www.facebook.com\/CIM.ICM","article_modified_time":"2026-05-20T19:39:03+00:00","og_image":[{"width":220,"height":296,"url":"https:\/\/mrmr2026.cim.org\/wp-content\/uploads\/2026\/05\/Toronto_Canada_David_Machuca.webp","type":"image\/webp"}],"twitter_card":"summary_large_image","twitter_site":"@CIM_ICM","twitter_misc":{"Estimation du temps de lecture":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/mrmr2026.cim.org\/uncertainty-in-the-metal-supply\/","url":"https:\/\/mrmr2026.cim.org\/uncertainty-in-the-metal-supply\/","name":"Uncertainty in the Metal Supply - MRMR 2026","isPartOf":{"@id":"https:\/\/mrmr2026.cim.org\/#website"},"primaryImageOfPage":{"@id":"https:\/\/mrmr2026.cim.org\/uncertainty-in-the-metal-supply\/#primaryimage"},"image":{"@id":"https:\/\/mrmr2026.cim.org\/uncertainty-in-the-metal-supply\/#primaryimage"},"thumbnailUrl":"https:\/\/mrmr2026.cim.org\/wp-content\/uploads\/2026\/05\/Toronto_Canada_David_Machuca.webp","datePublished":"2026-05-20T18:04:23+00:00","dateModified":"2026-05-20T19:39:03+00:00","breadcrumb":{"@id":"https:\/\/mrmr2026.cim.org\/uncertainty-in-the-metal-supply\/#breadcrumb"},"inLanguage":"fr-CA","potentialAction":[{"@type":"ReadAction","target":["https:\/\/mrmr2026.cim.org\/uncertainty-in-the-metal-supply\/"]}]},{"@type":"ImageObject","inLanguage":"fr-CA","@id":"https:\/\/mrmr2026.cim.org\/uncertainty-in-the-metal-supply\/#primaryimage","url":"https:\/\/mrmr2026.cim.org\/wp-content\/uploads\/2026\/05\/Toronto_Canada_David_Machuca.webp","contentUrl":"https:\/\/mrmr2026.cim.org\/wp-content\/uploads\/2026\/05\/Toronto_Canada_David_Machuca.webp"},{"@type":"BreadcrumbList","@id":"https:\/\/mrmr2026.cim.org\/uncertainty-in-the-metal-supply\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/mrmr2026.cim.org\/"},{"@type":"ListItem","position":2,"name":"Uncertainty in the Metal Supply"}]},{"@type":"WebSite","@id":"https:\/\/mrmr2026.cim.org\/#website","url":"https:\/\/mrmr2026.cim.org\/","name":"MRMR 2026","description":"","publisher":{"@id":"https:\/\/mrmr2026.cim.org\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/mrmr2026.cim.org\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"fr-CA"},{"@type":"Organization","@id":"https:\/\/mrmr2026.cim.org\/#organization","name":"CIM","url":"https:\/\/mrmr2026.cim.org\/","logo":{"@type":"ImageObject","inLanguage":"fr-CA","@id":"https:\/\/mrmr2026.cim.org\/#\/schema\/logo\/image\/","url":"https:\/\/mrmr2026.cim.org\/wp-content\/uploads\/2026\/01\/cropped-MRMR-Conference-Logo-with-dates-1.png","contentUrl":"https:\/\/mrmr2026.cim.org\/wp-content\/uploads\/2026\/01\/cropped-MRMR-Conference-Logo-with-dates-1.png","width":1920,"height":705,"caption":"CIM"},"image":{"@id":"https:\/\/mrmr2026.cim.org\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/CIM.ICM","https:\/\/x.com\/CIM_ICM","https:\/\/www.instagram.com\/cim.icm\/","https:\/\/www.youtube.com\/c\/CanadianInstituteofMiningMetallurgyandPetroleum","https:\/\/www.linkedin.com\/company\/cim-icm\/"]}]}},"_links":{"self":[{"href":"https:\/\/mrmr2026.cim.org\/fr\/wp-json\/wp\/v2\/pages\/2557","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mrmr2026.cim.org\/fr\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mrmr2026.cim.org\/fr\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mrmr2026.cim.org\/fr\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/mrmr2026.cim.org\/fr\/wp-json\/wp\/v2\/comments?post=2557"}],"version-history":[{"count":16,"href":"https:\/\/mrmr2026.cim.org\/fr\/wp-json\/wp\/v2\/pages\/2557\/revisions"}],"predecessor-version":[{"id":2654,"href":"https:\/\/mrmr2026.cim.org\/fr\/wp-json\/wp\/v2\/pages\/2557\/revisions\/2654"}],"wp:attachment":[{"href":"https:\/\/mrmr2026.cim.org\/fr\/wp-json\/wp\/v2\/media?parent=2557"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}