Seven Ways Metals, Mining, & Materials Companies Turn Data into a Sustainable, Competitive Advantage

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1 Seven Ways Metals, Mining, & Materials Companies Turn Data into a Sustainable, Competitive Advantage This guide focuses on streamlining the flow of information within Metals, Mining & Materials (MMM) companies. To transform your organization into a more agile, innovative, resilient and sustainable enterprise, effectively managing the flow of information is a necessary platform for success. At a time when the speed of business was accelerating at an unprecedented rate, Microsoft founder and chairman Bill Gates wrote The Speed of Thought: Using a Digital Nervous System. The most meaningful way to differentiate your company from your competition... is to do an outstanding job with information. How you gather, manage, and use information will determine whether you win or lose, he wrote. In the book, Gates develops the idea of a digital nervous system which must, like the body s nervous system, filter through a flood of information to identify what is truly critical. As a concept, the digital nervous system offers intriguing insight for businesses. As a comparison, the human nervous system translates environmental data through the senses into electrical impulses that integrate with subsystems (reflexes) and central processing centers in the brain, allowing for seamless interconnectivity and speed. Now consider companies. How quickly does data move through a typical MMM company? From instrument sensor to report, how quickly can you roll up the numbers for a regulatory report or an annual sustainability report? If you decided on a new corporate initiative, how quickly could you create the metrics and KPIs to drive that initiative? If you could do these things at the speed of thought, what impact might that have on innovation and agility? Does your organization operate at the speed of thought? When a metals company took a calculated risk in introducing the aluminum can to the consumer market, they had gathered consumer data to devise the pop top as a crucial aspect of the product. Imagine if you had quick access to what your customers were thinking and how they behaved how would that change what you produce? When lithium battery companies were searching for ways to lower the cost of raw material in order to reduce the cost of batteries, data showed that further process improvements to mine raw materials could not meet the requirements. Recycling batteries could, and so changes were made that met cost requirements. Imagine if you had overall visibility in your processes to reach these out-of-the-box conclusions faster, and the potential impact of first-mover advantage in establishing market leadership. We call this digital nervous system the data infrastructure. Organizing your data infrastructure to positively impact business is complex but not difficult. Below are seven aspects related to data that encourage your enterprise to think and act at the speed of thought, resulting in more value and impact for your business:

2 COLLECTION: collect everything. Be not only data-driven, but data-hungry. You will never know what will be important knowledge in the future. Develop a system that can interface with new and legacy data systems, gather and serve the right amount of data, and have the ability to scale as the business grows and concomitant data increases. Recognize that there may be special attributes of different data types that require special handling. Have the necessary systems to handle those types. Identify which information systems can act as systems of record and draw from those for integration and analytics. For mining companies looking for better ways to store and manage data, understand the subtle benefits and limitations involved in supporting a data warehouse solution for efficiency. For example, operational data is time-series based and is much different in nature than financial data, which is more manageable in data warehousing. PROVIDE CONTEXT TO to turn it into information. Create useful information that it is consumable to the target audience. A fundamental step here is the ability to organize assets and processes into hierarchies that mirror the mine or plant floor. This adds a basic level of context to data coming from these areas. Another layer of contextual information such as business logic or calculations can be associated with this organizational framework. Consider organizing your data using an enterprise data model or standard plant models such as ISA S95/S88, which can be applied to data coming from across all sites. This becomes the common language through which to transfer data-driven best practices across sites. Example: A global materials company was having indigestion after a series of M&A activity. When all sites across the enterprise incorporated a common data infrastructure, the desired efficiencies heralded during the M&As were realized. Corporate dashboards could be developed quickly to extract the right data for executives in order to guide high-level decisions. Turning Data into Valuable Business Information COLLECT GIVE CONTEXT DEVELOP METRICS ANALYZE DEVELOP ENTERPRISE CONSISTENCY INTEGRATE QUALITY TRUSTABLE STRUCTURED ACTIONABLE DECISION READY ENTERPRISE READY BUSINESS IMPACT 2

3 PROVIDE ONBOARDING AND TRAINING to the user base. Provide data resources and coaching to teach departments how to improve their knowledge through data. Even when you have ensured data is available to everyone who needs it, workers will need training to know how to use that data. Educating and encouraging workers will result in a positive feedback loop: the better educated the workforce, the more data will be used and scrutinized. An important result will be improved data quality which in turn influences reliability and credibility. This in turn breeds confidence in the information and business decisions made downstream. As your workers become more adept at using data, leave room for innovation by avoiding blind use of benchmarks. There are very few ordinary days in a mining corporation and these can be opportunities to push processes to a new level. If old benchmarks are ingrained, your workers won t see the new possibilities. DEVELOP PERFORMANCE METRICS AND VISUAL INSIGHT to drive appropriate action. First, establish appropriate models for mining and materials operations and processes. This in turn defines the right metrics to be measured. Be able to incorporate appropriate dashboards and alerting to turn data and metrics into actionable information. Determine if adding additional data models will further enhance insight; for example, cash flow and data rates are types of models that can provide further insight when formulated with and made into relevant metrics. These will allow workers to relate what is happening in the mine, mill, smelter, and plant to impacts on the enterprise. Example: For environmental monitoring, a metals company developed dashboards and alarms to measure fluoride levels and pot emission levels which allowed faster, immediate, response to abnormal levels. SUPPORT INVESTIGATION AND ANALYSIS to drive innovation and continuous improvement. Identifying the most useful questions to ask is akin to choosing the right search string on web searches: it s something people can learn to do better. If workers are allowed to look for patterns, improvements in downtime, slow-time, and production losses can be found. In support of innovation, quality, or continuous improvement initiatives, allow workers to quickly and easily access current and historical data. Users with little or no programming experience must be able to configure and deploy basic calculations and base analytics. Allow a variety of comparison methods such as by run (time frame) or by asset relationship (object orientation). Account for mobility and have solutions that allow for analysis in the field. 3

4 If done well, MMM companies can have visibility into total production losses by process area, ore-type, and shift. While the latest trends like Big Data analytics have the potential to find novel insights from old data, keep it simple for the majority of your organization. This may lead to bigger lifts from finding low hanging fruit. In general, we have seen that 80% of process productivity can be driven to high efficiency with three parameters, so have simple easyto-use tools for basic calculations and analysis available at all levels. Have a way to institutionalize that knowledge thereby strengthening the competitive advantages of your organization. Consider creating a Center of Excellence or Competence Center, which collects local insights and disseminates best practices across sites. Example: A metals mining company changed grinding operations from the same way for all ore to different ways based on ore types. This in turn led to significant improvements in water and energy consumption. DEVELOP CONSISTENT REPORTING METRICS AND PROCESSES for evaluation. Consider which decisions are being made by your organization and provide the decision-makers at every level with the best data to make those decisions. Be able to align your data systems to business drivers. Juxtapose, compare/contrast, and incorporate other data and domains to create a consistent set of enterprise metrics, dashboards, and performance measures, from plant to manufacturing quality operating systems to executive dashboards. Meet other data requirements such as market prices, regulatory standards, and industry standards. Also, realize that notifications (or alerting) are key to applying and embedding the learning from the large data sets that come from large enterprises. Doing this process by process or site by site can prove challenging. The key to developing consistent reporting quickly and efficiently is having one version of the truth, which is enabled by a strong, well-organized, data infrastructure. This will help connect the islands of best practices that have been developed in operational silos. INTEGRATE OPERATIONS TO THE BUSINESS: improve forecasting, modeling and comprehensive business intelligence. Most importantly - integrate and extend. Facilitate collaboration by shaping the way people deal with data through how data is visualized, modeled, and compared. Allow collaboration across disparate systems by integrating information systems. If you want to establish a culture of innovation, allow workers access to the kinds of data that may lead to inventive thinking outside their functional areas. Ensure important data is visible to all departments: for example, operational, laboratory, maintenance, customer complaint, and sales data. When different functional groups are allowed to collaborate, improvements in best practices will result. 4

5 Example: Maintenance teams at a mining company with access to operational data were better able to manage downtime. Downstream benefits included reduction in specific energy consumption and better compliance through fewer excursions and rejected materials due to unpredicted downtimes. Extending beyond the enterprise, MMM companies with an effective data infrastructure have benefited from external collaboration with equipment suppliers and support organizations, which has led to regional integrated demand and consumption operations working to detect energy, water, and environmental constraints. Mining and material processing involve complexity in the processes and assets that are deployed. Large mining and metallurgical complexes have more data than a small city. Much of it is in real-time and changing rapidly as internal and external conditions evolve. MMM companies also have to adapt to aging assets and mercurial market pressures. Compounding this, as corporations buy and sell assets and reengineer their staff, the structure and tools that were used to understand legacy data can be lost. The net result is a very significant challenge in maintaining a data-driven smart operation. Giving order and structure to the data and information flowing from this complexity starts with considering your realtime data infrastructure - your organization s nervous system. Every organization has one, effective or not, whether it consists of disparate systems connected by custom code and thumb-drives or is intentionally planned in a formal master data management and enterprise architecture strategy. Your data infrastructure starts at the sensor level measuring something physical, but it ends up being used to drive and reflect corporate initiatives through KPI s, quality metrics, executive dashboards, and annual reports. By incorporating data and process models into the data infrastructure, a standard way to communicate and analyze the data across systems and sites will simplify the view of operations. At its most effective use, extending this infrastructure beyond the plant floor by combining it with other business systems enables the seamless transformation of operational data into insight that will drive business decisions across the enterprise. Done well, we have seen this drive increased cost savings in process optimization, energy, water, and a reduction of production losses. For further information, visit: About OSIsoft, LLC OSIsoft, a global leader in operational intelligence, delivers an open enterprise infrastructure to connect sensor-based data, operations and people to enable real-time and actionable insights. As the maker of the PI System, OSIsoft empowers companies across a range of industries in activities such as exploration, extraction, production, generation, process and discrete manufacturing, distribution and services to leverage streaming data to optimize and enrich their businesses. For over thirty years, OSIsoft customers have embraced the PI System to deliver process, quality, energy, regulatory compliance, safety, security and asset health improvements across their operations. Founded in 1980, OSIsoft is a privately-held company, headquartered in San Leandro, California, U.S.A, with offices around the world. For more information visit 5 All companies, products and brands mentioned are trademarks of their respective trademark owners. Copyright 2015 OSIsoft, LLC 777 Davis Street, San Leandro, CA GU7MEN

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