In this special guest feature, Deryk Anderson, Director of Technical Product Management for GE Digital, explains that in the age of the industrial internet of things or IIoT, we will continue to see organizations shift their focus from simply collecting and managing large volumes of data to acquiring the most value from all data across business units – before it goes dark. Deryk is responsible for product delivery of the Machine and Equipment Health components of GE Digital’s Asset Performance Management solution. He has over thirty years’ experience in the management of industrial assets as a manager and consultant across a variety of industries including, oil and gas, mining, petrochemical, manufacturing, food processing and utilities.
Industrial companies have a critical tool at hand to mitigate risk and reduce costs from unexpected downtime: data. But in today’s digital world, oftentimes these organizations find themselves faced with too much data to make sense of. By the year 2020, data production will reach an all-time high — 44 times greater than it was in 2009.
Think of it this way: when something like unplanned downtime in an automotive plant can cost $22,000 per minute, industrial engineers often hone in on the data that only provides red flags to avoid incidents in the first place. As a result, they overlook other critical data in the process, losing out on opportunities to save money in the long term. This unused data — also known as “dark data” — has the potential to improve operations and significantly reduce costs. But where can dark data benefit industrial organizations the most?
Stock Control, Inventory Optimization and Other Business Processes
Organizations often fail to use operational data for anything other than historical record, so its value is ultimately lost. But by extracting value from this data, industrial companies can gain crucial insight into a range of business processes, such as stock control, inventory optimization and loss accounting. There is unexploited potential, for example, in inventory data to infer component failure rates and failure mechanisms. Even though work history data may be poorly coded for analysis purposes, the allocation of parts data to work orders and assets provides a rich source of data for deeper analysis of equipment failure.
Additionally, an effective “Bill of Materials” capability, or automated list of raw materials, can save organizations significant time and money in locating parts and avoiding losses due to incorrect part selection. Many organizations aspire to a comprehensive bill of materials, but are often frustrated by the time and effort required to gather the data. Inventory data, however, can be leveraged to identify links between assets and stock and non-stock items to create a bill of materials more quickly and accurately.
Finance and Accounting
Financial and accounting departments miss out on significant cost saving insights residing within dark data. Currently, companies are collecting mass amounts of data related to costs for things such as raw material, parts, labor, insurance, fuel, utility bills, among others, but few are actually putting this information to use or integrating data with financial planning applications in order to provide the most accurate cost analysis and adjust for future budgeting.
Industrial organizations need to operationalize data to reduce costs and improve efficiency. Engineers and operators need the end-to-end picture of operations to drive impactful change, which requires leveraging information from equipment and assets. This means that machines must be continuously monitored for valuable insights. Today’s advanced sensors and software systems measure asset health in real-time, but when that data is stored in silos across an organization instead of centrally managed, analyzed and put to into action, it loses real-time value, and with that comes missed opportunities to make more strategic decisions for the business.
In the age of the industrial internet of things or IIoT, we will continue to see organizations shift their focus from simply collecting and managing large volumes of data to acquiring the most value from all data across business units– before it goes dark. As industrial organizations look for more ways to improve operations, productivity and cut costs, expect dark data to come into the light across all business units.
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