Today, digitization across most industries is driving the next industrial revolution in manufacturing. As companies and OEMs seek to build digital capabilities within their production systems, smart manufacturing becomes standard for anyone looking to get ahead.This trend is most often explained in terms of data and advanced analytics as a path to improved quality, process optimization, and a host of other benefits. But when undertaking the digitization journey, what do you do with that data? And how do you ensure that it makes sense to the analytics engine meant to deliver actionable insights?Contextualization Opens the Door to Smart ManufacturingWhile accurate data is good, accurate real-time data is even better. However, raw data alone needs context to be valuable and without it, analytics may render faulty or misleading information. These insights could lead to misinterpretations that result in flawed decisions.So, what is contextualized data? Contextualization means moving beyond traditional data utilization to apply additional information relevant to the process or operation. This information can be as simple as time stamps, location, or source.In smart manufacturing systems, even operator-added data could be included to increase data importance and provide background for an operation. These elements allow interoperability across systems so that MES, QMS, ERP, SCP, and other programs work with real-time information.Digitize FirstThe first step in unlocking smart manufacturing’s impact through contextualization is to digitize your operation. Data importance in modern manufacturing cannot be overstated, and connecting physical assets in infrastructure to collect, cleanse, store, and analyze that data is critical.Different functional areas – like production, quality, maintenance, and engineering – need different types of information to perform their tasks. Traditional systems using manual data collection were siloed and delivered in weekly or monthly reports. But manufacturing is complex, and without digitizing an enterprise, data will be limited in its capabilities. Digitization requires a strong data strategy and implementation plan alongside a commitment to building out a system of connected assets.Directing Your DataIn a digitized production system, data utilization becomes more about adding industry-specific context to turn the raw data into usable information for analysis. Contextualized data can include:Traceability in Production – Traceability is a cornerstone in critical industries like automotive and food and beverage. This data identifies the specific location of production, its performance characteristics, and other details along the production process.Raw Materials Origin – A smart manufacturing environment will include a connected and highly visible supply chain. This enhances traceability and gives decision-makers the ability to know what raw material was used, its origin, lot number, and more.Asset Data – Manufacturing companies often have a wide range of capital assets across different OEMs and generations within the same OEM. Connecting these assets lets you connect equipment specifications, capabilities, and cycle times. It can also include parts consumption and other data specific to an OEM or generation of machinery.Event Data – Event data represents the ever-important “W” questions like what, when, where, who, and why. It’s vital for identifying trends to facilitate process improvement.Unlock Smart ManufacturingCompanies like yours can only unlock access to smarter manufacturing capabilities with complete digitization and contextualized data. Analytics and tools such as machine learning can be powerful allies for improvement. However, with fully contextualized data, the analysis is more precise and more likely to render actionable insights that truly improve performance.Contextualized data also benefits different functional areas within manufacturing. Formerly siloed systems now utilize the same data in context to how it applies to their mission. This improves communication and ensures that functional areas like production, maintenance, engineering, QA, and others are in sync and not working at cross purposes.The journey to smart manufacturing is not a switch or a magic pill. It requires a sound data strategy and implementation of digital technology. Once that decision is made, it’s critical to contextualize data to let the new infrastructure work at its full potential.Discover more smart manufacturing trends and tips for putting your data to work in our latest State of Smart Manufacturing Report.
