Technology Record - Issue 31: Winter 2023

107 INDUSTRIALS & MANUFACTURING 2. Plant data lacks context or meaningful metadata Many manufacturers have collected years’ worth of data in lakes and historians. However, some of this data may not have been properly labelled when it was captured. Organisations face a challenge turning what seems like a ‘data swamp’ into actionable insights. Manually processing thousands of tags to derive meaning and identify relevant data is timeconsuming and resource intensive. Consequently, teams tend to rely on a limited set of tags, utilising less than 10 per cent of available data to improve production performance. To tackle that problem, Sight Machine developed an automated and artificial intelligence-enabled system that can label and map millions of factory data points. The system, called Blueprint, was jointly developed with funding from Nvidia and Microsoft. 3. Data heterogeneity is extreme Plants have a wide range of data types, protocols, data sources, assets and hundreds of software solutions and systems. In Sight Machine’s datafirst architecture, all streaming plant data is transformed into standardised information early in the process, versus a machine-by-machine approach, which is hard to scale. 4. Modelling requirements are also extreme Plants have hundreds of assets and thousands of process steps. To understand and relate activities among all these steps, this variety must be commonly represented. Sight Machine’s composable digital twins use standardised schemas, including assets, lines, systems and parts to represent not only the machines and processing steps, but also the product itself as it moves from raw material to finished product, relating each batch or discrete item to the machine settings and conditions in place as that product unit flowed down the line. 5. The data environment constantly shifts Systems will change, machines will be replaced, and sensors will be added. Data solutions need to automate adjustments to these everchanging data environments without the need for burdensome wrangling and model revisions. An industrial data revolution Sight Machine’s data-first architecture provides data transformation within a software platform that is cloud native and built to address these five constraints, whilst reducing the total cost of ownership and eliminating the technical risks associated with traditional methods. The convergence of IT and OT within an integrated enterprise architecture, amplified by the force of AI and machine learning, promises substantial business value extraction from data, igniting a wealth of possibilities across the value chain. The accelerating momentum of data utilisation marks an unprecedented opportunity in the history of manufacturing. Jason Nienaber is chief revenue officer at Sight Machine Photo: Pexels/cottonbro studio