106 VIEWPOINT A range of variables can affect the production process, but when used correctly, operational digital twins can help identify and solve these issues JASON NIENABER: SIGHT MACHINE Five constraints facing digital twins Digital twins have been sparking excitement in the manufacturing industry for their potential to accelerate industrial digital transformation and unlock the untapped business value within operations technology data. The most common type of digital twin is the asset digital twin, which can model a single machine but is limited in its ability to enable comprehensive understanding of complex manufacturing environments. Whilst no single technology can resolve this challenge, purpose-built solutions have emerged over the past decade, bridging the gap between IT and operational technology (OT). These solutions bring coherence and context to OT data, opening a promising pathway to address manufacturing challenges on a scalable level. Sight Machine’s Manufacturing Data Platform transforms streaming factory data into a data foundation that models the entire production process. This allows manufacturers to identify how production variables like speed, temperature, force and raw material variation interact across the line to drive core factory key performance indicators such as output and quality. For example, consider a box of pastries. The product starts out as dough and proceeds through a series of machines for baking, sheeting, cutting and packaging. The mixer speed could affect the dough’s elasticity, affecting how well it rises, which in turn impacts the shape of the pastry before it is baked in the oven. Misshapen pastries create jams in the packaging machine. There is a vast array of potential interactions among production steps and manually discerning correlations can be challenging. Sight Machine’s Manufacturing Data Platform creates operational digital twins, modelling entire lines, machines, production steps and the product itself as it is transformed step by step from raw material into its finished form. Through the lens of an operational digital twin, we uncover the ability to quickly identify and optimise the interactions that influence our factory’s performance. For a digital twin of a production process to be effective, organisations must overcome these five constraints: 1. Insights must be delivered in real time To understand, control and improve production, operations teams need to work with high-frequency data to know what is happening in the moment. To achieve realtime insight, a data platform must automate stream processing to integrate all factory data sources, from sensors to data lakes, including time series and transactional data, as well as late and missing data. “The accelerating momentum of data utilisation marks an unprecedented opportunity in the history of manufacturing”
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