The application of AI has expanded beyond research environments into the real world. Simulation modeling facilitates this expansion by providing supportive environments to leverage the promises of AI. The combination of AI and simulation provide enterprises across the industrial sector with diverse opportunities to evaluate processes to gain the data-driven insight needed for accurate decision-making. …
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Digital twins are virtual representations of physical entities including objects, facilities, and processes. The cyber-physical space that compromises of the digital twin and what it represents facilitates the real-time exchange of data which provides a platform that supports diverse use cases. Use cases include the application of predictive and prescriptive analysis to discover future challenges …
The concept of a ‘machine that thinks’ or an inanimate entity that thinks is traced back to the scientists of Ancient Greece who sought to imbibe objects with analytical capabilities. However, the success of the Ancient Greeks in actualizing the thinking machine was severely limited due to limitations with understanding how the human brain functioned …
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Analytics has always focused on gaining insight into processes that enable its adherents to make data-driven decisions and to understand complex systems. One example of a complex system that produces big data sets is the supply chain. However, many companies struggle to harness data from their supply chain to successfully transform and optimize how they …
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The introduction of Industry 4.0 in 2011 came with specific challenges. First, the proprietary equipment used within the average industrial facility needed unification to implement data-driven strategies required a solution. Secondly, a reliable means of communication and data transfer was required to enable the interexchange of data and finally, deterministic networking was required to implement …
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Hear ye, hear ye! The largest simulation modeling students’ competition is once again, here! Simio’s biannual students’ competition provides graduate and undergraduate students with the chance to compete against the best teams across the globe to win fantastic prizes. Competing with like minds on the global stage is half the thrill our biannual Simulation Competition …
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Digital transformation technologies are key enablers of modern Industry 4.0 workflows where automation is required to run traditional processes. For manufacturers, implementing industry 4.0 operational models starts with going digital and in many cases; the digital transformation process is done incrementally from eliminating the use of paper to utilizing manufacturing enterprise systems (MES). Moving from …
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Dark data – this refers to the data collected from the shop floor that remains untouched or used to improve manufacturing output in any way. The average enterprise’s leaves 90% of its data sets untouched and this applies to facilities within the manufacturing industry. This statistic means that despite the increased rate at which data …
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Beating the competition requires the ability to continuously evolve business processes to deal with disruption. Today, the manufacturing industry experiences a high level of disruption and competition across the globe continues to increase at an intense pace. First-world manufacturers must contend with the growing manufacturing capability of developing countries and the affordable labor they leverage. …
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At the height of the pandemic, access to protective equipment such as masks and gloves, and the availability of test kits to improve tracing was at an all-time low. In a bid to secure citizens, countries turned to their manufacturing industries to repurpose production lines to aid the response efforts…and the global manufacturing industry’s response …
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How do you inspire creativity and empower creators? By recognizing the efforts creators put into designing ground-breaking animations and models that make a difference! The Model Maker Animation Design Contest is Simio’s initiative to put the spotlight on creators building awe-inspiring simulation and scheduling models across the globe. Putting it mildly, we want to reward …
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The manufacturing industry is no novice to applying technology solutions to innovate, improve planning, and productivity within the shop floor. In the 60s’ the industry rode thewave of technology advancements and adopted time-keeping systems and production-tracking systems to implement lean manufacturing concepts. Manufacturers also made a dent in the 80s’ by leveraging computer-aided design and …
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The manufacturing industry is known for the large data sets it produces. These data sets include both structured and unstructured data from production processes and for a long time they went uncollected until the advent of Industry 4.0. Earlier advancements such as lean manufacturing methodologies leveraged data. To implement lean processes, the manufacturer had to …
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Implementing digital transformation processes within the manufacturing industry has been an ongoing process for approximately three decades. First, manufacturers utilized digitization to collect data for storage purposes before moving on to more complex applications. Optimizing industrial design became the next logical step for applying digital transformation technologies. However, it would take the introduction of Industry …
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Realizing the dreams of a fully automated factory floor starts with utilizing the structured and unstructured data captured from manufacturing processes. Once captured, data-producing sources and assets on the shop floor must be capable of decentralized analysis to take accurate, independent decisions. For that, you need artificial intelligence. Applying AI to optimize or automate manufacturing …
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The widespread adoption of Industry 4.0 business concepts shows that the future of business operations is automation. Robotic Process Automation (RPA) borrows a leaf from the automation playbook of Industry 4.0 because it involves the automation of routine rule-based processes using artificial intelligence and machine learning. RPA is a next-gen technological concept and as with …
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As the adoption rate of digital transformation within the manufacturing industry increases, many still struggle with differentiating between digitization and digitalization. Although both terms sound similar, that’s where the similarities end. Many who use digitization and digitalization interchangeably end up confusing the technological partners they intend to work with. Thus, to eliminate the confusion associated …
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Delivering sustainable growth in the manufacturing industry starts with adopting digital transformation technologies and Industry 4.0 strategies. Digital technologies support the capture of shop floor data and the analysis of aggregated data to gain insight or take actions that improve operational performance. Captured data must be aggregated, stored, and analyzed. To accomplish these actions, a …
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The introduction of Industry 4.0 in 2011 could be seen as a necessary measure to wake up a declining industry. The decade before its introduction saw global manufacturing productivity drop to 1% and to spur growth a German study group considered business models that leveraged data sets produced within shop floors. These data sets were …
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The introduction of Industry 4.0 in 2011 and the digital transformation that enables it led to an influx of new technologies to optimize traditional processes in the manufacturing industry. Artificial intelligence, which was predominantly utilized in computer science and software engineering fields, was also introduced into the manufacturing industry because to achieve the ‘lights out’ …
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