Businesses that embrace AI have demonstrated a remarkable 12% improvement over their counterparts, according to Microsoft (2019). In this blog post, we explore the principles and benefits of automated model building in the manufacturing sector, examining how it enhances production processes, facilitates decision-making, and ensures sustainable growth.
Creating Value through Automation
Automated model building represents a paradigm shift where AI designs AI systems, saving time and enhancing adaptability and accuracy. In traditional process modelling, a data scientist constructs a model, a time-consuming endeavour often beset by challenges in scaling for large operations and handling exceptional cases effectively. Automated model building changes this narrative. Here, AI constructs the model, liberating engineers to focus on emerging issues and process improvement. By leveraging automation, experts gain actionable insights, fostering a proactive approach to problem-solving and securing a competitive edge for their production.
Democratizing Data for Informed Decision-Making
Central to automated model building is its ability to democratize information extraction. Forward-thinking manufacturers employ tools that monitor production processes, offering valuable insights to all on-site operators and engineers. Scalable model building automates the construction process without compromising accuracy, ensuring consistent delivery of important information. This empowerment enables operators to make informed decisions, enhancing the efficiency and effectiveness of manufacturing operations.
Navigating Challenges and Enhancing Relevance
Automated model building surmounts challenges of wide and siloed datasets. Highly accurate, generalized, user-friendly AI models minimize complexity, remaining relevant and up-to-date in the dynamic manufacturing landscape. Harmony modelling, for instance, can account for interdependencies between different tags in the process, thus identifying issues and operating conditions beyond the human eye, and offering continuous support to operators. Embracing automated model building becomes essential for staying ahead, enhancing productivity, minimizing risks, and ensuring safety in the factory of the future.
Conclusion
The manufacturing industry has seen a range of companies witness decreases in labour productivity, large and unexpected waste disposal costs, increased energy expenditure, and more due to manufacturing process inefficiencies. These serve as stark reminders of the tangible impact that technology and automated model building can have on the bottom line of manufacturing. The importance was further noted during the unusual circumstances of the COVID-19 pandemic. A survey by McKinsey&Co (2023) highlights that of the surveyed manufacturers that had not begun implementing Industry 4.0 technologies, 56% felt constrained in their responses to the challenges that they faced as a consequence.
Hence, manufacturing decision-makers must explore advanced analytics, leveraging flexibility to minimize production risks. These solutions bolster job satisfaction among operators and foster sustainable growth. The industry's future hinges on advanced analytics, and embracing automated model building isn't just a choice but a necessity. It drives informed decision-making, ensuring resilience amid evolving challenges.
