Using Advanced Analytics to Improve Efficiency in Manufacturing

In recent years, business intelligence and data science advancements have transformed manufacturing, triggering a Fourth Industrial Revolution. To succeed in today’s business world, manufacturers must invest in data-driven strategies.

What are the benefits of advanced analytics in manufacturing?

Smart manufacturing and data analysis go hand in hand, but what is advanced analytics exactly, and why is it a big deal? This umbrella term describes advanced analytics tools and techniques that go beyond the capabilities of traditional BI — including machine learning, artificial intelligence, predictive modeling, and statistical methods. Whereas traditional analytics and business intelligence can provide a great snapshot of a business, advanced analytics goes a step further by predicting future outcomes and analyzing complex, multi-domain relationships for deeper insights.

Other advanced analytics capabilities include smart data discovery, sentiment analysis, and interactive data visualization. Here’s how these powerful analytic tools, alongside data-driven business strategies, can improve efficiency and give business users a significant competitive advantage in manufacturing:

Automation With Machine Learning

When a computer system uses algorithms to find patterns in data without any human programming, the machine is learning on its own, hence the term “machine learning.” This application of artificial intelligence has huge implications for manufacturers, helping them identify new business models, automate processes, optimize operational efficiency, and perfect product quality. Deep learning, a subset of machine learning, uses algorithms to create artificial neural networks similar to the human brain. Advancements in big data analytics have allowed data scientists to create even more complex and sophisticated neural networks, providing deeper insights into business operations and finding hidden relationships.

Predicting Future Outcomes

Predictive analytics can also increase manufacturing efficiency by using historical data and statistical models to forecast future events, behaviors, and market trends. These simulations and predictions are also powered by machine learning and bolstered with powerful visualization tools. These analytic techniques and tools can be utilized in manufacturing for predictive maintenance, improving employee safety, and identifying promotional campaigns with the highest ROI. Manufacturers can also improve customer experience by predicting behaviors and delivering personalized marketing.

Optimizing Operations & Supply Chain

A top use case for advanced analytics in manufacturing is operations and supply chain management. Using reliable, real-time data and predictive analysis, manufacturers can maintain a more accurate inventory and prepare for future demands. Utilizing programs that analyze large amounts of data and can quickly generate visualizations boosts productivity by helping business users make smarter, faster decisions. According to Cognizant, businesses that applied artificial intelligence and machine learning to specific business processes reported an 11% increase in operational efficiency, with expectations that the number will rise to 17% by 2023.

Supporting Data-Driven Business Methodology

Even though deep learning might imitate the human brain, there’s still no substitute for the real thing. You can be armed with all of the latest advanced analytics techniques and tools, but you still need to have a strong business methodology to attain total quality management and continuous improvement.

Six Sigma is a data-driven set of techniques that’s been used for many years to improve business processes. By following the Six Sigma steps, you’ll learn how to become a process owner rather than a business owner. Businesses are just a collection of processes, after all. Optimizing those processes will create a more successful business. Lean manufacturing is another methodology rooted in getting more with less — reducing input while maximizing output. Lean Six Sigma is the best of both worlds, using data analysis and leveraging existing systems by integrating both methodologies.

Advanced analytics and artificial intelligence are the future of manufacturing. By using these innovative technologies alongside a strong business strategy, manufacturers can maintain a competitive edge in a constantly evolving industry.

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