Impact of machine learning in manufacturing in recent times
Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations are is achievable with machine learning. Attaining up to a 30% reduction in yield detraction in semiconductor manufacturing, reducing scrap rates based on machine learning-based root-cause analysis and reducing testing costs using AI optimization are the top three areas where machine learning will improve semiconductor manufacturing. McKinsey also found that AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs.
Asset Management, Supply Chain Management, and Inventory Management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today. The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimization.
Manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC.Analytics and MI-driven process and quality optimization are predicted to grow 35% and process visualization and automation, 34%. PwC sees the integration of analytics, APIs and big data contributing to a 31% growth rate for connected factories in the next five years
Improving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management.
Automating inventory optimization using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%. AI and machine learning constraint-based algorithms and modeling are making it possible scale inventory optimization across all distribution locations, taking into account external, independent variables that affect demand and time-to-customer delivery performance.
Combining real-time monitoring and machine learning is optimizing shop floor operations, providing insights into machine-level loads and production schedule performance. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using machine learning algorithms.
A manufacturer was able to achieve a 35% reduction in test and calibration time via accurate prediction of calibration and test results using machine learning. The project’s goal was to reduce test and calibration time in the production of mobile hydraulic pumps. The methodology focused on using a series of machine learning models that would predict test outcomes and learn over time. The process workflow below was able to isolate the bottlenecks, streamlining test and calibration time in the process.
Improving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE). OEE is a pervasively used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness. Combined with other metrics, it’s possible to find the factors that impact manufacturing performance the most and least. Integrating OEE and other datasets in machine learning models that learn quickly through iteration are one of the fastest growing areas of manufacturing intelligence and analytics today.
Source and Reference
Technology and Innovation for the Future of Production: Accelerating Value Creation
Digital Factories 2020: Shaping the future of manufacturing
Honeywell Connected Plant: Analytics and Beyond
Transform the manufacturing supply chain with Multi-Echelon inventory optimization
The Value Of Data Science Standards In Manufacturing Analytics
2 comments on “Impact of machine learning in manufacturing in recent times”
Comments are closed.
I'm currently pursuing Industrial engineering at Texas A&M. I also hold a bachelors degree in Robotics engineering from India. I have some amount of experience in Logistics and manufacturing. I originally hail from the southern part of India.
Hi Anush – these are interesting collection of numbers and industry data points. I’m curious about your perspective is on open challenges of ML in manufacturing. Is it related to a specific goal of manufacturing like improving QA tests, or reducing downstream errors? Or is it something edgy like printing components in products? What’s the big barrier that technology is aiming to overcome in manufacturing?
Impact of machine learning describes in a great way as you highlighted advancements in manufacturing. It has always been of outermost interest in the process industries to better understand the relationship between changes in various inputs to arrive at certain outputs. Let’s think of inputs in chemical industries for example: temperature, pressure, time and additives as reaction conditions. Multiple variables at almost endless possible values. Understanding machine learning a combination of programming and statistics shows us how we can expand our real-time understanding of processes beyond the typical Design of Experiments of past days. Machine learning allows drawing conclusions from heavy data sets that were not available or understandable before. We can use compute power to draw inferences in an instant where humans would have had to draw them before using endless hours for calculations.