9 Game-changing Use Cases of Machine Learning in Manufacturing

1. 0         Blog Post Home / Technology / 9 Game-changing Use Cases of Machine Learning in Manufacturing T ECHNOL OGY 9 Game-changing Use Cases of Machine Learning in Manufacturing       varunbhagat, 6 hours ago 6 min 42 Machine learning is no longer a buzzword, without a doubt, the most crucial technology to invest in in the twenty-?rst century. By implementing machine learning technology, your company can gain a signi?cant competitive advantage. Did you know? Arti?cial intelligence (AI) and machine learning (ML) spending will increase to $57.6 billion by 2021, up from $12 billion in 2017. (IDC) By 2022, the global machine learning market is anticipated to reach $8.81 billion (Research And Markets) Machine learning has revolutionized many industries, including healthcare, advertising, and e- commerce. But the impact on manufacturing hasn’t yet been fully realized; instead, it’s just

2. getting started. In this blog, I’ll go through the practicalities of machine learning and look at nine speci?c machine learning applications in manufacturing. Here we go:        Source  Top 9 Machine Learning Use Cases in Manufacturing Here are nine game-changing machine learning use cases that will radically transform the way manufacturers use data to improve their bottom line. #1 Pattern Analysis

3.        Machine learning and predictive analytics take in a slew of measurements and crunch them to predict future events. Pattern analysis is one way manufacturers can utilize machine learning.  It involves studying past data and comparing it to current measurements, identifying trends or patterns, and forecasting future results based on these ?ndings.  The point of pattern analysis is to predict when malfunctions will occur or when it’s time for maintenance based on these observations. This provides businesses with precise control over their processes, both increasing e?ciency and decreasing costs. #2 Smart Energy Consumption For power consumption forecasts and optimization, the energy sector can bene?t from AI. ML models, designed to recognize patterns and trends, forecast future energy usage by analyzing and processing historical data. ML models in this scenario rely on sequential data measurements computed using autoregressive models and deep neural networks. This machine learning approach provides a better knowledge of how energy is utilized at facilities and optimizes production processes more data-driven. For example, ABB, a Swiss corporation, supplied manufacturers with an AI-driven platform to help them avoid peak-time energy expenses. #3 Prediction Prediction is one of machine learning’s most lucrative use cases because it represents an avenue to pro?tability for companies dealing with excess inventory. By using predictive analysis, manufacturers can make better decisions about when to produce and withhold supply. If you think your organization could leverage from predictive modeling, here are some factors to consider:

4. Some manufacturers use machine learning to predict product demand and production schedules. For example, let’s say you need to buy $10,000 worth of raw materials for your next batch of widgets.       If it has historically taken you four days to move through your inventory, then based on past sales data and machine learning, you can predict how many units you will sell during that period.  #4 Improving Operational Ef?ciency Using machine learning to optimize the production ?oor improves operational e?ciency and lowers expenses. For example, Google cut its data center electricity use by 40% by employing bespoke machine learning to control the air conditioning in its server farms. Even though Google has already spent a signi?cant amount of time manually optimizing its procedures, the improvement was obtained. Google isn’t alone: a quarter of early ML users claim the technology has helped them improve internal operations e?ciency, and more than 80% say it has helped them reduce costs. #5 Forecasting Forecasting is one of those behind-the-scenes tasks that you probably don’t think about too often unless it goes wrong, but that doesn’t mean it isn’t important.  When forecasting—which uses machine learning to predict future data points based on historical data—goes wrong, your entire supply chain can be thrown o?. The result? Overproduction and underproduction of goods, wasted time and money spent restocking products, plus late deliveries to your customers. Furthermore, machine learning provides predictive monitoring, with machine learning algorithms predicting equipment problems and scheduling prompt maintenance before they occur. #6 Supply Chain Management

5. Source In ML-based supply chain management software, deep neural networks are used to assess data, including material inventory, inbound shipments, work-in- processes, market trends, consumer moods, and weather forecasts.        It is feasible to evaluate client behavior patterns and trends using demand forecasting approaches such as time series analysis, NLP techniques, etc. As a result of having data-driven forecasts, ?rms may make AI-based judgments about optimizing logistic procedures. Machine learning and deep learning algorithms can also be used to optimize logistic routes. ML-based models can determine the optimal solution for planning logistics routes by evaluating shipments and deliverables and determining their impact on performance. #7 Quality Control Arti?cial intelligence (AI) is also being used for quality control and product inspection. Machine learning-based computer vision algorithms can identify the “good” from the “bad” from a group of examples. Semi-supervised anomaly detection algorithms, in particular, just require “good” samples in their training set, obviating the need for a library of probable ?aws. Alternatively, a solution that compares samples to typical defect cases can be devised. Machine learning delivers di?erent savings in visual quality control in manufacturing depending on the specialty. Testing and failure costs can account for up to 30% of overall product costs in semiconductor manufacturing. “According to Forbes, using machine learning to automate quality testing can raise detection rates by up to 90%.” #8 Predicting Maintenance Issues One of the most crucial aspects of the manufacturing industry is maintenance. Predicting when machines need repair, rather than guessing and perhaps causing problems in the manufacturing process, will prevent unnecessary downtime. Sensors and powerful analytics built into the machinery provide quick and accurate information about potential machine problems. According to studies, malfunctioning machines cost manufacturers about 180 billion pounds in the UK each year, accounting for 3% of all working days. Many factors in?uence downtime, and many manufacturing facilities have found cost-e?ective results by integrating arti?cial intelligence solutions such as machine learning.

6. #9 AI-powered Equipment Failure Prevention       Successful manufacturers avoid equipment failures before they occur. Rather than relying on periodic inspections, the machine learning approach uses time-series data to uncover failure trends and predict future problems.  Various circumstances can lead to equipment failure. It is possible to forecast what causes equipment failure using data acquired by sensors and analyzed using machine learning methods such as regression models, classi?cation models, anomaly detection models, and external data sources. When abnormalities are discovered, the manufacturing process’ performance can be improved. The nature and frequency of anomalies can determine a failure event. Wrapping Up  For years, the manufacturing industry has relied on automation, robotics, and complicated analytics. In the manufacturing industry, machine learning is more likely to resemble evolution than revolution. If you’d like to learn more about how Machine Learning can be used in manufacturing, then get in touch with a Machine Learning development company in India. They can guide you properly and provide embedded analytics with attractive user interfaces.   Good Luck!   GENERAL  ﻎﻧوﺪﻠﯿﺒﺳواﺔﯿﻧﺎﻤﻟﻻاﺔﻐﻠﻟاﻢﻠﻌﺗ GENERAL  Jak nauczyć małe kotki korzystania z kuwety?

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