An Overview of Data Science in Manufacturing Data science has greatly increased various industrial applications over the last few years. Data science is now used in health care, customer service, government, cybersecurity, mechanical, aerospace, and other industrial applications. Among these, manufacturing has grown in importance to achieve the simple goal of Just-in-Time delivery (JIT). Manufacturing has gone through four major industrial revolutions in the last 100 years. We are currently in the fourth Industrial Revolution, where data from machines, the environment, and products is harvested to get closer to Just-in-Time's simple goal: "making the right products in the right quantities at the right time." The obvious solution is to lower production costs so that goods may be sold at lower prices. In this article, I will attempt to answer some of the most frequently asked data science in manufacturing questions.
What is the impact of data science on the manufacturing industry? There are numerous applications of data science in manufacturing. Predictive maintenance, predictive quality, safety analytics, warranty analytics, plant facility monitoring, computer vision, sales forecasting, KPI forecasting, and many more are just a few examples.
● Maintenance Prediction: Manufacturing machine breakdown is extremely costly. The single largest contributor to manufacturing overhead costs is unplanned downtime. Over the last three years, unplanned downtime has cost businesses an average of $2 million. In 2014, the average cost of downtime per hour was $164,000. By 2016, that figure had risen by 59% to $260,000 per hour. This has resulted in adopting technologies such as condition-based monitoring and predictive maintenance. Continuous tracking of sensor data from machines is performed to detect anomalies (using models such as PCA-T2, one-class SVM, autoencoders, and logistic regression), diagnose failure modes (using classification models such as SVM, random forest, decision trees, and neural networks), and predict the time to failure (TTF) (using a combination of techniques such as survival analysis, lagging, curve fitting and regression models)
● Vision in computers: Traditional computer vision systems measure parts for tolerance to determine whether or not they are acceptable. Inspecting the claims for defects such as scuff marks, scratches, and dents is also critical. Humans have traditionally been used to scan for such flaws. Today, AI technologies such as CNN, RCNN, and Fast RCNNs have proven to be more accurate than