In the last couple of years, data science has seen an immense influx in various industrial applications across the board. Today, we can see data science applied in health care, customer service, governments, cybersecurity, mechanical, aerospace, and other industrial applications. Among these, manufacturing has gained more prominence to achieve a simple goal of Just-in-Time (JIT). In the last 100 years, manufacturing has gone through four major industrial revolutions. Currently, we are going through the fourth Industrial Revolution, where data from machines, environment, and products are being harvested to get closer to that simple goal of Just-in-Time; “Making the right products in right quantities at the right time.” One might ask why JIT is so important in manufacturing? The simple answer is to reduce the manufacturing cost and make products more affordable for everyone.
In this article, I will try to answer some of the most frequently asked questions on data science in manufacturing.
The applications of data science in manufacturing are several. To name a few: predictive maintenance, predictive quality, safety analytics, warranty analytics, plant facilities monitoring, computer vision, sales forecasting, KPI forecasting, and many more [1] as shown in Figure 1 [2].
Predictive Maintenance: Machine breakdown in manufacturing is very expensive. Unplanned downtime is the single largest contributor to manufacturing overhead costs. Unplanned downtime costs businesses an average of $2 million over the last three years. In 2014 the average downtime cost per hour was $164,000. By 2016, that statistic had exploded by 59% to $260,000 per hour [3]. This has led to embracing technologies like condition-based monitoring and predictive maintenance. Sensor data from machines are monitored continuously 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), predict the time to failure (TTF) (using combination of techniques such as survival analysis, lagging, curve fitting and regression models) and optimal maintenance time prediction (using operations research techniques) [4] [5].
Computer Vision: Traditional computer vision systems measure the parts for tolerance to determine if the parts are acceptable or not. Detecting the quality of the parts for defects such as scuff marks, scratches, and dents are equally important. Traditionally humans were used for inspecting for such defects. Today, AI technologies such as CNN, RCNN, and Fast RCNN’s have proven to be more accurate than their human counterparts and take much less time in inspecting. Hence, significantly reducing the cost of the products [6].
Sales forecasting: Predicting future trends has always helped in optimizing the resources for profitability. This has been true in various industries, such as manufacturing, airlines, and tourism. In manufacturing, knowing the manufacturing volumes ahead of time helps in optimizing the resources such as supply chain, machine-product balancing, and workforce. Techniques ranging from linear regression models, ARIMA, lagging to more complicated models such as LSTM are being used today to optimize the resources.
Predicting quality: The quality of the products coming out of the machines are predictable. Statistical process control techniques are the most common tools that we find on the manufacturing floor that tell us if the process is in control or out of control as shown in Figure 2. Using statistical techniques such as linear regression on time and product quality would yield us a reasonable trend line. This line is then extrapolated to answer questions such as “How long do we have before we start to make bad parts?”
The above are just some of the most common and popular applications. There are still various applications that are hidden and yet to be discovered.
