Overview: Data Science in Manufacturing
You may be asking, “what is Data Science in manufacturing?”
Data science is an interdisciplinary field that controls every type of data, no matter how big or small. By feeding the correct data into an accurate model can be very useful for manufacturing processes. The reason for its popularity is the sudden growth of IoT devices. Data science is responsible for an increase in productivity and data processing in recent years, and it is going to revolutionize this field because of its accuracy.
Data science has been witnessing an enormous inflow in several industrial applications everywhere in the last few years. These days data science is applied in different fields such as customer service, cybersecurity, governments, aerospace, healthcare, industrial, and mechanical applications. With the most uncomplicated goal of JIT (Just in Time), manufacturing has been gaining more reputation among all these fields. Manufacturing has been through the four biggest industrial revolutions in these last 100 years.
At the moment, the most uncomplicated goal of JIT (Just in Time) is being achieved through data harvesting from products, machines, and the environment, which is the fourth Industrial Revolution. The Fourth Industrial Revolution can be best summarized as ‘making the right quantities of products at the right time.’ The question comes to mind why manufacturing must need JIT? The reason is to decrease the cost of manufacturing and make affordable products for customers.
Let me give you answers to some of the most frequently asked questions regarding manufacturing in data science.
What is the use of manufacturing in data science and its impact?
Manufacturing is used in several applications of data science now. Safety analytics, sales forecasting, warranty analytics, predictive maintenance, computer vision, KPI forecasting, predictive quality, plant facilities monitoring, and more are just to name a few.
Manufacturing has a costly machine breakdown process. Overhead costs for manufacturing have unplanned downtime as the single largest contributor. These random downtown have cost businesses an average of about $2 million over these last few years. An average downtown cost in 2014 was $164,000 per hour. In 2016, that figure reached $260,000 per hour (an increase of 59%). This explosion in price has paved the way for embracing new emerging technologies such as predictive maintenance and condition-based monitoring.
Anomalies are being detected continuously by sensor data from machines using models like on-class SVM, PCA-T2, logistic regression, and autoencoders); failure models are being diagnosed using models of classification like the random forest, SVM, neural networks, and decision trees). Sensor data is also being used in TTF (time to time failure prediction) using lagging, regression models, curve fitting, and survival analysis. It is also being used for optimum maintenance time prediction (with the help of research techniques for operations).
Parts for tolerance are being measured by traditional computers to find out if those parts are even acceptable or not. It is equally pertinent to detect defects like scratches, scuff marks, and dents to determine these parts’ quality. Before this, such shortcomings were being caught by humans.
RCINN, CNN, and Fast RCNN like Artificial Intelligence technologies are used, which has been more beneficial than human detection. These technologies also take less time for inspection. This leads to a significant reduction in product costs.
Profitability recourses have helped optimize the prediction of future trends. Different industries, like airlines, tourism, and manufacturing, have been using these prediction features. Before the actual time, knowledge of manufacturing volumes can help optimize resources like machine product learning, supply chain, and workforce in the manufacturing. Different techniques such as ARIMA, linear regression models, and some complicated lagging models like LSTM optimize resources.
Predictability has increased in assessing the quality of products produced by machines. Now, the statistical process control has some essential tools that can tell us about the process’s control. Methods that are statistical like linear regression on product quality and time can yield a sensible trend line. After extrapolation, these techniques can answer questions like ‘how much time do we have before we may start making bad parts?’
The techniques we have discussed so far are only a few of the most popular and typical applications. There can still be many applications that may be discovered later.
Is Data Science Popular In Manufacturing?
One of the US estimates says that in 2019, the Industry market in manufacturing stood at USD 904.65 million, and by 2025, it is projected to reach $4.55 billion at a CAGR of about 30.9% during the year 2020-2025. According to another estimate, TrendForce has forecasted that smart manufacturing type solutions will reach new heights globally and are projected to pass $320 billion in 2020. Another report states that intelligent manufacturing at the global level is projected to reach 395.24 billion dollars before 2025, making a CAGR of around 10.7%. A recent study conducted by Grand View Inc. has created this forecast.
Does Data Science Present Challenges In Manufacturing?
Data Science in manufacturing has proved very challenging. Here are some of the most extraordinary challenges we have come across:
Lack of expertise on this subject matter
Data science has relatively been a very recent field. For the data science field, every application needs its particular skill sets. Similarly, data science in manufacturing needs a thorough understanding of the supply chain components, process and manufacturing terminologies, rules and regulations, industrial engineering and business understanding, and lack of expertise can be fatal. SMEs are a must in tackling this set of problems, lack of which can lead to project failure, and most importantly, customer trust would be lost. For all this, a basic understanding of who a data scientist is necessary.
A Reinvention of the wheel
The manufacturing environment is different for every problem, and various stakeholders are present every time. Deployment of standard solutions is always risky, and on top of that, it will always fail at one point or another. The good news is that with the coming of every new problem, there is still a solution available already. The rest of the problem can be engineered. This engineering may involve writing novel ML packages or different model workflows for simple cases. This can lead to the development of new sensors or hardware if the problem is too complicated. I have enjoyed all of it, although I may have been on the extreme edge. My experience from a few years has been outstanding.
Do Data Scientists Use Specific Tools In Manufacturing?
A manufacturing data scientist employs different sets of tools for every step of the projects during their lifecycle.
Here are a few examples:
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- Concept proof: Python, R, SQL, MinIO, GIT, PostgreSQL
- Scale-up: Docker, GIT pipelines, and Kubernetes
Manufacturing is the one sector that can benefit hugely from progress in data science. Industries have a large amount of data. Moreover, there are increasing demands for budget-friendly yet high-quality products to cover every consumer base worldwide. The only way to cover this demand is by improving the data science field. If manufacturers get a chance to harness the data’s potential, they would forecast and predict with almost certainty what consumers would need and then manufacture the products that would be very well received.
Presently, the manufacturing data science application is relatively recent. Every day, new applications are available, and different solutions are being made continuously. In the case of various manufacturing projects or, in other words, capital investments, ROI has been used for many years (6-8 years). Most notable projects of data science have made their ROI in about half a year. This is a substantial improvement in the field. Just-In-Time goal is being achieved by most of the manufacturing industries by using data science in manufacturing. As a data scientist in manufacturing, my suggestions would be to truly understand the field’s problem, aim for an easily achievable target, achieve initial targets, and trust the institution.
In short, some top data science applications are general equipment effectiveness, predicting the quality of the products, sales forecasting, computer vision, material design, supply chain optimization, product development, and much more. Different statistic models can be used to make it smarter day by day. Discoveries keep coming up every day, and BI tools are reaching new heights. Data science would be able to bring thousands of scientists together in the future.