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  as shown in Figure 1 .
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 164,000. By 2016, that statistic had exploded by 59% to 320 billion by 2020. ” In another report it was stated that “The global smart manufacturing market size is estimated to reach USD 395.24 billion by 2025, registering a CAGR of 10.7% according to a new study by Grand View Research, Inc. ”
What are the challenges of data science in manufacturing?
There are various challenges for applying data science in manufacturing. Some of the most common ones that I have come across are as follows
Lack of subject matter expertise: Data science is a very new field. Every application in data science requires its own core set of skills. Likewise, in manufacturing, knowing the manufacturing and process terminologies, rules and regulations, business understanding, components of supply chain and industrial engineering is very vital. Lack of SME would lead to tackling the wrong set of problems, eventually leading to failed projects and, more importantly, losing trust. When someone asks me what is a manufacturing data scientist?, I show them this nice image in Figure 3.
Reinventing the wheel: Every problem in a manufacturing environment is new, and the stakeholders are different. Deploying a standard solution is risky and, more importantly, at some point its bound to fail. Every new problem has a part of the solution that is readily available, and the remaining has to be engineered. Engineering involves developing new ML model workflows and/ writing new ML packages for the simplest case and developing a new sensor or hardware in the most complex ones. In my experience for the last couple of years, I have been on both extreme ends, and I have enjoyed it.
What tools do data scientists who work in manufacturing use?
A data scientist in manufacturing uses a combination of tools at every stage of the project lifecycle. For example:
- Feasibility study: Notebooks (R markdown & Jupyter), GIT and PowerPoint
“Yes! You read it right. PowerPoint is still very much necessary in any organization. BI tools are trying hard to take them over. In my experience with half a dozen BI tools, PowerPoint still stands in first place in terms of storytelling.”
- Proof of concept: R, Python, SQL, PostgreSQL, MinIO, and GIT
- Scale-up: Kubernetes, Docker, and GIT pipelines
Currently, applying data science in manufacturing is very new. New applications are being discovered every day, and various solutions are invented constantly. In many manufacturing projects (capital investments), ROI is realized over the years (5 – 7 years). Most successfully deployed data science projects have their ROI in less than a year. This makes them very appreciable. Data science is just one of many tools that manufacturing industries are currently using to achieve their JIT goal. As a manufacturing data scientist, some of my recommendations are to spend enough time to understand the problem statement, a target for the low hanging fruit, get those early wins, and build trust in the organization.
I will be at ODSC East 2020, presenting “Predictive Maintenance: Zero to Deployment in Manufacturing.” Do stop by to learn more about our journey in deploying predictive maintenance in the production environment.
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|||N. a. T. G. Amruthnath, “Fault class prediction in unsupervised learning using model-based clustering approach.,” in In 2018 International Conference on Information and Computer Technologies (ICICT), Chicago, 2018.|
|||N. a. T. G. Amruthnath, “A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance.,” in In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), 2018.|
|||T. Y. C. M. Q. a. H. S. Wang, “A fast and robust convolutional neural network-based defect detection model in product quality control.,” The International Journal of Advanced Manufacturing Technology, vol. 94, no. 9-12, pp. 3465-3471, 2018.|
|||“Big Data Analytics in Manufacturing Industry Market – Growth, Trends, and Forecast (2020 – 2025),” Mordor Intelligence, 2020.|
|||Trendforce, “TrendForce Forecasts Size of Global Market for Smart Manufacturing Solutions to Top US395.24 Billion By 2025,” 2019.|
Dr. Nagdev Amruthnath is a Data Scientist III at DENSO and has experience working in manufacturing and full-stack data science deployment experience. He specializes in solving manufacturing problems related operations, quality and supply chain using ML and DL. He has published various articles in international journals and conferences along with various R packages on GitHub. Nagdev graduated with a Ph.D. in Industrial Engineering from Western Michigan University.