Growing use of Data Science in Manufacturing

As all of us are becoming aware, data science skills can and are being increasingly applied across all functionalities and industries  viz  Health care, Customer service, Retail, Oil n Gas, Governments, Aviation, Supply Chain, Logistics and so on. In Manufacturing Industries too , data science techniques are being used in myriad of ways especially focused towards Lean and Quality production.

Some of the important applications of data science in manufacturing are Predictive maintenance and Plant monitoring, Productivity and Scheduling Analytics, Safety Analytics, Quality Control , Anomaly detection,  Warranty analytics, Demand and Sales forecasting, Supply Chain analytics , computer vision etc.

Applications of Data Science in Manufacturing

In this article we will discuss some of these important applications.

Predictive Maintenance:

In manufacturing industries, unplanned downtime are the most significant reason for productivity and profitability loss as they contribute most to the overhead costs. Data Science techniques to identify and predict any abnormalities (anomalies) as well as potential failures can help in predicting time to next failure as well as find optimal maintenance schedules . They go a long way in reducing sudden breakdowns and unplanned downtimes. The data is collected from the sensors in the machines and combination of data science techniques like linear regression,  logistic regression, classification models such as random forest, decision trees, SVM ,neural networks ,survival analysis etc. are used to predict the maintenance needs.

Safety Analytics :

Similarly all aspects of Safety protocols and equipment can be measured and analyzed to identify reasons for any mis-haps as well as find most optimal procedures and predict potential safety breaches.

Computer Vision:

To inspect the quality of parts and detect any defects like dents, scratches , mismatch etc. earlier Human eye and different manual measurements process were used. However with computer vision and related techniques such as CNN, RCNN the inspection process has become not only faster but also more accurate.

Quality Control :

Statistical process control techniques have always been used  to maintain the quality process in manufacturing. This process has become faster and more accurate as well as capable of handling more data points with the use of advance data science techniques .

Warranty Analytics :

The  quality control data and data science techniques can be further used to predict failures and their potential impact on warranties and hence cost of warranty covers etc.

Demand forecasting:

Using data science to predict demand and sales helps in optimizing inventory , supply chain as well as the manufacturing schedules and reduces the cost . The concept of “Just-in-Time“ manufacturing is becoming more and more prevalent as it helps in increasing profitability in today’s competitive world. Various Data Science Forecasting techniques like linear regression models, Time series models like ETS, ARIMA are being increasingly used to forecast the demand and optimize the resources.

Estimated Market for Data Science in Manufacturing

It is estimated that  by 2025 the global smart manufacturing market size is estimated to reach USD 400 Billion  and the use of data science techniques and big data analytics will play a key part in driving it. However there is currently huge shortage of Data Science experts who are also subject matter experts in Manufacturing.

Hence it becomes an attractive niche for manufacturing and production engineers to learn Data Science skills to not only enhance their career but also raise the level of smart and data driven manufacturing in the industry.

Get in touch with us today to find how Ambeone’s Data Science courses for Manufacturing can help you as an  Engineer to  start using Data Science in your manufacturing and production operations.