The main driver of modern civilization is manufacturing, which uses labor, equipment, tools, and procedures. In a competitive world, all of the factors mentioned above are open to modification for increased sustainability and efficiency. As a result, data science is also widely used in production today to achieve the most excellent outcomes. Because there are several data science positions in the manufacturing industry, there are plenty of chances for data scientists and analysts.
Data Science in Manufacturing: What is it?
Manufacturing has undergone considerable development since its inception, and Industry 4.0 standards are currently being used. Through the use of technology, this new revolution combines data analytics and artificial intelligence to automate the current production processes.
Every product life cycle begins with product design based on market need and moves on to material selection, manufacturing equipment, necessary tools, labor, procedures, quality control, packaging, and supply chain after manufacture. It is necessary to thoroughly examine and evaluate the performance of each of these influencing elements to manage all of these tasks as effectively as possible.
Today, information on these components is gathered and evaluated utilizing data science methods to yield insightful information. This helps with increased output, preventing loss, optimizing resources, and appropriately adapting to current and future demands.
Numerous applications about manufacturing processes benefit from the use of machine learning and deep learning techniques. Therefore, it is intriguing to learn how data science might support these manufacturing-related endeavors.
Top Applications of Data Science in Manufacturing
1. Demand Forecasting and Inventory Management
Production-related inventories have a significant impact on the financial estimates of manufacturing industries. In a competitive setting, the Just-in-Time (JIT) strategy is essential for keeping inventory levels high. The level of inventory should be controlled such that it is neither more than what is now needed nor lower than what is required.
This JIT strategy lowers the likelihood of capital being stopped while yet providing enough to satisfy the demand as it stands right now. Using conventional guidelines like ABC analysis and related techniques is currently challenging for the management. In data science, it is simple to obtain precise estimates with a scientific foundation by using statistical tools.
2. Computer Vision
Image analysis has advanced significantly, thanks to developments in deep learning that use convolutional neural networks to train the models. For various analysis applications, image analysis aids in object detection, classification, and segmentation. This new area of computer vision in AI is also being used for practical purposes by numerous manufacturing businesses. Finding and manually detecting product faults such as scratches, non-conforming profiles, and microscopic cracks can be challenging.
3. Design and Development of Products
A product is first created and built in response to market demand, and it is then enhanced whenever such input is obtained. However, in the past, this method relied heavily on trial & error, designer experience, and prototype models, which took a long time and carried some chance of failure.
Product design and development are now more accurate and dependable thanks to new design software like CAD and simulation software like MATLAB. With the right software, which is currently widely accessible, it is possible to make rapid improvements to the features of existing items or create new, highly desired products.
4. Optimizing Supply Chain
The correct and timely delivery of manufactured items to the client depends on an effective supply chain. Likewise, the company’s inventory needs to be arranged appropriately in amount and timing. Both of these responsibilities are crucial for a company to ensure a timely supply. A precise examination of these firms’ data is essential for managing suppliers, supply plans, and inventory levels. Sitting on the manufacturer’s premises, one can utilize RFID and barcode scanning to track the whereabouts of warehouses and the items being shipped.
5. Forecasting Faults and Preventive Maintenance
A substantial amount of data is collected by sensors mounted on machines that detect temperature, speed, humidity, and other similar attributes like vibration. They are responsible for affecting the quality of the manufactured goods. The quality of the product is degraded to an intolerable degree by any of these whose values go beyond the specified range. These circumstances need costly product returns, scrapping, or rework. Similarly, failure can occur if machine health is not regularly monitored.
What Challenges Does Data Science Face in Manufacturing?
Data science applications in manufacturing are both promising and challenging. The following is a list of some of the data science issues that the manufacturing sector faces.
1. Lack of Technical Personnel
Even though data science is a trendy term, there isn’t enough readily available skilled and experienced labor. Due to frequent job changes and the potential for training expenditures to be wasted, shortages exacerbate existing financial difficulties.
2. Managing Large amounts of data
These days, obtaining the required amount of data is not a major difficulty, but handling it is. The corporation cannot use the data in its raw form until it is processed, and transforming and storing ever-increasing data is difficult. It’s not easy to decide whether to handle this data on the cloud or at the corporate level.
3. Coordination among Management
Implementing data science in the manufacturing industry requires coordination between all pertinent departments, particularly production, planning, marketing, and data science. A thorough understanding of business intelligence, data science, and manufacturing technology is necessary to profit fully from data science applications in the manufacturing industry.
It might be complicated to persuade upper management and all employees of the benefits of implementing data science in contrast to conventional methods. After this problem is resolved, more advancements can be made.
The tools required in the data science field are identical to those used in manufacturing. Data scientists make predictions to solve challenging real-world problems after extracting, altering, and pre-processing data from a dataset. As a result, they must be knowledgeable in various statistical tools and programming languages, such as R and Python.
1. TensorFlow
TensorFlow is a popular machine learning and deep learning platform that is considered an industry standard. This open-source framework is popular due to its high performance and computational capabilities. In addition to CPUs and GPUs, it now supports TPU platforms. Concerning the processing capacity of sophisticated machine learning algorithms, TensorFlow offers a clear advantage.
2. Power BI
Although it can also be used in the industrial sector, this Microsoft product is commonly used in the business intelligence profession. Key Performance Indicator (KPI) dashboards for industrial operations can be created by pre-processing data using Power BI GUI and DAX instructions.
3. Matplotlib
This well-known Python plotting and visualization module creates graphics using the data that has been processed. It is utilized for creating intricate plots with a few simple lines of code. With Matplotlib, we can rapidly produce bar graphs, histograms, scatterplots, and more.
4. Ggplot2
This widely used R package for advanced data visualization was created to take the role of R’s built-in graphics package. It offers a wide range of instructions for producing intricate representations. The tidyverse R package for data science includes ggplot2.
5. Jupyter
Jupyter Notebooks are used for statistical calculation, data cleansing, visualization, and the development of predictive machine learning models. It is free since it is entirely open-source.
The Future of Data Science in Manufacturing
Automation and simulation are already assisting the manufacturing sector with precise production. The next ten years will see more developments in data science tools and production technology. Massive amounts of data will continue to be generated by IoT (Internet of Things) devices installed on machines.
With the advent of the Industrial Internet of Things (IIoT), data exchange within internal departments and with other industries appears to be essential for further output growth. The idea behind augmented reality is that a technician or engineer can make essential modifications in addition to seeing what he is doing on a console in front of him.
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