Data Science in Manufacturing: Applications, Tools, and Future

Data and Data Science words have become ubiquitous in the business world as well as in daily life. How can manufacturing remain unconcerned with it when data science has a strong presence in all sectors like healthcare, aviation, travel, and agriculture? Gone are earlier days when data was neither abundantly available nor could be processed speedily. Today we have huge volumes of data, high computing speeds, and technology of fast processing. Everybody knows that data is a vital source that, after proper analysis gives crucial insights for decision-making beneficial for the business.

Manufacturing is the primary source of today's status of civilization and involves manpower, machinery, tools, and processes. Every factor stated above is subject to improvisation for higher efficiency and sustainability in a competitive world. Therefore, Data science in manufacturing is also very common these days to get the best results. Hence great opportunities exist for data analysts and data scientists, as there are several data science jobs in the manufacturing sector. A comprehensive Data Science Course content will build the required data science skills for this domain and prepare aspirants for a data science career in manufacturing. This article covers the scope and prominent areas of manufacturing where and how data science is being used.

What is Data Science in Manufacturing?

Since the start of manufacturing, it has seen a lot of changes, and we now have Industry 4.0 standards in practice. This new revolution uses technology to transform the existing manufacturing processes into automation by combining Artificial intelligence and Data Analytics.

We know that any product life cycle starts with product design as per market demand and is followed by material selection, machinery, tools required, manpower, processes, quality assurance, packaging, and supply chain after manufacturing. Managing all these activities in the best possible manner requires deep study and analysis of the performance of all these impacting factors. Nowadays, data pertaining to all these elements is collected, which can be analyzed using data science techniques to get valuable insights. This helps to get higher output, prevent losses, optimize resources, and adapt appropriately to current and future demands.

Machine learning and deep learning techniques are beneficial in various applications related to manufacturing activities. It is, therefore, interesting to find out how data science can help in these activities related to manufacturing. You can explore the best online Data Science Bootcamps to get started in your data science career. In the next section, we will look into nine important applications of data science in manufacturing industry.

Top 9 Applications of Data Science in Manufacturing

1. Computer Vision

The advances in deep learning to train the models through convolutional neural networks have made great strides in image analysis. Image analysis helps in object detection, classification, and segmentation for various analysis applications. Many manufacturing industries are also using this new domain of computer vision in AI for useful applications. Defects in products, like very minute cracks, non-confirming profiles, scratches, etc., are challenging to locate and identify manually. Computer vision techniques mentioned earlier assist such tasks through trained models. Dimensional accuracy can also be achieved through computer vision, saving time and money in a manual inspection.

2. Inventory Management and Demand Forecasting

Manufacturing Industries' financial estimations are greatly influenced by inventories needed for production. The Just in time (JIT) approach is vital in maintaining stocks of inventories in a competitive environment. The inventory level should be so managed that it should neither be more than currently required nor fall short of what is needed. This JIT approach reduces the chances of blocked capital but provides adequate to meet the currently required demand. This management is now challenging using traditional rules like ABC analysis and similar approaches. Using statistical tools, accurate estimations based on a scientific basis is an easy option in data science. Again, forecasting of required demand can be estimated accurately using Excel, Power BI, and Python packages. Based on this, inventory stocks can be managed, saving the company a lot of money. Hence data science is readily used now for inventory management and demand forecasting.

3. Product Design and Development

Initially, a product is designed and developed based on customer demand in the market and subsequently improved whenever such feedback is received. However, in earlier days, this process was mainly based on designer experience, trial, and error, prototype model approach that required a huge amount of time and some risk of failure. Today, new software for designing, like CAD, and simulation software, like MATLAB have made product design and development accurate and reliable. Quick changes in existing product features or developing new demanded products can be quickly done with necessary software that is readily available now.

4. Supply Chain Optimization

An efficient supply chain is necessary for the proper and timely supply of manufactured goods to the customer. Similarly, the company's own required inventory needs to be arranged in the proper quantity and right time. Both these tasks are very crucial for a company to ensure timely supply. Managing vendors, schedules of supply, and stock quantities depend on proper data analysis of these entities that can be used for efficient management. Tracking of goods in dispatch and location of warehouses can be done sitting in manufacturers premises with RFID and barcode scanning. Thus, optimization in the supply chain can be achieved through appropriate tools and techniques of data science.

5. Price Optimization

Determining the price of a product is very important for selling any product, as it has to be reasonable, competitive, and acceptable to targeted customers. The decision to fix the price of a product depends on expenses incurred on materials, machine time, labor, overheads, and customer demand. Product prices frequently see fluctuations as price deciding factors themselves change. This happens due to changes in demand, shortage in supply due to less availability or seasonal changes, and transportation costs. The data compiled based on past and current prices, along with the impacting parameters, can help data scientists to predict future prices. Correct predictions with the highest probability can ensure good returns for the company. Data scientists can use time series models like ARIMA to anticipate changes in market prices and then recommend measures to be taken by management for necessary actions.

6. Fault Prediction and Preventive Maintenance

 A substantial amount of data is collected through sensors fitted on machines that record temperature, speed, humidity, and similar parameters like vibration. These are responsible for affecting the quality of manufactured products. If any of these have values out of the specified range, they affect the quality of the product to make them unacceptable. In such cases, return of the product, scrapping, or rework is involved, which costs money. Similarly, if machine health is not monitored regularly, breakdown may occur. Maintenance of machines after breakdown results in downtime and loss of production, which again costs money. Hence, Preventive Maintenance, which aims at ensuring no breakdowns taking proper steps is crucial. Applying machine learning algorithm-trained models can predict possible locations and parameters likely to degrade machine performance through previously collected data or simulations mimicking real-time working.

Similarly, image analysis through computer vision can identify defects in machine components that need replacements. Quality control with statistical tools like sampling techniques can ensure good quality products. Thus, for fault prediction and preventive maintenance, data science in the manufacturing field can save vast amounts of money by avoiding breakdown and downtime and customer satisfaction through the supply of good quality products.

7. Warranty Analysis

A warranty ensures the fault-free performance of manufactured products for specific periods to customers. Manufacturing Industries must spend a good amount on providing customer claims in this period. Proper record maintenance and identifying false claims through a data analytics approach are possible through anomaly detection. Hence data science and manufacturing have close relations here too. Data Science adoption here cannot only save money from false claims but gives the opportunity to improve services and product quality also through feedback on data collected on legitimate claims of product failure frequency.

8. Robotization

Robots programmed and trained through different software developed in programming languages like C, C++, or Python packages are widely deployed in manufacturing industries. They can work 24x7 accurately and replace humans in dangerous tasks as well as routine or complex tasks. Wastage reduction, higher output, and safety are, thus, possible wherever robots can be deployed.

9. Cyber Threats and Data Security

Data collected by any organization or business is critical for analysis based on further decisions. Any manipulation or theft through cyber-attacks will hamper proper analysis. This will affect all decisions and actions taken in a negative manner, damaging the prospects and reputation of the company. Hence it is very crucial to use data science in manufacturing industries also to prevent cyber-attacks and provide complete security for data related to products, processes, and other strategic points. Storage systems in Hadoop can help monitor and ensure data safety.

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What are the Challenges of Data Science in Manufacturing?

Application of Data Science in the manufacturing sector is highly promising but challenging. Here are some of the challenges of data science faced by manufacturing industries, as listed below.

1. Handling big data

Nowadays, getting necessary large data is not a big issue, while managing it is a real problem. The data in its raw form is of no use to the company unless it is transformed, and it is challenging to transform and store ever-growing data. The decision of whether to manage this data at the enterprise level or in the cloud is not simple.

2. Scarcity of technical staff

Although data science is a buzzword today, trained and experienced manpower is not readily available in the required numbers. Shortage leads to further crunch as frequent change of jobs is also common whereby money spent on training can also go to waste.

3. Rapid changes in Technology

The Data Science field is witnessing rapid change as also manufacturing technology. Retrofitting and adoption of new technology need some time by which new options come to the surface. Hence changeover is not easy.

4. Coordination Management

Adopting data science in the manufacturing industry needs coordination amongst all concerned departments, specifically production, planning, marketing, and data science. There is a need for someone very well-versed in manufacturing technology, business intelligence, and data science to gain real benefits from data science applications in the manufacturing field. Convincing the advantages of adopting data science against traditional practices to all staff and higher management is a challenge. Once this issue is settled, further progress is possible.

What Tools do Data Scientists Who Work in Manufacturing Use?

All the tools required in the data science field are similar in manufacturing as in others. From a given dataset, Data Scientists extract data, manipulate and pre-process it, and finally generate predictions for solving challenging real-world problems. Thus, they are expected to be knowledgeable in several statistical tools, and programming languages, such as Python and R. Check out upGrad’s Data Science course content to learn more about data science fundamentals and applications.

There are many tools, both open-source and commercially available for Data Science. A few of these tools are listed below -

1. SAS

It is a closed-source commercial tool used by large organizations to analyze data. It uses the base SAS programming language to perform statistical modeling.

2. Apache Spark

It has many Machine Learning APIs that can help data scientists make powerful predictions with the given data.

3. MATLAB

It is a well-known commercial engineering software that is used in different industries across multiple domains. It provides a numerical computing environment for processing mathematical information. It is also a closed-source tool that supports matrix functions, algorithmic implementation, and statistical modeling of data. MATLAB is used in data science to simulate neural networks and fuzzy logic. Additionally, it can be used in image and signal processing. We can generate powerful visualizations using the MATLAB graphics library. Overall, it is a versatile data science tool for Data Scientists to handle data cleaning, data analysis, and more advanced Deep Learning algorithms.

4. Excel

It is one of the extremely popular Data Analysis tools developed by Microsoft. Excel was initially used for spreadsheet calculations, but today it is also used for data processing, visualization, and complex calculations.

5. PowerBI

This is another tool from Microsoft that is highly implemented in the business intelligence domain but can also be used in the manufacturing industry too. It is possible to pre-process data using Power BI GUI, and DAX commands to build Key performance indicator (KPI) dashboards for manufacturing operations.

6. Ggplot2

It is a commonly used advanced data visualization R package developed to replace the native graphics package of R. It provides many powerful commands to create detailed visualizations. ggplot2 is part of tidyverse (R package designed for Data Science).

7. Jupyter

In Jupyter Notebooks, one can perform data cleaning, statistical computation, visualization, and create predictive machine learning models. It is 100% open-source and is, therefore, free of cost.

8. Matplotlib

It is a popular plotting and visualization Python library for generating visualizations with the analyzed data. It is mainly used for plotting complex plots with a few lines of easy-to-use code. We can create bar plots, histograms, scatterplots, etc., quickly using Matplotlib.

9. Scikit-learn

It is an open-source Python library for Machine Learning. It is simple and easy to implement for analysis and data science.

10. TensorFlow

TensorFlow is an industry-standard tool for Machine Learning that is widely used for Machine Learning and Deep Learning. It is an open-source framework popular for its performance and high computational abilities. It supports both CPUs and GPUs and recently also supports TPU platforms. TensorFlow has a distinctive advantage in terms of the processing power of advanced machine learning algorithms.

Apart from the above tools, there are some specialized tools from renowned brands such as ‘SAP’ necessary in the manufacturing industry like:

• ERP (Enterprise Resource Planning): It is a tool to analyze and manage organizations’ resources most economically.
• CRM (Customer Relationship Management): A tool that helps to store and analyze customer data to improve customer experience with the company. CRM analytics, a type of OLAP (Online Analytical Processing) tool, can use data mining.

Future of Data Science in Manufacturing

Simulation and automation are already helping manufacturing industries with highly accurate production. Incoming decade, further advances will be made in the technology of production and data science tools. IoT (Internet of things) devices fitted on machines will continue generating huge data volumes. The concept of the Industrial internet of things (IIoT) has come into existence wherein sharing the data amongst internal departments as well as with other industries seems necessary for further output growth. Augmented reality is a concept in which an engineer or technician can not only see what he is doing on a console in front of him but can manipulate necessary changes. A company situated at a remote location and has supplied some plant to another company can help it troubleshoot in case of a problem without visiting. The use of drones for delivering items to customer locations will rise. Manufacturing flying cars will no longer be a dream. Manufacturing robots which can interact with humans and work together will also be possible. In short, data science will be used more and more in manufacturing and allied services.

Conclusion

It is now a well-accepted fact that data science has numerous applications in manufacturing industries. Data analytics, mainly through predictive and preventive analysis, can immensely contribute to higher and more accurate output. Manufacturing industries need data scientists for the adoption of data science in all stages of manufacturing. As technology improves, more avenues will open for implementing data science.

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