Applications of data science in manufacturing

Most industries nowadays are dominated by data science since they all rely on data – about the industry, stakeholders, inventory, finances, and much more. It has changed the way different industries see data. Given the enormous field of data science and its many uses, it is only natural that it will find a niche in manufacturing. 

The manufacturing industry is undergoing a shift in the digital era, which necessitates enhanced agility for customers, partners, and suppliers. Manufacturers face challenges when business scale and speed increase, which is where data science comes in. Data science in the manufacturing sector assesses consumer preferences and market trends before producing a new item or improving an existing one.

Read more about the importance of data science in the current world.

What is the manufacturing sector?

As the name implies, manufacturing creates new finished items from raw materials or necessary elements. This process of manufacturing or converting raw materials or unfinished goods into new finished goods may entail physical, chemical, or mechanical transformations.

Manufacturing does not always imply using machines and equipment; it can also be done by hand, as in small-scale manufacturing of handicrafts, hand-baked items, hand-created foods such as jam and pickles, and so on.

Scope of data science in the manufacturing sector

Manufacturing has changed dramatically since its inception, and we now have Industry 4.0 norms in place. By merging artificial intelligence and data analytics, this new revolution leverages technology to automate conventional production processes.

We all know that the product life cycle begins with product design based on market needs and continues with material selection, machinery, tools needed, packaging, people, processes, quality assurance, and supply chain after manufacturing. Managing these operations in the best possible way necessitates extensive research and analysis of the performance of these influencing elements. Hence, data on these elements are collected and may be evaluated using data science approaches to gain important insights.

Learn more on how data science helps organizations.

Application of data science

The application of data science in manufacturing is distinctive in certain aspects, given the field’s specific requirements. It is largely used to deliver valuable insights to firms seeking to maximize profit, minimize risk, and assess productivity. Here is a list of the important data science applications in manufacturing.

1. Product development and design

Big data enables manufacturers to better understand their client’s interests and preferences to meet their wants and needs. Data is also required to design the product to attract customers and estimate the dangers of competition to offer a new product or improve an existing one in the market. 

Data management solutions are also utilized to gain accurate insights when modeling, planning, and making decisions. Data science is also utilized to collect client input and generate new ideas.

2. Predictive modeling

Data scientists analyze current data to foresee and avert future problems. They assess the current issues and plan accordingly to avoid similar mistakes in the future. Manufacturers employ data to its maximum potential for monitoring company function and performance and identifying viable solutions to overcome challenges and prevent them from impeding future opportunities through predictive analysis.

3. Prediction of faults

Manufacturers use data science to foresee the failure of equipment to execute the task. As a result, these failures can be avoided or reduced to some extent. Manufacturers utilize certain preventative maintenance approaches, like time-based or usage-based methods, to avoid such failures. 

Proper planning is the goal here. The manufacturer may plan a break or shutdown to remedy any foreseeable equipment problems. Such breaks are frequently used to avoid future delays and frequent breakdowns.

4. Price optimization

Before deciding on a price for the goods, manufacturers must consider several criteria. A product’s price includes the cost of raw materials, manufacture, distribution, and maintenance, among other things. 

Manufacturers use price optimization to determine the ideal price to charge customers, which is neither too high nor too cheap and profitable. This boosts profit efficiency. Data science assists in the analysis of pricing and cost data from internal and external sources to gain a competitive advantage and produce optimized price variants.

5. Robotization and automation

Robots are widely used in manufacturing to undertake routine chores and activities. Every year, manufacturers invest massive quantities of money in robots and automation. 

Data science aids in robots’ programming and seamless operation, improving product quality. Every year, new robots are introduced to revolutionize and quicken the manufacturing process. Manufacturing robots are now more inexpensive than ever for manufacturing sectors.

6. Controlling the supply chain

Manufacturers use data science analytics to manage supply chain risks. The supply chain has always been complicated, but big data analytics has proven advantageous. Manufacturers use data science to analyze potential dangers or delays and evaluate the likelihood of serious issues. This allows them to do appropriate planning and discover backup supplies. 

Real-time data analytics is essential for keeping up with the changing world. For a successful manufacturing business, predictive analysis and preventative maintenance are necessary for the supply chain management.

Roles & responsibilities of a data science professional in manufacturing

The role of data science in manufacturing is to collect a vast quantity of data, analyze it, sort out the important information, and then use tools like SAS, R programming, Java, Python, and others. Data scientists extract insights that can be used to increase the productivity and efficiency of the organization.

Some of the data scientist duties and responsibilities in the manufacturing sector are as follows:

• Gather information and identify datasets
• Analyze massive volumes of organized and unstructured data
• Develop commercial solutions and strategies
• Develop a data plan with team members and leaders
• Combine several algorithms and modules to uncover trends and patterns
• Data visualization techniques and tools are used to present data
• Look into new technology and techniques for building unique data strategies

Read more on data scientist job role and description

Top manufacturing companies

The top largest manufacturing companies worldwide are as follows.

Conclusion

Data science applications in the real world are increasing company productivity. More improved versions of tools and processes will be developed to change the manufacturing industry. If you want to study more about data science, enroll in online M.Sc. program in Data Science at Manipal Academy of Higher Education (MAHE). Designed by expert faculty, this comprehensive program covers all the core concepts of data science and help you become job-ready. On the Online Manipal platform, you can also enroll in nine-month PGCP in Data Science & Machine Learning program offered by Manipal Institute of Technology (MIT). These programs prepare you to become future-ready and grow in your professional life. Enroll now!

Key takeaways:

• Data science aids in increasing output, preventing losses, optimizing resources, and adapting to current and future demands.
• Data scientists work collaboratively with business leaders and other important resources to understand organizational goals and develop data-driven strategies to achieve those goals.