In this blog on applications of data science in manufacturing, we will go through the following topics:
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How does the manufacturing industry use Data Science?
Manufacturing is the backbone of every other industry. Manufacturers use heavy machinery, equipment, tools, etc. to manufacture products. But, only manufacturing the products is not enough to compete in the market. They need to analyze performance, reduce errors in production, adapt to the changes in market trends, and upgrade the production system using new technologies. Nowadays, applications of Data Science are playing a major role in the manufacturing industry to boost the production system and revenue. Let us understand the application of Data Science in manufacturing with the help of a real-life use case of the food manufacturing industry.
In 2023, most manufacturing firms such as food production companies are using AI-based machines for production. These AI-based machines are capable of performing the following tasks as per the instructions fed into them:
- Creating an item
- Eliminating a defective item
- Packing an item
- Supplying the product in the market
To calculate the efficiency and performance of the machines, the data of the production process is collected. Then, using Data Science techniques such as visualization, companies analyze the number of products manufactured per day. Also, they keep track of the number of defective items and the cause of the defects. With the help of the collected data, they can predict revenue for the year as per the condition of the current production systems.
After that, the companies optimize these production systems consistently for boosting their performance and revenue. Further, they need to check the balance between the demand for a product in the market and its supply and then manufacture the product accordingly. With the help of Data Science techniques, companies can keep track of their growth. This is how Data Science applications in the manufacturing industry help them to grow.
Now, we will look into various applications of Data Science in manufacturing.
Applications of Data Science in Manufacturing
Below is the list of the major applications of Data Science in manufacturing:
- Price Optimization
- Predictive Analytics
- Demand Forecasting and Inventory Management
- Supply Chain Management
Price Optimization
One of the competitive factors in the market is the pricing of a product. There are various aspects on which the end price of a product depends. These are raw materials, machinery, labor cost, electricity, eliminated products, packaging, and supply. All combined determine the cost of the final product. If the cost is too high for customers, then it needs to be lowered.
Lowering the price of a product requires the proper analysis of all the factors mentioned above that are involved in the manufacturing process. Here, techniques of Data Science help companies visualize the need for making a product and identify and reduce the unnecessary costs that influence the overall pricing of the product. By this, they can optimize the cost of the product that would be reasonable for their buyers. Finally, this price optimization helps them survive in the highly-competitive market and fulfill the needs of the customers at an affordable price. This way, the companies can enhance the profitability of their business further.
Predictive Analysis
The economic performance of an organization depends on its knowledge of market trends, consumer needs, and business competitors. Predictive analysis is one of the factors that can help companies predict the future scope of a product as per customer requirements. In manufacturing companies, Data Science allows them to precisely analyze all factors that affect the growth of their business. Data Scientists here study the data of customers’ demands and predict future trends. Based on these predictions, the companies advance to further manufacturing.
Further, during the manufacturing process, Data Scientists can ensure to make the product is fault-free. Also, they explore new technologies that can make production faster. These tools help analyze the productivity of the business and make changes in the product accordingly. Manufacturers can thus build strategies in advance to avoid uncertain situations using predictive analysis. The predictive analysis strategies that use Data Science help monitor the overall functioning of the organizations. Finally, using predictive analysis, organizations can also build effective methodologies for effective production. This is how the applications of Data Science in manufacturing boost productivity.
Demand Forecasting and Inventory Management
Running a successful manufacturing business require on-time production. Packing and supplying products to customers is yet another high-priority task for manufacturers. In such a highly competitive world, it has become necessary to forecast the demand of the customers in advance. Therefore, almost all manufacturing companies use Data Science for analyzing and predicting customer demands. It helps them manage the production and the supply chain. Also, the objective of supply chain management is to prevent unnecessary production and congestion of orders. By this, the manufacturers have an advantage over inventory management as well. The stock of goods stored by them can thereby be used as per customer requirements and business needs.
Further, the benefits of demand forecasting using Data Science are listed below:
- It helps reduce the requirement of unnecessary storage.
- The analysis of data using Data Science helps in inventory management.
- It helps improve the credibility of the supplier and the manufacturer.
- One of the important uses of Data Science is the regulation of the supply process.
With the applications of Data Science in the manufacturing industry, businesses can enhance the process flow of inventory management and demand forecasting. In this way, they can perform well in the market and make strategies for the future.
Supply Chain Management
The supply chain involved in the manufacturing process has always been complicated. From the manufacturing of an end product to its delivery to customers, the process has been involving risks at all its stages. The importance of supply chain management cannot be overstated as it is critical to the success of any business, regardless of its size or industry. A few of the various complex stages involved in the business life cycle of the manufacturing industry are as follows:
- Gathering of the requirement
- Collecting raw materials
- Gaining information about the market demand
- Analyzing the resources for production
- Programming the machines for producing goods
- Employing skilled workers for operating machines
- Checking the quality of the end product
- Distribution of the product in the market
There are many more stages that may vary according to different organizations. All these processes comprise the supply chain of an organization. Any inconsistency in the supply chain may lead to business loss due to the delay of the product in the market. With the help of Data Science and automated supply chain management tools, the inconsistency can be figured out in an early stage.
The use of Data Science detects the events of overloading on machines and their failure and prevents them. Using Data Science in supply chain management predicts the future possibilities of delay in production or supply. This helps manufacturers create and keep backups for immediate supplies to maintain the supply chain. Further, to prevent business losses, Data Science tools analyze and repair the schedules optimizing the production process. By managing the supply chain risks, Data Science in manufacturing takes care of the entire business.
In the manufacturing industry, data scientists rely on a variety of tools to harness the potential of data analytics and facilitate decision-making based on valuable insights. The top 5 tools commonly utilized by data scientists in manufacturing are as follows:
- Python: Python is an indispensable tool for data scientists in manufacturing due to its extensive range of libraries and frameworks that enable data manipulation, statistical analysis, and machine learning tasks.
- R: R provides data scientists in manufacturing with a comprehensive suite of statistical analysis and visualization packages, empowering them to effectively explore and model manufacturing data.
- Apache Hadoop: By enabling data scientists to process and analyze large-scale datasets, Apache Hadoop plays a crucial role in manufacturing. It facilitates tasks such as predictive maintenance, supply chain optimization, and quality control.
- Tableau: Tableau serves as a powerful data visualization tool, enabling data scientists to create interactive dashboards and visualizations that effectively communicate insights derived from manufacturing data.
- TensorFlow: TensorFlow, a machine learning framework widely embraced in the manufacturing sector, serves as an open-source tool for constructing and implementing machine learning models. It empowers data scientists to undertake various critical tasks including anomaly detection, predictive maintenance, and process optimization.
By leveraging these tools, data scientists in the manufacturing industry gain the necessary capabilities to extract actionable insights, optimize production processes, enhance product quality, and drive operational efficiency.
Data Scientist’s Salary in Manufacturing
The salary levels for data scientists in the manufacturing sector can fluctuate based on variables such as experience, location, company size, and job responsibilities. As the manufacturing industry places growing importance on data-driven decision-making and process optimization, the demand for data scientists in this field is high. In order to attract and retain highly skilled professionals, competitive salaries are provided.
In the United States, entry-level data scientists in the manufacturing sector typically earn an average salary ranging from $80,000 to $150,000 per year. However, as professionals gain more experience and expertise, their salaries can significantly increase. Mid-level data scientists with several years of experience can expect to earn between $100,000 and $180,000 annually, while senior-level data scientists or those in leadership positions may earn over $200,000 per year.
Data scientists working in the manufacturing industry in India typically receive lower average salaries compared to their counterparts in countries like the United States. Nevertheless, it is important to recognize that salaries can still be competitive based on various factors, including the specific company and the qualifications possessed by the candidate.
For entry-level positions, data scientists in the manufacturing industry in India can anticipate an average annual salary ranging from INR 5 lakhs to INR 10 lakhs. With increasing experience and expertise, salaries can experience significant growth. Mid-level data scientists with several years of experience can earn between INR 10 lakhs and INR 20 lakhs annually, while senior-level data scientists or those in leadership positions can earn over INR 20 lakhs per year.
Kindly take note that the aforementioned salary figures are approximations and are subject to potential variations based on factors such as the company’s location, the industry’s reputation, and the individual’s qualifications. Additionally, due to disparities in the cost of living and market demand, salary ranges may differ across countries and regions.
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Future of Data Science in Manufacturing
We have already entered the era of automation. The applications of Data Science in the real world are enhancing the productivity of businesses. In the future, there will be more advanced versions of tools and techniques to revolutionize the manufacturing industry. One of them is Amazon’s delivery of products through drones. The drones built by Amazon are capable of finding the location of customers and delivering the products directly to them. It will help in the easy and efficient delivery of products.
Finally, the applications of Data Science in manufacturing will change the current trends of traditional methods used by the manufacturers. It will boost the revenue generation of these companies and contribute to the overall economic growth of the industry.