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Application of Data Science in
Manufacturing.

Published

3 days ago by

Admin


Hello everyone, I am Rajkiran and today in this blog, which is a continuation of my previous blog I will be discussing about the other applications of Data Science in Manufacturing Sector. These applications are very useful use cases to train as it covers a wide range of Statistical models as well as different Probability functions for predictive and prescriptive solutions. Later on, students can relate and start implementing their learning experience in other applications quickly. Today, we will start with another area of Data Science implementation to Forecast Demand and manage Inventory.

Now, both these problems are again utilization of Predictive Analysis. Let us first understand the problem and how they are related to each other and more important thing is we must also understand how this is related particularly to Manufacturing Industry. Demand forecasting is a complex process involving analysis of data and massive work of the accountants and specialists. Moreover, it appears to have strong relations with inventory management. A simple fact may explain this interrelation – if the demand goes up to meet that the manufacturing unit has to produce more. To produce more, more raw materials for processing is required over a given period of time. When the requirement increases for raw material the inventory has to be managed. The situation is also vice versa.

Now I believe, you can understand the accuracy of our predictive model will determine many things. To increase the accuracy level, we have to consider all possible factors as variables which may affect the market. But, considering all the possible factors is always a challenge. Let us look at it with a real-life example. Suppose Coke brings out a new pack size for Sprite at certain price which is going to increase the demand for Sprite. Here we are assuming number of customers remained constant and their per capita consumption is also not going up or down. The per capita consumption may rise or fall with the change in the weather. Number of consumers may go down, let’s say because of a community health campaign or it may also go up because of a marketing campaign. The most important thing is, till now we haven’t considered competitors activity. In this case, we have a very less chance to understand how Pepsi will react to this. Suppose, Pepsi also launches the same pack size for 7-Up. We do not know how much time Pepsi will take to react and we also do not know what will happen to the newly demand rise if Pepsi takes a counter move.

Hmmm! take a deep breath, I know there are lot of other external factors, like economy or markets, raw material availability, etc. are associated with this particular Data Science application. Let us now know about the benefits of demand forecasting for the manufacturers. First of all, it gives the opportunity to control inventory better and reduce the need to store significant amounts of useless products. Besides, the online inventory management software helps to collect data that may be of great use for further analysis. One more critical factor is that the data input for the demand forecasting may be continually updated. Thus, relevant forecasts may be made. Additional benefits lie in the improvement of the supplier-manufacturer relations, as both can efficiently regulate their stocks and supply process.

Application of Data Science also helps to develop new product for manufacturers. Big Data has brought big opportunities to manufacturing companies regarding product development. The manufacturers use the advantage of Big Data to understand their customers better, to meet the demand and to satisfy their needs. Thus, data may be used to develop new products or to improve the existing ones.

Using Big Data for product development, the manufacturers can design a product with increased customer value and minimize the risks connected to introduction of a new product to the market. Actionable insights are taken into account while modeling and planning. This data can strengthen the decision-making process. Also, data management tools are widely applied to optimize the operational aspects of the distribution chain.

Processing customer feedback and feeding this data to product marketers may contribute to the idea generation stage. Thus, a new product which would prove more useful to the customers and more profitable for the manufacturers may be developed. So, when I was talking about launching a new pack size of Sprite, Coke can actually do this requirement analysis based on Data Science.

One very important segment related to this is warranty analysis. The manufacturers spend a considerable amount of money every year on supporting warranty claims. Warranty claims disclose valuable information on the quality and reliability of the product. They help to reveal early warnings or defects of the product.

Using data science, the manufacturer can make improvements to the existing products or develop new ones, more effectively and efficiently. Modern warranty analytics solutions help manufacturers to process vast volumes of warranty-related data from various sources and to apply this knowledge to discover where the warranty issues are rising and the reasons for their occurrence.

Another application of Data Science in manufacturing area is Quality Control. AI-powered technologies and computer vision applications found their usage in manufacturing at the stage of quality control. In this respect object identification and object detection and classification proved to be very efficient. Usually, quality control monitoring was performed by people. However, now it is more common to rely on computer vision rather than on human vision. These monitoring systems usually consist of computer hardware and software, cameras, and lighting for image capturing. After that, these images are algorithmically compared to the standards to identify discrepancies.

Among key advantages of the computer visions applications are:

  • improved high-quality control

  • decrease in labor cost

  • high-speed processing capability

  • continuous operability 24/7

I hope you have enjoyed reading this article. I am really energized when I see you are resharing my post and liking it. But today I am looking for some feedback. Are finding it interesting to have Data Science as your new carrier start? Or you are already working on it and wish to know more about our developments? Guys please let me know. I will be happy.

See next time with some more implementations of Data Science. Bye!

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