10 Applications of Big Data in Manufacturing

  • Muskan
  • May 24, 2021
  • Big Data
10 Applications of Big Data in Manufacturing title banner

Big Data is literally being used everywhere and due to the fact that it carries a huge amount of relevant and related information which can be categorized as a storehouse of a lot of advantages if read properly.

 

 It is being a very trendy analytical tool these days and is being adopted by manufacturing industries to solve a lot of problems, various kinds of manufacturing industries including aerospace and defense companies, auto manufacturers, heavy equipment manufacturers, electronics companies, oil and gas companies, and other organizations that produce consumer and capital goods are adopting these advanced analytics to gain the benefits most suited for them. 

 

Let’s have a look at how it works in detail. 

 


What is Big Data analytics?


As data refers to the stored information and recorded operations which were performed by the computer in various forms, big data can be understood as the same data being present in huge amounts and numbers. This consists of information provided and both humans and the devices and on almost everything. 

 

Big data analytics is the collection of the data which is also growing exponentially with time. Big data is so large and complex in its size and storage that it is unable to be deciphered and understood efficiently by the traditional data management tools. There is a need for much more efficient tools and insights to be able to draw concrete and effective solutions from the available big data. 

 

Recommended Blog:  Big Data in Media and Entertainment Industry 

 

 

How do Manufacturers Generate Big Data?

 

Apart from the traditional source of big data like conducting online marketing analysis, regulated loyalty programs and social media monitoring the industries can use other software machines to collect such data. 

 

They use a variety of manufacturing softwares within the company, like MES, CRP, CMMS etc. and these systems are integrated with machines like machines such as assets like sensors, pumps, motors, compressors, or conveyors to be able to generate big data in the manufacturing industry. 

 

Patterns can be found, and problems can be solved, efficiently and without complexity, they can be solved and understood with the help of a data analyst and applications to identify the patterns and links within the data available to be able to generate solutions and implication based conclusions which cause thoughtful decisions to boost the production. 

 

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Applications of Big Data in Manufacturing Industry




1. Production Optimization 


There are a lot of factors that contribute to the production quality in a company which can be understood and analysed by calculating decades of data present in a company which hasn't been read yet. 

 

If that data is able to generate successful conclusions it will be able to give real insights  as it will quickly capture, cleanse, and analyze machine data and reveal insights which will help them improve performance and production of the company. The available conclusions will help the manufacturers optimise the solutions and production based on what’s most suited for them. 

 

 

2. Maintenance Regulation

 

Big Data Can help the manufactures understand the working capability of the machines and the track of their breakdown too. 

 

This can help in preparing the regular machine maintenance charts and plan regular downtime without affecting the production and in fact making sure that unexpected breakdown of any machine does not affect production and also somehow the repair does not add on to the costs of production. 

 

All of this can be easily prevented by keeping a track record of how and when the machines need maintenance and service so as to be able to function efficiently.

 

 

3. Quality Checks

 

There are a lot of systems and scenarios that affect the quality of the products and all of this can be kept a record of easily in the forms of which product is being manufactured where and when. 

 

This can help in keeping a track of the quality checks before putting the product in the market and also can help in understanding the reasons or kinds of defects in the products and the failures of production in the malfunctioned or under quality products. 

 

 

4. Tool Optimization 

 

There are certain tools in the production which get outdated and out of time as they get used over and over again. With the help of keeping a record of the same they can be used to reduce the number of anololisd in a system or a factory. 

 

Big data tools equipped with adequate softwares and alerting systems can help in solving the root causes of the tools before they wear out too much and be added to the production cost for the repairs and problems.
 

 


5. Supply Chain Management 

 

Big data tools help in understanding the sales and systems of distribution of products and by the data it can be made out as to where products are being needed in which quality this can help in maintaining the supply chain with respect to demand in the market and several other factors considered. 

 

This will keep the suppliers in coordination and take the supplies as they are needed, this will prevent them from stockpiling and extra manufacturing which might do in vain and as well as is not so much of a sustainable practice. 


 

Image Showing Uses of Big Data in Manufacturing Industry  1. Production Optimisation 2. Maintenance Regulation 3. Quality Checks 4. Tool Optimization 5. Supply Chain Management 6. Production Preparation 7. Yield Improvement 8. Market Study 9. Sustainable Development 10. Risk Evaluation

Uses of Big Data in Manufacturing Industry


6. Production Preparation

 

Now there are certain industries which have seasonal boom seasons and which need to excel their production to meet the demands of the people once in a while. 

 

Big data records can help them in forecasting the productions which might be needed in the market at a certain point of time so that they can produce the material beforehand and when the time is claimed they do not miss out on the customers and face a loss or be able to under utilise the resources and their capacities. 

 

This states that anticipating demand based on records available can help in preparing better for the future. 


 

7. Yield Improvement


Now, unlike production increase this has to do with the capacity increase without resources being increased. This goes on with keeping in mind the other factors that can help in production if the hidden patterns are understood clearly and widely. This will enable continuous improvement in the yield. 


For example a firm which sells the grains can understand the rainy patterns of their crops harvest season in order to avoid extra irrigation. This is the understanding of the pattern which is even being used in the primitive sense if the advanced analytics are being used properly. 


 

8. Market Study 

 

Big Data analytics combined with IoT technological growth and other advanced analytics can help in understanding the market and how it is proving to be for this one industry. 

 

This can be helped by conducting feedback and surveys and collecting the huge piles of data so as to implement product modification and finding a perfect niche in the market to function and maximise the profit. In the production this can help in pre formulating the strategy for the business and keeping a track on the requirement so that nothing of it gets wasted with respect to the industry .
 

 


9. Sustainable Development 

 

The issue of sustainability has been valued these days as resources are being depleted as a result of human activities and wastage. 

 

Big data in the manufacturing industry if it gets deciphered properly can help in preventing the wastage of resources as the monitored activities will keep an eye on supply chain production management and even machine management to not be able to increase production cost and keep it the most efficient. 

 

This will not just help in monitoring the optimum for the industry, be it any but this will also help in preventing a portion of the environment. 


 

10. Risk Evaluation

 

There are certain risks involved in the manufacturing industries be it in the form of casualties or in the form of huge losses and low margin profits. Big data can help in calculating and foreseeing such risks and hence taking preventive measures for the same. 

 

This can also help in pre determining the factors affecting the product sale in the market post manufacturing for example, productions can be increased with respect to the successful marketing campaigns of the product, as the demand rises. 

 

Considering the older data available if understood properly it can give clar insights into where were the issues initially which hampered the growth of the business and such things can be eradicated beforehand.
 


Investing in Big Data 


There are a lot of industries which are understanding the role and significance of big data in being able to help boost the production and efficacy at minimal costs. Oil and gas, refineries, chemical producers, automotive, plastics, metal forming, food and beverage manufacturers are the examples of such industries. 


As big data works by seeing a cumulative big picture which is conclusive in nature out of the data available. This can be done by hiring a data scientist or an analyst of the industry based field who can understand the pros and cons of the same and can be able to provide efficient results. 


Also, it will not be wrong to say that Big Data and other advanced analytics alritirms if adopted are of a great significance to the industries. These help in monitoring and keeping a track of things sometimes a normal human can fail to notice successfully. Investing in Big Data resources is absolutely worth it. 

 

Recommended Blog: Companies That Use Big Data 



Conclusion


Complex algorithms can solve problems and are even capable of finding them which one must be ignoring. Big Data can save a lot of purposes in manufacturing industries and is being a tool to make them more efficient and optimum. A lot of companies and industries are adopting such techniques everyday, improving their capacities to function and making the best out of what they have. 

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