Big data has revolutionized manufacturing through computational analysis. Manufacturers can now analyze massive data sets from assembly lines and product testing to improve quality, cut costs, optimize workforce efficiency, and enhance collaboration. However, many manufacturers have not fully leveraged big data analytics due to the complexity of integrating information technology systems and determining the right analytical tools. As manufacturers learn to better extract insights from their large amounts of production data, they can continue developing processes and increasing growth.

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Impact of Computers on Big Data
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The big data has led the revolution of computational power in industrial manufacturing
and spread the horizon of the market. It shows trends, reveal patterns, and relations of
human behaviour and interactions. Industry giants like Apple, Tesla, Uber and Airbnb,
have used it for the expansion into new big markets and used these trends to know the
customer needs and making their products accordingly to grab the market share. It has
improved the supply chain mechanism in markets. IDC study has revealed that the Sales
and analytics from it can reach up to $187 billion in 2019, which were $122 billion in
2015. One area which has benefited from it is the manufacturing industry which has
projected the growth in 2019 to be $39 billion for the revenues (Agrawal, 2016).
After the invention of assembly line anug=facturing has grown massively as before 20
century, the production processes were slow and these processes yielded few products.
The assembly line integration with other methods like lean thinking approach in
manufacturing has enabled the mass production in manufacturing with maximum
utilisation of their available resources; statistics used to analyse could result in more
accurate and more straightforward to understand results of its analysis (Oregon State
University, 2017).
Effects on Manufacturing Companies
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IT plays the major role in manufacturing revolution even the companies which have
generated the large number of data sets which yet don’t have realised what to do with the
available data which is in access than needed. For instance, the factory sensors which can
generate the number of data points by the inspection of the product defects across the
assembly line. When put into analytical software it can produce different types of data
which can be used to improve the manufacturing processes. It has developed the industry
in the number of ways.
Cost Savings
It has changed the thinking about the manufacturing processes through available data
companies can know the trends and methods from which the cost can be cut. The
information produced data which can help the industry lower their manufacturing,
packaging, transportation and normal loss costs and at the end huge saving in inventory
and warehousing costs.
Improved Quality and Safety
Most of the big organisations use data analytics for their product model simulation to
enhance the quality of their products. Companies used computerised sensors to along
assembly line which identify the defects in products and put out them from an assembly
line, by analysing these defects and the reason behind companies can improve the quality
and manufacturing processes to reduce these rejections. To make improvements in the
product before launching and delivering into the markets (Agrawal, 2016).
Improve Workforce efficiency
Through analysing the production floor data companies can improve the effectiveness of
the workforce by making improvements into the areas where the workforce is lacking and
where they are excelling, it can be used to study the error rate and identification of the
processes which needed to be optimised. This data analytics can also improve the
production line speed for higher output.
Also Read : What is the Role of System Support Engineer
Better Collaboration
Data analytics can improve the overall organisational efficiency by enhanced workflow
information in the manufacturing organisation. The synergistic flow between
departments of engineering, management, quality control, machine operators and other
departments within the organisation, this can improve access to massive data sets within
organisation’s different departments.
Implications
Despite delivering lots of benefits to the industry, the industry is slow moving and lacking
top reap the full potential of it analytics industry needs to integrate more with it.
Manufacturing is more complicated than other industries which have implemented the it,
and it makes difficult the integration of the it in manufacturing. Companies should know

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where to mine the data and what right analytical tools to make sense of the data. If the
software has a problem assembly line can suffer a huge loss and to implement fixes it can
take more time if the problem is not identified (Agrawal, 2016).
Industries have reaped the benefits from it analysis and still have a lot of data to use and
to make sense of the available data. It improved the manufacturing processes and helped
every sector which are using this software to analyse data and take industries, not only
tech but also manufacturing industries to incredible growth
Works Cited
Agrawal, V. (2016, 12 15). The Impact of Big Data and Analytics on Manufacturing.
Retrieved from tech.co: https://tech.co/big-data-analytics-manufacturing-2016-12
Oregon State University. (2017, May 11). Bridging computer sceince and statistics to
optimize results from “Big Data”. Retrieved from http://impact.oregonstate.edu:
http://impact.oregonstate.edu/2017/05/bridging-computer-science-statistics-optimize-
results-big-data/
The Network Impact of Big Data. (n.d.). Retrieved October 10, 2018, from
https://www.nojitter.com/post/240170228/the-network-impact-of-big-data
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