Data Analytics & Its Importance in Manufacturing Industry Today

Data analytics is the process of examining data sets in order to draw conclusions about the information they contain. This is done with the aid of specialised systems and software. In this report I will talk about data analytics, and discuss some of the strengths, weaknesses opportunities and threats associated with it. Finally, I will discuss the importance of data analytics in the manufacturing industry today. Data analytics technologies and techniques are widely used in commercial industries to enable organisations to make more-informed business decisions and by scientists, engineers and researchers to verify or disprove scientific models, theories and hypotheses. Data analytics can help businesses increase revenues, improve operational efficiency, optimise marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals, all with the ultimate goal of boosting business performance. Depending on the application, the data that's analysed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources.

SWOT Analysis Strengths

The fundamental strength of data analysis or big data is in the three Vs it represents: volume, velocity, and variety. The sheer volume, velocity, and variety of data that is getting scooped up opens up a new array of business opportunities in all functional areas from marketing, operations, accounting, finance, and human resource management, and in all sorts of organizations from industry, government, and non-profit. In short, the data itself is the opportunity for innovation.

Weaknesses

As big data analytics becomes more popular and a more standard part of modern business processes, there will need to be more training and knowledge transfer for the small to medium enterprises such that they are able to analyse the data they collect to gain a better insight into what their customers want and need. It was pointed out that there are not enough people comfortable dealing with large amounts of data and that big data should be incorporated into all aspects of an undergraduate degree so that more graduates have at least a moderate level of understanding in the field. Another major issue with data analytics is the risk of unintentionally, or deliberately, violating the privacy of individuals, as companies analyse large amounts of data the risk of this happening can be high.

Opportunities

Data analytics has an exciting set of opportunities in many different industries including healthcare, education, manufacturing, supply chain, and transportation. Similarly, the promise of big data spans all functional areas including, marketing, accounting, finance, operations, and human resource management. Big data can be used to tease out unique customer needs and wishes and develop products and services that satisfy those needs. Another area where data analytics could benefit a good majority of the population would be in the education sector. Having the ability to process the data to see if teachers are being effective in improving their students' performance would not only increase the testing scores to boost the school system's standing, but it would make for a more productive and educated future workforce.

Threats

As more and more data are collected, there is a risk that some of this data could be used inappropriately. For example, in the healthcare field, if a third party were analysing data, the data would have to be stripped clean of certain identifying information. Leaving someone's name or other personally identifiable information in a dataset that was sent outside of the company could not only endanger the client, whether it be from identity theft or some type of fraud scheme; but it could also have an impact on the company that released the information.

Data analytics in the manufacturing industry

Data Analytics is hugely important to the manufacturing industry today, as it is essential in achieving productivity and efficiency gains and uncovering new insights to drive innovation. With Big Data analytics, manufacturers can discover new information and identify patterns that enable them to improve processes, increase supply chain efficiency and identify variables that affect production. Manufacturing enterprise leaders understand the importance of data analytics in today’s industry.

A Honeywell Process Solutions-KRC research study found that 67 percent of manufacturing executives planned to invest in data analytics, even in the face of pressure to reduce costs. The majority understand that data analytics are required to compete successfully in a data-driven economy, and they are making investments in data integration and management assets to achieve digital transformation and gain a competitive edge. With the right analytics, manufacturers can zero in on every segment of the production process and examine supply chains in fine detail, accounting for individual activities and tasks. This ability to narrow the focus allows manufacturers to identify bottlenecks and reveal underperforming processes and components. Data analytics also reveal dependencies, enabling manufacturers to enhance production processes and create alternative plans to address potential pitfalls.

Data analytics also makes it possible to accurately predict the demand for customized products. By detecting changes in customer behaviour, data analytics can give manufacturers more lead time, providing the opportunity to produce customised products almost as efficiently as goods produced at greater scale. Innovative capabilities include tools that allow product engineers to gather, analyse and visualize customer feedback in near-real time. By giving manufacturers the tools they need to do a review on processes, data analytics allows them to identify points within the production process where they can profitably insert custom processes using in-house capabilities or postpone production to enable a partner to execute customisation prior to completion of the manufacturing process.

According to a Deloitte review of the rise of mass personalisation, the ability to postpone production gives manufacturers new flexibility that allows them to take on made-to-order requests. Deloitte also notes that the ability to postpone production can “help reduce inventory levels and ultimately increase plant efficiency.” A streamlined manufacturing process is not only beneficial in its own right — it gives manufacturers a way to maintain efficiency while performing customisations.

Conclusion

From my report you can see the main strengths, weaknesses, opportunities and threats to data analytics and why data analytics is so important to the manufacturing industry today. Data analytics use to be a perk for a company to have, now it is no longer a “nice to have” option for manufacturing enterprises. Nowadays companies must find a way to improve efficiency and generate insights, and data analytics provides this to the company and helps them to succeed in an increasingly complex environment.

Bibliography

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3. Consoli, R. (2018). Using Big Data Analytics to Improve Production. Available: https://www.manufacturing.net/article/2018/05/using-big-data-analytics-improve-production. Accessed: 27/09/18.