“Big Data Analytics”
“Internet of Things”
Every so often, we hear these buzzwords in the world of manufacturing. But what do they really mean?
Big data (large datasets) is increasingly being recognized as a valuable source of information for manufacturers through the insights it provides to their factories. A product of the current technological landscape or ‘Industry 4.0’, big data is often cited as the ‘new currency’ for the very reason that it helps managers make cost-effective and strategic decisions. The power comes from data through analytics; taking data sets and combining them to derive meaning through statistical analysis. LNS Research defines big data analytics in manufacturing as “...using a common data model to combine structured business system data like inventory transactions and financial transactions with structured operational system data like alarms, process parameters, and quality events, with unstructured internal and external data like customer, supplier, Web, and machine data to uncover new insights through advanced analytical tools.”
In other words, aggregating both unstructured and structured forms of data and using analytical toolsets to figure out insights.
So how does one introduce big data analytics into their manufacturing processes? With the use of traditional ERP and MES, manufacturers have been able to collect historic data for some time now. These solutions are able to collect data from manufacturing processes using sensors or wireless equipment installed on machinery. But just collecting the data is only half of the battle. From this point, the manufacturer will need to invest in making the data beneficial to them, by organizing the data into a central network and employing a person or solution that will analyze the data and report patterns. From there, manufacturers are able to glean important information through analysis; seeing where the variables and bottlenecks in your process occur.
There are many benefits to implementing big data analysis into your process, such as cutting down the number of errors, producing better quality products, higher efficiency, and less wasted time. Big data can also help management to figure out where some equipment or employees are falling short and use this information to act accordingly, and ultimately save money. Employing big data analytics is a cost-effective method that allows firms to stay competitive in the increasingly data-centric world we live in.
Sources: http://www2.deloitte.com/global/en/pages/manufacturing/articles/future-of-manufacturing.html http://www.mckinsey.com/business-functions/operations/our-insights/how-big-data-can-improve-manufacturing https://hbr.org/2016/05/the-biggest-challenges-of-data-driven-manufacturing http://blog.lnsresearch.com/what-is-big-data-analytics-in-manufacturing