by Jonathan Soriano
Companies have been using basic data analytics long before the term “big data” existed. As far back as the 1950s, firms would discover insights and trends by manually examining numbers in a spreadsheet. Until now, firms could gather information, run analytics, and use the information discovered to assist with future decisions. Today, the speed and efficiency associated with big data analytics allow firms to identify insights for immediate decisions. Due to the ability to work faster and stay agile, firms utilizing big data analytics have a competitive edge they did not enjoy previously.
Over a wide range of industries, manufacturers around the world have real-time shop-floor data available to them. Big data analytics involves taking isolated data sets, aggregating them, and then analyzing them to discover new insights. Tom Davenport, the Director of Research at the International Institute for Analytics, interviewed more than 50 businesses to determine the major benefits provided by big data analytics.
Cost Reduction: Technologies that are used to aggregate and analyze big data offer significant cost advantages for storing large amounts of data. Additionally, the insights provided by the analytics often uncover more efficient production methods through the removal of unnecessary costs.
Time Reduction: Whether its small-scale and straightforward decisions or large-scale and complex decisions, the amount of time required to arrive at a conclusion is greatly reduced with big data analytics. Firms may also discover new methods or production processes that decrease the manufacturing cycle time of products.
New Products and Services: Firms can collect and analyze data about customer needs and satisfaction, and then create new products and services that address these needs and desires.
Support Internal Business Decisions: Firms can incorporate external data into their analysis to assist with decisions regarding supply chains, risk management, pricing, and more.
Big data analytics will become a critical tool for discovering new methods to improve output per unit of input (yield). The ability to continuously enhance yield is especially important for firms within the manufacturing industry due to process complexity, process variability, and capacity restraints. Firms must begin taking the necessary steps to capitalize on the opportunity provided by big data analytics.
Before beginning any analysis, manufacturers must determine how much data they are capable of collecting. After discovering all of the data available, manufacturers must start using the data to improve operations, rather than just using the data to record operations. This may involve an investment in new systems or skill sets that allow firms to analyze their process information optimally. For example, data from multiple sources could be centralized or indexed, so the data can be analyzed more easily. Additionally, data analysts who have experience in discovering patterns and determining actionable insights from information could be hired.
Regardless of how confident a firm may be in its manufacturing process, using big data analytics will probably reveal more opportunities to increase yield. For example, a European manufacturer of functional and specialty chemicals for multiple industries was well-known for a strong history of process improvements since 1960. Additionally, its average yield was well above industry benchmarks on a consistent basis, so employees were unconvinced there was much to improve. However, after using big data analytics to measure and compare the relative impact of different production inputs on yield, several unexpected insights were discovered. The firm then reset its parameters, reducing its waste of raw materials by 20% and its energy costs by 15%.
The example above highlights that even firms with effective production processes can still use big data analytics to improve yield. More manufacturing firms in Ontario should begin to utilize the potential advantage provided by big data analytics.
“Big Data Analytics: What It Is and Why It Matters.” SAS Institute Inc., https://www.sas.com/en_ca/insights/analytics/big-data-analytics.html
Davenport, Thomas H., and Jill Dyche. “Big Data in Big Companies” International Institute for Analytic., SAS Institute Inc., May 2013. https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/bigdata-bigcompanies-106461.pdf
Hammer, Markus, Eric Auschitzky, and Agesan Rajagopaul. “How big data can improve manufacturing” McKinsey & Company. https://www.mckinsey.com/business-functions/operations/our-insights/how-big-data-can-improve-manufacturing