In his recent State of the Union address, President Barack Obama outlines a new public-private initiative to create 15 manufacturing hubs in the US. The plan calls for the private sector to work with the federal government to offset some of the domestic manufacturing jobs that have been lost for years to outsourcing.
Indeed, manufacturing is entering a new era, one with a competitive landscape that’s been forever changed by the recession that has stifled demand for many goods and services.
Yet, at the same time, manufacturers will soon encounter a new paradigm shift where emerging economies like China and India, once seen only as sources of cheap labor, will be home to the vast majority of the world’s consumer class.
The next era of global manufacturing will require a new kind of organization, “a networked enterprise that uses big data and analytics to respond quickly and decisively to changing conditions and can also pursue long-term opportunities,” the McKinsey Global Institute notes in a recent research report.
This is the third article in a series that details the critical role data analysis will play in driving the success of manufacturing in the future, from helping companies dissect the preferences of customers in different geographic locations to predicting and responding to volatile market factors like the cost of materials.
Because of their strong reliance on machinery, data analysis can potentially help manufacturers exploit the “Internet of Things,” making sense of the vast flow of data that’s generated from sensors that are attached to everything from product shipments to the most advanced production machinery.
“By monitoring machinery, [manufacturers] can prevent or work around potential outages before they happen,” the McKinsey report notes. “For example in chemical plants and oil refineries, sensors and telematics devices that monitor noise, temperature, vibration and other factors are used to gather data to predict breakdowns or safety risks. All kinds of companies can streamline maintenance strategies by using similar analytical techniques, allowing them to move from preventative maintenance plans that require replacement schedules that are often too conservative to more efficient predictive maintenance.”
Manufacturers can also use big data and data analysis to better manage complex supply chains. Toyota, Fiat and Nissan have all reduced new-model development time by 30% to 50% by allowing designers and manufacturers to share data quickly, create simulations to test different designs, as well as by allowing them to choose the parts and suppliers, the report notes.
Machine-generated data is expected to increase from 11% of all data in 2005 to more than 40% in 2020, according to a report from IDC.
But manufacturers must overcome a variety of challenges to successfully mine the reams of data streaming into their networks from sensors. Data is now considered to be the fourth factor of production, as important as land, labor and capital, according to respondents to a Capgemini survey.
The survey respondents estimate that, on average, they have seen a 26% improvement in performance over the past three years in the processes where data analysis has been applied. They estimate performance will improve by 41% over the next three years.
The study goes on to note that manufacturers cite three primary impediments to using big data:
- Too many silos – data is not pooled for the benefit of the entire organization
- Shortage of skilled people to analyze data properly
- Big data is not viewed strategically enough by senior managers
Moreover, 36% of manufacturers strongly agree and 54% of manufacturers agree that the challenge is not the growing volumes of data but rather being able to analyze and act on data in real time, according to the study.
To help overcome these common barriers to the successful wrangling of big data – from machines or other sources – companies must view big data in terms of their desired business outcomes, advises Harvard Business Review (HBR).
“Instead of asking, ‘How can we get far more value from far more data?’ successful big data overseers seek to answer, ‘What value matters most, and what marriage of data and algorithms gets us there?’” HBR notes. “Executives need to understand that big data is not about subordinating managerial decisions to automated algorithms but deciding what kinds of data should enhance or transform user experiences. Big data should be neither servant nor master; properly managed, it becomes a new medium for shaping how people and their technologies interact.”
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