The Magic Bullet for Boosting Manufacturing Production

The Magic Bullet for Boosting Manufacturing Production
Part of a special series from
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Why you should care

Because who doesn’t want to produce more for less?

The author, Andy Henderson, is an industry analyst in Heavy Industry/Discrete Manufacturing, GE Digital.

OZY and Predix from GE — the cloud-based development platform built for industry — have partnered to bring you an inside look at the future of digital industries, where people, data and productivity meet.

When it comes to manufacturing prowess, mastering the science of streamlined, efficient production is job No. 1. With so much money at stake — in 2015, manufacturers contributed a whopping $2.17 trillion to the U.S. economy, according to the Bureau of Economic Analysis — it’s all about the bottom line. And the bottom line relies on productivity. Output per hour for all workers in the manufacturing sector has increased by more than 2.5 times since 1987, according to the Bureau of Labor Statistics. How do savvy manufacturers get that number even higher? Two words: big data.

Right now, production decisions are typically made with partial information and by relying on spreadsheets that have limited data visibility. But what may seem like the “optimal” solution on a local level can create costly disturbances on the macro level. An example: Equipment needs preventive maintenance, but production schedules haven’t included the necessary downtime — so service is delayed in favor of continued operations. When this cycle continues for too long, the equipment may fail prematurely, causing costly disruptions on the line.

Advanced software and predictive analytics will be able to forecast potential staffing or supply-chain interruptions.

Enter smart production scheduling, fueled by big data. In the future, machine parts can be outfitted with sensors that stream data, constantly revealing the “health” of equipment and detecting potential failures. The win: optimal operations, with no unplanned downtime or interruptions to the production schedule. Advanced software and predictive analytics will be able to forecast potential staffing or supply-chain interruptions — like a flu outbreak that could cause a temporary personnel shortage or a blizzard that could disrupt deliveries. And big data will also relieve manufacturers from the challenge of managing replacement part inventories: It will “know” when machines are approaching failure and then query the maintenance inventory to see if the necessary replacement parts are available or need to be ordered. Risks to production are calculated and machine downtime is added to scheduled maintenance at an optimal operational time.

In the future, all data systems will seamlessly share information with one another. There will be a contiguous data record that exists from marketing and sales to services, including product development and manufacturing. This means immediate visibility to sales, customer issues, internal and external supply chain, personnel availability and qualifications and product design. Manufacturers will be able to make macroscale decisions while accounting for upstream inputs and downstream impacts.

A contiguous data record will also enable faster root cause analysis and corrective actions. Consider, for example, a component failure on a product at a customer’s site. The new production management system will connect the component to equipment, personnel and quality checks of each supplier in the supply chain, as well as the analysis and testing that was conducted on the design. And it will also be linked to the customer order that drove the product configuration. All of this will give the person who is diagnosing the issue an unprecedented view into all of the factors that impact the component’s ability to perform its function.

To be sure, reaching this level of efficient production scheduling will require time and financial investment. To tap into the powers of big data, manufacturers must outfit their equipment with sensors to establish a reliable and secure network to store and manage continuous streams of data. All of that might require bringing on new talent or outsourcing information technology tasks to a third party. But with so much manufacturing dollars on the line, an investment in productivity is the one that’s going to pay off.

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