Five ways real-time analytics can improve manufacturing
Manufacturers are continually looking for ways to improve efficiency and output, not just to increase profits but also to streamline operations and lower costs. Many new and emerging technologies have helped make optimisation possible, mainly through automation and a wealth of insights.
By Kayla Matthews
Those insights come from digital support systems, collected by smarter and more connected devices such as IoT sensors.
These data-driven solutions provide powerful, real-time analytics. This can allow for much faster reactions to various elements of the modern market. Everything from customer demands to material shortages can be planned for well in advance. It facilitates not just a more reactive operation, but also one that is almost entirely proactive.
Manufacturers only achieve 40% of their potential because they are spending too much time manually updating inventory controls, production reporting, pricing reports and more. On the other side of that equation, competitors are using real-time data to win deals and plan next-gen factories with innovative production cycles.
What is real-time information, and how will it improve big data in manufacturing throughout 2020?
1. Endless quality enhancements
With data flowing in, manufacturers can use the insights they gain to improve products and goods in a near-infinite loop. Rather than having items go out in waves or batches, the entire development process can be keyed-in to the live information.
Defects, faulty parts and poor design choices can be remedied more rapidly than ever before. More importantly, the revisions are informed, with inspiration taken directly from customers.
It’s a continual process that collects, tracks and measures various elements of the development phase, and then administers fine-tuning without stopping or scrapping procedures. Everything stays in motion, thanks to the steady flow of real-time data.
Smart factories with integrated IT systems provide real-time data to both sides of the supply chain, helping to boost production capacity by 20%. Quality then becomes even more of a priority, because it’s never sacrificed to improve efficiency.
2. Unprecedented transparency and traceability
Typically, to understand performance and quality, manufacturers would have to stop the presses, examine finished goods or components, and then make the necessary changes. It’s at this time when crews might notice a malfunctioning machine or system. They might also perform close inspections of the processes. As with quality, the goal is to fine-tune operations.
Thanks to PLC-based monitoring and machine-to-machine (M2M) platforms, it’s now possible to capture and analyse real-time metrics on working processes and equipment. It completely transforms the everyday use of key performance indicators (KPIs) by pushing them into more of a proactive role.
The result is an incredible boost in both transparency and traceability across the entire factory floor. Decision-makers can hone in on patterns, trends and informative insights that create an unprecedented level of manufacturing intelligence. Visibility and controls are improved to new levels thanks to the incoming data.
One unnamed chemical company was able to reduce its waste of raw materials by 20% and energy costs by 15%, merely by measuring and comparing the impact of different production inputs on yield. Neural-network techniques helped to analyse and extract insights from real-time data about related production inputs. The increase in transparency helped achieve waste and energy improvements.
3. Prolonged equipment lifecycles
Preventive maintenance suddenly becomes plausible with real-time data, mainly collected by smart hardware and IoT sensors. Crews can keep an eye on machinery and tools, making minor adjustments that prolong their active cycles. Servicing equipment before it breaks down improves the lifecycle of the hardware and extends plant efficiency.
If and when a machine does need a more substantial repair — or needs to be replaced entirely — all parties are privy to the information, allowing for more streamlined planning. New equipment can be ordered and on its way long before a major shutdown, which is true of individual parts and supplies, too.
4. Faster and more accurate audits
Regulatory audits are a necessary evil. Internal audits are another concern, often serving as a trial run for more official inspections.
Having real-time big data manufacturing information flowing in, alongside the technologies necessary to collect and generate it, is an immense improvement over old-school manual operations. The audit process analyses the incoming data and does a proper check to make sure the reported information is accurate.
5. New business models
The business world has become incredibly dynamic and fast-paced. Today’s customers demand higher quality and proficiency at hyperspeed — on-demand products and services have a lot to do with it. Manufacturers have a responsibility to keep up, but it must be achieved without sacrificing quality and reliability.
Data available to manufacturers via real-time operating systems can make new business models possible. It presents incredible opportunities, including new business models like build-to-order operations, on-demand specs and mass customisation. The benefits are amplified even more when combined with new technologies, like 3D printing.
It doesn’t help that these newer, fast-paced processes are perhaps some of the most challenging elements of the modern business world. Mass customisation is not a quick or easy feat, as product personalisation must occur on an individual level. Real-time data helps inform such processes, by flowing directly into the programs that matter most.
Imagine a new machining order that passes right to a control system via the network. It enables automation on an almost unfathomable scale.
Real-time analytics are required
Any manufacturer that wishes to thrive in today’s market must have real-time data and analytics solutions in place.
The information helps inform not just regular operations, but also future opportunities like mass customisation, preventive maintenance and inventory, continual quality enhancements and much more.
Kayla Matthews is a technology journalist and writer whose work has also been featured on a number of publications. To read more from her, find Kayla's tech blog here.