Manufacturers have long been known as early adopters of technology to reduce costs, innovate, and drive revenue. From the plant floor to the top floor, technology is used to expand worker mobility, gain real-time insights, and make data-driven decisions. Big data, in particular, is being used to bolster productivity, drive communications and maintain competitiveness.
Yet the manufacturing industry is one that is often steeped in tradition, especially when a veteran workforce is involved. While millennials and those who have yet to establish a career in manufacturing will be more open to big data integration, employees who prefer the tried-and-trusted methods might show some hesitation. In this case, it’s important to illustrate how big data can make their jobs more productive, enjoyable and, most importantly, easier.
Here are 3 ways big data can help your operations teams think bigger and perform better than before:
1. Monitor product quality in real time
Cloud computing, cloud data storage, the Industrial Internet of Things (IIoT). All these factors have collided to create a golden opportunity for manufacturers who can now use massive data volumes in unexpected new ways.
Using big data and advanced analytics, manufacturers are able to view product quality and delivery accuracy in real time, making trade-offs on which suppliers receive the most time-sensitive orders. Armed with this information, they can give preference to top suppliers and thus improve quality over time.
The use of big data also means that quality can be integrated throughout the entire value chain, rather than at the end of the production line. This level of knowledge increases quality while reducing waste.
2. Predict the future
Operational analytics is great for telling us what just happened and why. Manufacturers have been doing this kind of analysis for years. But they’re now using the predictive aspects of big data to monitor their operations against their quality standards. That often means predicting when a machine or tool is about to break before it starts churning out defective products.
Predictive analytics tells you what’s about to happen. It shows you how to make machines do what you want. Together, operational and predictive analytics are the crown jewels of business intelligence. Both require vast amounts of data, and the ability to analyse it effectively.
A manufacturer is only as good as the machines that produce its products. Unfortunately, machines break down over time – parts wear away, and the cost of replacing a single piece of modern equipment can easily cost thousands of dollars.
Predictive analytics in manufacturing is enabling manufacturers to make better use of machine loss. Automating the analysis of data from sensors within equipment as well as the actual operation of these machines can determine when machines may need to be brought online or shut off to prevent failures.
Integrating predictive analytics across the DMAIC (Define, Measure, Analyse, Improve and Control) process gives manufacturers greater insight into how each phase of a DMAIC-driven continuous improvement programme is working. The use of big data in this way shows great potential to make production workflows even more customer-driven than before. It also increases equipment uptime.
WATCH this video to find out how DuPont is driving sustainable improvement and ensuring capable, predictable and reliable manufacturing.
3. Get the data you want from your customers
A happy customer is a loyal customer and that translates directly into a healthy bottom line. Big data analytics can help manufacturers respond quickly to customer complaints or queries.
But you cannot use data unless you collect it first. The winners in the new data-driven economy will be the companies that can gather vast amounts of data and turn it into actionable processes within their value chain. For manufacturers, the data gathering doesn’t stop at the boundaries of the organisation — it includes information collected at customer sites.
Sensors come into play here, too. It’s becoming highly cost-effective for manufacturers to embed sensors into the products they deliver to customers — and the data they’re getting back is well worth the small investment in hardware. By extending the quality control process beyond purchase and throughout the life of their products, manufacturers now gather information that catapults their products to higher levels of performance, better design, and longer lifespan.
But as large volumes of data are generated, stored and processed, legacy technologies may no longer be adequate. New cloud technologies will support systems used in manufacturing by providing the necessary capacity to store and share data. These technologies also allow manufacturers to replicate best practices and technical advancements quickly throughout their global enterprises.
Manufacturers must evolve to stay ahead of their competitors, and the use and implementation of big data, specifically predictive analytics, will continue to take centre stage. By understanding where the prioritisation is taking place, why the IIoT is empowering it, and how it will benefit manufacturers, these companies can take the lead in making it the platform for dramatic change on a global scale.
|The TRACC framework helps organisations build standardised and integrated good practice and performance capacity across their Plan, Source, Make and Deliver functions. Simultaneously it accelerates their collaboration and alignment capacity to build world class end-to-end value chains, enabling the organisation itself to become the ultimate source of sustainable competitive advantage.|