5 ways big data is enhancing quality in manufacturing

Manufacturers are definitely the underdog in the game of consumer buying. With ever increasing pressure to reduce production costs coming from both global competition as well as consumers with powerful voices, manufacturers are really under the pump.

There is a definitive opportunity to get insight into various aspects of the manufacturing process, and it is simply through leveraging something most manufacturers already have, Data. Manufacturers are inundated with data, often from multiple sources and datasets and at various points in the supply chain. However, it is the intelligent use of big data in manufacturing that reduces processing flaws, improves product quality, and saves time and money.

The challenge for manufacturing managers lies in harnessing data when faced with limited resources, while still ensuring the optimum quality of their products.

To help, we’ve outlined 5 key areas where pragmatic use of big data delivers measurable results for manufacturing quality.

1. Real-time monitoring of machine performance

A big problem manufacturing managers face is in pinpointing the causes of production delays, maintenance downtime or product defects. Real-time monitoring can now be done by smart sensors that can pinpoint where errors exist, how long delays occur and give preventative maintenance alerts.

Nukon, for example, implemented sensors to help a manufacturing client identify problematic points in its workflow. The sensors identified not just the cause of delay, but also when it started and how long it lasted. One delay was caused by something as seemingly innocuous as a drill bit dulling, which added a full five minutes to production time. The simple fix? Keeping spare parts at hand for a drastic reduction of downtime.

Take-home: Real-time monitoring helps to identify problems quickly so manufacturers can intervene and improve quality output sooner.

2. Data integration breaks down ‘information silos’

Data is a great leveller: When presented correctly, everyone on the project can quickly reach the same conclusions.

Perhaps the most exciting impact of big data on manufacturing quality is its potential to break down information silos and integrate disparate data systems and sources. Data sets spread across multiple spreadsheets put manufacturers at risk of making ‘blind’ decisions. Big data integration lends insight.

McKinsey & Company demonstrate how advanced analytics helped a top biopharmaceutical company increase yield by more than 50 percent, with no additional capital expenditure. The company first clustered closely-related production activities in one central database. Analysis then determined how different process parameters were interdependent and how each impacted yield. This revealed where changes should be made within key process stages. The saving was substantial - with over $5million (and sometimes up to $10million) annual cost reduction in the production of a single line.

Take-home: Integration of multiple data sets can pinpoint exactly which processes to optimise. This can both increase yield and reduce the number of defects in production.


3. Big data drives better quality through 'predictive analytics'

Harnessing historical data sets can reduce the time needed for quality and compliance checking.

Computing giant Intel, for example, has used big data to optimise the quality check process for its chip manufacture. Every chip in Intel’s production line undergoes a check involving 19,000 tests. Predictive analysis of historical data sets made the business focus on chips at ‘wafer level’, to target specific tests and significantly reduce testing time. Intel reported a 25 percent reduction in chip quality processing time. The cost savings were impressive, with $3 million cost reduction in one line of core processors.

Take-home: Big data harnesses historical data to find future savings while not compromising on quality.


4. Big data raises supplier visibility, improving overall manufacture quality

A Forbes report points out that data technology has enabled greater visibility across the supply chain, meaning managers can monitor supplier’s performance historically, in real time and into the future. This feeds back into improving quality across the entire manufacturing supply chain for the business.

Take-home: Supplier auditing is easier and more transparent with big data tools.

5. Big data increases quality in customer services, including customer-driven production

Remember those machine sensors we mentioned earlier? Well they can extend as far as the customer. Tracking systems can indicate when customer products like automobiles or software might need maintenance, driving up the quality of after-purchase services. Such data can then feed back into improving production and can drive other business areas, such as new offerings based on customer usage.

IBM provides an automotive case for customer-driven data. In China, a leading automobile manufacturer was struggling to cope with fluctuations in customer demand. They used purchase order analysis and automated production rules to develop ‘what-if’ scenarios to improve decision-making. The company reduced production costs by 15 percent and boosted productivity by 10 percent.   

Take-home: Big data drives quality improvements by connecting customer data to production.

Big data in manufacturing: Next steps for practitioners

Appropriate use of big data in manufacturing can not only enhance quality and drive down costs; it can improve entire business systems.

With the right intelligence tools, managers can make the right decisions based on historic and real time data – and ultimately gain support from all aspects of the business.

Need help uncovering significant variables in your production chain? Download our free guide to driving operational efficiencies through real-time data today.

3 ways Nukon helps manufacturing engineers get better insight into their data

Topics: Manufacturing