Quality control and testing plays a crucial part in product development and manufacturing. Without those aspects, faulty products could hit the marketplace and cause reputational damage, excessive costs and can even risk lives due to potentially dangerous consequences.
However, big data is proving to be an essential technology in making quality control and testing more efficient and effective.
By speeding up the time to market
Examining the findings of big data interfaces can enable companies to significantly reduce the amount of time needed to handle validation testing before placing a product on the market. One instance involved Intel combining big data with artificial intelligence (AI) to capture tremendous amounts of data and process it more quickly than humans.
In an instance of validating the new features of computer chips, Intel's validation teams collect up to 250 GB of new data each week while working with tests that could have more than 1,000 parameters. Intel's AI solution checks through historical data and finds patterns. Then it uses that information to create tests. This process only takes a few hours, but if humans tried, the feat would require thousands of hours.
Moreover, depending on AI for test execution allows Intel to locate bugs more efficiently while eliminating tests that are not relevant. This approach, according to Intel, reduces the number of tests performed by 70%, helping products reach the market faster without sacrificing quality.
Giving compiled insights that inform improved product design and testing
Some companies may have product tests being carried out all over the world and plan to use the results from those experiments to inform new, enhanced designs. Before big data became prominent, collecting the information from those tests was time-consuming and often required locating users to gather feedback about the product.
However, today's big data platforms can quickly look at opinions broadcasted on social media or, in the case of an internet-connected device, keep tabs on how people use products in development without explicitly reaching out to them to get their feedback.
For example, big data could find out which features within fitness trackers that a tester uses more frequent and the steps they go through to do so.
Collecting data throughout a period of time and extracting the meaningful sentiments from it could also increase the likelihood of a new product's later success. Predictive analytics can examine various aspects of the product development process and find the factors within it that highlight the things people like the most, as well as the things that frustrate them.
Big data is also capable of predictive models, allowing brands to create thousands of versions of a product in seconds. Proctor & Gamble took that approach when designing diapers, and they used a similar approach to determine when a dishwashing liquid would release particular fragrance notes.
Improving testing relevance
The information gleaned from big data platforms could help companies choose whether highly accelerated life testing (HALT) or accelerated life testing (ALT) procedures are more appropriate for their products. HALT finds failures in products early in the development process, which could save a company money and lead to more customer satisfaction in the long run.
Conversely, ALT attempts to find out how long a product could perform before components start to break down by speeding up its aging process. Big data might show a potential unexpected weakness in a product, thereby spurring the manufacturer to see if a HALT could give more details about the causes for the failure. Then, the people overseeing quality control could target those problems before proceeding further with the development process.
Additionally, big data can make other kinds of tests more customer-focused. Julep, a company that makes nail polish, used online A/B testing to listen to its customers and find out if they'd rather have a nail polish wand designed for mixing colors or one that gave professional-quality results. Such investigations validate demand for a new feature or product before it arrives on the market. The company also collects the thoughts of thousands of its product testers and analyzes them. In these cases, companies aren't as likely to possibly waste money testing offerings that customers do not need or want, helping them cut unnecessary spending.
Bringing analytics into existing equipment
Some companies offer big data analytics platforms with plug-and-play functionality enabling manufacturers to swiftly incorporate analytics tools into factory equipment. Then, those entities can potentially spend less money than they otherwise might to start being more reliant on analytics for quality testing.
As an example, Oden Technologies produces analytics devices that connect to almost any existing equipment. They aim to spot problems in machinery that could lead to product defects. Then, quality control becomes more streamlined because companies become aware of issues — and can fix them — before they show up in future product tests.
BMW uses big data analytics to boost quality throughout its manufacturing process, such as by marking individual parts with laser-etched codes. Experts can then analyze the characteristics of parts or the different stages they go through in a factory, such as shaping or paint application.
Digging through data for enhanced results
Big data is already well established for quality control and testing purposes, but it's likely the technology and the respective tools to harness big data will become increasingly prominent as innovations progress and businesses explore ways to deliver high-quality, well-tested products to customers within reasonable time frames. With big data, consumers can benefit from enhanced products, and companies spend less time and money on irrelevant testing.