Applying Big Data to Tame Manufacturing Complexity

Things are becoming smarter. Inanimate things, things that you wouldn’t ordinarily expect to have a brain — teakettles, washing machines, refrigerators, and locks — now have WiFi innards that let you control them with your phone. While these newer, smarter home goods have definitely had their teething pains in terms of how they interact with the real world, those making them have problems of their own. To put it simply, everyday goods are becoming harder and harder to make.

According to a broad survey of the industry, 40% of respondents see a rise in manufacturing complexity within their vertical. The average manufactured good contains more mechanical, electrical, and software components than ever before. By virtue of this complexity, it is also now more difficult to manufacture goods than ever before. Designing a system, assembling the required components, and ensuring that it doesn’t break before its projected end of life is now a greater challenge than ever — and it’s one that big data can solve.

It starts with design. Once humans have a basic design to start from, machines can take over pretty well, creating millions of tiny iterations that vary the parts of a product for different outcomes. All that’s left for people to do is to pick out the most optimal combination of price and performance.

This process, known as simulation analytics, has been used to design everything from diapers to Patriot Missiles. Procter & Gamble, a consumer goods giant that serves almost five billion customers globally, uses modeling and simulation in the design of disposable diapers to create thousands of product iterations in seconds and find the best product design. When developing a new dishwashing liquid, they use predictive analytics to predict how moisture would excite fragrance molecules, to make sure the right fragrance is felt by consumers at every stage of the dishwashing process.

Research suggests that companies who use this method will experience a:

In other words, big data helps companies get products to market faster and cheaper. Moving forward, the same techniques help these same products work better and last longer.

The product lifecycle is another huge concern in manufacturing, and it’s another area that’s exacerbated by complexity.