Reducing production losses and preventing production process inefficiencies has always been a constant struggle for manufacturers of all stripes. Today, this is truer than ever, as growing demand meets increased competition.
On the one hand, consumers’ expectations are higher than ever before; global consumer habits are gradually “westernizing”, even as the population boom continues. According to numerous surveys in recent years, the global population will grow by 25% by 2050, equating to some 200,000 additional mouths to feed every day.
On the other hand, consumers have never had so many alternatives available to them, in almost every product imaginable. Recent surveys indicate that this wealth of options means consumers are increasingly likely to permanently ditch even their favorite brands if, for example, a product isn’t available on the shelf.
Against such a backdrop, manufacturers can no longer afford to take process inefficiencies, and their associated losses, in their stride. Every loss in terms of waste, yield, quality or throughput chips away at their bottom line and hands another inch to the competition — assuming their production processes are more efficient.
The challenge for many manufacturers — particularly those with complex processes — is that they eventually hit a glass ceiling in terms of process optimization. Some inefficiencies don’t have any obvious cause, and process experts are left at a loss to explain them.
Predictive Quality and Yield uses Industrial Artificial Intelligence to reveal the hidden causes of many of the perennial production losses manufacturers face on a daily basis. This is done via continuous, multivariate analysis, using Machine Learning algorithms that are uniquely trained to intimately understand each individual production process. The specific AI/Machine Learning technique used here is termed “supervised learning”, where the algorithm is trained to identify trends and patterns in the data.
(Learn more about the different types of Machine Learning, and how they relate to different manufacturing challenges, in our free mini-webinar)
Automated recommendations and alerts can then be generated to inform production teams and process engineers of an imminent problem, and seamlessly share important knowledge on how to prevent the losses before they happen.