In manufacturing, yield, consistency, and quality are of vital importance. Historically, it’s been difficult for companies to optimize manufacturing processes. They simply didn’t have access to the data or the analytical technologies to provide the insight needed to modify processes at a granular level. With the advent of big data technologies, the cloud, and ubiquitous sensors, that’s changed. Companies now have access to huge amounts of data from every stage in the manufacturing process.
By leveraging data for deep insights into the manufacturing process, companies can increase efficiency and consistency and thereby reduce manufacturing costs and increase ROI.
Increase Yield, Quality, And Consistency
As Mckinsey & Company authors have discussed, it can be extremely difficult for some types of manufacturing companies to control all the variables that might impact production yield and quality. The pharmaceutical industry has long been accustomed to variations in yield of anything up to 100% the manufacturing processes are complex and rely on the correct functioning of both chemical and biological factors.
By gathering detailed data about each stage of the manufacturing process and using statistical and machine learning techniques to analyze that data, pharmaceutical companies have demonstrated significantly increased yields.
Pharmaceutical manufacture is an extreme example, but almost every manufacturing process can benefit from big data. More data means a higher-fidelity view of the real-world variability of the manufacturing process, allowing analysts to spot areas for improvement.
Improve Operational Efficiency
Manufacturers have an abundance of information about the performance of each part of the manufacturing process over time. That data can be segmented down to the level of specific machines, employees, and times of day. With that data, companies are able to identify bottlenecks, non-compliance with established production workflows, and other points in the process that impact operational efficiency.
The Unknown Unknowns
Most big data statistical analyses require the manufacturer to ask the right questions, but there are occasions where unseen and unconceptualized factors impact the process. With machine learning technologies, which many companies are currently seeking practical applications for, these unknown unknowns have the potential to become amenable to analysis and change.
Google’s AlphaGo which recently beat the world’s leading Go player is an example of how far artificial intelligence and machine learning has come over the last few years. AlphaGo wasn’t “programmed” to play Go in the traditional sense. It learned to play Go by analyzing inputs and outputs over huge datasets. Its playing style wasn’t predictable even to its developers.
Machine learning is particularly useful when the underlying data is too complex for ordinary analytical processes:
“When the underlying physical reality is too complex to be described by manageable modelsas it is the case for complex devices such as integrated circuits or processes in semiconductor manufacturingit becomes impossible to directly interpret observations or to understand the effect of specific changes.”
Big data analytics is well established within manufacturing companies, and advanced machine learning techniques are on the cusp of being able to deliver real-world improvements to manufacturing processes. Over the next few years, the combination of sensors, data, the cloud, and analytics coupled with machine learning will revolutionize how we approach the optimization of manufacturing processes.
