Central to lean manufacturing is the empowerment of people. Lean can’t work unless there is a pervasive culture that will experiment, implement and evaluate. The lean methods enable employees to focus on fault diagnosis and to propose innovative solutions that minimise the resources required to maximise throughput.
A lean implementation will help build a good standard of information literacy amongst staff who might previously had very little experience of data analysis. And this could be useful if a factory embarks upon enhancing its data capture with IIoT equipment.
There is an added complication with IIoT/Industry 4.0/digital manufacturing data capture though; these initiatives assume the use of analysis techniques that are beyond the traditions of Statistical Process Control (SPC).
Machine Learning (ML) is being touted by many software vendors as a silver bullet (which it isn’t), but it does have a place when it comes to developing insight from data streams that are both fast and large. ML models can help us understand some of the complexities of processes by providing different perspectives of the data; such models will probably include a number of inter-related processes, rather than the single operation that is represented by a lean Statistical Process Control (SPC) chart.
SPC charts can often be challenging to comprehend until you have seen a few, or better still, plotted them yourself. They are an excellent way of using a data visualisation technique to communicate deeper understanding of a process.
But using Fast Fourier transform (FFT) to make sense of streaming data requires a different set of skills, as does the selection of random walk over a support vector machine (SVM) for classification. Hence the the demand for the ‘data scientist’ who can take such decisions and help translate the complexity to us mortals.
We are heading towards an era where there is a fundamental need for more sophisticated data analysis skills. Technologies such as IIoT are providing better, cheaper access to data. We can look beyond the confines of the individual manufacturing process and analyse operations at scale.
However, realising this potential means that we need to address a skills gap that is rapidly emerging. A lot of Computer Science degree courses do not teach these skills, some of which are more often found in electrical/electronic engineering courses. Some Data Analytics courses are starting to appear, and these will only increase as businesses see the need to make better use of the data that their IIoT devices are churning out.
So, lean might be a good way of introducing digital transformation technologies to a business. But this may also expose a need to develop advanced data literacy skills rather rapidly.