Relevant:

Comment:

All the experiments conducted have been interpreted within their business implication: a reliable system (i.e. robust SERENA platform and robust data connection) and an effective Prediction System (i.e. data analytics algorithm able to identify mixing head health status in advance) will have an impact on main KPI related to the foaming machine performances. In particular:

  • Overall equipment effectiveness increased 0.4% with an intended achievement of 15% 
  • Mean time to repair reduced from 3.5 hours to 3 hours
  • Mean time between failures increased from 180 days to over 360 days
  • Total cost of maintenance was reduced from €17400 to €8000.

 

Besides practical results, SERENA provided some important lesson to be transferred into its operative departments:

 

Data Quality 

Finding the relevant piece of information hidden in large amounts of data, turned to be more difficult than initially thought. One of the main learnings is that Data Quality needs to be ensured since the beginning and this implies spending some more time, effort and money to carefully select sensor type, data format, tags, and correlating information. This turns particularly true when dealing with human-generated data: if the activity of input data from operators is felt as not useful, time-consuming, boring and out of scope, this will inevitably bring bad data.

Some examples of poor quality are represented by:

a.         Missing data

b.         Poor data description or no metadata availability

c.         Data not or scarcely relevant for the specific need

d.         Poor data reliability

 

The solutions are two: 1) train people on the shop floor to increase their skills on Digitalization in general and a Data-Based decision process specifically; 2) design more ergonomic human-machine interfaces, involving experts in the HMI field with the scope of reducing time to insert data and uncertainty during data input.

These two recommendations can bring in having a better design of dataset since the beginning (which ensure machine-generated data quality) and reduce the possibility of errors, omissions and scarce accuracy in human-generated data.

 

Data Quantity

PU foaming is a stable, controlled process and it turned to have less variation: thus, machine learning requires large sets of data to yield accurate results. Also, this aspect of data collection needs to de designed in advance, months, even years before the real need will emerge. This turns into some simple, even counterintuitive guidelines: 

1.         Anticipate the installation of sensors and data gathering. The best is doing it at the equipment first installation or its first revamp activity. Don’t underestimate the amount of data you need to improve good machine learning. This, of course, also needs to provide economic justification since the investment in new sensors and data storing will find payback after some years.

2.         Gather more data than needed. Common practice advice is to design a data-gathering campaign starting from the current need. This could lead, though to missing the right data history when a future need emerges. In an ideal state of infinite capacity, the data gathering activities should be able to capture all the ontological descriptions of the system under design. Of course, this could not be feasible in real-life situations, but a good strategy could be to populate the machine with as many sensors as possible.

3.         Start initiatives to preserve and improve the current datasets, even if not immediately needed. For example, start migrating excel files spread in individuals’ PC into commonly shared databases, making good data cleaning and normalization (for example, converting local languages descriptions in data and metadata to English).

 

Skills

Data Scientists and Process Experts are not yet talking the same language and it takes significant time and effort from mediators to make them communicate properly. This is also an aspect that needs to be taken into account and carefully planned: companies need definitely to close the “skills” gaps and there are different strategies applicable: train Process Experts on data science, train data scientists on the Subject matter; develop a new role of Mediators, which stays in between and shares a minimum common ground to enable the extreme cases to communicate.

 

No additional information available