I recently came across a post explaining why it is urgent to start going Industry 4.0 with collecting big data. The rationale is built on the example of social media companies who base their business model on big data and paved the way to industry at large.
Just as the social media companies sift out keen understanding of people needs and desire out of their data ocean, manufacturing companies can do the same to better know their machines. The vast amount of data already available and increasing with the installation of more sensors and automation must be used to increased knowledge and convert it into concrete actions, the post pitches.
Doing so, valuable insights into the current state of a specific machine will be gained, enabling immediate reaction in case of irregularities and reducing machine downtime to an absolute minimum. The analysis of the collected data can “predict” the likelihood of a breakdown and warn about conditions jeopardizing quality, triggering the preventive actions.
As a result, a more effective use of human and machine resources and increased product quality.
Therefore, the author invites to start an Industry 4.0 process by collecting and utilizing the data that already exist in the production and the efforts will quickly be reflected on the bottom line.
Checking who the author is: a big data / analytics solution vendor.
The current reality
No doubt the benefits described in the pitch can eventually be reaped by manufacturing companies, but their current reality calls for others, more mundane solutions before going for big data and data science.
From my experience, many companies still suffer from inefficiencies due to multiple reasons starting with unexpected and frequent manufacturing schedule change, lengthy changeovers, material shortage, disrupted supplies, bottlenecks in the process and sometimes poor basic machine tending. If machines are left idle, it’s mostly because of external factors and not related to the machines’ intrinsic reliability.
What good would more data and high-tech analytics do in such situations? The causes for inefficiencies are known or easy to find, so are most of the countermeasures. At this stage there is no need for in-depth knowledge about the machines’ condition.
Withstand the hype
It is a vendor’s job to promote his stuff and in the current Industry 4.0 craze, it’s understandable that vendors push hard. It’s on the companies’ side to withstand the hype and analyze if for them the time is really ripe to embark in digitizing, going big data, advanced analytics, etc.
I am not at all basically resisting digital transformation, but in the situation I described above, big data and analytics seems to me like trying to get smart about a driver’s behavior analyzing a lot of engine data while the car is daily stuck in traffic jams.
My advice: consider more advanced technologies only when basic conditions are restored and sustained and when Lean and continuous improvement really are at their limits.Follow @HOHMANN_Chris