I am no IT expert nor data scientist. I am a management consultant specialized in operations’ performance. My analyses use “small” data, even so sometimes the number of records in a database are quite impressive, but I got some insight about big data and with these posts, I’d like to share my experience. What is … Continue reading Big data and IT
In this post, I assume in near future correlation will be more important than causation* for decision-making, decisions will have to be made according to “incomplete, good enough” information rather than solid analyses, thus big data superseding Six Sigma. *See my post “my takeaways from Big data” on this subject In a world with increasing … Continue reading Why Big data may supersede Six Sigma
I got my first explanations about Big Data from experts who were my colleagues for a time. These passionate IT guys, surely very knowledgeable about their trade, were not always good about passing somewhat complex concepts in a simple manner to non-specialists. Yet they did well enough to raise my interest to know a bit … Continue reading My Takeaways from Big data, the book
5S are meant to be the foundations of operational excellence as no efficient work is imaginable in a messy, dirty and unsuitable-for-quality environment. This is long proven in the “physical world” and until recently transposable into the virtual world of digital information. In short, 5S is a framework for sorting, organizing, tidying, set housekeeping and … Continue reading Can 5S survive big data?
This weird title “Play big on small data” suggests the utilization of big data principles on small data sets. “Small” is to be considered relatively to huge amount of data big data can manage, which is not necessarily only a handful. I came across big data with former colleagues who were IT experts and got … Continue reading Play big on small data
Maximizing the exploitation of critical Capacity Constraint Resources (CCRs), so called bottlenecks, is crucial for maximizing revenue. Changeovers usually have a significant impact on productive capacity, reducing it with every new change made on those resources that already have too few of it. Yet changeovers are a necessary evil, and the trend is going for … Continue reading What data for changeover monitoring and improvement?
Asking people to fill out forms in order to monitor performance, track a phenomenon or try to gather data for problem solving, too often leads to trouble when data is ultimately collected and analysed. The case is about manual data capture into paper forms and logbooks on production lines. A precious source of information for … Continue reading Trouble with manual data capture
In near future, technology and especially connected objects – smart things stuffed with sensors and so-called wearable devices – will supercharge Lean improvements. One example of such already used device is given in a Mark Graban podcast about Hand Hygiene & Patient Safety. In this podcast (Episode #205), Mark’s guest Joe Schnur, VP Business Development … Continue reading Lean in digital age: sensors and data
The success of a 4.0 transformation in manufacturing (and industry at large) is particularly dependent on the initial conditions for its implementation. The “founding fathers” of Industry 4.0 and the organizations who help its implementation soon recognized the importance of a suitable “ground” as well as a number of prerequisites for a 4.0 transformation to … Continue reading The success of a 4.0 transformation is strongly dependent on the initial conditions