Chris HOHMANN – Author
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 uncertainty, fast changing businesses and fiercer competition, I assume speed will make the difference between competitors. The winners will be those having:
- fast development of new offers
- short time-to-market
- quick reaction to unpredictable changes and orders
- fast response to customers requirements and complaints
Frenzy will be the new normal.
I also assume that for most industries, products will be increasingly customized, fashionable (changing rapidly from one generation to the next, or constantly changing in shapes, colors, materials, etc.) and with shorter life cycles.
That means that production batches are smaller and the repeating of an identical production run unlikely.
In such an environment, decisions must be made swiftly, most often based on partial, incomplete information, with “messy” data flowing in great numbers from various sources (customer service, social media, real-time sales data, sales reps reports, automated surveys, benchmarking…).
Furthermore, decisions have to be made the closest to customers or where decision matters, by empowered people. There is no more time to report to a higher authority and wait for the answer, decisions must be made almost at once.
There will be fewer opportunities to step back, collect relevant data, analyze them and find out the root cause of a problem, not even speaking about designing experiments and testing several possible solutions.
Decision making is going to be more and more stochastic: with the number and urgency of decisions to make what matters is making significantly more good decisions than bad ones, the latter being inevitable.
What is coming is what Big data is good at: fast handling a lots of messy bits of information and revealing existing correlations and/or patterns to help making decisions. Hence, decision-making will rely more on correlation than causation.
Six Sigma aficionados will probably argue that no problem can be sustainably solved if the root cause is not addressed.
Agreed, but who will care about trying to eradicate a problem that may be a one-shot and which solving time will probably exceed the problem duration?
In a world of growing interactions, transactions and in constant acceleration, time to get to the root cause may not be granted often. Furthermore, even knowing what the root cause is, this one may lay outside of the decision maker or company’s span of control.
Let’s take an example:
The final assembly of a widget requires several subsystems supplied by different suppliers.The production batches are small as the widgets are highly customized and with short life cycle (about a year).
The data survey – using big data techniques – foretells the high likelihood to have some trouble with the next production because of correlations between former experienced issues in combination of some of the supplies.
Given the short notice, relatively to the lengthy lead time to get alternate supplies, and the short production run, it is more efficient to prepare to overcome or bypass the possible problems than trying to solve them. Especially if the likelihood to assemble again these very same widgets is (extremely) low.
Issues are not certain, they are likely.
The sound decision is then to mitigate the risk by adding more tests, quality gates, screening procedures and the like, supply the market with flawless widgets, make the profit and head for the next production.
Decision is then based on probability, not on profound knowledge.
But even so the causes of issues are well-known, the decision must sometimes be the same: avoidance rather than solving.
This is already the case with quieter businesses, when parts, supplies or subsystems are supplied by remote unreliable suppliers and with no grip to control them.
I remember a major pump maker facing this kind of trouble with pig iron casted parts from India. No Six Sigma techniques could help make a decision or solve the problem: the problem laid beyond the span of control.
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