According to its promoters, the fourth industrial revolution (Industry 4.0) will bring big changes and re-boost industry in Western Europe with the integration of digital technologies in smart resources and connected objects.
Embedded smart technologies will free the future processes from some human weaknesses and limitations, if not from human attendance. With less needs for human management, what will happen to Theory of Constraints, Lean and Six Sigma?
I assume that ToC, Lean and Six Sigma will not only survive Industry 4.0 but keep naturally their place in smart factories and enterprises of the future.
Each of the TLS (Theory of Constraints, Lean and Six Sigma) body of knowledge in Industry 4.0 smart factories is described in a specific article, leading to the proposed conclusion.
Bottlenecks and constraints exist in any environment, any organisation and in all processes regardless to technologies. Hence, Theory of Constraints (ToC) will not be affected by foretold technologic drift in smart factories.
Since heterogeneous resources are used in production, experience told that balancing their utilization is impossible. Therefore, even in a fully automated plant, some machines or equipments will turn out to be bottlenecks, candidates for ToC application.
What will change in smart factories, are machines and objects able to communicate with each other and collaborate in order to optimize Throughput in real time. ToC will be embedded in those smart processes.
Smart machines will be able to learn and apply the most efficient scenarios, emit early warnings before malfunctions may affect bottleneck resources.
Conscious of the peculiar status of a bottleneck resource, smart machines and equipments in smart factories will take special care of the precious.
Markets or supplies of the future will continue to be external bottlenecks, another case for future application of ToC.
Decisions and policies, being of political, economical or regulatory nature, some even originated by the plant’s equipments themselves, will be constraints and obstacles on the road toward the company’s objectives. This is where Thinking Processes will be able to provide solutions to cope with or bypass constraints. More generally, problems will continue to pop up in future and the way to solve them (e.g. via Thinking Processes) will not change fundamentally.
Future processes will embed more technology and may become smarter, but they’ll remain processes. This trivial reminder is a hint for sustainability of Lean Thinking, Lean methods and tools. All Lean helped to improve so far remains valid in an environment richer of technologies and maybe poorer of manpower:
- Value creation for customers
- Lead time reduction
- Value added time maximization
- One piece flow
- Inventory minimization
- First Pass Yield
- Waste elimination
- Continuous improvement
What will change is anticipation, front loading Lean to the very early stages of a project, instead of trying to fix broken processes afterwards, when all is said and done, already decided and in place.
Much attention should therefore be paid embedding Lean in design and development, to Lean Engineering. Design For Manufacturing and Assembly (DFMA) will become even more necessary that fewer workers should be available to salvage poor product or process design or fix broken processes.
Furthermore, in the very early stages of design the most important decisions are taken, those choices and parameters that define future performances of the product and all processes for manufacturing, distribution, servicing and recycling.
Past experience learnt us that those decisions and choices may not be challenged afterwards, hence the importance of proactive and anticipated front loading of lessons learnt and Lean best practices.
The dawn of Industry 4.0 will not change the nature of these needs but will provide new opportunities for Lean application. Moreover Industry 4.0 will not change that much because technologies will be available to all competitors and differentiation can only be achieved with better offers and better use of resources.
Once efficient processes in place, random events and changes will continue to “plague” operations, bringing new occasions to use Lean tools and techniques.
Future processes will embed more technology and may become smarter, but they’ll remain processes. This trivial reminder is a hint that use cases for Six Sigma will still exist. Capabilities and deviations must be mastered, drifts must be controlled and there will be needs to pinpoint the few influent parameters in an even greater ocean of many.
With more automation and the potential faculty to mass-manufacture unique products, the traditional – and sometimes convenient – excuse of lack of repetition in high mix low volume production, hence lack of statistical significance should vanish.
Smart automated processes should produce more data and faster to which smart objects will add theirs, creating a permanent data flood. New parameters will be measured by embedded or affixed sensors, like temperature, moisture, acceleration, orientation, pressure, brightness, etc.
That’s where Six Sigma could need to evolve as this data flood will enter the Big Data world.
Analysis of such mass of data will not be done with usual Six Sigma statistical techniques but with scenario correlation analysis. The new statistical management and control models will be multi-criteria base, both because the techniques allow it and the new processes require it.
Those techniques are said to give predictive and self-learning abilities to machines, smart material and all objects associated; eg. molds, tools, test benches, ovens, etc.
Design of experiment, originally designed to reduce the number of required experiments to the bare minimum in order to isolate the few influent parameters, could go obsolete as High Performance Computing takes over. The latest uses low cost resources (massively paralleled multicore processors) to calculate at high speed large amounts of data, thus allowing exhaustive explorations of several scenarios.
Among the three TLS (ToC, Lean & Six Sigma) approaches, Six Sigma is by its nature the one most likely to be passed over to the machines. Future machines and equipments will take over, individually or in collaboration, statistical controls, real time analysis, self corrections, and so on.
If I’m right, Black Belt and Master Black Belts expertise can be passed over to machines, maybe even to smart objects. The need for experts will be reduced to those engineering the embedded Six Sigma intelligence.
In this future nevertheless, problems will still happen and the way to understand the causes and solve the issues will not change fundamentally. New tools will show up, but good old DMAIC, PDCA, Pareto or fishbone diagrams will still be used.
Despite possible transfers to smart devices, problem solving will still require intuition and the kind of reasoning (say gut feeling) machines should not have so soon. Therefore, until artificial intelligence demonstrates human-equivalent abilities, some (human) earthlings will still be required in smart factories.
“Conclusion”, when it come to a prospective thinking about a future 10 or 20 years ahead, cannot be understood as a definitive one. It’s more a wrap-up of likely-to-happen scenario, and likely to evolve according to technical and conceptual evolutions.
Lean, Six Sigma and Theory of Constraints should survive the fourth industrial revolution (Industry 4.0), even possibly find a revival with new use cases..
Lean and Theory of Constraints should demonstrate their robustness and universality just naturally applying in the new environment, without need for revision or update.
I am more sceptical about Six Sigma. No doubt Six Sigma will find its own use cases and demonstrate its relevance in a cybernetic environment, yet the mass of data generated by smart machines, devices and objects and their nature will need to reconsider their analysis, the methods and the tools.
My assumption is based on Big Data and High Performance Computing promises, new approaches and techniques, already handy (or in a very near future). Instead of applying statistical techniques to only a few critical-to-something parameters, its a global approach taking into account numerous parameters in scenarios that should emerge.
Besides, all the math of Six Sigma can be transferred (embedded) to smart machines and smart processes. What will be the need for (so many) experts?
I assume the survival of Six Sigma in smart plants will need some adaptation Lean and ToC won’t need.