How Lean Six Sigma prepares workers for tomorrow’s workplace.
People outside of manufacturing often imagine that technology’s next step is to turn factories entirely over to robots. While factory automation is growing by leaps and bounds, the reality is most automation is paving the way for workers to be far more involved in critical decision-making on the factory floor. Just as Industry 4.0 is the driving force behind smarter machines that automatically analyze and adjust processes as they inspect product, Lean Six Sigma is paving the way for a smarter workforce, capable of analyzing production trends and optimizing processes.
The benefit of Lean manufacturing philosophy is a holistic focus on eliminating issues that create bottlenecks, defects and wasted effort. It aligns well with an Industry 4.0 vision, since greater levels of automation help drive reduced variation, and eliminate excess handling and errors related to manual processing. However, while a Lean vision helps drive efficiency and improve throughput, factories with a lot of product variation, as is found in contract manufacturing, do develop inefficiencies that need to be addressed. Six Sigma provides a powerful methodology and toolbox for addressing these inefficiencies. Implemented correctly, it creates problem-solving discipline that teaches production teams how to make good choices in the problems they choose to solve, thoroughly analyze root cause, test their preferred solution and make sure the improvement is sustainable over time.
The DMAIC (define, measure, analyze, improve, control) methodology represents the heart of this discipline, teaching teams to use a measured process in their problem-solving efforts. The skillsets taught incorporate best practices in management, process engineering and quality.
In the define phase, teams learn to focus on the right issues and lay the groundwork for a disciplined process. The problem statement helps team members align on the core issue they are trying to solve. Building a business case and analyzing the financial impact helps ensure the ends justify the means – in other words, that correcting the issue generates sufficient savings to justify the time spent. Identifying critical to quality (CTQ) elements, defect metrics and voice of the customer (VoC) specifications helps ensure team members focus on analyzing process outcomes meaningful to all stakeholders.
In the measure phase, teams are introduced to a set of core tools that ensure they are collecting meaningful data on the process variances identified in the define phase and drilling down to the variances that impact process outcome. Core tools such as cause-and-effect, Pareto charts, SIPOC (suppliers, inputs, process, outputs, customers) diagrams, capability analyses to assess the ability of a process to meet specifications, and gage R&R measurements enable the team to collect the data necessary to move to the next phase.
In the analyze phase, teams apply critical thinking skills to analyze the data collected to determine trends and possible corrective actions. Tools such as the hypothesis test, cause-and-effect diagrams and IPO charts are used to establish whether the key process outcomes are truly the result of the identified process drivers and inputs. This is expressed as the equation Y=f(x), where Y is the key process outcomes, and x is the variables or drivers within the process.
In the improve phase, teams learn to implement improvements and utilize design of experiments (DoEs) to determine if the proposed solutions correct the problem. For example, in one SigmaTron DMAIC project focused on solder dross reduction, the team made the logical assumption solder dross residue on wave solder pallets was contributing to the dross accumulation issue they wanted to reduce. DoEs established the cleanliness of solder pallets had no impact on accumulation, and as a result, pallets are not being put through an unnecessary added cleaning step.
The control phase drives teams to implement measures that ensure continued achievement of desired metrics, since short-term improvements rarely justify the time spent to develop a DMAIC project.
Generally, those closest to a production process understand what works well and where it could use improvement. However, often these staff don’t have the ability to articulate a business case for why process improvements should be initiated. Lean Six Sigma training helps change that when disciplined DMAIC processes are part of the equation. In a world where production teams are increasingly required to focus on continuous metric improvements at a work-cell level, rather than mundane tasks at individual workstations or a machine, Lean Six Sigma training can help production operators evolve into the workers that the highly automated 21st century workplace requires.