A little-known secret in production and manufacturing is that you can actually break down every part of a process to mathematical and logical sequences. With a few changes in the process, the best possible outcomes can be drawn with a 99.99% consistency.
There are a number of techniques and tools used to ensure this level of improvement. However, what has been the most consistent is the process known as Six Sigma.
What is Six Sigma?
It is basically a set of strategies meant to help manufacturers and producers ensure quality in their outputs using a number of statistical and empirical methods. It mainly helps to allow businesses to identify flaws in their processes, removing these, and minimizing further signs of variability in the overall manufacturing system.
The strategy owes its existence back to engineer Bill Smith who laid down the basics during his tenure at Motorola in 1980. Jack Welsh further improved on the concept when he worked for General Electric in 1995 as part of his central business strategy.
Understanding how the Six Sigma process works can be rather confusing for non-statistical folk but it’s best to get acquainted with the process on a more technical side first. It’s basically a set of standard deviation figures collected from data in a manufacturing process.
Since defects are defined in statistics as specification limits which separates the bad outcomes from the good, then the 6S process is a set of standard deviations based on the nearest specification limit for each level or “Sigma”.
For instance, if the desired length of a metal pane produced by a machine is in between 2.5 and 3.0 meters, then the process mean is at 2.75 with a standard deviation of 0.187. That means, in order to get a near-perfect and near-consistent production run, almost every item produced by that machine should be within the range of 2.62 and 2.85 meters.
On a more production-focused perspective, you can use the statistical tool to lower the defects of each production batch by a percentage in each sigma level. Theoretically speaking, the success rate per level is as follows.
Level 1 – 33.45%
Level 2 – 69.98%
Level 3 – 93.32%
Level 4 – 99.38%
Level 5 – 99.97%
Level 6 – 99.99%
As one could see, reaching level 6 is not even necessary to ensure optimum production results. You could basically go as far as level 4 and the margin of success in each production batch has already attained near-perfection. This won’t ensure a perfect production run in each sequence, however. It only ensures that the defects in each production process have been reduced by the thousands or millions, depending on the case.
As of now, there are two schools of thought in performing Six Sigma. These two methodologies have 5 phases each and are known as:
DMAIC – This methodology is best for businesses with existing production system designs and could be broken down into:
Defining the System – this includes identifying the requirements usually set by customers and comparing them to the overall goals set by the company.
Measuring Key Aspects – this involves measuring and analyzing current production systems and determines their production capability as is.
Analyzing Data – In this process, one must identify how each factor correlates to one another and result in a specific set of outcomes. This will also involve categorizing each outcome and seeking out root causes for the ones that result in defective products.
Improving Current Systems – During this phase, the focus is on making the system “mistake-proof”, constant calibration and repairing are to be expected here if one wants to reach the margin of statistical success defined in each sigma level.
Controlling Future Production Runs – At this point, the focus is on making the changes as sustainable as possible. This will involve implementing rules that ensure each production reaches the required sigma level success rate.
DMADV – This methodology works best if you have to start from scratch as far as setting up your production processes are concerned.
Defining the Goals – This phase involves identifying what customers want, what the company can offer, and what the company intends to achieve.
Measuring and Identifying Critical to Quality Characteristics – Here, you will identify what tools and specifications in your production runs have to be met to ensure quality. Also, you’d have to identify the risks to avoid and the output to minimize production.
Analyzing and Developing Design Alternatives – At this point, you should come up with several production layouts and processes to ensure the level 4 to 6 success rates.
Designing an Improved Alternative – Once you have found the best possible production design, you have the option to improve it even further.
Verifying the Design – This phase involves setting things up and ding your first production runs. A bit of tweaking and upgrading might be necessary here if you want to reach the 99% success rate.
Once you have come up with a near-perfect production process, the rest of the process will include translating that concept to the rest of the organization. This should not come as a surprise to you but implementation should always start on top.
The executive management should learn what is Six Sigma and set up the rules on how to implement it. Lower management like department heads will then oversee the implementation while senior personnel like supervisors must coach the lower levels on how to ensure near-perfect production qualities. The employees would then receive the training and directly implement these processes by making sure that their work’s output follows the desired specifications. This will include every stage of the production process from the selection of materials to the production proper and even the quality control processes at several points through the chain.
At a glance, Six Sigma could look like this arduous strategy whose conditions for success are near impossible to achieve. However, you might just be surprised at how easy it is to implement and sustain. With constant effort in implementation and improvement, your employees could go about attaining the upper sigma of success within each production run.