What is once revolutionary will eventually become obsolete

The first industrial revolution was a result of the usage of steam engines to power manufacturing machinery. Henry Ford’s mass production of a single-car model at the lowest cost was essential to the Second Industrial Revolution. Toyota leveraged the concept of Ford’s line production model and evolved a methodology around it- popularly known as the Toyota Production System (TPS).

Lean Six Sigma approach, which derives its inspiration primarily from TPS, has been used widely in Manufacturing. It was so popular at one point that it spawned an entire Industry of “Lean Six Sigma professionals.” But in my perspective, lean Six Sigma had some drawbacks, which need to be addressed now, though they were overlooked at that time due to tech constraints. And Big Data technologies can help.

Big Data or no Big Data…Lean Six Sigma needs to evolve

When it was introduced, Toyota Production System was indeed revolutionary. Based on the Toyota Production System, Lean Six Sigma became the most widely used process improvement method in Industry and the military, creating the Third Industrial Revolution. Lean Six Sigma incorporated Little’s Law of cycle time reduction, allowing the TPS to be applied to any industry and process.

This was state of the art…..but in 2001.

Now let us explore some of the drawbacks of TPS that were ignored back then.

Lean Six Sigma of 2001 was based on the repetitive manufacturing model of Toyota Motors.  TPS allowed Toyota to set up a 2,000-ton stamping press in 4 minutes vs. GM’s 4 hours (in 1986). This allowed Toyota to reduce the batch size by a factor of 24, still have the same cost efficiency, and avoid dings and rust on parts as they were immediately assembled onto a chassis.

However, Toyota’s press and machine tool produced only a dozen or so different part numbers during its lifetime. What does that mean?

It means it was a highly repetitive manufacturing environment.

The Gaping Hole in Lean Six Sigma Approach

The typical manufacturer in America today produces both new products and spare parts – which essentially means that they make both repetitive and non-repetitive Manufacturing. Product designs have become more complex, and customization has increased, which means a reduction in repetitive manufacturing aspects.

Also, to gain the advantages of TPS, Lean Six Sigma used Pareto Analysis to find the repetitive 20% of part numbers that delivered 80% of revenue. The belief was that if we made 80% of revenue production highly efficient, we would be making giant strides.

Lean Six Sigma thus neglected all the waste in the 20% of revenue.

The data for this 80% of parts went into a data blackhole, which we like to call “Dark Data” now. No one bothered to collect that data, let alone analyze it. But within the last two decades, the customer expectation landscape changed.

Customer satisfaction now reigns supreme

Customers now demand quality and service, irrespective of whether that part accounts for a minuscule portion of your revenue. If you have 5000 low-volume parts and 20% of them are late or have quality issues- it will start impacting your high-volume high, revenue parts soon. Today’s customers have many channels to vent, and social media provides unlimited power. You are judged for every part you produce- even if it is a fastener.

How can big data technologies help?

As discussed earlier, Lean Six Sigma focused on value stream maps at the department level rather than evaluating costs and wastes holistically. They had Black belts work on removing local sources of waste.

The good news is that with advances in computing hardware (think processing power), software, etc., and the availability of cloud computing, it is now very much feasible to analyze massive datasets for all your manufacturing parts and processes. This capability will allow us to focus on the “vital few” vs. the so-called “irrelevant many.”

And as I have indicated in many of my posts , those same Big Data tools can be enhanced by leveraging AI algorithms on each part being manufactured to generate plans and recommendations in near real-time. Technology has opened a floodgate, and we should allow the current version of Lean Six Sigma to be washed away so that the next generation of LSS can sprout.