Continuous Improvement for Manufacturers

How to get better all the time

“Continuous improvement” is a business philosophy rooted in the belief that everyone in an organization — from upper management to factory workers — shares responsibility in improving quality and performance.

This philosophy has bred several popular methodologies in the manufacturing industry, the most common of which are called “Lean Manufacturing” and “Six Sigma.” Each approach has its own technique for improving the manufacturing process. However, you don’t need to choose one method over the other; you can pick and choose aspects from each practice to improve your manufacturing workflow.

Origins of continuous improvement

Continuous improvement was introduced by W. Edwards Deming, an American who honed his philosophy during the manufacturing boom in Japan. To this day, one of the fundamental tools for continuous improvement is the Deming Cycle, also known as the Plan-Do-Check-Act (PDCA) cycle:

1. Identify an opportunity to improve and create a plan to modify your process
2. Implement the modification on a small scale
3. Analyze the results of the modification and determine whether it was successful
4. If the modification worked, standardize it and continue to assess it

Continuous improvement tenets

In order for the PDCA cycle to flourish, a company must adopt the basic principles of continuous improvement:

- Customer focus, the belief that a business’s success is dependent on understanding and satisfying the customer’s actual needs (rather than what your company may perceive those needs to be).

- Valuing all employees and empowering those at all levels to suggest opportunities for improvement.

- Using data to make sure that fact is driving quality improvement (rather than guesswork). Customer surveys, time-tracking and quality comparisons are all common forms of continuous improvement data.

- Commitment to continuous improvement. This should not be viewed as a temporary project; once a customer need is resolved, new goals should be set. This obsession with pleasing the customer often requires a change in company thinking and culture.

Lean manufacturing

In the late 1980s, the Massachusetts Institute of Technology (MIT) identified Toyota Motor Co.’s process as the gold standard in automobile manufacturing, largely because it practiced “lean production” over “mass production.” The premise of lean production is that a company should pursue any feature that a customer is willing to pay for because it adds value. Conversely, an activity that does not impact the customer experience does not produce value and should be cut.

Lean also looks further down the supply chain, examining delivery times, suppliers’ operations and the impact of a product on a community. This is all aimed at eliminating waste and maximizing value for customers.

Kaizen

Kaizen was another American philosophy brought to Japan after World War II. The Japanese word kaizen translates as “the act of making bad points better.” Kaizen stresses the importance of getting rid of muda, the Japanese word for waste. Teams are assembled to tackle Kaizen projects that weed out muda — everything from superfluous meetings to mismanaged inventories to outrageous expense accounts.

One important distinction is that clearing your business of muda is not about laying people off. In fact, Kaizen is less about cutting costs and more about reallocating resources so that they can contribute to a more efficient company and a less frustrating work environment.

Six Sigma

One argument against lean manufacturing is that it fails to deliver on one of the core tenets of continuous improvement: using data. That’s where Six Sigma comes in. Six Sigma brings a manufacturing process under statistical control, with data-driven decisions geared toward achieving a higher level of consistency and fewer product defects.

First used by Motorola in the 1980s, Six Sigma is a problem-solving strategy guided by customer needs. Those needs are defined by consistency (rate of defects) and success is based wholly on statistical analysis.

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