A Few Tools for Continuous Improvement

July 31, 2013
Whichever flavor of continuous improvement program you use, it seems there are always more problems to tackle. Some tools geared specifically toward continuous improvement can really help.

These days everyone has a continuous improvement program of some kind. Six Sigma is used quite extensively, as is Lean Manufacturing. Some people even tailor their own programs using the best ideas from a wide variety of sources. Whichever flavor of continuous improvement program you use, it seems there’s always more to do; there are always more problems to tackle. But it is a continuous improvement program, after all.

However, some tools geared specifically toward continuous improvement can really help. They can have a significant impact on continuous improvement programs, helping those working in the programs do a lot more and solve more problems.

Collecting, disseminating, analyzing data
One category of tools is data collection. To really analyze a problem, you need lots of hard data. Guesses just won’t do; you have to know what’s actually going on. There are several ways to implement data collection, but the key is to get all the data you need and store it in a way that people can use.

PLCs and DCSs are usually a great part of any data collection system. Networking is required to get the data from one place to another, and some type of storage solution such as a data historian or general-purpose database is also necessary.

Once you have the data collected, you really need to get the data to the right people so they can do something with it. Some people hate the word dashboards and some people love it, but the analogy is so straightforward that it needs no explanation. There are lots of dashboard and reporting solutions, but the key is that they tie back to the data historian and the database and allow each person access to the data they need, when they need it, and in the way they need it.

Analytics takes data collection and dissemination a big step further by providing the tools to analyze the data—study large volumes, slice and dice, and otherwise mine the data to get the real information and the real understanding from it that you need.

It can be a lot more than just looking at a lot of data or even developing complex reports. Analytics is all about taking the data apart and putting it back together to see trends and relationships that you never knew about before and gaining a much deeper understanding of what’s actually going on in the real world.

With a slightly different approach from analytics, the idea with metrics is to define the key data that indicates the true performance of the manufacturing operation or process. Based on this, many people refer to metrics as key performance indicators (KPIs). The important point here is that you have to get beyond the data to the specific measures of the operation or process. And the measures need to indicate how well the operation or process is actually running, which is much more complicated than just how fast the machine is running or how many units are being produced per hour.

Overall equipment effectiveness (OEE) is one specific metric that has become a staple in a lot of continuous improvement programs around the world. OEE attempts to measure not merely how fast a line or piece of equipment is running but its true manufacturing performance.

It does this by combining three underlying metrics typically calculated with a lot of underlying data. Availability is calculated based on runtime and downtime. Performance is based on actual vs. theoretical rates. Quality is based on actual first-pass quality. Together, availability, performance and quality make up OEE and provide a very good standard approach to measure true performance.

A specialized version of general analytics is statistical analysis. In manufacturing, it usually takes the form of statistical process control (SPC) or statistical quality control (SQC). The short definition of SPC/SQC is the application of statistical methods to control the manufacturing process or manufacturing quality.

SPC/SQC is very valuable in manufacturing because the statistical analyses can help you anticipate and avoid problems. You can analyze trends in real time and take corrective action.

>> John Clemons is Director of Manufacturing IT for Maverick Technologies (www.mavtechglobal.com), which delivers system integration, industrial automation, process control and automation engineering services.