World Class Manufacturing / In-process Quality

The structure behind TQC for GGEC is to eliminate the opportunity to produce a non quality part, process or product. Therefore this is addressed up front, thus avoiding inspecting in quality and incurring high scrap levels.

Our world class manufacturing system is based upon a flow process in which people and machines that build the product are given the responsibility for quality. TQC tools, authority and methods are provided to achieve their goals. The responsibility must start with Design engineering and become a focus at each step of the build of a product. Once the TQC sequence of events in our SOP’s (Standard operating Procedures)have been developed and the quality criteria defined, these TQC points are highlighted in the SOP’s to ensure that the next operator in the process flow checks the previous operators work, before it gets covered up. This dictates the flow of our product in regards to the line layout.

GGEC’s Process Capability is constantly monitored

The process potential index, or Cp, measures a process's potential capability, which is defined as the allowable spread over the actual spread. The allowable spread is the difference between the upper specification limit and the lower specification limit. The actual spread is determined from the process data collected and is calculated by multiplying six times the standard deviation, s. The standard deviation quantifies a process's variability. As the standard deviation increases in a process, the Cp decreases in value. As the standard deviation decreases (i.e., as the process becomes less variable), the Cp increases in value.

By convention, when a process has a Cp value less than 1.0, it is considered potentially incapable of meeting specification requirements. Conversely, when a process Cp is greater than or equal to 1.0, the process has the potential of being capable.

Ideally, the Cp should be as high as possible. The higher the Cp, the lower the variability with respect to the specification limits. In a process qualified as a Six Sigma process (i.e., one that allows plus or minus six standard deviations within the specifications limits), the Cp is greater than or equal to 2.0.

However, a high Cp value doesn't guarantee a production process falls within specification limits because the Cp value doesn't imply that the actual spread coincides with the allowable spread (i.e., the specification limits). This is why the Cp is called the process potential.

The process capability index, or Cpk, measures a process's ability to create product within specification limits. Cpk represents the difference between the actual process average and the closest specification limit over the standard deviation, times three.

By convention, when the Cpk is less than one, the process is referred to as incapable. When the Cpk is greater than or equal to one, the process is considered capable of producing a product within specification limits. In a Six Sigma process, the Cpk equals 2.0.

The Cpk is inversely proportional to the standard deviation, or variability, of a process. The higher the Cpk, the narrower the process distribution as compared with the specification limits, and the more uniform the product. As the standard deviation increases, the Cpk index decreases. At the same time, the potential to create product outside the specification limits increases.

Cpk can only have positive values. It will equal zero when the actual process average matches or falls outside one of the specification limits. The Cpk index can never be greater than the Cp, only equal to it. This happens when the actual process average falls in the middle of the specification limits.

This is the capability of the process expressed in relation to a worse case scenario view of the data. It is denoted by the symbol C_pk.

We use this criteria to determine if a process or design, is within the normal variation, and is capable of meeting specifications.

Cpk = the lesser of ...

Cpk	=	(Upper Spec Limit - Mean)		(Mean - Lower Spec Limit )
			or
		3s Actual		3s Actual

Note: s = Sigma

X Bar - Mean - Average

X Bar is an average of a sample. It is the arithmetic mean. It is calculated by adding the observation, (subgroup using stratified sampling), values and dividing by the total number of samples. This data is typically charted on a line control chart with the center line being X Double Bar, (an average of the averages), and upper control limits and lower control limits. You can also use Pre-Control to establish control limits.

We use averages because they are more susceptible to change than single values. To see how to use this chart, see statistical process control overview and the X-Bar and R-Bar explanation pages.

x is an individual observation

n is the total number of samples

X Bar	=	x1 + x2 + x3 + x4 .... xn
		n

Example:

X Bar = (.05 + .05 + .051 + .049 + .05) / 5

X Bar = .05

Root Cause Analysis

Our teams work to understand the process and its associated variables. We use an example of this “Fishbone” diagram, also called an Ishikawa diagram. This is used to associate multiple possible causes with a single effect. Thus, given a particular effect, the diagram is constructed to identify and organize possible causes for this effect.

The primary branch represents the effect (the quality characteristic that is intended to be improved and controlled) and is typically labeled on the right side of the diagram. Each major branch of the diagram corresponds to a major cause (or class of causes) that directly relates to the effect. Minor branches correspond to more detailed causal factors. This type of diagram is useful in any analysis, as it illustrates the relationship between cause and effect in a rational manner.

Our Total Quality Control is based upon internal process control at the source of the work. The goal is to find the “root cause” to the problem. Enforced problem solving is a program that uses all of the team members, including Finance, Sales and Marketing, Procurement, Engineering and of course Quality. We use a process that minimizes or eliminate boundaries, and top management is also assigned and actively involved with teams.

Control Chart

The control chart is the fundamental tool of statistical process control, as it indicates the range of variability that is built into a system (known as common cause variation). Thus, it helps determine whether or not a process is operating consistently or if a special cause has occurred to change the process mean or variance.

The bounds of the control chart are marked by upper and lower control limits that are calculated by applying statistical formulas to data from the process. Data points that fall outside these bounds represent variations due to special causes, which can typically be found and eliminated. On the other hand, improvements in common cause variation require fundamental changes in the process.

Pareto charts are extremely useful because they can be used to identify those factors that have the greatest cumulative effect on the system, and thus screen out the less significant factors in an analysis. Ideally, this allows our QA team to focus attention on a few important factors in a process.

They are created by plotting the cumulative frequencies of the relative frequency data (event count data), in descending order. When this is done, the most essential factors for the analysis are graphically apparent, and in an orderly format.

At GGEC we spend countless hours on QA and product training. The core team and the people building the product undergo training secessions in Product Build and TQM quality. This helps build a team commitment in building a better product. We invest in this knowledge up front as this allows a more cohesive team as the product goes into production.

While we at GGEC do everything possible to minimize the variables in both process and design we must still have the tools necessary for validation / confirmation

Our quality department uses all the internationally recognized Quality tools to achieve our quality goals.

Quality in process Management

GGEC controls the incoming components by using professional testing equipment on the production lines. It also carries out statistical data inspection at the QC points for effective quality control. Each finished product has passed product tests before delivery.

In addition, each batch of products will be sampled for selective testing to ensure reliable delivery.

Line testing facilities:

All loudspeaker systems must undergo frequency response testing. We work carefully with our customers to create systems that are easy to communicate results back to the customer. This is done in data file sharing and sometimes duplicated testing systems.

Electronics testing:

We do all typical In Circuit bed of nails testing, and we create functional testing fixtures to test completed electronic packages before they go into final assembly. We put procedures in place for data transfer that allows the customer to read our files and determine both functional and set up issues. This process allows for the long distance communication in the process. GGEC also provides burn in facilities at the customer’s request.