, the leader in Hi-Resolution process improvement technologies, is offering a fixed-rate project analysis
for any X-Y type problem. This means that a show-stopper quality emergency can now be quickly resolved for as little as $1,000. Why spend $30,000 for Six Sigma Black Belt training that does not guarantee results!
GeneMetriX is able to model process outputs (Y) versus process inputs (X) for any cause and effect relationship. Unlike other linear modeling tools such as linear regression, Analysis of Variance (ANOVA), and design of experiments (DOE) that merely report the model coefficients and their significance, GenemetriX can automatically compute the optimal X settings that maximize process capability. You don't have to be a Six Sigma Black Belt to have an impact on bottom line profitability with our assistance. We can help you reduce the cycle time of process improvement by integrating optimization techniques into a smart tool that automatically makes suggestions on how to improve the performance metrics of the process.
Note that this listing is not for the purchase of any GeneMetriX
Why do X-Y Problems Occur in Industry?
During processing, different product units undergo slightly different conditions that ultimately result in product variability. For instance, different conditions could be due to a change in raw material, different operators, a change in ambient temperature or humidity throughout the day, multiple test fixtures that are in parallel, etc. As a result of the different X conditions, the variability of Y performance metrics increases, which reduces the capability of the process.
What are the Typical Symptoms?
Often, there is a breakdown in a performance metric
Show-stopper quality problems (severe capability problem that halts production)
Processes that have difficulty conforming to specifications
Significant cost impact - typically 10% of conversion cost.
Are there any Generic Process Traits that tend to Cause these Symptoms?
Yes, unstable performance metrics usually result from varying X input conditions
. These changes in the process are often planned events and can be recognized by multiple levels of a variable - multiple suppliers, multiple operators, multiple test fixtures, etc
. Although there is a sound business argument for multiple suppliers, using raw materials from multiple suppliers tends to increase product variability in most industries including electronics, food, rubber, and plastics. Multiple operators and test fixtures increase the number of processing paths through a high volume production system, which unfortunately increases overall product variability.
Is this limited to Manufacturing Processes?
, the X-Y problem is generic to any industry.
Instead of determining how pressure affects part hardness, we could examine how the timing of a traffic light affects traffic flow. The only difference is that the context of performance metrics in certain industries like service deals more with time, i.e. cycle time or response time.
How can GeneMetriX
help you Save
Time and Money?
's Process Scout software is a state-of-the-art diagnostic tool that models process outputs (Y) versus process inputs (X). Unlike other linear modeling tools (regression, ANOVA, DOE) that merely report the model coefficients and their significance, Process Scout
automatically computes the optimal X settings that maximize process capability
. Not only does this reduce the expertise requirements of users, but it guides users towards an optimal recipe in minutes instead of months. Once receiving your data, GeneMetriX
will respond with a quick solution � so that you can take appropriate corrective action to quickly resolve your situation.
What Companies would Benefit
from this Analysis?
Process Scout inverts the X-Y relationship to quickly find the best X input conditions. The cause and effect relationship is so generic that it affects every industry. From Process Scout's perspective, it doesn�t matter if the data pertains to the service time at a casino or the surface roughness of an engine block. However, there are certain applications where Process Scout may have a greater impact than others, such as when: (a) You have the ability to control the input conditions (if the best conditions were known),
(b) Speed is of the essence, (c) Too much product variability exists. Companies with the following characteristics would also benefit from Process Scout:
Have experienced show-stopper emergencies
Need to quickly improve a performance metric, perhaps in real time
Want to improve the capability of their performance metrics
Desire to reduce the cost of poor quality
Processes with several different processing paths
Products with several performance metrics
Product variability is affected by multiple suppliers of raw materials.
receives payment and your dataset, GeneMetriX
will provide the results/solution within the agreed upon time frame. Please select a desired Time Frame and corresponding price from the table below. If for any reason, GeneMetriX
is unable to deliver the results/solution within the desired time frame, the next broader Time Frame will be become applicable - and you will receive a refund for the price difference.
7 Days $500
24 Hours $1,000
6 Hours $2,500
2 Hours $5,000
Where does the X-Y Data come from?
Some companies record the X input conditions and the resultant Y performance metrics for every unit processed. In this case, the data would already exist for an analysis to be performed.
record X-Y data only after a problem has been identified. After a problem has been signaled, some practitioners choose to observe the natural process and record the X-Y data. Others may choose to force the X input conditions via a designed experiment. Process Scout will use naturally observed data or experimentally designed data equally effectively.
The analysis will require an X-Y data set (preferably in an Excel or .txt file format) and specification limits for each Y variable. The first row of data should contain a header of each column. Thereafter, each row represents a record of data. Each column of the data set represents an X or Y variable.