Statistical Process Control had been deployed for almost
100 years in many established manufacturing organization such as Western
Electric, General Electric etc. to monitor their process for any potential
special cause. However for the past 20
years, I found that many manufacturing organizations which produce computer/IT
gadgets parts had been struggling to use Statistical Process Control as process
monitoring tool mainly due to the chart exhibit too many false alarm out of
control situation.
So what had changed over the past 20 years? The boom of
computer industry had monopolize the manufacturing eco system globally
with supply chains that are much more complex and longer compare to other types
manufacturing industry such as automobile.
In order to stay competitive, IT gadget suppliers such as computer or
hand phone companies had offer many options which include different shape and
size of product to satisfy myriads consumer preference in the market. Gone are the days where a factory shop floor
can run products for a few weeks or even months without changing model. There were some manufacturers continue to run
a single product till end of life such as model T Ford automobile in the last
century!
Today, daily or even hourly model conversion had become a
norm in many manufacturing industry with high complexity and low volume to
cater for different consumer needs. Manufacturers
are making smaller lot size where some could be less than 100 pcs batch and the
batch run only last for an hour or less.
If the control chart sampling frequency to collect data is 5 pcs per
hour, then only one data point manage to
be collected. There is even not enough
data point to calculate control limits.
Most manufacturing line use 2 approach in traditional SPC to manage frequent model change
and both has its own problem:
Traditional
SPC method
|
Type
of run
|
Problem
|
Use one chart for one model or same part number
|
Single production
run.
|
There
is not enough point to read the potential out of control situation where some
out of control situation need about 7 points.
Increase
sampling frequency is not an answer as it will increase the cost of
production.
|
Use
one chart for one model or same part number.
Whenever there is a production run for a part number, the same chart
will be used to plot the SPC point.
|
Multiple production runs
|
There
will be too many false alarms such as center line shifted due to different
production lot throughout the supply chain.
|
Below
is an example of notebook cover length data collected on different production
run. It is really impossible to apply
conventional SPC method to monitor the process as there are shift of trend with
each new production run.
 |
This chart show that there are parts from
different batches in different date with different process center and
possibility different data spread.
|
In traditional SPC method, the manufacturing engineer could have use one chart for one single lot date code run. This is not a practical method as there would too many charts to monitor for a single product.
We shall look at how we can simplify SPC chart in the part 2 of this
article for such scenario where parts with different process center and even data spread for different production lot.
