I played a CS on Sunday and it's pretty beat up. Hit some trees, hit 2 cart paths. How do I slice mine open without the device?
Butter knife?
You can use a splitting maul. Probably won’t have anything left though.
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I played a CS on Sunday and it's pretty beat up. Hit some trees, hit 2 cart paths. How do I slice mine open without the device?
Butter knife?
The ball in the OP is pink inside, yours is blue. Are you sure they are the same model ball ?
I played a CS on Sunday and it's pretty beat up. Hit some trees, hit 2 cart paths. How do I slice mine open without the device?
Butter knife?
You can use a splitting maul. Probably won’t have anything left though.
That looks like last years Chrome Soft core.
That looks like last years Chrome Soft core.
MGS is getting people to voluntarily destroy perfectly good golf balls. Sure the ones you open might be good... but what about the ones you are playing with? Ah now it's in your head!
Out of sight, out of mind IMO
And according to their IG post, they have talked to Callaway's CEO and Callaway plans on investing in improving their golf ball facility. Didn't they already do that? lol
And according to their IG post, they have talked to Callaway's CEO and Callaway plans on investing in improving their golf ball facility. Didn't they already do that? lol
Kind of my point. A blanket statement to appease them.I'm sure it can always improve. This is just Callaway getting ahead of it. I doubt we will hear what steps (if any) will be taken *that is if steps even need to be taken
Rather than saying 'no way that never happens' just say 'we will look into it, thanks" ... and now back to our regularly scheduled programming
PVC cutters work better. Just not on Rock Flites.Melodramatic at its core.... pun intended. This does nothing to influence my opinion of the OEM.
Like every other industry where QC tolerances are in play, sometimes these controls are imperfect and need to be revisited and I’m sure these are on a continuous basis... improvement is dynamic
I like @Nate’s suggestion for a money back guarantee.... I’m heading to Home Depot to purchase a band saw....
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Masterfully explained.Here's some context to help the conversation along. Using fake data, I plotted a scenario where an OEM who makes roughly 5 million golf balls a year is experiencing some level of defects in their process. Meaning, bad golf balls are produced (which happens all the time). 5 million balls a year is about 104,166 golf balls a week.
View attachment 40395
That graph, a "P-Chart", shows proportion of defective units.
The red lines = control limits (3 standard deviations above and below the mean, or the green line in the middle)
Blue lines = % defective that day, which is labeled on the x-axis on the bottom.
We want to look for data points that fall above, or below, the control limits. Those are called special cause variance, or signals. A signal suggests something needs further investigation.
EVERY process in the word has some level of variance, btw.
In this example, assumed around 5000 defective golf balls are produced each week. If those make it to customers, with regularity, there's an issue. However, only TWO instances did higher than expected defects occur (2/5 and 3/19).
One way to interpret: something happened on 2/5 that caused significantly higher defects to be produced. Does this mean we need to completely alter the way we manufacture? Is there a systemic problem? What about all the other days that are in control?
Here's some context to help the conversation along. Using fake data, I plotted a scenario where an OEM who makes roughly 5 million golf balls a year is experiencing some level of defects in their process. Meaning, bad golf balls are produced (which happens all the time). 5 million balls a year is about 104,166 golf balls a week.
View attachment 40395
That graph, a "P-Chart", shows proportion of defective units.
The red lines = control limits (3 standard deviations above and below the mean, or the green line in the middle)
Blue lines = % defective that day, which is labeled on the x-axis on the bottom.
We want to look for data points that fall above, or below, the control limits. Those are called special cause variance, or signals. A signal suggests something needs further investigation.
EVERY process in the word has some level of variance, btw.
In this example, assumed around 5000 defective golf balls are produced each week. If those make it to customers, with regularity, there's an issue. However, only TWO instances did higher than expected defects occur (2/5 and 3/19).
One way to interpret: something happened on 2/5 that caused significantly higher defects to be produced. Does this mean we need to completely alter the way we manufacture? Is there a systemic problem? What about all the other days that are in control?
Hear me out. What if this ball was made purposely wrong and "leaked" so it could be cut open prompting others to cut a part their Chrome Softs. After finding good balls, these cutters would have to go out and buy replacement Chrome Softs
This was just an elaborate ruse by Callaway to boost their ball sales.
Conspiracy Theorists Unite!!! I like it! lolHear me out. What if this ball was made purposely wrong and "leaked" so it could be cut open prompting others to cut a part their Chrome Softs. After finding good balls, these cutters would have to go out and buy replacement Chrome Softs
This was just an elaborate ruse by Callaway to boost their ball sales.
Although this is disheartening, its kinda like buying a car and worrying about it being a lemon as you drive it off the lot. Anytime you mass produce anything, there's a margin of quality loss.
The cynic in me wonders how many balls he cut in half before he found this one...
Am I alone in the fact that it’s all just noise to me and really doesn’t show me anything?
Am I alone in the fact that it’s all just noise to me and really doesn’t show me anything?