![]() ![]() More times than not, our response times are anything from not good to down right terrible. I’m scoping several projects now that are similar dealing with response times. It’s usually because your process is crappy somewhere. When your histogram, or other visual chart looks “crappy”, it’s not always that you picked the wrong analysis tool. At some point you will most likely find a range of late delivery time which, while not JIT, is such that any attempt at further reduction in late delivery time will not be cost effective.Īll great points. Once you have the upper 5% in “control” you can choose a new cut point for maximum late delivery time and repeat. I would also guess once you do this you will find all sorts of things you can change/modify to pull late delivery times for the upper 5% into the late delivery time range of the 95%. Having dealt with things like this in the past my guess is you will first have to really understand the issues surrounding the delivery times of the upper 5%. Once you know which parts are in the upper 5% (we’re assuming the spread in late delivery time for 95% of the parts is the initial target) the issue becomes one of looking for ways to insure a delivery time for the 5% is less than or equal to whatever the upper limit in late delivery time is for the 95% majority. What is the time spread for 80,90,95, and 99%? Since all parts are created equal and all that matters is delivery time then the focus should be on those parts whose delivery time is in excess of say 95% of the other parts. ![]() You know 75% of the parts have a time spread of 0-6 days. Under these circumstances the issue becomes one of minimization of the time spread for late delivery. Given: Based on your post – just-in-time (JIT) for everything is a non-starter. I would insist it is just what you need to start thinking about ways to address your problem. I would disagree with respect to your view of a histogram for delivery time. ![]()
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