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I posted this question in the math forum on redddit, but it was deleted because it is not a math question.

I am hoping someone here can enlighten me.

In my life as an estimator for a machine tool builder, I have always used a learning curve model to estimate future repeat production.

That model is: Every time a quantity of parts is ran, the time to complete should improve; that improvement will be seen every time the quantity is doubled. In other words - with a 95% learning curve, a 5% improvement (100-95) will be seen between the first and second part, and again from the 2nd to the 4th, from the 4th to the 8th, etc. Every time the quantity doubles, the same percentage improvement is realized. (There is a little more to it, batch sizes and a 'forget factor' and so on, but I think you get the point.)

Every time a job is repeated, it is expected to go faster/cheaper because of experience and lessons learned.

If a job is able to be improved considerably every time it is re-ran, then the cost/time goes down considerably on subsequent runs. That is, a plot of cycle times or costs vs quantity, would show a very big drop from part 1 to part 2, from part 2 to part 4, from 4 to 8, etc. A big drop = a steep curve. Going down that curve happens quickly.

If a job is very difficult to improve upon, then the curve is considerably shallower, because the time to produce does not get better over time = shallow learning curve.

I have always used 'steep learning curve' to denote 'rapid improvement over time' but am now being told that is the opposite; apparently my definition is backward, and a SHALLOW learning curve means rapid improvement over time.

Could someone please set me straight on this?

I posted this question in the math forum on redddit, but it was deleted because it is not a math question. I am hoping someone here can enlighten me. In my life as an estimator for a machine tool builder, I have always used a learning curve model to estimate future repeat production. That model is: Every time a quantity of parts is ran, the time to complete should improve; that improvement will be seen every time the quantity is doubled. In other words - with a 95% learning curve, a 5% improvement (100-95) will be seen between the first and second part, and again from the 2nd to the 4th, from the 4th to the 8th, etc. Every time the quantity doubles, the same percentage improvement is realized. (There is a little more to it, batch sizes and a 'forget factor' and so on, but I think you get the point.) Every time a job is repeated, it is expected to go faster/cheaper because of experience and lessons learned. If a job is able to be improved considerably every time it is re-ran, then the cost/time goes down considerably on subsequent runs. That is, a plot of cycle times or costs vs quantity, would show a very big drop from part 1 to part 2, from part 2 to part 4, from 4 to 8, etc. A big drop = a steep curve. Going down that curve happens quickly. If a job is very difficult to improve upon, then the curve is considerably shallower, because the time to produce does not get better over time = shallow learning curve. I have always used 'steep learning curve' to denote 'rapid improvement over time' but am now being told that is the opposite; apparently my definition is backward, and a SHALLOW learning curve means rapid improvement over time. Could someone please set me straight on this?

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[–] • 0 pt

Steep to me would mean the learning curve is more difficult, thus a steep learning curve to me, would mean that its a more difficult task. I always thought of it as just an expression and not really a term with an express definition like you're saying. I'm not sure.

[–] • 1 pt

A learning curve is literally a graph of how well something has been learned over some period of time. Your Y axis should represent ability, and your X axis should represent time. As you go up on the Y axis, you've learned more, as you go along the X axis, you've taken more time. Try drawing a few curves and think about what the different shapes would mean.

[–] [deleted] • 0 pt

There is the crux of the discussion.

I'm not interested in the time to figure something out for the first time. That's just a starting point.

What I'm looking at is the time it takes to figure it out the second time. And all the next times.

Like this:

"Go to the store and get me a bottle of water."

It may take you an hour to drive around looking for the store, find the aisle with water, get caught by the speed trap on the main road on the way back, and so on. Whatever, you don't know my neighborhood.

But tomorrow, when I send you back for another bottle, you will be faster because you remember where the store is, and which aisle has the water.

After a week, you have found a shortcut that saves you another 30 seconds, after a month you just buy a 6 pack every 6 days, after 6 months you know which register checks you out faster, after a year you know which traffic lights are 3 seconds faster, and so on.

Just because it is harder the first time isn't really the point, it's all about how much of an improvement can be made over subsequent times repeating the process.