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Unformatted text preview: Manufacturing Progress Function Empirical evidence demonstrates that
the # of direct labor hours required to complete a unit of product will
decrease by a constant % each
time the production quantity is
doubled. A manufacturing progress
model is an expression of the
anticipated reduction in direct labor
hours in a manufacturing operation. Manufacturing improvement is some
times referred to as "learning".
Improvements may come from direct
labor learning, management & support
staff, tooling, production methods,
scheduling, materials handling, and
quality control. The Shape of the
Progress Function The manufacturing progress function
describes a constant percentage improvement as the production
quantities double. A 20% improvement curve means that
as production quantities. double,
there is a 20% reduction in d.i. hours. Note that the smaller the percent
rate of improvement, the greater
the progressive improvement with
production output. e.g. a 90% rate of improvement means that as
production quantities double, the average time per unit will decline 10% a 80% rate of improvement means that as
production quantities double, the average
time per unit will decline 20% The learning curve begins to flatten
once the task is mastered. It is best
to determine labor standards then.
Other considerations: Performance Rating
Effect of relearning (new design may have parts similar to old design)
"LearnForget” Model (/ 2"?! The Unit Formula X = the unit.# YX = the number of direct labor hours
required to produce the Xth unit K = the # of direct labor hours
required to produce the tst unit 935 = the slope parameter of the
manufacturing progress function n = log ¢J (log of learning percent)
log 2 n
YX= KX n
The equation YX= K X is referred to as a “loglinear” function. When plotted on loglog paper the
a straight line results and its ¢ slepe is given by an: 1%,}? _"" L’qug)? quimmvz an 907C. frojnm ¥Mnt+flﬂﬂa WL'EVLJ {Aan #1 A+ 302000 (infch dia é“ LUMI‘J . jdi‘v‘? 4:01” 73;, PEI}? 1H: a‘F cl. If?(% I lager Aﬁﬂrd N714er ‘14:: 6M: ’4 {‘j‘le MW?“ W” J M 33—0 =~ {’13
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if 5.9m»: Fer mmf. XI : (gnawingElm frail«({‘LN {a ram‘li one (foamif} K1: (annual411;“ (arcémfuk fa lam311E {We (’05’iw5ﬁi) E : 2 (ILf. rrclHi{HN $H‘5‘h—E’4Y ’3 clﬁ'tzgllf‘j)
Xi
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a————~d—+———~ ‘ ———————_._‘ "gjﬂrf L93 5:: — iaj 596' Laﬁg‘iw 2.0000 a 0, 30M at“;
—CI.":''~$' 2 : 7'4: Afﬁrmh (HYVQ frfq'rra‘géfr) : ¢ __r'" n 2 Ellog Ylﬁlog x — 210g grim
Zﬂegx ~ 2 log MIL/I)2 ElogY—n Elegx IUK:
Us M M: 1H: 0,? Ain't; rpfn'ki M 1.4. FITTING THE PROGRESS FUNCTION BY LEAST SQUARES Jog 1’sz03 3:  E Jog foJ The slope can be determined as _ 0.ﬂ74l n — = —0.2432 0.304? are 3133:
1 Y‘I ...
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 Fall '11
 14:540:201
 Derivative, Learning curve, PEi, Xth, slepe

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