A Guide to Implementing the Theory of
Constraints (TOC) |
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Do you remember where we got up to? We were spilling red ink! The table is repeated below to remind us of
the situation.
So, what can we do?
We know now that by following our experience and trying to reduce cost
or otherwise locally optimize we may not always end up with a desirable
result. And yet in this example we were already aware that a
constraint existed, in fact we tried to maximize the output of the constraint
consistent with our knowledge of what is good for a system. What we must have failed to have done was
to exploit the potential of the constraint fully. Maybe now it is clear to from our continued
discussion on the measurements page as to what the problem is. We didn’t try to maximize the throughput
per unit time on the constraint! Let’s
have a closer look at this. How long does it take to process a P on the
constraint – resource B? We can see
from the table that it takes 15 minutes.
And for product Q it takes 30 minutes.
How much throughput does each product produce? P earns $45, and Q earns $60. Let’s look at the ratio then of throughput
per minute on the constraint for each product. P = $45 per 15 minutes or $3.00 per minute Q = $60 per 30 minutes or $2.00 per minute. Amazing. P
generates money for the system at 50% faster than Q. Which would you now chose as the product to
push ahead of the other, P or Q? P of
course. Let’s do the calculation. 100 of P requires 100 times 15 minutes (raw material
1) or 1500 minute in total. Leaving
2400 less 1500 or 900 minutes for Q.
One Q takes 30 minutes of resource B’s time (15 minutes for raw
material 2 and 15 minutes for raw material 3), thus 900/30 = 30 units. Let’s tabulate the result now fulfilling the market
demand for P of 100 units and then the remainder for product Q.
What did we get this time? Not red ink that is for certain. This time we can show that we do know how
to generate a profit. Let’s highlight the key point, the throughput per
unit time on the scarce resource.
The P&Q is a very important learning tool. I hope that you found working through the
example, or even just reading through it to be beneficial. And don’t be surprised if your floor staff
can tell you with some precision which of your company’s products produced
the most throughput through the constraint.
They already have intuition for this problem. Let’s continue on again with the measurements page
hopefully armed with a renewed understanding of how companies produce money. To return
to the previous page press Alt key + left arrow.
(1) Goldratt,
E. M. (1990) The
haystack syndrome: sifting information out of the data ocean. North River Press, pp 72-78. This Webpage Copyright © 2003-2009 by Dr K. J.
Youngman |