A Guide to Implementing the Theory of
P&Q Answer - Part Two
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.
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(1) Goldratt, E. M. (1990) The haystack syndrome: sifting information out of the data ocean. North River Press, pp 72-78.
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