TOA: OK, welcome back to another edition of Ask The Business Bro, last time we dug a little into the main ideas discussed in The Goal. This week, we’re back to learn a little more about how to apply those ideas. BB, what’s up?
BB: Not too much, I think we left off last time with a question about how to determine excess bottleneck capacity, might as well start there, right?
TOA: Let’s get to it.
BB: As usual, the first answer is deceptively simple. The excess capacity here is just the answer to two questions. First, does the bottleneck resource ever sit idle? Second, could it performer better or faster while it’s working? If the answer is no and no, then you are maxed out, and if the answer to either question is yes, then you can figure out a fairly simple estimate of the excess capacity for the bottleneck.
TOA: OK, so let me see if I get it, let’s say your bottleneck produces five units an hour and it works for seven hours. If you can get up to eight hours, then the spare capacity is five units. If you can get it to produce six units per hour, then the spare capacity is seven units. And if you combine both improvements, then the spare capacity is thirteen units.
BB: Bingo.
TOA: OK, so why is it more complex than that?
BB: It’s not the math that’s tricky, or I should say, the formula is easy, the complex part is getting the right numbers. You might think you produce five units per hour, for example, or that the bottleneck is utilized for seven out of eight hours, but in reality capturing those measurements is the real source of difficulty.
TOA: I imagine Goldratt has some ideas about how to calculate these numbers?
BB: He certainly does. The key to getting the math right is to understand the fluctuations in the process. Each fluctuation creates variation that threatens the reliability of your numbers. If you understand the variation, you can factor those into the calculation and go from there.
TOA: What are some important factors to consider in terms of fluctuations?
BB: Well, before I get into that, I think it’s important to talk about certain assumptions.
TOA: OK, what are the assumptions?
BB: The most important one is that fluctuations average out. This assumption sounds good on paper and anyone who has studied the normal curve in a basic statistics course was trained to think this way. If you crunch your numbers thinking that they could go up or down by 20% with equal probability, you’ll end up with a much different perspective on your operation than someone who thinks differently.
TOA: But why is it automatically the case that those probabilities aren’t equal?
BB: Well, they could be equal, but remember that everything we are talking about ties back to the bottleneck resource. Let’s go back to your numerical example. Suppose the bottleneck resource operates as you defined it, five units per hour for seven hours for a total of thirty-five units. The raw material you need to feed into the bottleneck is therefore equivalent to what produces five units per hour for seven hours.
TOA: Right.
BB: Let’s accept the assumption that positive and negative fluctuations of one unit per hour in the available level of raw material happen with equal probability. On day one, we have a normal day and produce thirty-five units. On day two, we get a negative day and only produce four units per hour for a total of twenty-eight units. We’re down seven units, and we aren’t getting those back.
TOA: Hold on, but what if the next day we have a positive day? Six units per hour means forty-two units, right?
BB: OK, so here we go, six units per hour, we think we’ll get forty-two units because we have the raw material, but remember that we defined the bottleneck as being able to process only five units per hour. In this case, the positive fluctuation on day three doesn’t matter. The unprocessed raw material becomes inventory and at the end of day three we’ve produced thirty-five units for a total of ninety-eight units over three days with seven units of raw material inventory. This means we fell short of the production estimate by those seven units in the inventory.
TOA: But what if we run the bottleneck for an additional hour?
BB: You do the math. We could have ten thousand units of raw material but we can only produce up to the bottlenck capacity.
TOA: OK, five units per hour, so we end up at forty units, with two units of raw material.
BB: The point of The Goal, at least when it digs into the detailed level of how to apply its main principle, hinges on understanding why in most systems fluctuations do not even out. It’s because in most systems, a negative fluctuation accumulates faster than a positive accumulation can make up for it. So even if the probability of the two fluctuations is equal, the reality is that since most bottleneck resources operate at pretty close to capacity most of the time, it’s hard to make up for a bottleneck sitting idle during a negative fluctuation by having it do more work when there is a positive fluctuation.
TOA: I’m seeing the point – you can be stuck in traffic all day but you can’t drive from here to there in an instant.
BB: Right.
TOA: I’m still not sure. Can we go back to the example?
BB: OK.
TOA: Well, what happens if we start with a positive fluctuation? Wouldn’t the accumulated raw material come in handy later on to make up for the shortfall after a negative fluctuation?
BB: It sure would, but let’s do the math. If we have one positive fluctuation, we produce as expected and leave at the end of the day with seven units of raw material inventory. This means we are covered in the event of a negative fluctuation. But keep in mind, inventory is costly, and by holding this inventory we’ve raised the cost of each unit produced. We pay that cost every day until we have a negative fluctuation.
TOA: OK, right, I forgot that we pay for inventory, I guess.
BB: Right. Let’s hammer this point home by looking at a very positive example, let’s say we have a full week of positive fluctuations and we run our bottleneck resource for eight hours instead of seven. At the end of the week, we’ve produced forty units per day instead of thirty-five and we have two extra units of raw material inventory per day. On the surface that looks good, but we're only about two consecutive days of negative fluctuations away from eating up that spare inventory and producing less than the thirty-five units per day.
TOA: But can’t we afford that since we overproduced the week before?
BB: I’m sure it’s true in some cases, but it’s not a given. Remember that we’re working off of an assumed output of thirty-five units per day, so unless the sales team responds quickly and sells those extra units we might not be able to recognize the revenue for those products right away. If the completed product has a short shelf life, we might not recognize the revenue at all. Plus, since we have to buy space to store those extra units, each extra unit above our estimate comes at a slightly higher cost, so we might need to discount the revenue gained from each additional unit to account for this.
TOA: This is getting pretty complicated.
BB: Right. The short version is that if we overproduce, we run the risk of learning the hard way why we don’t overproduce all the time.
TOA: Are there any specific strategies we can use to get around this?
BB: There are, and Goldratt outlines a few, but unfortunately I must say that is where it gets complicated.
TOA: Oh, now it gets complicated?
BB: I think it might be best to leave the tactical discussion for next week.
TOA: OK, that will work for me.
BB: The key thing to remember is that negative fluctuations tend to accumulate faster than positive fluctuations will even them out.
TOA: Yes sir, I will try to do that. Same time next week?
BB: Sounds good to me.
