Imagine two restaurants. The first one is making cheeseburgers all day long. The second one is making cheeseburgers and veggie sandwiches. Fifty cheeseburgers, 50 veggie sandwiches, 50 cheeseburgers, 50 veggie sandwiches. In between, however, since the folks eating the veggie sandwich just don't want to have any of the animal's fat of cheeseburgers on the sandwich, we have to clean the kitchen. Clearly, the first one, the cheeseburger only restaurant will have the highest capacity. The reason for that is the second restaurant every time they change over from one sandwich type to the other is they're going to lose some of its capacity. In this session, we will introduce a concept of setup times. Setup times require us to tweak our definition of a capacity that we have introduced in the process analysis module. Since we've had many sandwich and restaurant-related examples in the previous two modules, we'll look at another industry. Customer apparel. So you might be able to tell from seeing me on video now for a couple of sessions, apparel and fashion are not my core competence, but this should not get into the way of the session. There are hundreds of online stores luckily thousands and thousands of Taylor's out there that have a business model around customization. This is how the process works. It starts with measurement in an online store, you take some measures. In the case of a Tailor, the tailor goes and starts taking your measures of the body geometry. You then have to make a couple of choices around the shirt style as well as the colors. This combines the three forms of variety that we saw in the last session: faith-based, performance-based, and taste-based variety. Once your order is in, a tailor typically takes a couple of days if not a couple of weeks to turn the order around. Oftentimes, there is an incentive for you to order multiple shirts as opposed to just ordering one shirt. Alternatively, I noticed that many online retailers have minimum orders where you can only get shirts custom-made if you order at least five shirts. Apparel production is a very manual process. It can be broken up into three steps. The first step is the cutting department. In the cutting department, we're going to take a layer of textile, and have to cut it in pieces. This is typically supported by templates. Templates basically capture the apparel that captures to your specific body geometry. They might have a specific arm size, then there might be the front of the shirt, the back of the shirt, the collar piece, and the cuffs, and those are programmed into a machine that will, later on, do the actual cutting. Now, notice that the setup of programming the machine or building this template takes a certain amount of time that is independent of whether we're going to produce one shirt, 10 shirts or 100 shirts. Once we have done the setup of the machine, the actual cutting can begin. Oftentimes, they even multiple layers of textile piled on top of each other, and then cut in one go. The next step is the sewing department. This is a real assembly line where these multiple pieces that we get out of the cutting are put together. Finally, in the finishing department, the shirts get ironed and fold it together in a package ready for delivery to the customers. When we analyze the production process of the apparel company here, we have to look at the capacity of each of the resources. For the cutting machine, the capacity calculations are a little bit more complicated than in the past. The reason for this is the setup time. When we're setting up the machine, when we're programming the sizes and the style into the machine or when we're building the template, this eats up capacity and thus can determine whether or not the cutting machine is the bottleneck. To compute the capacity of the cutting machine, we first have to introduce the concept of a batch. The batch refers to a collection of flow units. More specifically, we refer to a batch as the number of flow units that are produced between two setups as we change the template or the programming parameters from one shirt size to the next, we start a new batch. So here's how the capacity calculation work. Imagine we have a batch size of 15 shirts. We're going to find the capacity, we are asking ourselves well, how long will it take to produce these 15 shirts? The production process starts by setting up the machine for these specific shirts, which will take 20 minutes. Once the machine is set up, it will take 15 shirts times the processing time of four minutes per shirt to produce this batch, thus our capacity is simply 15 shirts divided by 80 minutes. Suppose this is expressed in shirts per minute. This is the capacity, given the batch size. Now, that was just the capacity calculation for the cutting machine. To find the bottleneck of the process, we have to calculate the capacity for each of the four resources at the process. As we said before, for the cutting machine, we had 15 shirts be reproduced in 20 minutes setup plus four times 15 minutes of production time which is equal to 15 divided by 80, which is roughly 1.88. Notice that cutting step is the only resource here in the process that requires a set-up time. For these reasons, the fact that this is batch operation is not relevant when we calculate the various capacity levels as the assembly steps over here, over here, and then finally at finishing. Here we calculate the capacity just as we did in the case as before. We define capacity as the number of workers divided by the processing time. Eighty divided by 40 is equal to 0.2. Similar for the next step, we have five divided by 30, which gives us 0.1666, and then finally one divided by three equals 0.33. So we notice that the step with the lowest capacity is over here at section 2, and we'll define this step as the bottleneck. Earlier on I mentioned that many production companies require the customers to order in a minimum order size. Why would they do this? To see this, consider the following calculation. Let's ask ourselves how the batch size is impacting the capacity at a particular resource. Remember in our example a moment ago where we had a setup time of 20 minutes and a four-minute processing time. For every unit that is made. Now, ask yourself what's the capacity if we just make one unit? Well, if we make one unit, we're going to get one unit, and it will take us 20 minutes of setup plus four minutes of production. That is roughly one divided by 24, which is equal to 0.04 and a little bit. Now, increase the batch size from one to 10. We're having 10 units and it would take us 20 minutes to set up plus 40 of production, which is 10 divided by 60, which is equal to 0.1667. Now, think about a really big batch size. Imagine somebody's buying 1,000 shirts. How long will that take us? Well, you're going get 1,000 shirts, and it will take us 20 minutes for setup plus 4,000 minutes of production. This number is amazingly close to 0.25, which is simply one over the processing time. Let's graph this effect in this chart here, where we have the batch size on the x-axis and the capacity on the vertical axis. We see that as a batch size increases, so does the capacity. The [inaudible] ultimately here is one over the processing time. Even with an infinite batch size, the capacity of this step here you will never be faster than one over four. However, as the batch size decreases, I have to spend more and more of the resources time and setup mode. Thus batching really is a form of scale economies. Larger batches means extra free capacity for the company, and that is something nice. This is what the company offers; quantity discounts or might require minimum purchase orders. When we change from making one type of flow unit to making another type of flow unit, we oftentimes incur a setup. Setups are by no means limited to the production sector. Think about the service example, think about an underwriter in the bank who is producing or is underwriting residential mortgages all day long. Compare the productivity of this underwriter with an underwriter who is alternating between underwriting, consumer loans, and residential mortgages. Setups reduce capacity, and for this reason, we have an incentive to run long production batches. This really creates a form of scale economies. Long production batches are good for capacity. However, as we will notice in the following session, long production batches lead to big inventory, which oftentimes is a problem.
